Tempus AI Aktie

Perfect. I think we can get started. Kallum Titchmarsh here from the life sciences team at Morgan Stanley. Welcome to Day 2 of the Global Healthcare Conference. Really pleased today to be joined by Eric Lefkofsky, Tempus' Founder and CEO. Thanks for being here, Eric.

Thanks for having me.

A lot of news to discuss, but maybe we can just hit on first the second quarter results. I think very strong across the board, broad-based performance. Anything that surprised you within that? Maybe just go through the good, anything that surprised you to the bad? Maybe just level set us, and then we could dive into some specifics from there.

Yes. I think the -- both the quarterly performance and I think the year performance has really been anchored around 2 main themes. One is the core strength of our diagnostic business, in particular, around comprehensive and genomic profiling, and how kind of durable those growth rates -- unit growth rates are, both in terms of solid tumor profiling and liquid biopsy. So you have this like very strong therapy selection business in terms of like demand that also has rising ASPs. And so that's kind of one of the big bellwethers of our business, the biggest part of our business. So even though MRD has high growth rates -- higher growth rates, it's the biggest part of our business.

So the diagnostic -- our diagnostic unit has been buoyed by really strong demand that we don't see slowing down. And on the other side, the data business continues to perform really well. I think our data licensing business grew about 36% or something in the last quarter. So really strong -- continued strong growth, and we've had something like 3 quarters in a row of more than $100 million of TCV. I think last quarter it was around $200 million of TCV add, meaning our bookings for that quarter, people signing new data licenses were $200 million or whatever. And so really strong demand that I think is starting to really be catalyzed by migration to AI solutions by biopharma. Every time you turn around, they're announcing a deal to bring in NVIDIA chips or cut a deal with Anthropic or OpenAI for inference and compute. And they just -- we're the fuel that makes a lot of that spend intelligent. And so we just have seen kind of a frenzy of demand.

Amazing. You've talked about expectations for at least 25% growth for the business over each of the next few years. Maybe just go through what has given you that confidence and where you have the most visibility across the different business lines to that sustained growth rate.

Yes. So we have long said that we would rather have a business growing at 25% for a decade than 35% for a few years. So we just kind of architect ourselves around long-term growth. On the diagnostics side, that business is really going to be benefited by not just this robust demand in terms of therapy selection, but really significant rise in ASP. We have 2 big levers that are going to work in our favor. I think one is our main solid tumor assay got FDA approved, not just tumor normal but also tumor only, which allows us to migrate that entire platform to ADLT pricing. That should add somewhere in the neighborhood of $80 million to $100 million of kind of incremental ASP lift and profit and free cash flow and all the things that comes with it if you didn't reinvest next year alone.

And then our second largest product, our liquid biopsy, which is also quite large in terms of units. That is in front of the FDA now. We expect that will be approved. At some point, that also will have ADLT pricing. And the kind of benefit to us is that, that assay is kind of roughly similar in size to Guardant's larger assay that just got approved and they signaled that they're getting ADLT pricing or they expect ADLT pricing north of $8,000. So we had historically thought that assay would be priced at let's say, $5,000 or $6,000, but now it looks like it will be priced closer to $7,000 or $8,000. So that should add something like $250 million or $300 million of lift on an annual basis once that's approved.

So you just have these really big kind of revenue drivers that will accrue to the benefit of the diagnostic business in therapy selection. And then we have the same thing happening in MRD. We have even stronger unit growth rate, rising ASPs. And so like it's that just -- that business for the next, whatever, 3 to 5 years will likely just have really robust revenue growth. At the same time, as I just mentioned, the data business, you have record demand and you have this migration to people leveraging these large language models for their benefit is not going to slow down or stop. In fact, it's probably going to accelerate as these people are just now starting to leverage those technologies. And so I think the data business also has really long-term visibility. And so if anything, if you said to me today, are you going to under or overdeliver that 25% growth rate, you got to bet it all every day of the week and twice on Sunday, we overdeliver.

Amazing. And you announced the deal to acquire Personalis. You've obviously been involved with the company for some time before that commercially. Maybe let's just start with the rationale behind that acquisition and the path you see forward for MRD across the market.

Yes. So I mean, we have long thought that Personalis' assay was best-in-class. It's whole genome based. It has incredible sensitivity and specificity. It's limits of detection are really extraordinary. So we've long thought that it was a best-in-class assay. And we had cut a deal with Personalis years ago to be their exclusive distributor in the U.S -- in the world. I'm not sure it's U.S. or world based -- U.S -- in the U.S. And so we thought that, that was a great way to enter the market. And the deal was structured where they were paying us basically a sales and marketing fee to distribute the assay, whether or not they got paid or not.

And so for the last several years, this deal was largely in our favor, right? Because they were -- we were getting paid about $400 per test. They weren't getting paid and they didn't have reimbursement approved. And so it was just in our favor. And I had been pretty vocal that like why would you want to change that? You have to wait until the things flip. When they got lung and breast and recently IO approved by MolDX, it became apparent to us that we were getting close to that point, that their ASPs would rise pretty dramatically and that there would come a time in the next few months, few quarters, whatever, where their ASPs would actually be higher than they're paying us.

So we began having conversations. This is all kind of detailed in the S-4 that was filed. We began having conversations. Those conversations were accelerated when another party showed up and made an offer to buy the company. So whereas we may have bought them toward the end of the year or maybe early next year, we accelerated it by a few months because there was all of a sudden some activity. But it makes a ton of sense in that it's a best-in-class assay the unit growth is really strong. We're already distributing it. So it's already a part of our portfolio. And they will have ASPs that will rise to get to $1,000 or north of $1,000, similar to Natera, and it will become a really strong financial product. And so it makes a ton of sense inside our platform.

Interesting timing as well with the Merck, Moderna V940 melanoma vaccine data. It seems like you had some involvement there in kind of excluding what Personalis is doing. So can you maybe just detail how involved Tempus is and then how that Personalis addition would kind of come into the picture?

Yes, yes. So Basically, Personalis was selected to be the sequencing partner for that clinical trial some time ago, and they've been performing the sequencing for that trial. At some point, Moderna and Merck felt they needed a national partner for the rollout. And so they ran an RFP that I think most people participated in scale. And they ran an RFP, and we won that RFP, which we put out on social the morning it was -- the news came out. We won that RFP. So once the product is FDA approved, Tempus unrelated to Personalis will be handling a significant amount of the kind of revenue and volume associated with that approval.

In terms of who does the sequencing, those details are still being worked out in terms of what percentages Personalis may do some sequencing, we may do some sequencing. If we're one company, it won't really matter. But in terms of like if you were to look at this like pizza slices, we were getting the majority of the pizza slices anyway by virtue of winning that RFP. It's a really important program because it's really the first time that I'm aware of where the sequencing is actually a component of the manufacturing process and product. So it's unlike kind of any other form of companion diagnostic where somebody could get a drug approved using Foundational Medicine, but Caris or Tempus could do the same sequencing or Guardant could be part of the SERENA-6 rollout, but I can sequence for ESR1 as well, and you can prescribe the drug.

Here, if we don't sequence you, like you can't get the drug. And so you can't use somebody else. The manufacturing process is approved by the sequencing we do and a whole bunch of other logistical things we do as part of the process. So they need -- Moderna needed a partner that could handle that kind of national scale, redundancy, can't go down, all that good stuff. So I think it now works out great that we're going to be one company, but that process kind of ran its course at the end of last year. So for a long time, we've been a big beneficiary.

And perhaps this is more my job, but have you worked to size that opportunity up for what this could be. You obviously have melanoma today and obviously, other indications perhaps coming through the line. So how should we be thinking about how big this could perhaps be?

Well, it's okay. So -- and I said this. I mean, honestly, I don't know because I don't know how the other trials will read out, and I don't know how big this will ultimately be. My best guess is that it's quite big, both because the performance of this drug, its ability to essentially allow patients that don't respond to immunotherapy to respond is pretty extraordinary. I suspect it will work quite well in multiple subtypes, and then it will be a very big drug. So there's revenue associated with the work we do as part of those clinical trials, of which there will be many, and that's great.

And then there's the ancillary benefit that we -- one of the reasons our unit growth rate is so strong at Tempus is we just have given our oncology partners like more and more reasons to work with us. We said this years ago, we said you people are all kind of chasing performance of assays if that's why you make decisions, but it's never why you make decisions. Oncologists make decisions because of a whole bunch of other reasons, including ease of use, logistics, administration, contextualization. But like do I get the information I need in a timely manner in a way that's better than other people.

It's the same reason we shop at Amazon and we don't shop at eBay. If you go back in time, 10 or 15 years, eBay and Amazon were kind of neck and neck or 20 years or whatever. And today, that's just not the case. We all go to Amazon and we don't go to eBay. And it's all those kind of logistical benefits. And so this is just another -- this program will be another reason to use the Tempus because why would you want to sequence to somebody else? And then if you want to get this drug, which is a part of your mainstay therapeutic options, you then have to kind of resequence with us. So I suspect that we'll have both real-term revenue benefits as these trials roll out and then ancillary benefits as more and more people choose our product.

And then just reading through the S-4, one of the things that caught our eye was just the differences in how Personalis and Tempus was underwriting the kind of projections for the 2 businesses for the 1 business. So maybe just talk through where that difference was. I think Personalis came out at $758 million of revenue in 2030. Tempus $333 million. So why do you think there is a big difference there?

Yes. It's almost entirely ASP. So our expectations of unit growth, I think, are both quite robust. We have a robust pro forma. They have a robust pro forma. We expect the unit growth to be really strong. You can make different assumptions about how ASP rises. What percent of the ordering -- what percent of the orders are for like IO response? How fast do they get, for example, CRC approved? These would radically change your projections. We have kind of tried to take the approach of being conservative with those particular estimates. And so we just don't feel any reason to kind of like be overly aggressive. But the short answer is their forecast could be better than ours. I don't know.

Yes. Makes sense. And there are some unique features of the deal. Personalis is currently trading above the deal price. How do you think about that?

I mean I think it makes no sense. And I don't know what it's trading at. I didn't look at it yesterday, but there was a while it was trading like $17 or $18, which made no sense. I mean, first of all, you can read -- you can read in the S-4 that it was a competitive process. There were all kinds of people contacted. They ran a full and robust process. And the spread between the bids was only like $0.75. I think somebody at $17, we were $16.25. So it looks like there's kind of no logic to think there's some kind of magical price out there that's much bigger I don't think exists.

On top of that, it's the idea that somehow you would be trading on the price when you have Tempus is the largest shareholder, obviously voting in favor. Merck is the second largest shareholder also voting in favor. I think ARK is the third largest shareholder and they're a huge Tempus fan. It just doesn't make a ton of sense to me that in and of itself could be 35% or 40% of the vote and these things never get 100% of voting. So I don't see any topping bids coming. There's no kind of logic there. We already have significant shareholders voting in favor of it. So there's just doesn't -- there's no kind of rationale to be buying their stock at a significant price above where it's going to close. The only counter to that would be some kind of like nefarious short-term trade, like you somehow think because there's an exchange ratio that's calculated a few weeks before closing that you could like short us or buy them or whatever. But those traits, in my opinion, are kind of riddled with risk that I wouldn't be taking.

Makes sense. Maybe we can transition on to the genomics business and focus specifically on oncology for a little bit. I think at the Investor Day, you described 3 key trends that will drive therapy selection in the years ahead, physician penetration, earlier stage testing and more comprehensive testing as well. So when we look kind of 3, 4-plus years out, how do you think that market evolves? Obviously, it's a more established market in the oncology space, but we still obviously have plenty of room to run, I think.

Yes. I mean I think there are some fairly good studies that came out that kind of penetration rates are in the roughly 50% range for kind of later-stage cancers, Stage III, Stage IV metastatic high-risk. And I think that's probably right. In addition to that, we're going to be sequencing patients earlier. More and more biomarkers will show up. The evidence behind concurrent testing is extraordinary in terms of the benefits you get from doing solid tumor profiling and liquid biopsy. The benefits of RNA are extraordinary in terms of enhanced fusion detection.

So I suspect -- and then the benefits of MRD are obviously kind of also well known. So I think we will be in a cadence of broadly sequencing newly diagnosed cancer patients, probably all Stage 2 plus, even certain Stage 1 categories like liver and pancreatic and then broadly monitoring patients post therapy. And I think over the next decade, I can't see anything that's going to slow that down. And then I think you'll start to see other disease areas that begin to catch up because they've seen the benefits in oncology, certainly rare and undiagnosed disorders, certain immunological conditions. So I think molecular profiling for therapy selection and monitoring post therapy will be a growing space for a while.

I know there were some headlines earlier this year, CRUSH RFI that came out of fruit to few investors out. Incoming [indiscernible] has kind of cooled off since then. But any way you're thinking about the durability of reimbursement for therapy selection, mRNA -- sorry, RNA, your kind of dual XTR -- xR orders, how are you thinking about that, I guess, in the years ahead?

Yes. I mean -- so the -- on the Medicare and Medicaid side, reimbursement seems to be quite stable. There are kind of took a long time to establish national pricing, which has been like kind of fairly stable for the past, let's say, I don't know, 4 or 5 years now at this point. So I think the space has pretty good pricing in terms of solid tumor profiling and liquid biopsy. We obviously benefit from national coverage policies that are in place by virtue of the fact that our main assay is FDA approved, liquid biopsy will be FDA approved. So we're even out of some of that in that we'll have ADLT pricing and be part of national coverage.

I think -- but I think the space has quite durable reimbursement in terms of Medicare and Medicaid. It took a long time to establish it. I don't see any material pressure coming anytime soon. On the MRD side, right now, those assays are basically being reimbursed by MolDX. I suspect the other MACs will start to pay for those tests as well because I just -- that tends to be a pattern. I think -- so I think over the next, let's say, 3 to 5 years, you're likely to have very good, very stable reimbursement from Medicare and Medicaid.

I suspect over time, commercial payers will start to pay more for these tests. They're still radically underpaying. I think others have said the same thing, whether that's Guardant or Natera. So I think that's a pretty common supposition at this point, which means I think you're going to see margins in this space from the top providers that get extraordinarily high. One could argue too high. Long term, I think that will start to normalize a bit. If you fast forward 25 years from now, you might see margins in the 60% range, but I would not be shocked if over the next decade, you see margins in the 80% range.

Amazing, we have 2 of those highest volume tests, xT and xF going through pretty significant regulatory and reimbursement upgrades. I think perhaps it's underappreciated in the story, at least from my conversation. So maybe just remind investors how important that is, the kind of uplift that we could see into next year.

Yes. So as I mentioned a little while ago, we have 2 main products in therapy selection. One is our solid tumor assay and one is our liquid biopsy. The solid tumor assay was divided really 2 parts, tumor normal and tumor only. Tumor normal represented a minority of the volume. We got original FDA approval for tumor normal and that was -- that had ADLT pricing at $4,500. And so we couldn't -- we weren't able to migrate our platform fully to the ADLT pricing. A few months ago, we got tumor-only FDA approved with similar ADLT pricing, identical ADLT pricing. So effective January 1, we will be running all of our solid tumor assays under that pricing.

So you have this kind of immediate step-up for more than half of your solid tumor portfolio from like $2,923 at list price to $4,500. And then sometime toward the back half of '27, we expect both approval and pricing of our liquid biopsy to be in market, whether that's Q3 or Q4 is unknown. But at some point in the back half of the year, you'll have that. That step-up goes from, I think, $3,200 which is our current -- roughly our current liquid biopsy pricing to somewhere in the mid- to high $7,000 range likely. So that's a very significant step-up. So whereas the ADLT pricing for tumor-only adds, let's say, $80 million to $100 million of revenue and margin benefit, liquid biopsies is like $250 million to $300 million.

Amazing. I want to hit on the hereditary business quickly. I think expectations for growth have fluctuated a little since you acquired Ambry. Is mid-teens the right way to think about this business longer term? And maybe just touch on the underlying drivers you think of this business 3, 4 years out?

Yes. So we have bounced around a bit like a yo-yo on their growth rate. I think -- fortunately or unfortunately, we've only owned Ambry for like 18 months or something. So we've had to learn a lot about how their business is -- performs and is forecast. We could see early on that the growth rates they were experiencing, let's say, 6 quarters ago felt extreme to us. So we tried to call that out. And we tried to call out that it felt onetime to us, that it was not onetime, but not repetitive in that it was largely a function of Invitae going bankrupt and a shift of volume from Invitae who was one of the largest suppliers over to Ambry.

So I think we tried to call some of that out. But certainly, as you lap it, we now can kind of fully see the impact of that. So you had these kind of -- you have a business that should be growing in the 15% range that was growing at 30% or 40% for a while that then was growing in the low-single digits or is now growing in low-single digits, and we begin to lap that toward the end of Q3. So you'll start to see growth rates kind of look better because we're just lapping that period of excessive growth. So I think we get back to mid-teens toward the end of this year because it's kind of almost just math, and I suspect we'll get there.

Long term, I think the business -- I think the business sustainably grows in the 12% to 18% range, let's call it, mid-teens, low mid-high teens. I think it grows at that range just based on like current dynamics, meaning understanding hereditary risk is important. We keep -- just like in cancer profiling, everyone is publishing papers, looking at genes that are correlated with risk. People want to understand risk. And so this is a mid-teen grower space. I think the best estimate of the space is growing around 12%. So we should be a little better than that.

If you can ever unlock what is to me the most insane amount of latent demand, I think this becomes a really big business. And so -- which is at the present moment, we run about 2 million of these tests a year, something like that. And yet there's current coverage policies in place for about 70 million tests. And we are gated by the number of genetic counselors that can order these tests and genetic counselors are not revenue generating for hospitals. So they don't like to make money off that. So you have this like massive amounts of people that are in categories where there's reimbursement established. They're Black, they're Ashkenazi Jews. They have known familial risk and yet we don't test them. So like I think that problem has to get solved. And if that problem -- when that problem gets solved, and we're thinking through lots of ways to solve it, this could become a very big business very quickly. So I think conservatively, this over the next 3 to 5 years, grows mid-teens. If we get any of that right, it should have growth rates that are equal to or greater than our cancer business.

Understood. And I want to make sure we cover the data business. I think a major theme in '26 is AI-driven drug discovery. It feels like you have the head start here in the market. So what part of the AI-enabled drug discovery thesis feels credible to you? What feels a little more speculative? And like what role do you think Tempus can play in this evolution?

Yes. I mean none of it feels speculative. It all feels like very well established at this point. We've been licensing data in oncology for probably 7 years, 8 years, 2018. So this has been going on for a long time. People thought our data business would never get to $25 million, would never get to $50 million, would never get to $100 million. It's obviously now way, way larger. And we have had now multiple people enter into $100 million-plus long-term data licensing deals with us, whether that's AstraZeneca or BMS or GSK or Merck or BioNTech, it just goes on and on. And I say to people all the time, our pricing works identical to AWS. You can license one file from us for a few thousand bucks.

So the only reason these people are entering into long-term contracts is they want access to data and they want discounts. And so I think that speaks to when you have this many people licensing this amount of data for this amount of time, it just speaks to the durability of that business. And AI is only a catalyst to that. Our data was invaluable -- is invaluable for understanding synthetic controls, understanding how to design a Phase II and which mechanisms of action are driving response. Do you have the right design for your Phase III? How do you think about site selection? How do you think about commercializing that asset given that therapies are changing like there's so many reasons to buy our data. And spending $25 million a year on our data when you can make decisions that are going to save you $200 million or $500 million, is just a no-brainer. So I think in oncology, right now, we have a significant number of people that are these very large strategic clients. I don't know how it's not almost everybody over the next 3 to 5 years. And I think that extends into biotech and it extends into other disease areas.

I think you mentioned at the Investor Day, those top 20 pharma, biopharma companies that only really just dipped their toes in the water with respect to those data offerings. So how big do you think this could become? And I'm just trying to, as an investor, think about that time line and the cadence when this could really expand meaningfully.

I think it's kind of similar -- in my mind, it's similar to genetics. So only the base case is a much higher growth rate. So the business probably grows at 30% just in oncology just under current trend line. So if things just keep going as they are, and we're really mostly oncology-based, this thing can grow 30% for the next 5 or 10 years easily for a while. If other disease areas really take off in big ways, and we've got some big projects in flight or if the hyperscalers choose to get into this space, which I suspect they will at scale, then those growth rates are going to seem small because the amount of money people like OpenAI or SpaceX or Anthropic or Google or whoever have to try to use our types of data to train their AI models is extraordinary relative to the amount of money pharma has which is crazy because pharma has lots of money.

Yes. And maybe just talk through that competitive moat as well. You obviously have the data from your tests and then the identified data from the hospitals as well. So like why couldn't someone come in and replicate this model that you've established?

Well, I think -- I mean, I've been asked that question. So we went public 2 years ago-ish, a little over 2 years ago, and we began that process, we were delayed for a couple of years. And then we had a testing the water. So for 5 years, people have been asked that question at scale, meaning every 3 months for 5 years, people have been saying the same thing. So I guess there's a question, at what point does it -- is it like it just -- it's -- so it starts with to build the data product we built, we had to connect the thousands of hospitals. We had to enter into legal agreements, have BAAs in place, build pipes, ingest the data, harmonize, structure that data so that it's usable in a longitudinal format, match it to molecular data at scale, match it to digital pathology slides, the radiology scans, then build tools around that data because it's -- otherwise, it's just 500 petabytes of useless data.

So we've just done all that. And if you look at it, everyone who's tried to launch a data business, which is most of our major competitors, if you look at the launch of their data business, it's 5, 6, 7 years old, have just had no traction basically. And we continue to have significant traction. So in terms of our immediate cohort, the people like us that have rich molecular data, I think we've just outpaced them dramatically. It doesn't mean there aren't people out there with competitive products. I mean, there are. ConcertAI did a deal with Caris and people have done deals with Flatiron and Foundation Medicine. So there's -- you can buy data from lots of people today. And in certain use cases, people do. They'll license Flatiron data, they'll license IQVIA. So there's lots of competition in the market. But this competition isn't affecting on any level, our growth rate or the kind of proprietary fuel driving that growth rate.

Is it fair to assume that pharma is just going to demand more and more data from you guys as we look forward? It seems like that's the obvious play. It seems like that should flow through nicely into the growth rates into those years ahead. So anything we should be keeping in mind there in terms of like the quantity of data that pharma is demanding, anything you've seen?

I think if you look at R&D budgets and not just biopharma, but researchers, payers, life science companies, certainly the big hyperscalers, anybody who wants to build products that advance health care in any way, shape or form are going to need vast amounts of de-identified multimodal data, and we just happen to be sitting on a very large lake, 50 million patients, 10 million in cancer, 40 million outside of cancer. So we just have an enormous repository of data that I think is going to power a lot of this AI development.

Amazing. Eric, thank you so much.

Thanks for having me.

Ladies and gentlemen, thank you for standing by. My name is Angela, and I will be your conference operator today. At this time, I would like to welcome everyone to the Tempus AI Second Quarter 2026 Financial Results Conference Call. I'd like to remind everyone that this call is being recorded [Operator Instructions].

I would now like to turn the call over to Liz Krutoholow, Vice President, Investor Relations. Please go ahead.

Thank you. Good afternoon, and welcome to Tempus' Second Quarter 2026 Conference Call. This afternoon, Tempus released results for the quarter ended June 30, 2026. The press release and overview of the quarter and our latest presentation are available on our IR website at investors.tempus.com.

Joining me today from Tempus are Eric Lefkofsky, Founder and CEO of Tempus and Jim Rogers, CFO. Before we begin, I would like to remind you that during this call, management will be making forward-looking statements that are subject to risks and uncertainties that could cause actual results to differ materially. For a discussion of these risks, please refer to our 10-K and other subsequent filings with the SEC.

During the call, we will discuss non-GAAP financial measures, which are not prepared in accordance with generally accepted accounting principles. Definitions of these non-GAAP financial measures, along with reconciliations to the most directly comparable GAAP financial measures are included in our earnings release, which is available on our IR page.

I would now like to turn the call over to Eric.

Thank you, and good afternoon, everyone. Q2 was an exceptional quarter for Tempus. Overall, our revenues increased 22% to $382.5 million with this being the first quarter where we are lapping Ambry being fully integrated into our results. Our Diagnostics business delivered $289.3 million of revenue, an increase of 20% year-over-year, as slower growth in hereditary cancer testing was offset by higher growth in CGP testing due to acceleration in the business.

Momentum continues as June saw some of the strongest growth we have seen to date across the portfolio. Hereditary revenue for the quarter was up 5% to $107.4 million as Q2 of 2025 was a period of abnormally high growth, which we are now lapping. Data and apps revenues were $93.2 million, increasing 28% year-over-year with our data licensing and modeling business insights, growing at 36% in the quarter.

There were also several notable highlights in the quarter. We received FDA approval for tumor-only xT CDx. This approval allows the migration of our entire solid tumor DNA portfolio to be under unified ADLT pricing. We expect an estimated $200 uplift in ASP, which equates to approximately $85 million on an annual basis beginning in 2027. It's also important to note that we have our liquid biopsy, xF in front of the FDA now. And when that is approved and in market which should be in the latter half of 2027, we expect the incremental ASP lift to be an additional $550. Between xT CDx and xF approvals we anticipate approximately $400 million of revenue uplift in 2028.

We introduced initial results from and successfully delivered the first version of our foundation model to AstraZeneca. The model was used to predict which patients responded in several public and blinded clinical trials. We're thrilled to have achieved this milestone and are now working on the next version of the model. We signed a large multiyear data licensing and modeling agreement with BioNTech, who now joins the ranks of AstraZeneca, GlaxoSmithKline, Bristol Myers Squibb and others. This, along with Merck last quarter, is further evidence that our data and modeling capabilities are becoming instrumental to pharma.

We also signed large deals with Daiichi Sankyo, LevelSet Bio and Insight Pharmaceuticals contributing to the approximately $200 million in total bookings this quarter. We completed a $460 million offering of 0.0% convertible senior notes due 2032. The proceeds of this offering were used in part to repay an outstanding loan from Ares Capital. Importantly, this transaction allows us to save over $30 million annually in interest expense, enabling us to achieve positive free cash flow by year-end.

GAAP net income was $5.6 million and adjusted EBITDA was $8 million, a $13.6 million year-over-year improvement. We finished the quarter with $820.7 million of cash, cash equivalents and marketable securities compared to $643.8 million last quarter. As expected, cash used in operating activities improved significantly to negative $7.5 million in the quarter.

On top of all this, on July 20, we announced an agreement to acquire Personalis, minimal residual disease, MRD testing, represents a $20 billion-plus market and is one of the fastest-growing segments in oncology diagnostics. Bringing Personalis under our roof accelerates commercial adoption of our MRD test rounds out our overall portfolio and strengthens the multimodal data flywheel that differentiates our business. Given their improving financial profile, we felt now was the right time to pursue a strategic acquisition.

Up until now, we have phased our sales efforts. That's only about 10% of our sales force is selling MRD today based on these reimbursed indications. Even with that, we are delivering growth rates that have exceeded our expectations, running approximately 6,500 tests in Q1 and approximately 9,000 tests in Q2, growing 38% quarter-over-quarter.

With reimbursement in place for several indications and more coming, we believe volumes will be materially higher as we equip additional sales reps with Next over time. The transaction is structured as a 100% stock transaction with Tempus having the option to elect payment in cash capped at 50% of the consideration paid. We have already begun working with parties to put a debt facility in place as our intention, obviously, depending on our stock price is to finance a large portion of the proceeds with debt to minimize shareholder dilution. Even with this acquisition, we intend to see continued improvement in adjusted EBITDA and free cash flow in 2027.

Turning to guidance. We are increasing guidance to $1.595 billion to $1.605 billion in 2026, representing approximately 25% growth. We expect 2026 adjusted EBITDA to be approximately $65 million, an improvement of about $72 million over 2025, we're exceptionally proud of our results this quarter and look forward to carrying this momentum into the second half of the year.

Operator, we are ready to open the line for questions.

[Operator Instructions]. And your first question comes from the line of Kallum Titchmarsh with Morgan Stanley.

Great. Maybe one for Jim, just on Personalis. We've had quite a lot of questions coming through. Just on your underlying assumptions on the ASP front and just how those economics could become more favorable to you with time. So just any incremental color on that would be fantastic.

And then, Eric, I think you touched on this a little on the call last week. But maybe just talk us through how the incremental MRD data you'll now have access to could feed back into your data business? And I guess why that would perhaps be more of a compelling data set now for customers?

Yes. So I'll start on the ASP, and then Eric can take the second piece. On ASP, obviously, they've gotten coverage in several indications over the last several quarters. And so there's been improvement on the Personalis front. They have more indications that are coming down the pipeline as well. And so over time, obviously, we would anticipate ASPs to continue to improve as they secure coverage and additional indications.

And then also from a volume perspective, our ability to kind of expand the sales force that is able to sell that test, which today is around 10% will help us drive volume. So they're early on in the ASP kind of curve, but they've obviously had a tremendous amount of success in getting the first couple of indications approved, and we anticipate that continuing.

Yes. And just maybe a bit more color there, and then I'll jump into the data. So I think the part of their story that is so compelling is that they have a really a nice pipeline of studies that are being run. And we, like others, are watching and reacting to those studies that read out to turn into papers that eventually turn into approvals. They've done a great job of getting 3 approvals so far. They have a whole pipeline of others coming. And so the real clarity that's come into focus over the last 30, 60, 90 days is that you can start to see how this ASP story is going to turn for them in 2027 and all of a sudden, the economics that were more favorable for us or that are more favorable for us today because we get paid and don't lose money will actually flip and all of a sudden, they'll be getting paid, they'll have more margin, and we'll kind of wish we had that deal instead of our deal. And that certainly is a great piece of the story is -- as Jim mentioned.

There's also, I think, compelling aspects in terms of their data. Almost every major biopharma client we have that's running large studies is trying to understand the endpoint of those studies. And historically, we think a lot about scans as a major endpoint to understand if disease is recurring or there's progression or what's happening. And more and more, you're getting earlier signals from these kind of MRD tests that are showing signs of cancer recurring 6 months or 12 months before a scan. And so as you can imagine, if you're a drug company, being able to see when patients recur and be able to get them out of drug earlier is a really big deal. And so we have a consistent stream of people wanting us to include MRD data with the GERD data that they're using for licensing and modeling purposes. And I would suspect over time, it becomes a really compelling component of our overall data offering.

Your next question comes from the line of Brad Bowers with Mizuho.

First off, congrats on the large deals that you got this quarter. I wanted to focus specifically on the AstraZeneca piece, another congratulations on kind of delivering the first version of the model. So maybe just to double-click on what that looks like. And then I think there's a little bit of the elephant in the room on kind of what the agreement looks like for 2027 and beyond. To me, I think it seems that the foundation model is obviously, a big piece of that. Maybe just some help on where that contracting kind of fits. And just a reminder on the kind of escalators that can exist, whether the foundation model catalysts come at some point after this year such that the contract needs to be in place?

Yes. So the foundation model was accepted by AZ, that was a big deal because we had to hit certain criteria. And the cool part of that is you train this very large multimodal model trained on billions of parameters, very complicated and it had to perform as well as certain models that both we had developed and they had developed that were like highly tuned for specific use cases, including predicting response to both public and private trials. And so we would send them these models, and they would basically see how our big model performed against their own internal models and in a blinded manner, we didn't have access to a bunch of that data.

So the fact that we've met the acceptance criteria means that they're comfortable this model is predictive and can now serve as the foundation even though it's a foundation model for all kinds of R&D and development work they're doing. So that's a huge hurdle and we're ecstatic. And we're consistent to invest in that.

Separate from our foundation model efforts, they're obviously a licensee of our data and a whole bunch of our products, the current agreement we have with AZ, I think, goes for other couple of years. So it doesn't end at the end of this year. I think the current agreement goes -- I don't even know, through '28 or something, I have no idea, but it had several years left on it even at the end of this year.

So there are certain criteria that they can opt into preferential pricing. And if not, they would just pay more for the data they are licensing. We have -- first of all, there's a bunch of projects they've already committed to that will extend into 2027. So they will be a very large client in 2027, no matter what happens. And we would -- I can't imagine a scenario like literally where they don't want to lock in for a longer period of time to avail themselves of discounts. I mean, it just wouldn't make any sense. They haven't given us any indication that they're not going to want to lock in for a long period of time and avail themselves to discount.

So I would suspect that we will be delivering a similar amount of data and revenue to them next year. I would suspect that at some point, we'll have a long-term extension in place or they'll just use the contract they currently have and commit to similar kind of dollar amounts of data. And every indication we have, including their CEO, talking about, I think, on CNBC or whatever is that they're super happy and tend to be a long-term partner of us.

Your next question comes from the line of Kyle Mikson with Canaccord.

Congrats on a very good quarter. The first one on the excess FDA clearance tailwind, that looks like it's now $550 using 2Q data compared to $230 that you had at the Investor Day that was using 4Q data. So just -- I don't think you called out the reason for the change there, if you just comment on that.

And secondly, with your shares trading below $46, is it possible that Personalis to terminate? Can you just talk about what you can do to avoid that as well as what makes you confident that they don't do that, they don't terminate.

Yes. So on the xF pricing as kind of others have gone down the approval for liquid biopsies and kind of indicated the price that they're going after that our price -- our thinking around the ADLT pricing for xF has evolved. And we think that there's additional upside from what we had pegged for the kind of earlier on. So that assay is in front of the FDA now, as Eric mentioned, as we get later into '27, we would anticipate getting approval and then following kind of the ADLT pathway, but that's the rationale behind the change.

And look, it's an evolving market. Our assay is most comparable in terms of size, like literally size like megabases and size totality to Guardant's recent assay that they got approved. And I believe their ADLT pricing is something like $8,300 or $8,400. And so we -- it would be very hard for us to go to the market with a almost identical, at least in terms of like size and complexity assay that's radically less expensive. And so we have to follow people who've come before us that have set ADLT pricing when we have kind of comparable products in terms of complexity and size. And so the pricing here is just higher than we expected. And so it's a significant benefit to us -- will be a significant benefit to us once it's approved in the market. So that's the big uplift.

In terms of Personalis, I can't see a scenario where they would want to terminate even if we were slightly below the floor. We established the floor because we weren't willing to -- we weren't willing to have more dilution than x amount. And so we obviously have cash as a lever. We've got stock as a lever. We don't want to have more than x amount of dilution given where we're trading now, obviously, my preference would be to fund maybe close to half the transaction in cash and the balance of stock to keep the dilution quite low. I believe we'll have that opportunity and I can't see any scenario upon which this doesn't close.

As you can imagine, they very much want to do this deal. We're a current partner of theirs now, it would be highly disruptive if this deal didn't get done on their side. And I just can't envision any scenario even if they end up getting a few less shares where it doesn't go forward.

Your next question comes from the line of Ryan MacDonald with Needham.

This is Matt Shea on for Ryan. Eric, you've seen some really nice momentum in the Data and Insights business throughout first half of 2026, including the BMS expansion in May and a number of deals you announced today, maybe can you talk about the level of momentum you have going into the back half of the year? And then maybe for Jim, as we layer in that BMS expansion and $200 million of bookings in the quarter on top of the $350 million of TCV that was already earmarked for revenue in 2026, how much visibility and confidence you have in hitting the implied $410 million of data revenue guidance, if that's even still the right number might be a bit higher with the guidance raise? And how are you thinking about levers for upside.

Yes. So I mean, I can -- you may add on, but my comment, I think, will tackle both, which is in light of the deals we've been signing , first of all, we've kind of have more momentum. I mentioned this, I think, on the last call before that the data business is just on fire. We've had more momentum in terms of signing deals than we've had in like a long time in years. Other than the foundation model, it's probably the single best run of 3 or 4 quarters we've had ever in terms of momentum. So we're having just an awesome moment. More and more people want our data.

And more importantly, what's really exciting is they don't just want our data, they want access to Lens. They want us connecting and provisioning GPUs for them in Lens, they're uploading data. They're building models that remain in Lens. So the business just feels super healthy, super sticky and it's just -- and we just have a stronger pipeline and more demand than we've had, which means we have great visibility into our growth rates, not just in 2026, but 2027. And we -- that's how we think about the data business. We really are interested in maintaining long-term growth in that close to 30% range plus or -- and we kind of want to plot these things out in a way that we feel like we can grow at that level for years, 3 years, 5 years, 7 years. And so we feel great. We're in a great spot for '26. We're in a great spot for '27, and we now spend a lot of time thinking about '28.

Your next question comes from the line of Mark Massaro with BTIG.

Congrats. I wanted to start maybe just to clarify the higher pricing assumptions on xT CDx or pardon me, the xF. Maybe can you just walk us through what rates or what prices are you estimating on the Medicare side? Because I know you cited Guardant, but if you could be more explicit, that would be helpful.

And then, Eric, when do you think you can sort of take that 10% promoting the Personalis test now? Why not take that up faster? And so do you think you could take that up sooner rather than later? Or are you waiting for the deal to perhaps close?

Yes. For xF, Mark, we're assuming a $7,500 ADLT price.

And in terms of taking MRD up faster, the same constraints we had when we didn't own Personalis will be the same constraints we'll have even after this transaction closes, which is we just want to time the full unshackling of these efforts to having the tests on an ASP level be basically breakeven. If you're losing money, if your margin is negative and you kind of rush to run an extra 100,000 tests, you're just burning money.

And if we felt like this market was such that this was beachfront real estate that you had to procure, we would do that. We would tell the world, hey, we want to earn a bunch of money and here's why we think it makes sense. We don't believe that. We didn't believe it with therapy selection. And if that was the case, Foundation Medicine would dominate the space instead of Tempus and Caris.

So we don't believe this is -- there's like beachfront real estate to be procured. We do believe it's important that we're in market with an offering that is comprehensive that people want. We think we can meter this out and not lose the market opportunity. Obviously, we're growing super fast. We're 38% quarter-over-quarter, and we're getting to some real scale, and we will get to even more significant scale in '27.

And at some point, you'll see this pivot where the ASPs will start to climb up and you kind of -- you can see breakeven in sight, and that's the point where I think you should expect us to kind of ramp up the sales force pretty dramatically. That said, you won't even notice it because the core economics of our business from a gross margin growth perspective, and the variable investments we make are so significant that if we wanted to invest an extra $50 million in the sales force, we just would spend $50 million less on cloud or things that you don't even see and we still would be EBITDA positive. We still would be cash flow positive. So we just are in a great spot where the core business is now starting to generate so much gross margin and gross profit dollar growth. And we're making so many incremental investments that are like long term in duration that we can make some of these investments like sales force growth without negative EBITDA or negative cash flow or going backwards. So I think we're in a good spot.

Your next question comes from the line of Subbu Nambi with Guggenheim.

This is Ricki on for Subbu. So following the launch of GenomeNext, do you have any updates on your outlook for the rare disease ramp within Ambry? And in the letter, you mentioned you're expecting this to pick up in the second half. Would you be able to quantify this for us? What would a successful second half for rare disease with an Ambry look like?

Yes. I'll take the first. The launch was great. Great, meaning we had an expectation for the first month. And I think I'm going to say something like 2 or 3 weeks in, we were already 50% higher than our expectation. So that said, it's -- these are small numbers. Like at the end of the day, this is a new product for us. So when you get to market and sell 500 or 1,000 tests, like it's a good start.

So I do think there is some upside that is going to come in the back half of the year related to whole genomes. We don't yet have enough insight to know. Right now, it's not cannibalistic to our whole exome business. Does it become cannibalistic at some point? So far, it's not, but we only have 1 month of data. And obviously, we're trying to ramp up hereditary growth rates. And so we kind of view that business as getting to like mid-teens growth by the end of the year. We're being conservative about our whole genome estimates, although it will pick up. And so I think we're in a bit of a wait and see on how that's going to shake out. And again, fortunate that we don't need it because our 2 main businesses, oncology testing and data are overperforming, and so we'll be fine.

Your next question comes from the line of Brendan Smith with TD Cowen.

Maybe just another follow-up on the data and insights business. I guess, kind of following up on your commentary about momentum in that part of the business. I mean you mentioned some of the newer deals being -- it sounds like potentially more expensive with some of these pharma guys in to leverage like Lens and you mentioned the other data and apps offerings. I guess just in terms of economics to Tempus, should we assume that some of the kind of construct of those deals drive potentially better revenue to you all over the course of the partnership? Is it maybe faster recognition of booking res versus backlog? I guess, I'm really just trying to understand how some of the levers there manifest and how we should think about the ramp in reported versus TCV as more of those guys get online and get the use of the platform up and running?

Yes. I can start and then Jim can jump in. So I mean so it's probably maybe worth some history. So we used to have a business where we would like go to people and say, we have this de-identified data, if you want to license it, we'll send you 5,000 files and you can pay us and our revenue was very lumpy, but we recognize revenue instantaneously. And we made a shift several years back, where we kind of stopped all that, like upfront revenue and moved people to 1-year, 2-year, 3-year, 5-year licenses and really deferred a bunch of that revenue which was tough to swallow back then, but great for the long-term health of the business because we now have like awesome visibility multiple years out.

So I don't expect these new deals where people are getting more ingrained with Lens and getting more ingrained with building small or large models in our environment accessing GPUs at some scale, I don't think they'll change revenue recognition at all. They just are another kind of element of stickiness that locks people into our ecosystem. They are kind of first locked in because they signed long-term contracts that are fixed in terminate, you can't cancel whatever the fixed term is. And number two, they're now locked in because they're building models in our environment, they can't take the models.

That said, the main reason they're locked in, we think, is because our data is awesome and the tools are really helpful. And if that weren't the case, we wouldn't have this healthy of a data business and one that continues to grow really fast.

And in the interest of time, and our last question comes from the line of Robert Bamberger with Baird.

You guys have cited about a 40% algorithm attach rate on solid tumor. Is that still the case in Q2? And I guess what's the algorithm that drives it and what attach rate is then embedded in your guidance here?

Yes. So the algorithm attach rate in Q2 was 45%, so a slight uptick from the 40% that we had quoted in Q1. And it's really broad-based. Obviously, we've got a suite of algorithms that address a number of different kind of questions or insights that physicians may be asking for. And so it's pretty broad-based in terms of which algorithms are being ordered.

And then in terms of the guide, many of those algorithms remain not being paid. And so there's no impact on revenue from the number of algorithms, although it does highlight again, our advantage in diagnostics are the insights that we provide physicians beyond just the test results. And so it helps drive kind of that core volume growth, which accelerated to 31% in Q2, it's just another factor of the data advantage that we have.

That concludes our question-and-answer session. I will now turn the conference back over to Liz Krutoholow for closing remarks.

Thanks, everyone, for joining us. If you have any questions, please reach out to the IR team. Have a great day.

Ladies and gentlemen, that concludes today's call. Thank you all for joining. You may now disconnect.

Ladies and gentlemen, thank you for standing by. My name is Nova, and I will be your conference operator for today. I would like to welcome you to Tempus AI Company Update. [Operator Instructions] Now I'd like to turn the conference over to Elizabeth Krutoholow, VP of Investor Relations. Please go ahead.

Thank you. Good morning. Thank you for joining us to discuss Tempus' agreement to acquire Personalis, which we announced this morning. Joining me today are Eric Lefkofsky, CEO of Tempus; and Jim Rogers, CFO. We issued a press release and posted an investor presentation this morning, both of which are available on our Investor Relations website. As a reminder, during this call, management may make forward-looking statements. Slide 2 of our presentation and the press release issued this morning contain additional information on forward-looking statements and other important information on the proposed transaction. We welcome any questions specific to this transaction. Please be advised that we are currently in a quiet period, which limits our ability to offer further comments. I'll now turn the call over to Eric.

Thanks, Liz. This morning, we announced that Tempus has entered into a definitive agreement to acquire Personalis. For the terms of the agreement, Personalis' shareholders will receive consideration of $16.25 per common share of common stock, representing $1.5 billion net of Tempus' existing ownership interest. MRD represents a $20 billion-plus market and is one of the fastest-growing segments in oncology diagnostics. It is transformative for cancer care, allowing clinicians to detect disease recurrence earlier than traditional imaging, enabling more informed treatment decisions when cancer recurs.

We've been the exclusive distributor of Personalis' tumor-informed MRD assay, NeXT Personal since 2023, which we believe is a best-in-class assay given its ultrasensitivity. By combining Personalis' tumor-informed assay with our tumor-naive offering, XM, we're able to offer solutions that meet each oncologist's MRD needs and provide a wide variety of solutions across tumor types. Bringing Personalis' testing portfolio under one roof accelerates commercial adoption of NeXT Personal while strengthening the multimodal data flywheel that differentiates our business with longitudinal patient data providing insights.

Our partnership with Personalis has been very successful. NeXT Personal is now reimbursed across multiple use cases in breast, non-small cell lung cancer and IO monitoring. As we've discussed historically, we phased our rollout of the assay based upon reimbursement of various indications, and we're on track with growth rates that have exceeded our expectations, having run about 6,500 tests in Q1 of this year and roughly 9,000 tests in Q2, growing 38% quarter-over-quarter. And that's just the test that we distribute for Personalis. They sell some of their own tests, which makes that even higher.

This growth is exceptional when you consider that only 10% of our sales force is currently selling MRD solutions today. So when you think about that kind of 38% quarter-over-quarter growth rate, it puts it into context. Going forward, we believe volumes could be even more material and higher as we equip additional sales reps with our offering and at more indications, secure reimbursement. In addition to strengthening our MRD leadership, the Personalis portfolio enhances our biopharma offering through profiling and IO capabilities. The potential addition of the identified longitudinal MRD data also creates really interesting opportunities to enrich our models to provide differentiated insights for our biopharma clients.

Serial measurements reveal disease dynamics, treatment response, resistance and recurrence, which are helpful for biomarker discovery, patient selection and trial optimization with reimbursement in place and more coming Personalis' exiting a period of heavy investment and losses. Given the improving financial profile, we felt now is the right time to pursue a strategic acquisition. Under the agreement, Tempus will acquire all outstanding shares of Personalis not already owned by Tempus AI at a price of $16.25 per share, representing a 6% premium to Friday's closing price and a 28% premium to the unaffected 30-day VWAP. Consideration will be structured as 100% stock with Tempus having the option to elect payment in up to 50% in cash. Personalis' shareholders will receive a floating exchange ratio of Tempus common stock for each share of Personalis common stock at closing, subject to a maximum exchange ratio of 0.3356.

Cash consideration can be financed with cash Tempus has on hand and original borrowing we procure between signing and closing. Both parties expect the close of the transaction to be late 2026 or early 2027. We'll provide additional detail on the transaction's financial impact on our outlook during our Q2 earnings call on July 30, which is about a week from now. But as we've highlighted in previous calls, there's a certain amount of discretionary investment that we are able to make each year given that we have increasing gross profit dollars from the growth of our core business across therapy selection volumes increasing, ASP tailwinds, which we've discussed and continued growth and strength in our data business. And we'll utilize some of those investment dollars to drive our MRD offering growth while continuing to demonstrate leverage in the business, both from an adjusted EBITDA and cash flow perspective. Even with this acquisition, we intend to be EBITDA and free cash flow positive in 2027. Thank you for your time this morning and for your continued interest in Tempus and our evolving growth story. Thank you. With that, it's over to you.

Great. We can now open the line for questions.

[Operator Instructions] Your first question comes from the line of Kallum Titchmarsh from Morgan Stanley.

Obviously, still somewhat in the initial innings of the launch, but maybe just talk to some of the feedback you've been hearing on the ground on NeXT Personal that I assume supported this decision. It seems like pretty nice growth out of the gate. So any sense of whether you're seeing competitive shifts here or it's more kind of a market expansion from accounts that weren't utilizing MRD tests beforehand?

Yes. I would say the growth in terms of -- obviously, percentages is pretty extraordinary. Anytime you have a business that's growing almost 40% quarter-over-quarter, those would be exceptional year-over-year growth rates. These are quarter-over-quarter growth rates. So I think it's safe to say that we are quickly gaining market adoption. And I think that is that over the last several quarters has been both a function of the market -- the overall MRD market is growing. It's a very healthy market and one that's growing pretty rapidly, I think, as evidenced not just by our growth rates, but by Natera and others. But I also think that given the really fantastic performance of Personalis' assay in their suite of products, you're going to -- I would expect to see more market shift -- and this really kind of speaks to the -- I think, the Tempus -- when the Tempus real flywheel gets humming, it's a function of a best-in-class diagnostic test and certainly NeXT is that, combined with all the other technology attributes we have from broad connectivity to hospitals all over the country to a whole suite of AI-enabled solutions that make ordering our products easier to a variety of AI insights we're able to deliver through the models we build. And I would suspect all of those will be more tightly embedded into our MRD offering over time. And as they continue to get more and more indications covered, we will dramatically expand the amount of our sales force that can sell it.

Your next question comes from the line of Kyle Mikson from Cannacord Genuity.

Congrats on the deal here. Just first, maybe just talking about like why now? As was discussed in the last question, it's kind of early for this -- for Personalis. I know they have a lot of reimbursement and so forth, but not a lot has improvement. So maybe why is now the best time? And also just talk a little bit about the dilution kind of road map here, the burning $20-plus million a quarter. You have that target to a positive cash flow in '27, but it's a little -- this doesn't help you kind of get there. So just maybe expand upon those factors.

Yes. I think the -- we looked at this -- we looked at Personalis back in 2023 and decided to do a commercial deal in large part because we could see that there were several years of significant investment at the time that they were going to have to make. And in fact, I think you can see from their financials, they've made those investments in '23, '24, '25. So now we're almost at the end of '26. So I think it was kind of the right decision for us to let them make those investments and get the assay to this point. The point that it's at now is it is beginning to get and will continue to get, I would assume, a very broad coverage and the economics of these assays begin to turn pretty dramatically once they are covered more broadly. So you kind of -- unlike other assays where you can get coverage quicker, here, you have to basically demonstrate analytic validity and clinical validity, you have to publish, you have to get MolDx approval. And then all of a sudden, one day, you just turn on reimbursement. So you go from like 0 revenue for some of these tests to significant revenue. And they are now entering that part of the cycle where their financials should improve dramatically. So that's why it was the right time for us to decide to acquire them. And in terms of like it being a proven test in market, I think it is widely considered if not the best, one of the best best in the market today. And so as the financial profile of these assays gets better, I think you'll see pretty dramatic expansion and really strong operating results in terms of revenue.

Yes. And then on the second part of your question, as Eric kind of noted in his prepared remarks, we're fortunate that the core business obviously has good tailwinds, both from like a therapy selection volume growth plus the ASP tailwind that we've highlighted over the last several quarters getting the tumor-only for xT FDA approved and then having xF in front of the -- so we're generating a lot of incremental gross profit dollars. And as we've previously discussed, we've always intended on investing a certain percentage of those kind of back into the business. MRD was a big area of investment, and that allows us to kind of absorb some of this burn given the strength in the core business.

Yes. I should jump in. I think Jim makes kind of the most compelling point just I want to highlight it, which is we have -- we're fortunate that we have this high-growth business that just generates lots of gross profit and lots of gross profit dollars. And we look for what are the best places to invest that. And as Jim mentioned, this, in our opinion, is the best place. So we're kind of thrilled that we're able to kind of lean into growth and position the business for long-term success.

he next question comes from the line of Dan Brennan from TD Cowen.

Maybe just I'll ask one, obviously, but a couple of other. Eric, I think you mentioned at the onset, 10% of the sales force is directed towards, I guess, MRD today or maybe specifically Personalis. So is the implication that, that number goes up and the growth rate accelerates from what we've seen? B, I know you mentioned the ability to integrate their data more. So I'm just wondering if you could share what the relationship was prior to owning the business outright in terms of the ability to use the data within your pharma offering and how that might change now? And then C, like does this impact your own plans on your own MRD assays? And then the final one would just be on the Personalis Pharma business. They have an important pharma business. They've got deals with, I think, Merck, Moderna. There's some outcomes data coming out later this year, early next year. Does this deal impact in any way the relationship with those companies and that offering?

Yes. So I learned a long time ago, I'm not smart enough to remember 4 questions in a row. So I cover some part of that. The pieces I can recall. So yes, we have a limited -- rough -- somewhere around 10% of our sales force selling the MRD product today. We will continue to ungate that and invest in additional salespeople in the field. It's more a function of the balancing act between when they get additional categories reimbursed and so on and so forth. And so I think they've got a really strong R&D portfolio, which they've disclosed in their own investor meetings. So you can get some sense as to when various things are coming to market. And as it's going to line up here likely in '27, somewhere in -- it's hard to know when, early, late whatever. But at some point, you'll get to this tipping point where the revenue generated from these assays is high enough that you can kind of more completely unlock and fully unshackle the sales force. And so we'll just keep people informed as to how that's going. But we expect really strong growth rates. We said this and we said this in our Investor Day a month ago or so, we expect really strong MRD growth rates to continue. And when you have a business growing 40% quarter-over-quarter, like that gets very big very quickly, and we expect that to continue. As it relates to data, yes, the deal was originally structured where we had broad clinical distribution rights, but they had their own biopharma business. They had their own data rights. And so post closing, we will more tightly couple these things together. And I suspect it will be catalytic to both their pharma business and our pharma business. So I think there'll be some really nice data benefits as we don't really fully bring in these MRD time points in a way that they do. And then finally [indiscernible] thing, as it relates to our own tumor-naive product. We have told folks over the last several quarters that we're seeing the market had shifted really pretty dramatically to tumor-informed in terms of volume. And the tumor-informed part of our business represented 95-plus, high 90s of our -- of the orders we were receiving. And I suspect that will continue for some period. We still believe tumor-naive has an important place. We'll continue to invest in tumor-naive. We'll continue to bring it to other indications. We're working on a more sensitive version of our assay now, and that's moving along well. But the market is just really leaning into these ultrasensitive tumor-informed assays that have incredibly low limits of detection. And we're kind of excited to ride that wave for the next several years. But longer term, I would suspect both will do quite well.

And then, Dan, I think on your final question around kind of their biopharma business. Obviously, we also have a large data business with biopharma. We also do some sequencing for biopharma as well. And so again, we can integrate that business with kind of the current offering and think it can be helpful in expanding the overall relationship with biopharma.

Your next question comes from the line of Brad Bowers from Mizuho.

Maybe just a 2-parter on the revenue side. Just wanted to hear about kind of the pathway to reimbursement. Obviously, the opportunity to have significant reimbursement here with the Signatera test at $3,500. So I wanted to hear about the time line for that process. And then on the other side, what does market share kind of look like in the deepest Personalis accounts? What does MRD penetration look like since you're kind of the first, I guess, alongside Personalis, the first payer to kind of come at this market here. So I wanted to hear about the deepest accounts that you're in and what that might imply for future market share.

I'll cover the market share. Jim can take reimbursement. I don't think we're prepared to kind of go too deep in reimbursement largely because they've got a road map, but Jim can cover it in a second. On the penetration side, we have been -- kind of been very judicious with who we let carry the MRD product within our world. We have hundreds of sales reps in the field across hereditary profiling and comprehensive genomic profiling and therapy selection. And so we've been very restrictive in terms of which of our accounts can order MRD and how and so on and so forth. So I would say most things are underpenetrated or not fully penetrated. And it really does come down to the balancing act of reimbursement across enough indications that you're able to generate an ASP high enough that you're not losing money on every test. And what's happened to them is they're just beginning -- that pendulum is starting to turn, and you'll see ASPs of this particular test will rise -- should rise pretty precipitously over the next year, and you'll kind of go from losing money to breaking even to then making money. And it's in that journey that we'll start to penetrate these accounts more fully, but they are kind of highly underpenetrated.

Yes. And then just quickly on reimbursement. NeXT Personal is reimbursed across kind of multiple use cases in breast, non-small cell lung cancer and IO monitoring. They've kind of laid out their road map for kind of additional indications and have a pretty robust kind of plan to bring additional indications to MolDX for approval. And so we think that they're set up, obviously, with what they have in place today, that's allowed us to kind of start ramping as they continue to get more indications, as Eric indicated, that allows us to kind of ungate additional volume and have more reps kind of selling. So they're making really good progress over the last 12 or 18 months from a reimbursement standpoint, and we anticipate that continuing as they submit for additional indications.

Our next question comes from the line of Subbu Nambi from Guggenheim.

This is Ricki on for Subbu. Most of the focus has been on MRD, so maybe something that hasn't been asked about NeXT Dx. Is there anything we should be thinking about in terms of that NeXT Dx clinical therapy selection test? Where does it fit in the portfolio? And is it additive or competitive with xT, xR?

I think I'll just quickly say, I think the portfolio at this point is just kind of holistically complementary. And now I think getting kind of very complete or post the closing of Personalis will be very complete. You have this kind of range of assays from best-in-class, am I at risk of getting cancer to best-in-class? I have cancer, how should I be treated? Whether that's from a tissue biopsy or a liquid biopsy to I'm post treatment and I need to be monitored and across a variety of subtypes of indications, what's the best test to order for that monitoring and for that early detection of recurrence? And so we just have a really incredible portfolio. Obviously, in our world, I think that portfolio is -- with this acquisition is really as good as I guess, the only place that we still have work to do is obviously on the MRD tumor-naive side, where we're just earlier in that game. And so we'll continue to try to make investments there to figure how to get those assays over time up to the same quality as what Personalis has been able to develop on the tumor-informed side. But it feels to us like we have a really strong portfolio across diagnostic, and we're in an interesting position in a world where these kind of tests will be ordered far more often, I think, across all the different categories we're in, both in cancer and then increasingly in noncancer. And so I would be kind of very surprised if a decade from now, we're not sequencing just multiples of the number of patients we sequence today clinically. And so you just -- in a world where we're going to generate an incredible amount of molecular data, it's going to become increasingly important for health and wellness and helping people fight disease. The data is going to become increasingly critical for biopharma to make decisions. You want the best portfolio, you want scale and you want to be in the best position to kind of win in that world, and we think this helps us, and we are. So I couldn't be more excited.

In interest of time, your last question comes from the line of Mark Massaro from BTIG.

Congrats on the deal. If I remember, I think Personalis has talked about scaling to gross margins of about 50% to 60% over time. Can you just share with us whether or not you agree with that margin target or if you think there could be upside to that? Also, would you mind just confirming that some of the reimbursement dollars for Medicare have trickled in? And then just to confirm last question that ImmunoID NeXT will remain part of the portfolio.

So I'm hesitant to kind of go too deep into some of the intricacies of Personalis' business before they provide some of that color. They are collecting dollars on the clinical side that is -- those funds are flowing. So there's certainly no issues there. In terms of long-term margin target, we'll provide more color on our call in a week. But obviously, we wouldn't have made the decision to acquire them if we didn't believe the margin profile was going to be super healthy. We're, I think, financially disciplined in that regard. We try to be conscientious when we're buying assets that we're paying the right price. And we -- as we said historically, we believe a business like ours that is 10-plus years old, should be generating EBITDA and cash flow and run a significant operating income, and we're on that journey. And so we don't intend to go backwards. And so for us, the timing, as we talked about a few minutes ago, was really important. And the reason we didn't do this a year or two ago is we wanted to be at the point in the curve where this was going to quickly turn into a really healthy business from a gross profit perspective and a margin perspective, and they're getting close to that.

That concludes our question-and-answer session. I will now be passing the call back over to Elizabeth Krutoholow, VP Investor Relations for closing remarks.

Thank you. Thanks, everyone, for joining us this morning. We look forward to speaking with you on our Q2 call on July 30.

Thank you, everyone, for attending this call. You may now disconnect.

Great. Good afternoon, everyone. I'm Evie Koslosky, the Life Science Tools and Diagnostics Analyst here at Goldman Sachs. I'm joined here with Eric Lefkofsky, the CEO of Tempus AI. Thank you so much for being here.

Thanks for having me.

I guess to kick us off, maybe you start by walking us through Tempus' business model. How are you structured differently from most diagnostics companies? And then I guess, what was the genesis of your unique kind of connected data strategy?

Well, that's the unique part. So I think we didn't -- like other people, Tempus is almost 11 years old and like other people, we didn't set out to just be a lab. We set out to kind of solve a problem, which was how do you contextualize these sequencing results in a way that would allow you to figure out what's the right path for this patient? What drug should they take, what adverse events might they expect, what clinical trial can they actually enroll in. And so to do that, we needed clinical data.

And so from our earliest onset, we said we have to connect molecular data and clinical data at the same time. So the challenge back then was we would go to hospitals and say, we want to sequence your patients. We just opened up this lab. So we're already kind of not established in a world where foundation medicine kind of dominated. But oh, by the way, you have to give us your clinical data because these things don't make a ton of sense, and we want to use technology to have them make sense. And so it was a very friction-filled sales process.

But fortunately, people realized that it was kind of a mess and they needed someone to help them make sense of all of it. And so people started giving us their data, and we were sequencing and these things just began to scale. And then obviously, over the last 10 years, we've become the biggest players out there.

Conversation becomes a little easier. So I think Diagnostics represents the majority of your revenues today, but I guess how do you see that changing over time? Like should we expect Diagnostics to be core engine longer term? Or should we expect to see the data and applications business become more essential to the P&L?

Yes. I mean I think we have long said that they're both great businesses. Now they're both spaces in flux. Our data business is in flux because AI is having such a catalytic effect on the business. And how you can structure data, what you can do with the data. Years ago, we were just selling data for the purpose of kind of analytics. Today, we sell most of our data is sold for the purpose of model building.

But the diagnostic business is equally in flux. You -- what's happening is pricing, which people thought, let's say, when we opened up our lab maybe 8 or 9 years ago, people generally thought prices in next-generation sequencing would be commoditized down. They've actually gone the other way. And with companies like ours and Guardant and others that are getting FDA approved and ADLT status, the pricing has actually gone up. and most recently gone up quite a bit.

The last two prices of big assays comparable to some of the things we have in market aren't in the $5,000 range, they are really in the $8,000 range. And so even we who -- we just got approval for our tumor-only assay. So we'll have kind of 100% of our solid tumor portfolio under FDA ADLT status. We're also in the getting the FDA approval on our liquid biopsy product. We would have thought a month ago that, that product would have been priced at, let's say, $5,000 or so. But it's now likely to be priced at $7,500 or $8,000. And so you have this kind of precipitous rise in prices in sequencing. And let alone the fact that still the majority of sequencing is paid for by Medicare and Medicaid, not private payers. And that also is starting to turn and crack.

So I think you're going to have this really significant rise in ASPs for sequencing companies over the next several years, contrary to what people thought. I suspect, like in the aggregate, the margins of NGS companies will be like really extraordinary over the next, let's say, 3 to 5 years. That may at some point, normalize. But it's going to be a pretty extraordinary ASP story for the next 3 to 5 years.

Great. And then I guess, just digging more into diagnostics. So one of the differentiators of your strategy is connecting the dots for physicians and patients and the unique insights that provides them. Maybe walk through these competitive advantages in the market and how much health systems and doctors weigh that aspect when deciding to use a Tempus test versus another provider?

I mean, one of the hardest things we've had to do when I talk about the difficulty of like convincing people to sign up with us, it's also not just giving us their data, but it's connecting to our ecosystem, so we can get the data. And that entails this really long process of getting through legal, signing the appropriate BAs where it needs to be in place, then getting through the IT implementation, which can take forever. And so it's a really long race.

When we went public, I would equate it to mowing 3,000 lawns like you just have to -- there's no way to cheat the system. And so we've done that with something like 5,500 hospitals in the United States. So we have built connections to a significant percentage of providers and those connections allow us to sequence patients, collect data, generate some kind of diagnostic insight and put the insight back in the hands of treating physicians at real scale and not just one form of data, but typically many forms of data. So we're able to move a digitized files, like pathology slides or radiology scans in addition to structured, unstructured notes.

So I think it's a huge advantage if you want to be in the business of kind of wrapping AI around a laboratory test result and connecting to hospitals. And so the people that have to -- want to do what we do will have to in some way, shape or form, build all those connections.

Okay. And I guess within your Oncology portfolio, I mean, you have offerings across the entire cancer care continuum. Maybe starting with therapy selection, though that space is growing really well broadly. And then you recently got FDA approval for the xT CDx. So what are you seeing there from a competitive standpoint? And how do you expect the FDA approval to change volumes or ASP?

I don't think the FDA approval will change ASP. So it could add $75 million, or $100 million of revenue. And obviously, with no cost, and so that part is great. But I don't think it changes adoption. Our adoption is really driven off of the connectivity we talked about a second ago. I mean if you look at our unit growth, which has kind of been in the low 20s, so somewhere in that range, versus others who might be in the low teens or not growing at all, it's predominantly because we offer this contextualized connected technology-enabled platform that physicians really like.

And I would say this years ago, there's only 14,000 or 15,000 oncologists in the U.S., and they have extraordinary influence and power. They don't have to use anybody. And so you can just follow their ordering patterns to know what is or isn't working for them. So when they really like a company and order a lot more, that company must be doing something well. And I think that's what we've been able to achieve in therapy selection.

Not to mention the fact that the overall space continues to grow both as we move from sequencing only later-stage patients to sequencing earlier-stage patients and as the guidelines call for more comprehensive profiling more often. So you have like therapy selection is a good space that's growing. We're growing faster by virtue of those connections, and that's driving our units. The FDA stuff just drives ASP, but the combination of both for us means that we expect kind of 30% revenue growth of our main diagnostic business for some time.

Okay. Great. And then how should we think about the conversion to the xT CDx throughout the remainder of the year?

At this point, the conversion of xT CDx is for us kind of largely irrelevant because the only benefit of the conversion was that we were moving from, let's say, [ $2,923 ], which is the typical price we get paid as an LDT to the ADLT price of, let's say, $4,500. But now that both assays are approved under the same ADLT status doesn't really make a difference. So it will be -- it's less about migration. It's more about flicking the switch and we expect the switch to be flicked kind of January 1. And so for all of 2027, we would expect higher price.

Okay. You also recently launched the xH whole genome test for heme. I mean maybe just talk us through the whole genome approach, how to improve the workflow of the test? And then do you plan to do this add WGS for solid tumors?

Yes. So there's some real benefits to the whole genome sequencing. There's obviously massive workflow benefits because you don't have panels that have all kinds of complexity. The historic challenge of whole genome sequencing was that you needed to sequence at a high enough depth of coverage to get those really critical genes like EGFR and ALK and things that you just needed, you couldn't afford to be at low pass at this.

As sequencing costs continue to come down and there they've been dropping at, let's say, a 40% reduction of total like sequencing cost ex labor for the past like every 3 to 5 years for the past I don't know, a decade or more. You're in a place where if you're planning kind of your next generation of assays, there's no reason not to plan them to be whole genome in orientation. So we will migrate our entire solid tumor portfolio over some period of time to whole genome at a relatively high depth of coverage because it's now cheap enough that it's roughly what we're paying for our panels.

The challenge won't be -- like we could turn that switch on tomorrow. That's a relatively easy switch to flick. The challenge is that the process of getting these assays to be approved in New York, in MolDx with the FDA, that's a really long process. So even at the point when we said, okay, we've got a whole genome assay that we would like to bring to market, it could take 3 years or 5 years to get it to market because you have to replace each one of these products. Otherwise, you'd have disruption in pricing, which we don't want to have. So I think whole genome is coming. The technology and the cost is there, it's just going to take some time.

Okay. And then touching on MRD, maybe can you talk through your strategy to go after both the tumor-informed approach and the tumor naive? And why do you think it's important to have both within your portfolio?

We have this approach from the onset, when we first got into this space like 3 years ago, I think people thought that approach was crazy. But now I think everyone's got the same approach. So clearly, it's resonating.

Our approach is always the same, which is we try to think like an oncologist, right? So how would an oncologist view this problem, and that's going to be the winning solution. So oncologists don't think about informed or naive. And the vast majority of oncologists aren't fixated on purely like limits of detection or things of that nature. They really want a comprehensive solution that's going to kind of meet their needs. That's easy and simple and like it helps them make decisions.

And so the challenge is like, especially in practices where the majority of patients are treated. Sometimes you got lots of tissue, sometimes you have very little tissue, just bouncing between, for example, colorectal cancer and lung cancer, you'd find very different kind of tissue repositories by virtue of like how the tissue is collected. So to say to somebody, "oh, when you have lots of tissue, you use me when you have a little bit of tissue to use somebody else", it's not a great experience. So we've always thought there are places for tumor naive and places for tumor informed and you should have both in market.

We chose to begin developing our own naive product, developed the first generation, got an assay in market, realized that the limits of detection and the PPM of that assay was not competitive, given how fast the market was moving, chose to work on a next generation of that product, which we're working on now, not just in colorectal cancer, but across a whole variety of indications. So it will really be on chassis pan cancer. We'll still have to get approved each disease at a time, but one chassis pan cancer. And that is going on now. And we expect those assays to be in market maybe in '27 and may be paid for in '28.

At the same time, the vast majority of our volume is actually tumor informed and comes through a partnership we have with Personalis, where we distribute that assay. I think we have through like the next 3 years or something, where we're the exclusive distributor of that assay. And that has had really great adoption because it's a well-developed product that performs super well in the market.

Can you maybe dig into a little bit the reimbursement on the Personalis test and where you stand today, where you would like it to be before you start kind of driving more volumes through your sales engine?

The issue for us is, so we get paid -- we have like a marketing and distribution deal with them. So we get paid I think something like $470 today per test. And we -- I think our ASP is about $350 because there's some accounting that moves around. But at the end of the day, as I said during our Investor Day, a few weeks ago. Our net margin on the test is probably what it would be, even if we were one company. Like it's -- and we're collecting like totally normalized reimbursement. So we have a very healthy net margin on the test. Even if our ASP is $1,000 per test, we probably wouldn't be any better off than we are today based on the net profit we generate from that test.

So for us, it's not about -- that's not why it's gated. It's gated because as Personalis gets reimbursement one indication at a time, its ability to run the test at greater scale goes up. So if we sent them 200,000 orders tomorrow, and they -- let's say, their ASP is $300, and their cost is 3x that whatever it is, I have no idea, they would burn an enormous amount of cash. So it's not so much like what we want to do is really more that we're in this partnership with them, and we don't want to flood them with too many orders.

So I think again, we also discussed this in our Investor Day. I think we have about 15% -- the sales force selling MRD, it's about 15% of the sales force selling a tumor therapy selection. So we could ramp up our efforts probably 6 or 7 fold if we wanted and generate way more volume.

Okay. Okay. And then timing of when you think from their front, they might be ready to kind of have you generate more of this revenue for this?

They're making great progress. I mean, they now have three indications that are approved. They've made some progress in breast, lung and response. And so obviously, CRC is a big piece of the market. So that is a piece of the puzzle that they'll need to solve. But I would think over the next year or so, their reimbursement is going to be a lot healthier and our ability to really ramp up volumes will keep going up. And so I think every quarter, you'll see us ramping up volumes, and it will just be the steady incline.

I mean our growth rates now are extreme. It's like, I don't even know what it is like hundreds of percent annual growth rate. So it's not like it's not growing quick.

Yes, definitely. And then on the hereditary side of your portfolio, you've talked in the past about the largely untapped hereditary cancer testing market. You recently acquired Ambry. I guess what initiatives do you have in place to start penetrating that market going forward? And what were some of the challenges that previously prevented some of that market penetration in the past?

So we began -- we've long -- again, we've long felt you have to be comprehensive. And comprehensive is, are you a risk of getting cancer, you have cancer, now you need to be treated that could either be, I have tissue, right, I have blood. And then I'm post-treatment, how do I monitor you and how do I detect disease when it comes back. So you need to be comprehensive.

So we began offering an inherited risk assay in cancer a while ago. We had several different partners eventually chose Ambry because they are the gold standard. And then acquired them early last year.

I think it's taken us a while to really understand that market. So our thoughts on how they would grow have moved around a bit. We originally thought low to mid-teens, then we thought like high teens, low 20s then we thought like low to mid-teens. In part, it's because in the middle of that, Invitae went bankrupt and all of a sudden that created like massive market fluctuations. But it's a really interesting market. That for-sure is going to grow dramatically as the market is unlocked.

The craziest part of the market is there are something like, I don't even know, 1,500 to 2,000 genetic counselors who order something like 1.5 million to 2 million tests a year. And that is really the gate that keeps the volume from growing. And yet based upon private payers and coverage policies in place and Medicare Medicaid, about 70 million Americans are eligible for testing and would be fully reimbursed. So it's one of the very few markets I've ever seen where you have, I don't know, 68 million tests you could run that aren't ordered. And so like that's going to change. I don't know how fast, but that's going to change.

And especially in a world where the trend among individuals that it's also unstoppable is I want to understand risk and I want to take control of my health care. So like if we fast -- if I come back a decade from now, do I think we'll run 10 million tests a year? For sure. Do I think Ambry will win that market? I don't know. But we're in a good spot.

Yes. A very exciting opportunity. And then maybe also talk to your exposure in rare disease. I mean how big is that market? How are you positioned in it? And then any recent trends you're seeing within volumes or ASP there?

It's a new market for us, so we're quite small in it today. It's -- essentially, it was a market that Ambry was in. And then they got out of it because reimbursement was terrible. And then GeneDX began getting -- and others began getting good reimbursement, and so they got back into it. And then just as they got back into it a year ago, the market moved from exome to genome. So it's been kind of also a bit of a bumpy road. But they have a good product. And I think those volumes will start to grow at a good clip towards the back half of this year.

And again, long term, it's a market we like a lot because we don't want to just run a test. We want to contextualize. We want to use AI to contextualize it. If you want to run a test, somebody else can run the test, and it's fine. But if the test requires contextualization, we have a dramatic advantage by virtue of the fact that we have 700 software engineers, 500 petabytes of data, as much compute capacity as the entire rest of all of health care like times multiples. So we want to be in that business. And there's almost nowhere I can think of a better place for a Tempus than in rare disorders, where the clinical journey and the phenotypic story is honestly equally, if not more important, than the genetic story or the genomic story. So we should do really well there. It's just a matter of building out those products over time.

Okay. Great. And then I guess shifting to the data and applications business. Maybe start by walking us through kind of your solutions, trials next and then algos and what customers you serve and then how you actually get paid?

Well, we don't get paid for a lot of that stuff much, which is why those are fairly nascent businesses. Our -- the cool part of our story is that our main businesses our data licensing and AI modeling business and our diagnostic business, especially in therapy selection, and those are like good businesses that grow and kind of pay for everything else.

But the revenue story of AI applications in health care is quite small. And it's quite small because there are literally structural problems to getting paid, namely Congress, the American Medical Association and the entire infrastructure doesn't yet know how to pay for AI. So like this isn't a Tempus problem, this is a general health care problem. And so the good news, I think, is that it feels to me like -- and certainly, this administration is more innovative than others, but it feels to me like people are really working hard to get their heads around how do I pay -- why wouldn't I pay, for example, for a digital pathology algorithm that can call ER and PR and HER2 status for or patient for $20, why would I pay $100 like that and have it be done worse. That doesn't make any sense. Why wouldn't I pay for an algorithm that's going to predict a heart attack. Why would I let someone have a heart attack.

So it feels like that is -- there's actually movement there, a bunch of really good movement. But I don't know if those are going to be big revenue businesses in '27 or '31, like there's no doubt in my mind, one day, they're going to be very, very big businesses, but I don't know when. And so we have a long history of like, I think, trying to be very candid with our forecasts. And so our whole point is like if we can't see it, we don't forecast it. So we don't -- I don't -- so these are all kind of cool businesses. They actually -- all of them operate at real scale. So like we have our cardio algorithms deployed at like 50 or 100 hospitals. We have our next -- our care gap algorithms deployed at another 50 or 100. So which means they're touching like millions of patients, thousands of doctors running like an enormous volume of these things, we just don't collect lots of money.

Okay. And then I guess when you were building out your business model, like why was it important to own the data? What moat does this provide to you relative to maybe someone just joining the market?

I think that the -- if you think about the value of kind of AI businesses, businesses that in theory are going to leverage the kind of prevailing frontier models, meaning ChatGPT, Claude, Gemini, Grok, Llama, pick a model, they basically have to have two things. You have to have two things: one or the other or both. One is proprietary data to train [Audio Gap] and then proprietary distribution. So that if you generate [Audio Gap] you can put it in the hands of somebody will pay you.

And what's weird is we had the same mental framework 10 years ago when we started Tempus we just were thinking about the benefits of machine learning and big data and optical care recognition and natural language processing. We weren't thinking about the benefits of Large Language Models because they didn't really exist at scale back then.

But the same logic was there, which is we want to be in the business of like training something proprietary and distributing an insight. And so you have to own the data or at least have proprietary access to data. Otherwise, you do run the risk of like somebody else being able to do it, and they don't need you.

Yes. And touching on some of your large pharma contracts. I mean, I think, yes, you recently mentioned that you're seeing increased penetration within some of these accounts. What's the feedback been when they decide to expand the contract? And then, are there any particular use cases that they're finding with your data that maybe they didn't expect initially?

I mean I think it's -- we're just becoming at a really rapid pace. What's been interesting for me over the last 6 months, is like how fast we're becoming an indispensable partner to big pharma. And I think the best example, I posted this a few hours ago, in the last 10 days, we had two CEOs, the CEO of Merck and the CEO of AstraZeneca, both of whom, without our knowledge, we had no idea, independently, commented on the strategic importance of Tempus to their AI initiatives, one in an earnings report and one on CNBC. So -- and you just don't -- I mean, if somebody -- if you follow big pharma, that doesn't happen. So I think it just speaks to the fact that like -- and this is just the beginning. I mean, I really believe that the value we can provide is extraordinary.

I mean, another example is we had a big pharma company came to us and they wanted to load 25 or 30 trials onto our network. And we have this just-in-time network that now is like it's -- we have too many people that want in. So there's like a long waiting list, okay? So we looked at the trials that they had. And we said to them, we'll take these 13 trials, but these 13, remember, we're not taking because they won't enroll well. And it was this aha moment for the pharmaceutical companies saying, what do you mean the -- like how do you know they won't enroll well. And we can just show them like literally in real time, the fundamental changes to care that exist today that probably didn't exist 5 years ago when they designed the trial that make this Phase III like problematic.

So like in the world post antibody drug conjugates in a world post HER2 low in a world post whatever, things have changed. And so all of a sudden, your funnel now of patients is just different. And more, I think, big biotech and big pharma are like, "I need to have that". I can't be blindsided when I have a $1 billion franchise or a $5 billion franchise that like unravels on me at the last minute because I wasn't collecting real-time molecular clinical data in oncology.

Yes. I mean that's great. The other kind of question that comes up with pharma in their pipelines is AI is helping to drive better return profiles on their R&D dollars, I guess, speed up time to market for therapies like do you think eventually pharma would look to build out data sets of their own? And how do you weigh that?

I mean, I don't know if this is right. This is from our Investor Day, so I'll take it -- I mean, I haven't verified it. I'm sure it's right because it was from an analyst that I think covers the space. And they had said that they were kind of amazed that Eli Lilly had announced it was building its own model, and that it was like kind of touting its 700 terabytes of data that it had collected over the past century.

I mean I think we generate 700 terabytes of data every 12 hours or something. I don't know, every 16 hours or something. So like it's that crazy.

So like -- so I don't know. I mean I just think at the end of the day, we're adding something like 20 or 30 petabytes of data a month at this point. And so -- and we purge data. We're adding so much data. We are purging data because we're like -- we just -- it's like the storage cost is so extreme.

So I think ultimately, we're just in a unique position in that even if big pharma wanted to build these models or if the big front foundation model, people wanted to build these models. They're going to have to come to somebody like Tempus that has this kind of unrestricted data, at least in the identified format and try to license it.

Yes. I did want to touch on the foundation model with AstraZeneca. I mean how significant are the initial findings from this model? And then how do you see pharma utilizing these in future workflows?

Yes. I mean the good news is again -- I mean, Pascal talked about some of this on CNBC a couple of days ago, so you can hear it from him. But it's a big deal. I mean, we took an enormous amount of data and disparate forms of data, molecular data, clinical data, imaging data, digitized H&Es. And we essentially trained a model based on all these different data modalities and then said, can you predict the outcome of this public trial and the outcome of this private trial.

They had -- we had set up with AZ two benchmarks. One was a -- we had to cross a C-index score on a public trial and one was a private trial that we didn't know. They had trained internally a very sophisticated small model to predict that trial, highly tuned, highly trained years. And our large model was able to hit both benchmarks, both the public trial and the private trial. So that was the massive hurdle that we -- that they needed us to get through to be like, okay, this is going to work at scale, which we obviously thought it would. And so now it's just a matter of like now you go. You make model 2, Model 3, Model 4, you refine your post training and you try to get really predictive. And I think we're building these digital twins and so I believe we will be able to predict with high fidelity, the likelihood of patients that are going to respond and not respond in a trial.

And we're predicting these things blindly. So it will be like, okay, here are 42 patients. We think those eight had a super response. We think those eight had no response and these 24 whatever were in the middle. And the righter we get, the more important we become.

Okay. And with just about a minute left, I mean, what do you feel is the most underappreciated part of the Tempus story from investors? And I guess what are you most excited about as you look at your business over the next several years?

I mean, I'm excited about all of it, both the diagnostic business, I think is a ton of tailwind and the data business. I think the challenge for us, which I have at times not appreciated is just how difficult it is for these two investor worlds to commingle.

I mean you have the diagnostic investors just hate the technology and data business that they don't understand and the technology investors just hate the diagnostic business. And they just don't -- they just have not spent no time trying to understand it. So it feels very destabilizing for them.

And I imagine the same thing happened with other companies that had kind of similar dual business models going. But that has been I think that's the most underappreciated part of the story. If you -- if we took our data business public under normal terms, it would probably be worth significantly more than the entire -- than Tempus in totality. So that will either correct itself or will at some point just me to correct it.

Okay. Great. Thank you so much. Thanks for being here.

Thank you.

We're going to get started. Great. Thank you. Welcome, everyone. Welcome to our inaugural Investor Day. Great to have you here in person, and thanks to those who are joining us virtually today.

Before we begin, just a quick reminder, today's presentation will include forward-looking statements, which are subject to risks and uncertainties that are described in our SEC filings. We have a number of our speakers today that are outlined here on the slide and are joining me here on the stage that you'll be hearing from throughout our sections. And here's a quick look at the agenda. We'll be starting looking at our diagnostics business, and then we will turn it over to data and applications. And then finally, take a moment to walk through the financial outlook. Each section will be followed by Q&A. We'll take questions both from the audience live and virtually.

And with that, I'll pass it to Eric.

Thank you. Welcome, everybody. We're going to go kind of quick, but we'll have a Q&A section throughout. So hopefully, we'll be able to get to all your questions. We also had a few -- we have a few releases out this morning, so just giving people a heads up. So one is already, I think, hitting the other ones coming short me.

10 years ago, we started attempts to solve a single problem, which is could you use AI to essentially unlock precision medicine. See if my clicker works. No clicker. Technical difficulties on clicker. The laptop isn't connected. So we need to... Just want it's like you feel like here at your house where you're just kind of moving around the room. Given that this is not our space, it's the building space, it's hard to complain. It were nice enough to just use it. We disconnect the laptop. Okay. Hold on, I'm now clicking, which is exciting.

Okay. Given our clicking capabilities are back, I'll start over. So 10 years ago, we started attempt to solve a single problem. Could we use the artificial intelligence to unlock precision medicine. In order to do that, you basically need 2 things. You need vast amounts of proprietary data to build models to bring the benefits of [ AIDA ], health care and you need a distribution system to take those insights and deliver them to the hands of physicians and patients. Tempus is unique in that it has both. We have vast amounts of data. We have vast compute and modeling capabilities and a vast distribution system to essentially target the main use cases within health care, which is can you match patients to the right drug, the right trial, the right therapy.

But in order to do this, you have to build a sustainable operating system. And that, I think, is one of the first places that makes Tempus truly unique is that we have built this connected ecosystem that allows us to essentially generate vast amounts of data from the clinical workflow, turn that data into insights and then essentially feed those insights back in the U.S. health care system, which then makes people want to connect with us more, and therefore, feed us more data, generate more insights, deliver more applications. And this whole ecosystem is now sustainable, which is probably most exciting parts about it. It's large. It operates at scale and it's sustainable, meaning we don't have to invest billions of dollars consistently in making it work. It works every day.

This integrated and sustainable system is essentially spanning vast amounts of health care data, morphologic and molecular data. So this is a molecular data that you would generate from sequencing patients or producing molecular data across a wide range of data. It's DNA data, RNA data, methalomic data, imaging data, from CT scans and MRIs. It's digital pathology slides, it's clinical data in structured and unstructured format. And all this data comes together in real time to essentially create this -- I think, I lost my mic, this network of bag system, something will always not work, and we'll just kind of bounce to hit it to.

The ecosystem we built now exists at scale. So we have -- we're connected to about 65% or 2/3 of all academic medical centers in the U.S. The vast majority of oncologists in the U.S. are now connected to Tempus in some way, shape or form ordering our tests. We run a large volume of tests, and so we generate lots of that molecular data at the beginning of that flywheel. We're connected to more than 5,000 institutions throughout the United States, which means we're connected to a significant percentage of the U.S. health care system. There's maybe 8,000 hospitals or so. We generate enormous amounts of data and then have built a world-class team to make sense of all this data. So in addition to being sustainable and running this operating system, we are sustainable at scale. The output of all this activity, where we're essentially trying to generate data as part of the clinical practice, turn that data into something useful generate an insight and put in the hands of everybody who needs it, whether those are physicians, patients or researchers. This has now produced an enormous amount of data. Over 500 petabytes of data for people who have been watching the growth of our data base over time. It's really quite extraordinary. I mean, not long ago, we were 50 petabytes of data. Here we are some 5-plus years later at 500 petabytes of data.

It spans over 45 million patients. There's over 9 million images in that data set, which is just one of the largest digital imagery data sets that we know of in the world in terms of digitized pathology slides and radiology records also connected to clinical outcome response, including a large volume of samples we've sequenced over 4.5 million. And then the very bottom of this data set or this funnel is over 400,000 of these really, really rich multimodal records. These are records where we have typically DNA and RNA and clinical data and outcome data response data, adverse event data, we have imaging data all the totality of what you would need to basically interrogate real-world data and figure out all the insights we're going to talk about in a minute on the biopharma side.

In order to make this business sustainable, we've divided it into essentially 2 parts. We built a diagnostic business and then a data and applications business. So I want to first start with the Diagnostics business. What makes our Diagnostic business unique is the comprehensive nature. If you're going to generate vast amounts of molecular data and then be in the business of connecting that molecular data to clinical data so you can contextualize it, the question you'd ask us why? Why am I going through all this effort to contextualize molecular reports? And the answer is that if you just run sequencing and generate like here's a patient that have a mutation and I'm going to hand that to somebody at best, you're basically in the business of targeted therapies or targeted medicine. You're not really in the business of precision medicine because you know nothing about that patient.

And so the journey we set out 10 years ago was could we essentially make diagnostics smart. Could we help contextualize them and basically wrap technology or AI around them to help physicians make really high-quality decisions and help researchers do much more efficient research. On the diagnostics side, that begins at not just generating an insight in a comprehensive manner to get an answer or result, but then bring it all the way through. I know some -- I've learned something, how do I connect it to clinical data so I can essentially figure out like if I find a mutation, I don't want to recommend a therapy that a patient just took in a prior line and failed, recommending that again would be pointless. I don't want to recommend a clinical trial that, that patient is not eligible for because the patient happens to be a smoker. And one of the exclusion criteria of this trial, I would recommend you can't be a smoker.

So by connecting rich molecular data or any kind of laboratory test results or diagnostic data to clinical data, you can contextualize it to go from kind of answer to insight. So I found something interesting through EHR connections I'm now going to contextualize that, so I can make a more intelligent decision, which is really powerful for clinical care, but that same data, that same vast amounts of multimodal data where you have an insight connected outcome and response is also what's needed for research. So all the research that people are trying to do to figure out how to make sequencing more useful for cancer patients. Our technologies are empowering that at scale. And then once you're generating lots of molecular data and you're contextualizing it and you're powering a bunch of research, you end up with this last mile, which is how do I put that in as many hands as possible we can. And so the investments we've made in connectivity and AI essentially allow us and will allow us in the future to distribute these insights at scale.

And our goal is not just to distribute them to all cancer patients, but to all patients in the United States. Tempus is successful over time, we'll be connected to every hospital in the United States, almost every hospital across all major disease areas. And every time there's a diagnostic insight, we will be in the middle of that trying to figure out how to take that diagnostic insight, contextualize it, wrap a whole bunch of insights around it that only people like us have because of the nature of the data we have and then deliver those to every clinician in real time so that patients are always on the right therapeutic path.

Begins with having -- we started in cancer, you have to pick a place to start. So we started in cancer. We started trying to make molecular testing in cancer as intelligent as we could or compressional profiling as intelligent as we could. And we started -- we made a decision early on that if we were going to be in the business of contextualizing tests, we want it to be as comprehensive as we could be. We didn't want to just give somebody part of an answer part of the time wanted to give them the answer all the time. And so in cancer, that begins -- if you look at the compendium it starts with who's at risk of getting cancer, it then translates into who is just being diagnosed with cancer and how do I essentially put them on the right therapeutic path. And that kind of bifurcates into solid tumor profiling or liquid biopsy because not all patients have enough tissue to be a sequence.

So you need both liquid solution or blood solution and a tissue solution. And then post treatment, how do I monitor these patients? How do I look for when their disease might be coming back or see if the therapies I'm giving are sustainable. And so Tempus operates across the entire spectrum. We are strong in hereditary profiling, strong in therapy selection, both solid tumor and liquid biopsy and then -- and strong in all the ancillary tests that come along with that and then strong in MRD and monitoring. So we'll cover all that shortly.

Here's just a quick snapshot of the comprehensive nature of that portfolio. We have a series of FDA-approved assays. We just added to our portfolio this morning. We had a tumor only FDA approved historically, and we now have tumor only approved, which is -- which for us is quite significant in that it expands the amount of FDA-approved tests we can offer to essentially 100% of our DNA portfolio. And given that we have a DLT pricing, that's quite powerful. So that was a big approval for us. And then we have a series of other -- as Jim will talk about a little bit. We have a series of LDT tests. And those cover RNA, liquid biopsy, other areas, all ex some things of that nature. Several of those are also going down this kind of FDA regulatory approval path. We have a series of pharmacogenomic assays we offer things that have become super powerful these days, whether it's DPYD or UGT1A1, we also pharmacogenomic profiling of patients with neurological issues such as major depressive disorder, bipolar disorder. We have a whole bunch of algos that sit on top of these diagnostics, we'll talk about in a second.

On a series of tests that typically ordered alongside these, whether it's immunochemistry stains or other tests of that nature. And then we have, obviously, a fairly large and growing portfolio in rare disease and cardiology and obviously, hereditary profiling. So a significant body of assays. And essentially, when you -- to understand Tempus' strategy, it begins with what is the diagnostic that's going to be most commonly ordered in this disease area and how do I either offer that diagnostic or partner with somebody that's offering that diagnostic, both of which are perfectly fine solutions. Generate that diagnostic data, begin to generate or consume that diagnostic data at scale in real time across a large percentage of the U.S. market and then begin to collect clinical data that's connected to that diagnostic, so I can figure out like what's happening? What drugs are patients going on? Are they responding? Are they not and essentially create a self-learning system to make that diagnostic better and to contextualize it, to make it personalized so that when a physician orders that diagnostic, it actually helps them figure out what to do next.

I'm going to bring up Mike to talk a little bit about some of these tests in greater detail.

Great. Thank you, Eric. So just starting off at kind of the top -- one of our kind of core workhorse assays on the solid tissue side is our xT assay. This is a 648 gene assay that combines both the molecular insights that are coming out of the genomic testing as well as how Eric mentioned contextualizes that with structured clinical information. We've had the tumor normal FDA approval for some time.

Obviously, as of this morning or as of last night, I should say, our tumor-only FDA approval came through. So we're very excited to be able to extend now this series of FDA approvals beyond just those 2 normal patients. Because as many of you may know, there's many cases where we're able to capture that blood. In other cases, we may not for a number of reasons. And so now this will allow us to service all of those patients that are coming in through the clinic.

On the RNA side, there are many cases where the RNA signatures that we're able to identify extend beyond what we're able to find via the DNA findings. So for example, with fusions and rearrangements we're able to now find many of these patients who might otherwise not have a therapy that's found by a DNA. They are able to be found when we're looking at the RNA signatures. So this is an area where we're seeing a preponderance of orders kind of coming in both inclusive of DNA and of RNA. And I'll talk about next a little bit some of the studies that we've done that show kind of that incremental benefit that we've been able to see as a result of adding RNA onto that DNA finding. So we have published a number of cases where we're looking at the incremental benefit. So what are we able to find when we're looking at patients who received both the DNA result as well as with an RNA result.

And there's a significant portion around 21% of patients that do find these driver mutations, they're able to now find FDA-approved targeted therapies that go beyond what would have been possible had this patient only received a DNA result. And so not surprisingly, we're seeing a large portion of our patient population include these RNA xR orders in addition to our xT orders.

So moving from the solid tissue side of the portfolio to our liquid biopsy. So in these cases where we're not able to obtain tissue or in many cases where clinicians are looking for additional insights beyond what they're able to find by just looking at that tissue by itself. Because of the shedding characteristics of these tumors, liquid biopsy allows for an incremental set of findings in a similar way to how RNA is additive to the xT result our xF portfolio, which is inclusive of 2 assays. So we have 2 flavors of liquid biopsy. The first is 105 gene and the second is a 523 gene assay that allows us to look at and again, find these shedded tumors and the DNA associated with other mutations that are again, looking at those 2 different flavors. This is a faster test from a turnaround time perspective. So in some cases, not only when these patients don't have tissue to be able to be sequenced, they are also optimizing for turnaround time. And so in this case, we're in that 6- to 7-day range, which has been helpful for many clinicians in many patient cases.

Importantly, what we're seeing is that these clinicians are also looking at this liquid biopsy and the evolution of tumor biology over time. And so this translates to having multiple xF tasks that are ordered to understand what are the resistance mutations that might be happening in a given patient case. So for example, with EGFR in lung cancer cases, a resistance mutation may be able to be found during the course of therapy. And so we're seeing an increasing cases, clinicians looking at what are the multiple time points that might be appropriate to order a liquid biopsy test. And this is our mechanism for being able to help identify that.

Similarly, when clinicians are ordering our solid tissue test, they are, in many cases, adding the xF liquid biopsy test, as I mentioned, and this does provide this incremental benefit. We've done some publications on this that find around 9% additional actionable variants are found when liquid is added into that solid tissue case. So clinicians are, in many cases, including this as part of their standard routine course of care. And then as we do look at what the -- those kind of incremental actionable findings are similar to a number of the studies that we've done on the RNA side and then looking at those resistance mutations, we've isolated that looking at specific subtypes and what are the benefits of xT and xF in different cases.

And so this is data that represents what are we finding incrementally beneficial in cases like lung and in breast and in prostate and in CRC. And there are these kind of incremental additive benefits. And so again, additional evidence as to why clinicians are increasingly looking at both the solid tissue profile as well as a liquid biopsy profile.

So just for a second to kind of provide some context. So when we -- when Tempus is about 10.5 years old [indiscernible]. And when we started, Foundation Medicine was the leader by far in [ solo ] profiling and had a liquid offering in [indiscernible] is obviously better and liquid. And at the time, people thought it would be very hard for us to kind of catch up and make progress. And if you look at the progress we've made over the last -- in the labs, maybe 8 or 9 years old, over the last, let's say, 8 plus years where we become #1 or #2 in both of those spaces and have unit growth that is kind of a best-in-class.

It really speaks to not only the comprehensive nature of these assays and how kind of good they are, we are. The gold standard, I think we're in that top-tier gold standards in both solid tumor profiling and liquid biopsy, but also the technology we wrap around these tests. We'll show you in a few minutes, our main ordering system, which is called Hub. But in addition to that, we don't just run these tests in isolation. We connect all these tests to each other, and we connect all these tests to clinical data and outcome data. And so what ends up happening is the ecosystem, if you're a doctor ordering our tests, it just gets smarter and smarter and smarter, which has led to such a high new physician acquisition rate and such a high physician retention rate.

We talked a little bit in the last quarter about the algos or algorithmic diagnostics that sit on top of these tests. And we have a variety in market today. We have algorithms that predict homologous recombination deficiency, algorithms that predict cyto tumor origin, algorithms that predict a whole bunch of other clinically relevant topics. But also one of the newest algos we've deployed is called our immune profile score, which Ezra's going to talk about in a second. These algos now have an attachment rate of greater than 40%, which essentially means a doctor is choosing to bolt on one of these algorithms, 40% to the time they -- more than 4% of the time, they order with temp which means they find enough value to say, I want that additional insight.

Ezra, you want to talk little bit about the IPS?

Yes. Thanks, Eric. And we set out -- I'm Ezra Cohen, I'm the CMO of Oncology, and I'm a medical oncologist by background. We -- if we can advance the slide. We set out about 2.5 years ago to solve a problem with the data that Eric was talking about. Because we have the clinical longitudinal data associated with both DNA and RNA, we were able to answer a fundamental question on oncology, which patients benefit from immunotherapy. Not in just 1 cancer, but across all solid tumors. And that's exactly what we did with IPS.

IPS is a quantitative score. Here, you see an example of a patient who scored high. IPS high predicts a benefit to immunotherapy, IPS low predicts a patient that will not benefit from immunotherapy. It goes beyond the traditional biomarkers that we already have and it gives providers that extra insight to make the decisions on whether they're going to use immunotherapy for their patient or whether they should go to a different modality. It's provided a tremendous amount of value, and the feedback has been incredibly positive. But that's a perfect example of what we can do with what we have built at Tempus, as Eric described. Thanks, Eric.

And the benefit of these algorithms that they're essentially providing insight on top of something else that might be previously known. So there are historic markers for IO response like tumor mutational burden or TMB. Unfortunately, it's just wrong too often. It's a -- these are kind of course scores that might be right, might be wrong. And if you have vast amounts of data, you can kind of refine the score. And in this case, we've unlocked roughly 20% of patients who you wouldn't think would respond to immunotherapy that will, and 20% of people you think should respond to immunotherapy that won't. And that journey, which we'll talk about a bit more we get into the foundation model in a second is what we expect to happen across all biomarkers and all diagnostics, not just in cancer for the disease areas.

It generates some kind of diagnostic insight, laboratory test result. You connect it to rich outcome response data over time. You track what's really going on, you refine the diagnostic, it becomes smarter, collect data, track, refine, so on and so forth.

Before we leave therapy selection or comprehensive genomic profile and go to MRD, I want to talk a little bit about the growth drivers of CGP. We believe that the unit growth rates we're experiencing, which are pretty extraordinary are sustainable for a long period of time for a few reasons. One, CGP is still not saturated. There have been a significant number of reports count recently that estimates the percentage of doctors ordering these tests in the kind of 40% to 50% range. There's a significant number of folks that still don't order these tests even though they should in areas where NCCN guidelines call for these kind of tests being ordering. That's not a small percentage of cases. And so we suspect the whole market will grow. And since we're growing faster than the market, that will accrue to our benefit.

The second is that we're the beneficiary of 40-plus years of research where you're essentially tying a biomarker to some kind of therapeutic benefit. It started in cancer. It started with [indiscernible] and it started with all the genomic work we've done in that area. And so if you've looked at the trend, we've been sequencing patients earlier and earlier in their diagnosis. And so we suspect over time, the vast majority of patients even in the Stage 2 and Stage 1, depending on the disease area are going to be profiled. So that's another benefit. The third benefit is that there's been a significant migration to more comprehensive profiling and therapy selection. We've broadly published on the benefits of doing solid tumor profiling and liquid biopsy profiling as have many of our competitors. So there's now a large volume of work around the benefits of doing both. There's an equal body of work about the benefits of doing DNA and RNA, which is kind of having an explosive moment in terms of therapeutic relevance. And so we suspect that trend will continue.

So you have a bunch of docs that are ordering that will you have stage expansion into earlier stage and you have this trend being more comprehensive ordering more tests. So we suspect the top companies in therapy selection or CGP will continue to do well. Well, we suspect we'll do better than that group or most of that group in large part because of the technology investments we've made, which integrate our platform, contextualize it, make it really smart. And so doctors have been flocking to our platform over the last 5-plus years, and we don't see that trend slowing down.

With that, I want to hit MRD and monitoring. Let me provide a quick overview, and then I'll bring up Kate. When we decided -- just in each area, we have to think strategically, we got into therapy selection. That's where we began. We began in solid tumor profiling, had to earn the right to go to liquid. That was a conscious decision we made. We then began doing hereditary profiling and realize that the best way for us to win in that space long term and really to tap into the bigger part of the market that's currently untapped today was to acquire Ambry, who was the gold standard of that assay. And we'll talk a bit about that in a second.

With MRD, it was a bit trickier. It was an emerging space. It was a new space. And so we had to make a series of decisions. One is, what did we think was going to win long term was it going to be a tumor-informed profiling or tumor naive. So -- and that is still, I think, up for debate. And the next question is, how is the science going to evolve in terms of these assays and their ability to detect cancer earlier and earlier. And the only thing we knew is that unlike other spaces, this space was exploding in large part because of Natera very rapidly, meaning what took Foundation Medicine, let's say, 10 years to do from a commercial perspective, Natera was doing in like a year or 2. So this space was growing very, very quickly, getting to scale, generating lots of money and yet the space hadn't yet really kind of landed in the place that's going to land long term. So our approach was to kind of derisk that by having multiple irons in the fire.

And so we made a decision to partner with whom we thought was best-in-class on the tumor and form side, Personalis had a whole genome assay at the time and to make investments developing our own tumor naive portfolio, knowing that those investments would be significant over time. So I want to cover -- I want to say one thing that I'll bring Kate. We have a distinct advantage in this area. One is we generate enormous amounts of data. So that data -- the same data that's allowed us to build best-in-class assays in solid tumor profiling and liquid will allow us to build best-in-class assays in MRD, we just -- we've got enormous data points and can learn and refine over time. The other is that were connected to a vast number of oncologists in the United States, and so we're a partner of choice for most people that have emerging technologies.

With that, bring up over to Kate.

Thanks, Eric. As Eric mentioned, our main goal is to be able to offer solutions across the spectrum for all patients at all time points in their journey. And there are advantages, as he mentioned, to both tumor-informed and tumor naive. And we like to think that we have the best of both worlds.

So first of all, we've partnered with Personalis for their tumor-informed assay, which we mentioned we really feel is best-in-class in terms of sensitivity levels that it can get to, and that's because of the technology that they use. So Personalis and xM NeXT leverages whole genome sequencing from tumor tissue and then actually looks at up to 1,800 different variants kind of leveraging that whole genome information to be able to sort of personalize them and follow and track that patient's variants that are contributing to the disease. So this really results in ultrasensitivity and allows you across breast, lung, IO monitoring and multiple indications to get a really sensitive result way ahead of when you would pick up disease on other modalities like imaging. Personalis has been in the game for a little while. They've been investing heavily alongside us in different clinical studies to be able to show the validity and utility of this assay.

And of course, with MRD, one of the challenges we all have in the field is that you really need to tie this to outcomes. And that means that you have to follow these patients for a long time to be able to understand how they're going to ultimately do in the clinic and with different treatment modalities or with different clinical decision-making, how will that translate into survival and other types of clinical benefit. As great as tumor-informed assays are and the fact that they can get to this really low ultrasensitivity, which is very useful. There's also the challenge that not all patients have tissue. Certainly, some indications like lung or breast or certain time points along a patient journey, there really isn't the advantage to be able to leverage that tissue. And so if you want to cover all solutions for all patients, you also need a tumor naive assay or a liquid approach.

And we have developed that technology here at Tempest, we've launched a first assay in CRC, where we're able to now just from a liquid test to be able to monitor for sensitivity and pick up early stages of tumors, very similar to the tumor-informed version, and we anticipate to continue to improve on this technology. This is a technology where, as we've been talking about the power of the Tempus data and the fact that we have so many different types of data coming into our ecosystem, this is one space where that data will help you continue to improve when you don't have that solid tumor tissue to lean on.

We are also in the journey of continuing to generate a lot of clinical evidence across many different tumor types. So we started in CRC, but we are looking at lung and breast, in [ PINK], in head and neck and all of the indications where a patient luckily may need to have an ultrasensitive assay or an assay to monitor for therapy response. This is good news for patients at large because it means that therapies are able to achieve really deep responses. And now we actually need these diagnostic tools to be able to understand still within patients who've had great responses who might ultimately relapse, why are they relapsing and what therapy do they need next. So over the next few years, we'll continue to expand and roll out these studies and then be able to follow these patients, validate the assays and improve the technology.

So before we get to hereditary, I want to cover a few things. So on the tumor inform side, by partnering with Personalis, we have a best-in-class assay in market. Obviously, they're expanding the number of indications where they're getting coverage. And so we will be kind of unlocking volume over time as the unit economics continue to improve on their side. They started like a year ago, had 0 approved. They now have 3, and there's more coming. It also, I think, speaks on the tumor inform side to the benefits of our platform. We went public, we told people that at heart, we're a tech company, and we didn't expect to run every single diagnostic in the world across every disease area. And so we would partner with people and essentially open up our platform much the same way Apple Assay platform where you have apps that they make money off of. If you look at the unit economics of MRD on tumor informed, it speaks to that.

We essentially generate the kind of profit at the present moment from that test that we would generate if we ran it at scale on our own. So we're a bit agnostic as to whether -- from a profit perspective, we're agnostic as to whether or not we partner with somebody and generate the EBITDA we would love to generate or whether we run the test and eventually generate that. So we happen to have -- we're fortunate in that regard. On the tumor-naive side, we launched our first version of this assay, and it was performing pretty well in terms of its stratification of patients. But the market is moving so quickly that the limits of detection in PPM you have to hit is just much lower. And so we were out there with an assay at, I don't know, 500 or 1,000 ppm and the market was at 100. And so we began working on the next generation of our tumor-naive assay, about a year ago, I think. And we are already hitting -- getting close to some of the levels we put on this slide. And so we intend to migrate the entire platform to this new version.

At the same time, we'll replace our CRC assay, we're also focused on the fact that given how fast things are moving, if we go one indication at a time, by the time we get to the third or fourth indication, the market will move again. So we have to kind of skip a bunch of that and really go from redoing CRC to focusing on pan cancer. So that's our strategy in naive. Given that it represents 2% or 3% of our volume, it just isn't material today, but eventually, hopefully, will be.

On that note, let me hit hereditary for a second. We're going to proceed with Tom, and then we'll come back. It's all yours.

Thanks, Eric. Yes. So with the acquisition and now the integration of Ambry Genetics into Tempus Diagnostics, Tempus picked up a pretty significant footprint in hereditary cancer testing and an emerging and growing footprint in rare disease. And so I'll talk briefly about both of those.

Those of you are familiar with the marketplace. There's 3 large buckets of testing orders. The biggest being the genetic counselor space, they order roughly 50% to 55% of all the genetic testing for hereditary cancer testing goes through the genetic counselors. We have about 1,700 active ordering genetic counselors. The other buckets are medical oncology and OB/GYN. The opportunity in territory cancer testing is quite immense for a few different reasons. One is there's an established NCCN guidelines. There's a robust reimbursement model and every commercial payer as well as Medicare covers this testing for patients who'd be criteria. The big untapped opportunity for us is in the unaffected patient population. There are literally over 70 million people in the United States who meet NCCN criteria for hereditary cancer testing, and they're just going undetected. Roughly 1.5 million to 2 million tests per year is what's happening right now. So there's a huge opportunity, and Tempus is very uniquely set up to capture the unaffected patient population.

And actually about 2 years ago, which tells me our strategies are working, we've finally surpassed where we're doing more unaffected patients than we are affected patients. So things are moving in the right direction, but there's a huge opportunity here. We have an automated high-risk platform called Care. There's roughly about 250 sites and growing across the United States right now. And what Care does with our partners, we proactively engage with patients. We extract information to assess the risk profile based on NCCN criteria. We do pretest education for those patients, and then we flagged them for our clinicians, then when they walk into the clinic for their next visit, they get informed again about the testing. We get the testing done. Obviously, we run the testing in our lab. When the results are ready, we send those back out we can also send those directly to the patient if they desire and do the post-test counseling. So this automated platform allows us to access that unaffected patient population, which we see as really prime for significant growth.

This year, we're adding enhancements to care. I won't go to all of them, but on the front end, we're going to be seamlessly integrating into EMRs starting with Epic that happens this summer. And then on the back end, we have what I call like a safety net wrapped around our patients. patients going through the health systems create lots of care gaps just because of the workflow and the complexity of it. We're launching a product in the summertime for starting with breast cancer and then moving to other cancers that have guidelines for germline testing. Just to help with the scope of how big the opportunity is. One of our pilot sites looked at breast cancer patients alone for the past 60 days that are active care throughout that health system, identified 5,000 patients, breast cancer patients that should have had germline testing that didn't get it done.

The Care platform will be able to wrap a safety net or umbrella around that, make sure we identify those patients for our clinicians and then send them back a fly to get tested. So a huge opportunity for growth for us there. From a testing product portfolio, we have roughly 100 tests. I won't go into all those tests, the most popular or CancerNext pan-cancer test. Cancer [ desk ] expanded and the BRCAplus. The advantage for BRCAplus mostly for breast surgeons, from the time we receive a result to the time we get to report in their hands is roughly 3 to 5 days. So that allows them to order a test for a patient and schedule them for surgery within a week, which is a very big advantage for them.

On CancerNext and CancerNext-Expanded. These are the most popular tests that we have. I won't go into all the details here, but I do want to highlight the RNAInsight. RNA is in addition to these tests that we launched in 2019. RNA provides us data that other laboratories don't generate. And so for deep intronic mutations, splice site variants at a high level, we basically identify mutations other labs don't and we can classify mutations other labs can't. We did a publication in early 2024, looking at roughly 40-plus thousand RNA patients. It increased our diagnostic yield by almost 9%. So this is the first time in [indiscernible] cancer testing and then like a decade where you can actually prove that you have a better test on the marketplace. We crossed over -- well over doing well over 1 million RNA patients to date, we'll probably do another 400-plus thousand this year.

With regards to rare disease, [ AmREIT ] actually been in rare disease for quite some time. We launched the first commercially available exome test way back in 2011. So our product portfolio currently, we have [indiscernible], we have Exome. In the fall of 2024, we launched a test called ExomeReveal, where we took our RNA expertise and added it to Exome. We saw roughly a 20% increase in diagnostic yield over standard exomes, also a nice bump in volume when we launched that test. And then in this summer, we're about to launch our first clinical whole genome sequencing test.

From a market perspective and diagnostic yield perspective, roughly 80% of these diseases. There's about 7,000 rare diseases out there. It impacts roughly 8% to 10% of the United States, and about 80% of those are genetic, 50% of those were children. These folks go through this diagnostic odyssey. It takes typically 5 to 7 years to identify the genetic disorder for these children. Unfortunately, with the advanced technology, diagnostic yield is increasing. So ExomeReveal again added more diagnostic yield to our test, and we're about to launch whole genome sequencing, which we expect to see another 5% pickup. So instead of 1/3 of these patients being identified, it will be a little over 40%.

And then the last thing I want to touch on because this is also very unique to Tempus Diagnostics or Ambry. We have a program called Patient for Life. And so if you get tested with Ambry, our scientists are constantly reviewing the literature and looking for a new genetic disease connections. When we find these, we reanalyze our patient data and then we contact the physician, provide them with an updated reclassified report for that patient and then have our genetic science liaisons to work with them to answer any questions that they have. This impacts roughly one in 20 of the patients, so about 5% of our total patient population, which is a pretty significant increase in diagnostic yield and identification for our patients, and this is very unique to [indiscernible]. Thank you.

So just really quickly, if you'll notice like one of the common themes here is multiple tests, generating multiple amounts of data connected to other forms of data like data about this patient over time for life and it kind of yields over time a really a more intelligent platform that can grow. And so that's just our playbook area by area. [indiscernible].

So really, I just want to cover one thing. We're going to tally give you a quick demo of Hub for a second. We haven't demoed our 2 main systems, Hub is our main system that physicians use and Lens is the main system that researchers use on the biopharma side. We'll demo those today for a second. But all this comes together, right, in this giant connected ecosystem. Like every part of this company is working on this platform that essentially generates rich diagnostic data, typically molecular data, connected to other form different data modalities, again, phenotypic data, morphologic data, some kind of text or image or [indiscernible], whatever puts it all into this big giant environment where we run compute, generate an insight, put the insight back into the test put the insight back in the hands of a physician. And so this is all happening at scale.

And because we're connected to over 5,000 hospitals, where we have data connections and BAAs and legal agreements and IT connections and all these really complicated things, we're in a unique position to pull data out, generate an insight by augmenting it and then putting it back in. So with that, let's talk a little bit about our main platform. The platform essentially, you're going to see this both today in Hub and in Lens. Hub is essentially an ordering tool that we use for physicians to connect with. You can go to it directly on your iPhone or iPad. It's integrated in with most major EHRs in some way, shape or form. And then sitting inside Hub is this brain, which we call Tempus One or One. And this is essentially all the benefit of the agents we have built live inside One.

We have built a ton of agents. I don't thousands of agents that essentially take disparate miserable siloed multimodal data and make sense of it and the challenge with large language models, regardless of what model you're using, Quad Google's models through Gemini, ChatGPT, these models were not trained on health care data. They were trained on Internet data. So they just don't work with perfectly with digitized pathology slides or DICOM files from CT scans or rich molecular data, if you dump in trillions of As and Bs and Ts and Cs, you don't get much out of a multimodal, but a large language model. So sitting inside these products is our Brain 1.

And with that, Laura is going to give you a quick demo.

So Laura Elster, Chief Commercial Officer at Tempest. I'm going to walk you through the demo of Hub. Great. So I am a provider. I've logged into the platform. This is where they can order tests or order kits and interact with the results. So I'm going to go down to a fictitious patient, [ Christina Collins ]. And you see that [ Christina Collins ] here, her provider ordered comprehensive testing. So you see our tissue test, our liquid test, we have DNA, RNA, a handful of IHCs as well as some of our algorithms. So the liquid biopsy on day 5 came back and it surfaced a [ PIK3CA ] mutation as well as a low blood tumor mutational burden. There weren't any driver mutations found on liquid. The tissue test came back and actually confirmed no driver mutations, but there was a high tumor mutational burden as well as a positive PD-L1 score.

And so this is an opportunity where a provider may want to chat with Tempus One, as you've heard and asked something like how common, how common is it? to have a low blood TMB, for example, and a high tissue TMB. And so they can ask a question like that. And in seconds, Tempus One will surface a result. And what you'll find is we're -- including the citations. So here, I might have missed type there. So you can see we can include the citations here. as to where the -- we're getting the information from. And then if you go to next then, at this point now that we have this the provider needs to decide what to do for the patient. And so they're going to think about having a -- putting them on a combo chemotherapy and immunotherapy.

So if you look at the RNA results here, we can see the patient has -- finds a rare NRG1 fusion. And so this is something that often wouldn't be surfaced on DNA alone. In fact, we've copublished that 40% of these rare fusions are found through RNA. So at this point, the patient starts on the combo chemo immunotherapy and chemotherapy. This patient also had the immune profile score, which you heard the team talk about and the result was an IPS low in this case. So now the provider says, okay, we have a low IPS. We have that rare NRG1 fusion, both are associated with poor outcomes so that chemo immunotherapy combination. And so because of that, the provider might think to order a MRD testing to now monitor how the patient is doing. So you see here, we've got this time line. We show all the results over time.

And in this case, for Christina, the ctDNA, so the circulating tumor DNA in the blood starts to rise mildly. And then over time, at the follow-up time point, we start to see significant elevation.

So just to provide some context, sorry. So you're a demo environment because we have in the production environment or we -- someone shows up with like [indiscernible]. But essentially, what you can see here is that we have all this technology wrapped around the report itself. So we have the ability to take the clinical data we're connecting and have the system automatically generate a summary of that patient or a summary of their current therapeutic regimen or whatever is going on. We -- off to decide each one of these things in the main production system, you could click on these things and actually go into the raw note and read the note itself because we pulled the note out, we've identified it as a notice there.

And then the system is just consistently as you make decisions that maybe you want to go from putting a patient on a combination of immunotherapy and chemotherapy to maybe looking at a target on the RNA side, it's keeping track of all that. So at the end of the day, you can -- it's going to get these cases that are going to be very complex and being able to summarize what happened, being able to ask questions becomes pretty powerful.

Yes. So in this case, they're tracking the MRD and becomes elevated, so that's when the provider might go order imaging. And in this case, then the CT confirmed progression. And so this is an example, again, where a provider might want to type in a question. like tell me if I'm now thinking about zenocutuzumab, they might want to ask questions like what are the adverse events associated with zenocutuzumab. Given that this patient has -- from that clinical time line that you saw on the right, this patient is having significant weight loss and questioning is that [indiscernible], is that related to the therapy? And so you see it quickly surfaced here now information with kind of linking out and sending you to more information.

So it is a bit of a deep dive into a particular case, but I think the beauty is the way that all these results are accessible, we have summaries and it sort of anticipates the information that providers may need and leave them together and pulls it using the technology and the testing together.

Cool. So just at a really high level, this is also this technology just doesn't really exist other places. So other people have ordering systems where you can track and order and get a result. But here, it's all brought together, allowing you to contextualize it. And the challenge with cancer cases, and I think most of the things that kill us, heart attacks, stroke, cancer at latent stages. They're complex, the comorbidities, the amount of things you have to consider just complicated. And so as an oncologist or a physician, you think you're treating one disease, but pretty quickly, you're treating another or another complication. And here all that information is accessible.

In the case of Tempus, you can order a whole variety of tests to begin with, you can track patients, but you can also communicate with the outside world and figure out like hey, if I put this patient on this drug, what's the most notable adverse event? And what does that mean? And how should I think about it? Maybe I'm going to bring somebody back sooner or dose them differently or be careful. And this is the reality of treating patients, right? You're treating a patient, but all of a sudden, they have major weight loss. And so you can't put them on the next dose of chemo or give them the next IO because they're just not doing well. And so being able to get ahead of that is super powerful. On that note, we're going to jump into the foundation model for a second. These systems, we began building software to kind of integrate all of this, that agent One that sits inside the system that allows you to basically bring all these different disparate health care data sets into one place is the same technology we use for our large-scale foundation model.

Just for people that don't know, I'll provide a quick overview. About a year ago, we made the decision to build a large-scale foundation model in partnership with AstraZeneca and [indiscernible] providing the majority of the funding they may invested about $200 million to build this model. And then we began building it. The model is quite significant. It sits inside a cluster of about [ 1,080 ] fairly large compute cluster. We loaded in an enormous amount of the identified data across all these major modalities and we've begun generating insights from this data. We had to do significant pre-training significant compute as in run this cluster for like 90 days at 100% capacity or thereabouts, and then do a post training. We announced this morning some of the first insights from that model. And at a high level, what's amazing about this model is you're taking enormous amounts of multimodal data like BAM files at scale. Clinical data, we have billions of notes at scale, things of that nature and then asking it to predict what's happening and it's performing in many instances as well as super small, highly tuned models, which Kate will cover.

Yes. So as Eric mentioned, we're really excited now to kind of enter this next era. You've heard this morning already about the algorithms that we develop and put on top of our diagnostic tests. And part of the future promise here is that we can start to do that at a really broad amazing scale by using Foundation models. And so one example that we've been working on is just starting to take this multimodal data that you've heard about all morning, the clinical notes, the EHR data, the molecular data that we have, both DNA, RNA and images. And to combine that and instead of developing algorithms, you heard about IPS and some of the amazing tools that are already out there today, those were developed by traditional computational data science teams really coming through massive data and coming up with those algorithms and then validating them.

Future state is that we're going to have models that will be able to surface those insights very rapidly in a more automated fashion, and then we'll be able to validate them quickly. We've started by just looking at very traditional biomarkers. We know that we already have good biomarkers like EGFR, [indiscernible], we can name a whole laundry list. So we asked the question, could we go even further and contextualize those patients. So in the IPS example, when you use biomarkers like PD-L1 or TMB, which are very well established, we can add insights on top of that and be able to separate patients who will do well or not do well beyond the standard biomarkers. Our model is now able to do that in addition across many different clinically relevant biomarker.

So here, we're just talking about EGFR. This is meant as an illustrative example. You can think about any other clinically relevant biomarker and the model now being able to say what patients would do well or not well on sort of standard of care therapies. And so we'll be able to then also look at other biomarkers. So we looked at things that were already well known about EGFR patients. So P53 other co-mutations, other comorbidities. And the model is actually able to pick up beyond those standard biomarkers, other signs that a patient may or may not respond to standard of care therapies. So you can imagine a future where any biomarker, any test you just saw Hub and Laura walked you through that. This type of information could be layered on top of that for future state for physicians to really be able to get a more global and deep understanding of the patients that they're looking at.

Yes. So really quickly, and then I'm going to bring Ezra back up. So this is our strategy, comprehensive tests add on a bunch of alrimic insights that make those tests better, which we are doing today, which is driving our unit growth rate to be so high. And then the kind of next level of that would be run large foundation model at scale, have the system instead of generating one insight every 6 months or a year, generate an insight a week. That's just that people didn't know is the patient going to respond to EGFR inhibitor, are they going to respond to an ALK inhibitor, does the [indiscernible] fusion matter? Are they going to respond to immunotherapy, what adverse event is most likely, how long are they going to be in this therapeutic, whatever it is, generate those insights at scale analytically and clinically validate them, we have a machine to do that, put them into the report.

And over time, you just become like it's hard not to get those insights because over here, you're ordering a test putting your patient on a drug, not knowing whether they're likely to respond or not and over here, you can, and we suspect that's the future. Whether -- we're the only company that can offer this or other people offer it. I don't know, but I'm 100% convinced that old world of targeted therapy will die and this new world of precision medicine will show up. On that note, we want to talk to you a little bit about how these algorithms are also affecting our clinical workflow. We have another product called Paige Tempus preview, which is essentially leveraging our digital pathology library to generate a whole bunch of insights. Some of those insights are making calls early. Some of them are making calls when a doctor can't get that information.

So I'll pass it off to Ezra.

Thanks. Thanks, Eric. And so here, we have -- as Eric was saying, 2 examples of how we've leveraged Path AI, this Path AI into the diagnostics. The first I'll talk to you about is Tempus preview. And there are certain situations where rapidity of the results is critically important because the therapy depends on that result. And if you choose the wrong therapy, the patient could be harmed.

The first example is MSI high. We know that these patients have a very high response rate to immunotherapy. And not only that, -- many of those patients will be on that immunotherapy for years and potentially cured. That's a result the provider wants to know right away because you don't want to put that patient on chemotherapy, you want to put them on immunotherapy. The same is true of EGFR mutations, especially in non-small cell lung cancer. Here is a situation where if you put this patient on immunotherapy, they actually do worse. So you want that result right away. The third example that I show you here is FGFR alterations in -- across several cancers, especially cholangiocarcinoma.

Again, the rapidity of that result is critical. And so how will we address that problem? We've addressed it through Dig path. Here, through an H&E slide, the algorithm can be highly predictive of the presence of that alteration, whether it's MSI high, EGFR mutations or FGFR alterations. Giving the provider that quick response, this patient may have or likely has with a high degree of certainty and EGFR mutation. And while the provider is waiting for the confirmation through NGS, they can now select the appropriate therapy and get ahead of it rather than select the wrong therapy and potentially harm that patient.

On the other end, it can be incredibly frustrating to providers and to patients to get a QNS result. There are situations where we just don't have enough tissue or the NGS testing for whatever reason fails. That happens in about 7% of the time. And they're really is very few methodologies that can get us below that 7% threshold. So we decided that we would address this problem in a different way. And again, bring in the capability of Dig path. Here, this is called Page Predict. And I show you this is a real-world example. Obviously, the patient's name is different, where we can use the Ditch path to tell the provider that there is a high degree of certainty that this patient has a specific alteration. In this case, it was a patient with cholangiocarcinoma, a highly deadly cancer, and that tumor contained an FGFR2 fusion. Those fusions are highly responsive to specific inhibitors. The result for the NGS came back QNS, just not enough tissue, but with the Dig path, we were able to inform the provider that there was indeed an FGFR2 fusion.

That provider got a confirmatory test, and that patient was put on the right therapy with a high degree of benefit versus chemotherapy that was unlikely to work. And again, 2 examples of how we can bring in the multiple capacities and capabilities that we have to provide the best insight to that clinician to help that -- to help get the patient on the right therapy at the right time. Thanks again, Eric.

Okay. On that note, I'm going to turn over to Jim to talk a little bit about the financials and diagnostics.

Thanks, Eric. So I think that gives you a good overview of kind of what is the big driver in terms of our volumes, specifically on the oncology side. We've obviously experienced strong sustained growth over the last several years.

In Q1, we had 28% volume growth in oncology, which was building out a very strong kind of accelerating growth rate throughout 2025. We have favorable ASP tailwinds that have led to kind of the revenue growth, and we'll hit that in a slide in a second. And then obviously, with the addition of Ambry and some of their outsized growth, given some of the share gains that led to additionally outsized growth in 2025. Here's just an overview of kind of trends in clinical oncology of volumes and ASP over time. I think the big takeaway here is that we had very strong growth in Q1, about 28%. As we look at April and May, that growth has continued in terms of the orders that are coming in, so they're tracking at a very similar pace. Again, highlighting the durability of the growth from a volume perspective.

And then again, we've seen ASPs tick up over time. We've talked previously about this path to kind of achieving $500 of incremental with the announcement this morning of xT [ TO ] CDx being approved, that allows us to capture that $200 at the beginning of 2027. So we're on track xF is sitting with the FDA currently that was submitted earlier this year. So it won't impact ASPs in 2026. But as we get into 2027, that should be accretive as well. And then there's also commercial coverage over time continues to tick up. that's not kind of a flip of a switch, but we will chip away and see improvements over time there as well.

On the regulatory side, we saw growth rates moderate in Q1 which was anticipated given some of the large kind of share gains that they had back in Q1 and Q2 of last year. We would anticipate similar growth rates in Q2. But as we get in the back half of the year, we'll see that acceleration of growth in the hereditary business again, as we've done kind of lapping some of those share gains. And lastly, we've talked previously in this 25% growth rate over the next 3 years. that would put us at about $1.9 billion, just to give you kind of the size and scale of the diagnostics business. We provided a rough breakdown of where that's coming from primarily in clinical oncology. But hereditary obviously moderating back to kind of the mid-teens that we had talked about previously. And within that 3-year period, there's going to be periods where ASP may outpace and you may be growing faster than 25%. But what we really want the takeaway to be is that this business is really set up for durable long-term growth. And on the right is kind of the list of initiatives, both near term, which are all being executed on today, but then also kind of the longer-term growth drivers that will allow us to move beyond that for 3-year period.

So with that, I think we're going to do some Q&A. I know that they are going to grab some mics to walk around.

Yes Dave Westenberg from Piper Sandler. I wanted to talk about the trajectory model. So the AI model predicts how patients will do over time, significantly outpacing the models at predicting survival. You've demonstrated this across 3 cohorts of famous studies. I believe they are all in lung cancer.

Two questions. First, of all 3 of these done were done using historical patient data. What's the plan for using this test on data outside the institution, a hospital or registry that Tempus didn't generate to prove this works in real-world samples. Secondly, and more importantly, at what point does a pharma partner move? This as an interesting research tool to actually make a go or no-go decision on multimillion dollar trials.

You want to start with first and I'll cover the second?

Yes. Yes. We kind of -- we're going to talk more about this actually in the data section in life science, but that's okay. You jumped in Yes. So first of all, just in terms of how this model compares to other more traditional methods that you're mentioning and reading from the press release.

So this model, we have many different models. We'll talk more about that, but the model that we're highlighting there is what we call a patient trajectory model. It's actually able to look at patients over time, which is one of the benefits that we think we're really excited about. One of the things we were highlighting is that it's very good at predicting outcomes. That's sort of for that particular model, it's able to look at outcomes, in particular, survival. And so you can start to do interesting things like take cohorts of patients that are very well known and understood from more traditional methods and ask how well the model can uncover other prognostic factors that might change survival.

And so that is a use case that can be used. You're right, for clinical trials, and we'll talk a little bit more about how pharma partners can think about that. But it's also a space where we can then start to validate against sort of well-known and understood trials that have that outcome data and then be able to move into new spaces and ask new questions of new cohorts. So that's the way we're thinking about it. In terms of validation, you asked how would we validate this with other sites or other institutions. That's absolutely part of the plan. So we have a vast and broad network of providers and institutions that we work with and a vast network of pharma partners and life science partners who have their own data, both retrospective and perspective. And so the future state will be taking some of the models, asking questions and then working on validation in a lot of those different spaces. So good to up for what's going to come later.

And we'll talk about it in a minute. But at a super high level, we generated inside these foundation models. One main large foundation model, lots of micro models. You generate the insight and then you essentially have to figure out if that insight works across data that you have that you didn't use to train the model. We're fortunate that we generate so much data that we have a huge bolus of data we've used to train the models and a large bolus of data that sits off to the side.

So we can make a prediction using that data and then see if it holds up in other data that we have. And then once that's true, you know you have something that's working, then you go seek to basically go to third-party sets and validate it. Diagnostic insights will essentially, for the near term, live on our diagnostic tests because that's where they live. And life science insights or biopharma insights are already being used by the people that have access to these models. There are half a dozen biotech and pharma companies today to have access to one or more of these models that are using those insights to interrogate their R&D portfolio, design more intelligent Phase II, so on and so forth. So they're already being used. And we'll talk about that insight. I don't know you want to call people on it, [indiscernible].

Kyle Mikson from Canaccord. Great stuff. I want to ask a multipart question. So first, on the LRP, I guess. I know you've been talking about the 25% for a while, but the Street's at like 20% revenue CAGR for Diagnostics. So maybe just talk about what we're missing in terms of ASP volumes, MRD and AI and rents up? And then secondly, maybe just talk about the diagnostics M&A kind of strategy. You haven't done one since Ambry, but you're having Personalis 15% or so. Maybe just talk about like how you're viewing dilution versus growth in that segment in terms of M&A?

We can both cover it. I mean, I'll start. I mean I don't follow the stream. Some of the models I've looked at essentially are they have like high growth rate in '26. There was a high growth in '24, low growth rate in '26, '27. Then they had high growth rate in '25, low growth '27, '28. Now they have high growth rate 26, low growth rate '28, '29. So they're just nonsensical. I mean they're essentially saying you're growing really fast, but one day you won't. And is there any logic behind that? I have no idea if what that logic is.

We look out at our portfolio. If we're going tell the world, we think we're going to grow 25%. We don't want to look stupid. So we have to believe that like we really think that's going to happen. So there is no street model that I would look at and be like, oh, they know something we don't know, we have more information. And at the present moment, we believe we're going to grow at 25% roughly. Some of it will be -- our volumes are pretty healthy right now. We're growing in the low 20s, so that's awesome. Some of it's going to be ASP lift. We just the one big -- you had kind of 2 big drivers of that. Roughly half of that gain showed up this morning when we got FDA approval for [ TO ]. That's $200 of lift across a massive number of tests. I don't know the number, but it's probably $750 million of gain. So that's a real number. XF when that's approved is another big piece. So part of it's ASP lift, part of it's volume lift.

And then you're launching new tests and other things are happening. There's always pluses and minuses. Everything doesn't grow equally up into the right. So some tests will overperform, some will underperform. But as Jim mentioned, the benefit of having this kind of a comprehensive portfolio is we're big enough to absorb that and still deliver that 25% per year. In terms of MRD, we chose personnel because we thought they had a great test. We invested in the company, obviously, as of this morning, that's been a great investment. I don't know how much money we made, but it's a lot of money. And at the end of the day, they've been a great partner, and we're executing that strategy.

In terms of whether or not at some point, we'll look to consolidate those companies. That's obviously not for this meeting here, and I don't have a good answer for that anyway. But I will say this, our strategy of not needing to run every diagnostic is the right strategy. like I know it sounds a little crazy to diagnostic investors, but I promise you, Amazon doesn't make every single product. So -- and nor does Apple make every single app. There's going to have to be technology platforms that take the U.S. health care system, which is very complex and translates it to physician care and patient care across the board and the companies that do that can't do every single thing themselves. They'll have to find ways to partner with third parties.

So we've always been focused on not just running tests and well but figuring out ways to make money partnering. And as I mentioned earlier, our net unit economics today are as good with personnel as they would be if we own the company. So if we -- one day own the company, all you get is revenue gain. -- you get no net income gain.

Great. Casey Woodring from JPMorgan, and thanks for hosting us today. Can you talk a little bit more about the attach rate of xF and the tissue test. What's the current attach rate there? What percentage of these cases are reimbursed for both tests. You mentioned that I think 9% of patients had unique actionable alterations that were found in xF that weren't observed in xT. So just wondering what a payer would say to that. Is that something that would preclude them from paying for both tests? Just how should we think about that?

Yes. So in terms of the attachment rate, it hasn't changed over time. I think we've published previously with around 25%. It largely has stayed intact over the years in terms of the number of physicians that are ordering it. And reimbursement depends on, obviously, the payer we're fortunate from a reimbursement standpoint that we're in this period where we're seeing expansion of reimbursement. And so there will be some tests that don't get reimbursed, but we're still going to see a net add to the overall reimbursement since we're not at parity with our peers.

So we think we're well positioned to continue to win in this space. It's another thing that arms doctors with additional information that is incredibly useful, and that's why we offer.

Yes, it's also worth noting, when we went public 2 years ago, people were like, "Oh, you're running DNA and RNA, and it's not going to get paid for it like it felt kind of like you guys are on the edge". If we were on the edge 2 years going to be public, we're now in the middle of the bus like maybe getting toward the back of the bus. I mean you've got reimbursement rates from our competitors that are 2x ours. You've got these portfolios being rolled out where we've got competitors that are like, click this button and you have 12 tests. They just will keep showing up forever.

So like we are not cutting edge in terms of like, hey, order a bunch of stuff and is it going to get paid for. Like there are people that are way further ahead of us that are driving all kinds of unit growth rate by being aggressive. We view ourselves as not being aggressive. We view ourselves as being comprehensive, but not aggressive and that's where we want to be. We want to give -- we want physicians to be able to like logistically make a decision and order things in an administratively intelligent way, but we never want them ordering test they do want and we never want to bundle in 5 tests like a year when they don't really want that. So I think to the extent we were on one end, we're not.

If I can make a comment, Eric. You don't know who that 9% is a priority. That's the other thing to keep in mind when you're thinking about reimbursing these tests. As a provider, I don't know who falls into that 9%. So I have to order both in order to get that answer. The same is true with RNA. I don't know who's going to fall into the that's only going to be surfaced by RNA. So I have to order both. So logically, it makes sense to reimburse. And the trend, I think, is -- I just want -- certainly on the RNA side, that bus cannot be, that training has left the station. We could argue whether or not there will be rules over time about how often you can order MRD test how often they're going to get paid for, how often treatment response modern is going to get paid for. I do think that space is going to be -- as I mentioned earlier on, I don't think fully like all the puzzle pieces are in the right spot.

So I don't know where that's going to land. But the RNA trains left the station. It just matters way too often. We're in the middle [indiscernible]. Our competitors been a bunch. You come back 5 years from now and there will be dozens and dozens and dozens of RNA-based therapies that you will need our express data for. We'll do one more -- we have more Q&A, so we'll do it in rounds.

Kallum Titchmarsh with Morgan Stanley. Maybe just on the oncology business and the volume growth you're seeing today, could you just break out that growth between the existing account penetration versus kind of new adds? And then I guess, have you seen any examples of physicians switching to Temps' tests as a result of the technology infrastructure behind it like Hub and Paige Predict?

We'll start with Mike. Any thoughts on the new versus existing?

Yes. I mean we've held pretty steady in terms of addition of new clinicians and new ordering systems. We monitor this really, really closely. So we look at what is the kind of reorder rates over time and then how do we actually add net new physicians that have never order with Tempus or physicians that had previously gone sale has had an order with us within a 12-month period and they've kind of come back in that. Number continues to, at the very least, hold study, and we've actually seen some modest growth in terms of that new ordering position base. So there's really kind of 2 vectors that we're seeing this growth come from. One is in this kind of addition of new customers and two is in deepening relationships with our existing customer base.

I would just add one quick thing. There's only 14,000 oncologists in the U.S. And so when we -- Eric talked about, we think about 50% of oncology as actually order that. So it's always a combination of continuing to have the physicians identify more patients that should receive this type of testing and then tapping into that untapped market of folks that aren't ordering at all.

In terms of some of the new products like Paige Preview or Tempest preview, I think we just are deploying these things now. We acquired Paige, I think maybe 6 months ago or 9 months ago or something. And so it took us a while to get these data sets aggregated and bring some of the benefits of those products into our platform. This QNS thing is not small. I mean it's just -- there's no way to solve that problem. We have that problem. All of our competitors have that problem. You just occasionally don't have enough tissue. occasionally, alumina sequencing process just doesn't yield the results you want. So being able to make predictions so no patient is left behind is pretty powerful.

On the other side of that, there's just a certain number of diseases where doctors want answers in 1 or 2 days. And we will be the first people that can offer that at scale. And so that's also powerful. So none of that stuff is currently showing up in our unit growth, and I suspect it shows up over the next, I don't know, 3, 6, 9 months, 12 months.

Let's jump to the data business, and then we will come back to Q&A, if you don't mind. Okay. So to be sustainable, 2 main businesses, a diagnostic business and a data business. Our data business is the one that I think for a lot of diagnostic investors is like unfamiliar. So we're going to spend some time trying to walk through like how it works and why it's growing and so successful.

First of all, if the question is whether or not data and AI and technology are going to permeate drug discovery and development and health care, the answer is like 100% yes, it can't not happen. Every industry whoever has said it's not going to happen has been washed away by technology. And I just give you one example, like you can go back to 1960, 1970, when people were like trading on the New York Stock Exchange like orange futures and would have bet you their life that this could not be replaced by technology and it's all been replaced. So like that's just the unstoppable nature of technology and health care will be a beneficiary of that, but it is coming.

In our case, we have spent the last 10 years really building the piping to generate a healthy and sustainable data business. That piping is all about how do you pull data out of the U.S. health care system at scale. How do you combine it with something else that makes it super interesting molecular data, how do you produce an insight? And then how do you package up that insight. If you think of it, we've got 2 end customers. We have to package up an insight for a doctor. Your patient is not going to respond to this particular EGFR inhibitor, you should know that. [indiscernible] it up for biopharma company is far more complex. So this is -- it's not -- it's typically not a simple answer. They're designing trials. It could be novel discovery. There's a lot going on. So you have to give them the tools to interrogate this data. And in that regard, we stand on. If people are wondering when we started the IPO process, 2 years where we went public and people -- I think if I would have asked 9 out of 10 investors, they would have never thought our data business to be this big and they all would have thought we'd have met massive competition. Both of those are -- have proven the opposite.

Our data business is big and growing, and we have almost no competition. And it's really the technology and tools we that wrap around this data, the connected platform, the analytic capabilities, the ability to deliver data at scale, interrogate the scale that's unique. And this data comes from many sources. You can't just be a sequencer that generates DNA and RNA data and be like, I'm in the data business. We have data coming from our care gap products, our clinical trial matching products, our real-time clinical trial matching products our AI tools and technology, our diagnostic business, our radiology products, our pathology products, our cardiology, we have many, many ways to get data, which allows the data set to be real, contemporaneous. And it's also -- it's part of this network effect that fuels our diagnostics business is also helping our data business. The more data we collect, the more insights we generate, the smarter our platforms get, the more people want that data, they're making -- they're licensing it, which allows us to invest in those tools and it becomes this really positive virtuous cycle.

This is used across the entire competitive of decision making. What I think a lot of people understand is like, why are people licensing your data? And Ryan is going to get into some use cases and the tools around it in a second. But it's really the entire R&D, the discovery and development life cycle. Do I have the right target going after the right indication, do I need a biomarker, how do I design my trial? These kind of questions aren't worth $1 million or $5 million to a big pharmaceutical company or big biotech, they're worth hundreds of millions. You get it wrong, you've got $1 billion failure. You get it right, you've got a $10 billion franchise.

Our data and our modeling tools are used really across every aspect, from early-stage R&D through clinical development, now into commercialization, we just touch our products touch really anywhere you could use data or AI or modeling to help make more intelligent decisions where they're certainly not in oncology at scale and will be the other disease areas over time.

Ryan, do you want to jump in?

Yes. So this is a quick snapshot of the platform. And like Eric was mentioning, we've been licensing data to our biopharma customers for several years now, and we have been in the business of licensing multimodal records for longer than anyone in the industry. This is -- when we say multimodal, we mean combining DNA, RNA, treatments, outcomes, images so that you can really understand what's going on with these particular patient populations in the real world.

And so this is the kind of a snapshot of what it looks like, but I actually rather just show it to you so you can see what it looks like because to many in our space, analyzing multimodal data is not easy. And so acquiring the data is one feat, but organizing it under a common data model, being able to make it useful for people that are coders or noncoters is essential to get -- to turn data into insight. And so this is a platform that sort of is the front door of our data set. And so our customers, we build these data sets for them and they can interrogate that data through this system. We've now actually embedded AI into every step of the workflow. And we'll actually -- I'm giving you an early preview that we're announcing in a few days here about sort of the new launch of Lens with these AI tools.

And so for every step in your journey as a user of the system, we have Tempus One as sort of as a copilot or coscientist to really help you on your journey to generate insight. And so one thing that I can do is I can start to build cohorts from simple natural language. This is an essential step in order to make sure that the cohort is sort of built fit for purpose. And so while this is building, you can start to see that we have various other data sets and projects that I, as a user, have already created. So things around particular either tempest data sets, public data sets things of that nature are all in this space so that I can start to compare and contrast different cohorts over time. And so what you can see is that we can now get deeper into things like not just a particular biomarker that's known today like KRAS, but I'm building a cohort for lung cancer, adenocarcinoma. For those patients that were treated with first-line therapy that have a KRAS mutation and then quickly identify we have 3,000 patients already in the system.

I can now either save this query, I can adjust this filter, and I can quickly start to refine this cohort over time. And so what you're seeing now on this left-hand side is that my copilot help me build this cohort, but I can take the steering wheel and actually refine this further with these filters on this left-hand side. Each filter is essentially an inclusion or exclusion criteria that our pharma customers are thinking about, right? So they start with a population of interest. And then they're trying to understand for the particular population that I'm going after for my trial, is there patients in the real world that I need to better understand, again, with these kind of this combination of DNA, RNA treatments and outcomes. And really the essential step is that you have to be operating at scale to get to the bottom of the funnel that is significantly powered thousands of patients.

So having millions of patients at the top of the funnel is essential in order to really get to something of real interest. This system allows our users to be able to visualize these different modalities of information and interrogate this information in much more granular ways. And so for that 3,000 patient cohort, I can start to see basic things like, okay, what are the demographics of these patients, the distribution of age, things of that nature. But I also maybe want to understand co-mutations like Kate and Ezra were mentioning as well, and you can start to see that not just in terms of prevalence, but you can also start to run feasibility on treatments were these patients given of the 3,000 because I may want to select some of those prior treatments as part of my inclusion criteria.

Now one of the things that our customers are doing all the time, and the question that we got earlier on was how are our customers using this for trial design decisions. And one of the most important aspects is to make sure you have your patient selection strategy correct, which means am I going after the right patient population with my drug or not, right? And so one of the things you can quickly do in this system by just -- even by clicking a few steps here. I can start to compare cohorts over time. And so I can start to look at other data sets that I may have added, I can start to look at that -- those changes and start to build these kind of graphs on the fly. I can go a bit deeper as well, and I can look at even things around the things that we only Tempus can provide, something like co-expression analysis as well.

And so I can start to dig a little bit deeper, and we allow our users to get to this type of insight in -- literally in a second or in days. And so looking at things like beyond EGFR and KRAS that we know of today, but looking at novel biomarkers that are coming in the future, like MTAP deletions is essential for our drug developers. And so this is sort of a detailed breakdown of that sort of data set that I've already built that's looking at not just the KRAS mutations that were treated in first line, but looking at something specific as MTAP biomarkers and what that is doing to the various kind of behaviors in the real world. I can look at the distribution of gene expression. I can look at sort of the correlation or the pairwise expressions between not just one marker, but 2 markers at a time. So looking at MET versus EGFR, MET versus KRAS, MET versus MTAP. These are the kind of the various iterations of questions that our users are going through, and they can get that insight all within this tool.

Now this is useful for people that -- even if you're not a coder, but we also have connected our data to a computational platform like our and Jupiter notebooks to be able to go even further for those that actually want to code, right, and actually have that capability. And so again, we've embedded Lens or Copilot here from lens to be able to help people ask certain questions around how do I create an onco plot, looking at the most top 10 most frequently altered genes. I can quickly do that, figure out sort of look at what tools that I need to call but I can also start to see the actual code that was written. And I can pull that up here. It starts to run the actual sort of analysis in my environment. And again, we're giving the user the control here. The code is populated, but they can actually edit this code no different than their -- what they would do in our environment.

But the most important thing is that I can quickly get to insight I can refine this, and I can have my files generated instantaneously. And so these types of outputs are the things that really drive our business today. And so maybe we flip back to the slides. Being able to have that go through those iterations very quickly get to these types of outputs is really the first step in a multistep journey for our drug developers. So if we can switch back to the slides. I can kind of then cover some of the other aspects. All right. So I already walked you through sort of the kind of the step 3 steps query, building data sets through natural language, using agentic workflows to be able to analyze this data in much more granular ways. And one of the things that we're really excited about is connecting this rich multimodal data to the compute environments that we use to actually train our foundation models, but actually connecting compute infrastructure to help our customers build and fine-tune models as well.

And so one of the things that we do is not just helping understand different co-mutations and helping early development also are helping clinical development in late-stage development for those high-risk, high-reward types of decisions like a Phase III global trial but even growing bigger part -- growing part of our business that is addressing what is happening in the real world is really helping those commercial and medical affairs teams around better understanding of things around clinical care gaps and things beyond that. But I want to spend most of the time maybe addressing that question head on around like what are our customers doing and what are they getting out of this type of unique data set.

So I'll walk you through 3 examples. The first one is a global biopharma company that really was interested in advancing their immunotherapy franchise. But they really need to think through, could they uncover additional novel biomarkers in a particular patient population. And so here, for this particular project, we were able to assemble a data set that really didn't exist in the world. A 5,000-patient data set where we had biopsy samples and DNA and RNA segmenting pretreatment and posttreatment. That type of data set will allow us to figure out what these particular immunotherapy treatments are doing to tumors that ultimately are leading to different outcomes in the real world. So again, this data didn't exist before, but what it allowed us to do is uncover 4 different novel targets that we're able to advance their drug discovery pipeline. And so they spent time and effort and money investing in these types of projects. But one project alone, and if you just think about like the return on investment on a single asset in an immunotherapy franchise, we're seeing ROIs calculated by our customers.

In this particular example, be 30 to 50 of what they spent. And this measurement of value is kind of the common, I would say, motion for us in our collaborations, so that we cannot just make sure we're delivering value now but also it's why many of our customers have expanded with us over time. The second example was exactly the question you asked earlier, which is the most sort of significant investment decision that these companies are making is a go-no-go decision in refining the trial for a Phase III global study. And so here it was a different global pharma company that was faced with this kind of critical decision. And so here, we were actually trying to better understand what was going to be the comparator arm? Can we actually understand standard of care and make sure we establish a good baseline for what those patients are facing and how their outcomes are performing today, but also to make sure we're stress testing the inclusion and exclusion criteria is not just based on clinical measures, but also looking at can we understand the tumor biology of those patients to make sure that there isn't a heterogeneity or surprises in our Phase III study.

And so again, in colorectal cancer, this is essential. And so we were able to be able to not just deliver this type of insight with our biopharma company, but it is essential to be able to derisk a decision and ultimately creates a net present value for our customers. of somewhere north of $500 million. And so you can start to see each project starts to stack up from an ROI perspective and how we build over time. The last piece is really another global biopharma company that was faced with a slightly different decision which is really around not just investing in a global Phase III study, but do I go first line, do I stay in the second line, that type of decision is a high-risk, high-reward type of play. And so again, we start to look at what data sets do we have, what multimodal data sets we can build so that we can analyze the molecular distributions of patients that have high PD-L1 versus low PD-L1 because this ADC sort of decision for that particular drug was going to go up against that type of landscape. And so we've built the data set, we worked with the teams, and we were able to derisk a number of the decisions by building these patient subgroups but also the go decision was made to ultimately allow that customer to proceed.

And we talk a lot about like probability of success, but we've been in these collaborations for long enough where our customers have actually seen success. We've seen approvals for the programs that we've supported and that makes it not just a perceived benefit, but an actual ROI metric that ultimately leads to why our business has grown over time.

So with those 3, I pass it over to Eric.

Great. Thank you. Okay. So at a high level, I just want to cover this kind of scale and scope of our data business. We're connected to -- I mean we're working with something like 19 of the 20 largest pharmaceutical companies in the United States. That number has helped -- or that metric has held pretty constant over the past several years. The good news being -- we're still working with all these folks. We work with over 250 biotechs. We've signed in excess of $2 billion worth of data licensing deals.

Our revenue last quarter on the data licensing side was $87 million. We've now large partnerships in place with not 1 or 2 big pharma companies, but lots. and This number continues to grow as we sign more and more of these kind of multiyear $10 million to $20 million or $30 million a year engagements with folks. And we also delivered an enormous amount of data. I think as one of the things when we talked about the Lens platform, this is -- these capabilities as they scale not just to generate data, structure, harmonize, clean it, analyze it, but also deliver those insights, including the raw underlying the identified data to biopharma.

When you think about delivering petabytes of data, it's just not a small task. Ryan got into a bit of the ROI that we are used to measure, but increasingly, folks are looking at this probability of technical and regulatory success and whether or not we're actually generating ROI if they're making investments in their licensing a $20 million of data, are they generating $60 million or $100 million of gain. And we've been through rigorous analyses over and over again with people who are increasing their spend where they're roping in finance they're hoping in [indiscernible], they're roping in other teams to validate the return on this data licensing and over and over and over again, it comes back that this is accretive. And so people increase their spend. We have a long history now of people increasing their spend, which we'll get into in the next slide. This is typically how it works. People very rarely to somebody say, "Hey, I'd like to sign a $100 million deal and license $25 million a year of data for the next 4 years".

More often than not, they start with, I'll do a $250,000 project. $0.5 million project or $1 million project. They get the data, they have to try it and test it. Multiple teams are involved. They have internal computational biology resources and bioinformatics resources and biostatistical resources R&D teams, and they interrogate this data and try to figure out, is it good? Is it clean? Can they use it as a representative. So you have to get through all these hurdles before you get to the next project and the next project. We have a long history of going from one program, one project with one asset to multiple assets to multiple assets over multiple years to expanded partnerships and ultimately strategic partnerships. As I have said for a long time, you can -- we -- our pricing model is similar to the large cloud providers, AWS or GCP or Azure. You don't have to sign a big deal to be on Azure or GCP or AWS. You can spend $100 a year with AWS, and they're happy to have you as a client or you can spend $1 billion a year.

The only thing that you gain by making a longer-term commitment, both in terms of years and dollars is a reduced price. So if you think about all the people that sign these multiyear, very large agreements with Tempest, the only thing they're getting is a discounted price, meaning they value the data so much, they're willing to make a multiyear commitment because they want to save that money. So I think it just speaks to the value of the product we've built. Obviously, here's a great example that our first strategic partnership with a big pharma, we have several strategic partnerships with big biotech.

But it was AstraZeneca that was signed, I think, in 2021. Obviously, that relationship is going strong, and we -- they're funding our foundation model, and that project runs for the next, I don't know, several years. And so we've got a long standing relationship with AZ. GSK was another large partner that came on board. There's a few years left in that agreement and then Merck recently signed up as another large farm who came on board in a strategic way, and that partnership is just starting to kind of grow and prosper in every which direction. And it just speaks to the fact that the biggest cancer companies increasingly realize they need our data to do all the things that Ryan talked about a minute ago.

If you look at the -- just the -- we'll get into some of the metrics of the business, but in terms of like overall relationships and concentration, work with about 240 companies in 2025, and that number should be up in 2026. We work with 35 in 2020. So over a 5-year period, we went from basically 35 people in total licensing our data to 240. Back then in 2020, 85% of our business came from our top 5 clients. Now it's 59% and shrinking dramatically. That number is just kind of on a free fall down. So the good news is the business is diversifying itself over time. Increasingly, people don't just want our data, they also want models.

Our -- it's actually what we call it data and applications, but in reality, it's not a great name because what's happened to us over the last year is it's rare that people just want our data. More and more and more, they actually want models. You can almost call it modeling and applications. Data in and of itself is interesting, but in a world of large multimodal models, which all of our companies have some exposure to, they want to know how they can use this data to build their own proprietary models or take their proprietary models and hyperscale them with more data and actually figure out how to build something that's proprietary and advantageous to us. Almost every conversation we have now is a blend of license some data and use our capabilities to build models, build those models on the lens platform. We have both CPUs and GPUs connected to that platform at scale or we'll partner with you to build models in some way, shape or form.

We're going to -- I'm going to bring Kate to talk a little bit about the benefits of the foundation model on the biopharma side. But again, this is a huge cluster, a massive amount of data that's producing insights. Some of those insights have therapeutic relevance. Some of them have research and discovery benefits, and Kate will cover that.

Yes. So we talked earlier about -- a little bit more detail about our model. And actually, when we say model, we're really moving quickly towards many models. So you can imagine an ecosystem of these models. And then layering on top of that, things like agents that can leverage the models, tools and capabilities, you just saw a Lens. And so we have things there. We mentioned the coscientist type of approach. So future state is that this is moving towards a platform. We talked about it earlier from a diagnostic perspective and how that can help us uncover insights that can become algorithms on top of tools. The same thing is true here for our biopharma partners.

And so the ecosystem we have today already includes multiple models. So with the acquisition of Page and their team coming in, we already have several foundation models that are very good at using images and being able to look at different signatures or outcomes. You heard a lot about that from the diagnostic component this morning. You can now imagine how a biopharma company might want to use those same tools and technologies for their trials. So rather than having to run sequencing, they can now use an H&E image to understand which patient that they should enroll and to pull those in. In a similar way, we're actually able to take all of our clinical data that you've heard about this morning and to be able to leverage that and pull that into a model.

And so when we talk about multimodal, what we're really saying is that these models can now incorporate things like clinical notes, clinical labs, clinical images on top of the molecular data that we've spent 10 years being able to build on our platform. So when others talk about multimodal, they often are thinking about 1 or 2 modalities. When we talk about multimodal, we're talking about a really large library of unimodal models that we can then combine and start to fuse together into true multimodal. And so the future state that we'll move towards, as you see here, just kind of building upon lots of models that are very focused and specialized.

So yes, we can have a genomics model that combines DNA, RNA TCR sequencing, BCR sequencing, we can also then combine that with the clinical model we just talked about in terms of patient trajectory, thinking about patients over time and what's happening to them in the clinical space. And then we can layer in things like images and others. So as we think about how to actually make this useful, we gave some examples already in terms of use cases for our pharma partners. And in some cases, as Ryan showed really nicely, computational scientists, both within Tempus or in our partners are still going in and doing a lot of that work in a more manual way using kind of standard machine learning and data science future state well that they may be able to just ask the model a question and the model will be able with agents and other workflows to be able to produce that analysis. And so what we're really talking about very quickly is the ability to uncover insights that would take perhaps weeks or months to generate to now be able to do that in a very quick fashion.

And then to leverage those insights, of course, you will need to do follow-on work to validate them and show clinical utility and make sure that they're actually correct. But the workflows here will speed up all of that process and will allow pharma companies to ask the really critical questions that we just talked about in terms of what type of INE criteria should I think about? How do I design my trial to make sure I stratify patients appropriately for other factors that may impact the outcome of the study? And so we're really excited about -- we've really reached a moment where all of these things come together. They are helpful on the diagnostics side for providers, but they are also helpful for our life science partners.

And again, I think the -- and we've released this morning cover some of this, we've also -- papers coming out that go in much greater detail about how these models predict what they predict and you can take a look at it. But ultimately, we've crossed the major hurdles we had to cross when we entered into the first foundation model agreement with AstraZeneca. They had essentially 2 criteria we had to meet. One was a CNX score for an open for a trial that was it was out in the public while there was a trial that they had data for. We didn't have data for, for which they had trained a very specific model.

And so the question was both, could this large scale model replicate trial outcome data that's publicly available and privately available, both where there's no model that's predictive and a highly tuned model that's predictive, and that was the hurdle we had to get over. And at the time -- and that hurdle was not seen as being like easy to get through given this is the first time we were building a large-scale multimodal model in oncology with all of our data. And models just get much better over time. Think about that ChatGPT 0.1 versus ChatGPT 1 versus whatever for on now. And so the fact that it performed this well this quickly, I think, is an indication of what is to come. once you have these kind of models performing at scale or insights permanent scale, you end up saying yourself, okay, and this is the last part of our business is what do you do with them? How do you distribute them to the broader ecosystem.

And so we've long focus not just on the diagnostics side of the business and the data side, but also the application side. How do you take these applications or algorithms and distribute them broadly, given that we have this connected ecosystem of 5,000-plus hospitals we're in a unique position to be able to distribute AI into the U.S. health care system at scale in ways other people can't. And there's all kinds of questions that people are answering every day. we know what critical biomarker should I target, what are the therapeutic options I have? What clinical trials my patient eligible for, did I overlook something. And there are also questions that they're not asking like ambient in the background is a mistake occurring that no one knows about where care gap is being kind of broken. And so we built technology once we had these connected rails, and we had date flowing in and out of all these hospitals, and we had the ability to kind of in real time, take that data in, generate insights and put the insight back into the hands of a provider, we chose 2 starting places to focus on.

One is, could we use this technology to match patients to clinical trials? And can we use this technology to close care gaps. The third is could be used this technology to develop entirely novel algorithmic diagnostics and distribute those. In the first 2 clinical trial matching, we call Time and our Care gap program we call NEXT. And both of these things are operating at scale. They're operating at scale, they just don't generate lots of money, which I've said many times. But they operate at real scale. This is not like it's not like, "Oh, we've got a few people using these things". We have many, many providers using them multiple care gaps deployed, millions of patients being screened. We are enrolling lots of patients in trials. We are closing lots of care gaps in real time. these things operate at scale. If they were paid for, like I suspect they will one day be paid for, this would already be a large business.

So we're fortunate that our 2 main businesses, diagnostics, data generate enough gross profit and enough investment dollars we can invest that we're able to really lean into some of these forward products like our applications business that we think will one day be quite big. In cardiology, 60-plus algorithms deployed across multiple conditions in oncology, a whole body of algorithms. And in radiology, we've got a few in markets today, including our module that operates at scale. And then in terms of clinical trials, we have dozens of providers enrolled in our program. I can't remember, it's 80 or 70, some very large number of providers that spans 1,000-plus, 2,000-plus oncologists. And we have, at any given moment in time, a nice basket of trials that we're able to enroll patients in, in a rapid manner and this program is starting to really scale as we are moving from the space of kind of -- we've been over the last 3, 4 years, proving that it works. And now all the conversations are about enterprise engagements where big pharma is like, "I want to give you 15 20 trials". And we now are in a position where we're like, it's too many. So we're now fortunate that we're able to say to people like we can't take your 15 or 20 trials, which is creating a really unique market condition.

All of these technologies that allow you to scan clinical and other forms of data in real time and then generate an insight, essentially are part of this future where there will be ambient AI-enabled copilots that exist in the wild that essentially make good doctors, great doctors and great doctors superhuman. And that is essentially what these products are designed to do. They're designed to kind of be there in the background, structure all this data that historically was unstructurable generate these insights and make sure that they're enhanced people. This is -- not only does this require a connected ecosystem, which we have, which we've invested enormous amounts of time and money in building but requires really intricate technology. We have an entire technology stack surrounding this products like Edge and air and Locker, which are enormous in scope. We can allow a health care provider to give us access to this data without it losing security and protection of that data. We can bring in different forms of multimodal data that don't even reside in their EHR DICOM files, CT scans, pathology slides, other forms of data, like, for example, when you get a 12-week CG, it sits in a different way fall from GE is not even in [indiscernible].

So we can basically with our edge server pull all these different data forms in, structure that data and then airs our technology platform that allows us to basically take the insights and put them back in the hands of doctor through Epic or through other HRs that they're using. So all of these technologies are necessary to kind of pull off this, how do you listen in the background, find an insight and deploy it. In addition to our applications business that is growing rapidly. We also have an algorithmic business, which we call algos. We believe that in the future, these algos will be pervasive. We've talked about that historically. We are now not alone in that conversation. We have, over the last several months, I think, been dragged to Washington several times. People have been here. I think the entire world now is thinking about how do we -- how are we going to manage a world where we don't just generate wet lab diagnostics, but dry lab diagnostics, how do we make sure these things can get validated and paid for, how do they get ordered, all of that.

They can't be stopped. There's going to be more of them. Patients are going to want them and the U.S. health care system will have to adapt. And we want to be front and center in that change. We started in a couple of areas where we're most pervasively engaged. One is pathology. We have a variety of pathology, algorithms, some which are FDA approved like our Paige prostate Algo, which actually has FDA approval. Others that are in flight with the FDA, for example, our pan cancer suite has a breakthrough designation is going through that process now. We have a variety of cardio algorithms we've talked about historically, 2 which are FDA approved are ECG-based atrial fibrillation or [indiscernible], algo and our ECG-based low ejection fraction or low EF algo. Many more in flight, many more coming. We envision a world where Tempest is able to basically generate hundreds or thousands of these algorithms. We are convinced they will one day be paid for by the normal reimbursement process. And this will eventually be probably the largest of all of our businesses by far.

I'll go through one use case, which we talked about a little bit historically, which is the ECG-based use case because I think people kind of understand maybe oncology or pathology, but this just go to a new area. So obviously, heart attack is the own killer of people in the United States. One of the most common diagnostic insights you would get is from an ECG. This is the thing that's kind of pervasively in primary care. Especially for older patients, you go in and you see your doctor and you might get this as part of routine care. And yet this test that comes back is effectively wrong 3% of the time. So we run a few hundred million ECGs a year in this country and 3% of the time were telling people something that's wrong or sing you're fine, but you're not fine. You're likely going to have a heart attack or stroke within a year, and we don't know it.

As you can imagine, the technologies that were built that are most in market today, are now 30 or 40 or 50 years old, so they didn't use AI to make these decisions. And so we've taken millions of ECGs, connected them to outcome response data and other critical diagnostic data like echo cartograms and the like. And so we just built a really powerful portfolio of ECG-based algorithms that are either FDA approved in the middle of being FDA approved. These things are also deployed at scale. We have lots of algorithms to put in lots of hospitals, 140-plus hospitals, touching millions of patients, not small, but again, doesn't generate meaningful revenue yet. Here's the basic use case of how these things will get big. We have a hospital that recently rolled out our ECG platform. It's one of the top academic medical centers in the United States.

It's rolled out. They see a large number of ECGs a year. There is currently a code to reimburse part of this world at $128 per ECG, that code relates to part of the population. The code will be expanded to, we think, the entire population. And so you can imagine, as these things get to scale, that one hospital alone could generate a few million dollars of revenue and multiply that by lots of hospitals and lots of ECGs and you get to a several hundred million dollar business just on our API predictor, low EF is even bigger. There's other algorithms coming. And so I would imagine that we will, for sure, be running some kind of algorithmic diagnostic on all ECGs in the future. And somebody will generate $1 billion or $2 billion of revenue just from that product alone. And that same thing is going to happen with echos and CAT scans and MRIs and mammographies and digital pathology slides.

Each one of these data modalities that's being generated is an algorithmic diagnostic opportunity that's being missed today. And even at small dollars, $50 or $100 per algorithm the impact you get is enormous. So you're going to spend -- even if you spend $1 billion generating an [indiscernible] insight, you're likely going to save the U.S. health care system, $50 billion or $100 billion of mistake because as these patients don't get caught as they don't get found, they show up with complex disease and the most money we spend is in the last 90 days of life.

So just to summarize, the data and applications business is growing rapidly, the bellwether of that business is our data in a modeling business, which is having a moment and growing quickly, and we're excited about the future of apps.

On that note, do you want to hit the financials?

Yes. Quick, and we can jump back into Q&A. So as Eric mentioned, we've seen strong growth in data applications, largely driven by the Insights business the data licensing and modeling component of that. So 41% growth in Q1 of this year, the Insights business growing even faster, kind of partially offset by some of the smaller businesses. We announced expanded collaborations with Merck, Gilead, BMS over the last couple of months.

As we've said, the pipeline with biopharma remains very strong and engagement with our customers given all the value that Ryan described is in a good spot. In terms of the financial metrics, these are new, but net revenue retention for 2025 was 126%, again, highlighting how these relationships kind of expand over time and people come back and spend more money. The TCV at the end of the year was north of $1.1 billion. So again, in a very healthy spot in terms of kind of that forward-looking visibility of revenue in the future. And then we've previously kind of talked about a 30% growth rate for this business. insights, again, outpacing that offset by some of the smaller businesses for 2026. This is just the same slide that you've seen probably previously around the kind of the breakdown of TCV, $350 million of that $1.1 billion related to 2026. And so again, as these relationships expand and we kind of stack these large strategic collaborations on top of each other, that just gives us a tremendous amount of visibility bolt into this year and next year. And so as we keep adding them, that visibility continues to grow, which is great.

And then the 3-year CAGR, again, probably north of 25% for the data business. You'll notice that almost all of this is coming from insights. As Eric mentioned, while we are incredibly bullish on the app space, and we think that these things do get paid at some point in the future, it is not what we're counting on to deliver over the next couple of years. And so while we continue to push those things forward, we do think reimbursement will come in many of those instances, the bread and butter of the business over that time is going to be data licensing and model building. So with that, more than happy to take any questions on the data side.

I have 2 questions. As you develop more AI agents and applications, are there examples you would highlight as practice changing for physicians today, one? And second, Tempes has the largest multimodal oncology data set. Is the Tempest data set comprehensive enough? Or do pharma companies still look to sign additional data deals with other companies?

I'll take the first, Ryan, you can take the second. Look, I think where this is going, we talked a little bit about it a minute ago. I'm convinced where this is going is you -- over the next several years, we will begin to layer real-world data insights on top of every biomarker that is really therapeutically relevant. And so it will no longer be a sequence a non-small cell lung cancer patient to see if they have EGFR ALK. It will be -- once I know someone has EGFR [indiscernible] , what does it mean? Are they going to be in the 1/4 of patients that have almost no response, the 1/4 of patients that will be on that drug for 5 years or the 50% of patients that will be somewhere in between.

And I will want to know that because that will determine what I do next. If I know my patient is very unlikely to respond, then I want to bring them in right away. On the other hand, if I know they're likely have been in this drug for a long time, different paths. So yes.

Yes, Eric, if I could add there [indiscernible] immediately practice changing. And we are actually going to present that at the ASCO meeting in a poster. We looked at early stage non-small cell lung cancer, and the frequency of EGFR mutational testing in the face of osimertinib improving survival in that population by 80%. Only 1/3 of patients were being tested. We piloted it in 5 large health care networks across the country. Within 6 months, it was 100%, and that retained over the next 12 months. That's immediately practice changing and has a tremendous impact from patient lives.

Yes. And the second question, not all data is created equal. And so there are many different facets of that data market where there are existing players that have been selling data for decades, right? Those groups are actually addressing a different type of question really around what is happening in the real world, right? It's very descriptive of like looking to see what happened, playing back the news. The reason why our business has grown and why -- what we see is we don't see any competitors in our space is that we're addressing why is this patient not responding to this existing standard of care.

And that question, that why question is what's at stake for when they're designing that clinical trial. So when people are making a Phase III global investment decision, again, $200 million to $500 million in that decision, that's at stake. I really need to understand why are these patients not responding? And my hope is that my drug is going to address that. And so really, we're providing data to those types of customers in those use cases and others. But that ROI, delivering that type of value and helping those biopharma companies increase the success rate of those investment decisions is why our data business has grown to a scale that hasn't really been seen in our space. And so that maybe addresses the kind of what we see in the broader market.

And I've said this earlier, like I think per vacate, we said like there will come a time when no Phase IIIs ever fail. And some company like Tempus will be responsible for that. And if you think about it, it's not -- I know it sounds crazy, but it's just like from a tech perspective, of course, it's going to happen. I mean if a large Phase III fails, you either didn't understand the mechanism of action that drives your drug or what you saw in a Phase II is not being seen in a Phase III. There's no other reason. Like literally, it's like that.

So both of those are solvable with real-world data at scale. You can understand what drives people to respond to your drug and then you can look at and interrogate at a comprehensive level, the population that's in your Phase I and Phase II and then look at the real world to see is that like representative of what I'm going to see and then track it as you start to enroll patients in our Phase II. In almost every instance, when you have big Phase IIIs that fail, we've gone back and looked at several massive base fees that have failed, and we basically set ahead of time, we could have predicted this failure, like here, you can see it. I could have predicted it from just digitized HNEs. Let alone more complex data. So this -- I think the R&D spends are going to get very efficient as AI becomes pervasive in R&D.

[ Brendan Smith], TD Cowen. Thanks for all the great input today. And I appreciate the color on kind of the monetization of data and applications here. I wanted to maybe double-click a little bit on that. And just can you speak to how you're thinking about evolving the actual monetization of the data business and even the foundation model itself, kind of just within the biopharma customer end market.

I guess you mentioned deal size has grown per customer over year, some are bigger than others. But you're kind of integrating new data, you've got all these agents now moving forward. So I guess, how should we think about actual value switches over the coming quarters? And I guess also, is there any differences in terms of revenues to Tempus on what their customer expands with in early discovery, clinical, commercial, just kind of cadence over the next few years?

Yes, I can start. So I think the there's no big seismic change that we see coming. The big seismic change, I think, in terms of like just the general business was -- we sold this very large foundation model deal with [indiscernible], and that came with both a very large data license and some compute and the compute is at a lower margin, right? So that's like, "Oh, wait, that feels a little different than what is normally there". And there was a time when I thought people would sign a bunch of these very large deals. The way the market has evolved, is they're actually signing they'll sign lots of smaller deals, but not, I think, these giant big deals like just you're seeing it now. People are saying, I want to build a lung cancer model. I want to build a prostate cancer model. I want to build a digital pathology model. I want to work with you to build -- to find a new biomarker using Scan Data River.

And so I think it's just moving so fast that that's the way it seems to be moving from lots of teams across these big pharma. So I think what you're going to see is the margin profile of our data business will look similar at what it is today, if not better. I think you'll see a blend between models and data that looks at some point, almost -- you won't be able to tell which what, what's what? Are you paying me $2 million to build a model or licensing $2 million of data, it won't matter. And most deals will have some of both.

I think the most sophisticated biopharma companies that we see that are really embedding AI into those critical decisions are actually using the data now at a higher level, which means they're just going to consume more, right? So the AI systems and the use cases you deploy within a pharma company will need that, especially if you're seeing high ROI use cases for every -- for not just 1 Phase III, but you want to see it for every Phase III in every Phase III or every trial. That's what we've seen for the most sophisticated.

I think for a majority of the market though, they're not all as sophisticated as the leading early adopters. And many of those use cases are really just using AI to go a little bit faster to build a little bit of a faster car, but aren't addressing like how do I increase my success rate, right? I can go faster, which is still valuable. But when you think about the inefficiency of drug development, where do we waste all our money, it's in the failures. And so if I can increase my success rate, now the ROI can quickly flip on one trial and now -- and this is what we've seen across that expansion like Eric was mentioning, once you see sort of a successful trial readout and you saw what you did differently, now you're asking the question internally, why aren't we always doing that, right? And so that's what we see is like it's just a natural consumption more data. And so not just more in terms of volume, but more in the sense of having real-time data of not 3 years ago, but like literally of last year.

But similar to like the big compute guys, like you don't go to Azure and Azure or GCP. Let me see your menu. How much of this is storage? How much of it's compute, how much of it's large cluster compute, small cluster compute? How much of it's ingress, how much of it's egress. You're like, "I don't care. Just give me the number." Okay? because the margin profile is all pretty similar. Same thing with us. We're doing a deal right now that's -- we're just licensing embeddings, licensing, modeling embeddings.

No files are moving, just the insights from those files that exist from a model we built on top of those files. So it looks and smells and feels like a data license, but it's really just someone -- giving somebody something they can use to build a model. So these things are going to merge together.

Eric, just a quick follow-up. Dan Brennan from TD Cowen. Just there's no doubt the interest from pharma appears to have really surged in AI, right, over the last quarter or 2, you listen to the public commentary from them. I'm just wondering you've discussed coming into the year, a really strong TCV and a high conversion rate. So you're enthusiastic about the data growth. Like are you -- kind of how would you characterize the interest today? Because it appears it's gone up dramatically. Are you seeing that will that translate in coming quarter bookings? Or just like -- any way you contextualize that just in terms of the real increase that we are seeing right now?

Yes. I mean I think you've already seen a piece of it you've already seen, which is we've had 3 quarters in a row of $100 million-plus bookings, some significant way higher than that and TCV growth. So TCV is just going up and up and up, even at our scale where we're licensing a lot of data. So I think that certainly is a part of it. But I think it's nothing compared to what's going to come.

I think the first step is you have the CEO saying, this really matters. And for that to translate into a signed contract, takes time, and we're in the middle of that world. But I suspect over the next one to whatever, 3, 4, 5 quarters, you're going to see a lot of these folks that are realizing they have to jump in with 2 feet jump in with 2 feet. And I would say -- what I would say to you as a year ago, we had a pool with like 1 or 2 people thinking about diving in. Now we've posed like 20 people think of diving in. So the question is how they're going to dive.

Andrew Brackmann from William Blair. I wanted to ask on the Algos business and recognize it's not a near-term driver for until probably after 2028. But maybe can you just talk about the distribution system that you're putting in place here. In oncology, I get it, right? You have the lab, you have the report, you're giving that molecular information in those contextualized results. But I guess how do you distribute these in those non-oncology settings, what's the look like to these institutions?

Yes. So I mean, let's just take -- we use -- start take Northwestern. So we have spent an enormous amount of time building pipes between us and Northwestern building an infrastructure where that data can flow freely, deploying these algorithms at scale -- by the way, which took like, I mean years and years of effort, legal, IT, not small. Then once you deploy these algorithms to when you bring into the health care system, you inevitably break some part of it. These systems weren't designed for AI.

So we began running our ECT algorithm [indiscernible]. And all of a sudden, we were producing an enormous number of patients that needed an echo or needed to wear a patch. Like they don't have like doctors lying around being like, great, so then you're like, well, wait a minute, how do we know, what's the change management side of this. And so you have to go through that. And so that's why it's going to take time for these businesses to actually really scale. You have to lay the pipes or do all that kind of foundational work for years. And then as the revenue starts to come, it really can be like a river. The failure of almost every AI company in our space is that the revenue is always way further than you think, and the cost is always way higher than you think. And so we just had 100 AI companies and looked at their decks you'd be like, "Oh, my God, every one of them was too optimistic and failed?" And most got to business. They just can't not they can't keep investing in that horizon that's always further out.

We're just super lucky that we have a business that allows us to make those investments and still generate incremental EBITDA improvement. And that's just like compounding. I mean our xT, our TO, the approval this morning adds like a ton of additional revenue that we either drop to the bottom line or choose to invest. But if you look at the kind of EBITDA generation of the business today and what coming in 2027, we just have a lot of money that we're able to invest and still be EBITDA positive and cash flow positive, where most of our competitors that also make a lot of investments are just burning money.

I think just to add, Eric, a lot of the work that we did to build the integrations for oncology can transfer over to other disease areas. We become a trusted partner within these hospital systems. And so it is easier than being a new company that shows up that tries to connect with somebody like Northwestern, we've been a trusted partner with them for a long time, and so that makes it easier as well.

Ryan MacDonald with Needham. Eric, Jim, Ryan, thanks for hosting this today. I thought it was really helpful to -- that you laid out sort of the use cases in terms of how your pharma clients are using Tempus. And I was kind of curious to understand, as you think about the data business today across the early-stage clinical stage and then the commercial use cases, where does the majority of that revenue why where are you seeing sort of the most demand this year in terms of the use case? And where is the greatest sort of white space for you to go after in sort of applying the platform?

Yes, I -- so really early discovery and clinical development are kind of merged together under the R&D budgets of these pharma companies. And so that's the bulk of our business. Again, we're addressing the why are in certain patients responding to the existing therapies. Every discovery team needs to know that. Every development team needs to know that. And that's kind of the lion's share of the budget that's being allocated to our contracts. Now that being said, we have customers that are also working with us in commercial, but that's a growth area for us, right? And so our data can be used there as well, but again, no one has been able to address the why aren't these patients respond to therapies.

And that's why it's -- we haven't really had to compete with others and why we've been so focused on that. Because even though we are working with 19 of the top 20. We're working at the same level for all 19 of those companies. And so our growth expansion still has opportunities just within R&D and then you kind of have expanded customer segments that can grow even beyond just the R&D teams.

Yes. I'll say one thing that we'll jump to financials, then we'll do Q&A again in a minute, so if you have questions. But like this is -- I think people don't -- people have not historically interested our data business, and they don't understand the moat around it.

So here's a great example. We have been one of the largest abstractors of cancer patient data for the last decade. I mean, literally, when ASCO decided to partner with somebody, we were one of the 2 partners. We've been doing this for a long time at scale. We also have invested, I don't know, a few billion dollars in technology. We have like 600 or 700 software engineers have been focusing on this problem. So just assume we're a massive abstractor and assume we built unbelievable technology, and we only know oncology, like that's -- if you think of our last 10 years. Okay. Now the world of large language model shows up and you're like, can I just do this in an automated way? Why do have abstractors? That journey, and we are now crossing that journey in 2 of the largest indications. That journey has taken us all this time to be in a world where we can be. We have the ability now to take 100% of our lung cancer patients, breast cancer patients do automated abstraction at scale. So -- which we're doing right now.

So all of a sudden, I as a data client can now access 100% of all the notes that exist across this millions of patients, but that's taken a decade of unbelievable time just to get there. And even then you still need humans to ensure that it's right. And so it becomes another powerful tool as we leave early-stage R&D and move deeper into development and commercialization. Should we hit the financials for a second?

Yes. Quick, and then we'll flip back. So I think we've covered kind of the economic model over the course of the session today, but it's obviously a framework for durable growth operating leverage, which we demonstrated over the last 8-plus quarters and long-term value creation.

So it starts with diagnostics, where all the data is being generated, but it obviously serves a very important use case for physicians. We have improving margins and a scaled kind of infrastructure that allows us to get that leverage. And then we move over to the data and application side, where we've got a very good backlog of things through our TCV. We have a very broad customer base. As Ryan just noted, still kind of relatively early on in days. And we have a history of high retention rate.

So looking at '26, we've given guidance of 1.59 and 1.6, which represents about 25% year-over-year growth, $65 million of adjusted EBITDA. The drivers are exactly what we talked about today. Within Diagnostics, we have very strong clinical oncology growth, improvements in ASPs. We have hereditary in the back half kind of normalizing after lapping some of the share gains. And then in data and applications, it's really just executing on the agreements that we have in place as well as expanding some of those relationships to give us more visibility into '27 and beyond.

One other note from a balance sheet perspective, we did the convert a few weeks ago to take out the remaining term loan that we put in place, that gives us about $30 million of annual savings, allowing us to achieve positive free cash flow around the end of the year. This same slide we presented before, just how we think about kind of balancing profitability in growth. And so again, if we expect kind of a 25% top line growth over the next 3 years, the way that we view the world is that incremental gross profit dollars that are generated, we'll reinvest about 2/3 of those back in the business with 1/3 dropping down to the bottom line. And then after that third year, probably flipping that to a 1/3 is reinvested in 2/3 because at that point, you're generating enough gross profit dollars that we can maintain the level of investment that allow us to capture all the things that we think play out over the next decade or so, but still demonstrating operating leverage and significant free cash flow.

Yes, we can go back and forth. So just to wrap up really quickly, then we're happy to take questions. I think just a summary of hopefully what you've gathered from today, we have a diagnostic business that's strong. The integrated nature of our technology platform is driving higher growth than most of the other folks in the space, and it's sustainable. The trends we saw in 2025, the trends we saw in Q1 are continuing into Q2. So it seems to be a long-term pattern of us taking share from other folks. In terms of MRD, we have a comprehensive portfolio of both tumor naive and tumor-informed products.

On the inform side, that assay is doing super well. We've got great market traction. And as we unleash more demand because it's currently gated, we expect it to grow pretty dramatically. And we continue to invest in R&D. We've got something like, again, 5,500 patients in studies right now. I think we don't get a ton of credit because we don't spend most of our time focused on the readouts of these kind of interim studies. But at the end of the day, I think it escalate something like 35 posters and papers and [indiscernible]. So the kind of scientific rigor of what goes on here is pretty extreme. And I would suspect that we're able to build a tumor naive product that's quite good over time.

The data we're generating is compounding. The moat around our data business is growing. Our AI applications are really taking hold. The foundation model seems to be performing at or above our expectation. We'll certainly, over the next 3, 6, 9 months, have far more that hits the market in terms of insights that flow from that model. And ultimately, the company just has a really good financial profile, which was just made materially better by of this FDA approval this morning. And so we're just in a great spot. We're growing at a good clip, generating leverage, reinvesting it in forward growth.

On that note, yes. So I don't know if someone's going to...

Yes. I think I've got it over here. Catherine Schulte with Baird. I had one on data, but we can maybe loop it into financials as well. If we look at some of the comments you made on customer growth and customer concentration in the data business, if we look at kind of value per customer outside of your top 5 customers that went from a little under $200,000 in 2020 to a little over $0.5 million per customer in 2025. So I was just curious, as we think about if we sit here 5 years from now for your data business, how much of that growth is from extracting value from your kind of per customer basis versus that customer growth?

I would think it's -- I would suspect it's actually on this kind of a curve. In other words, I really didn't know that. So if you said it went from $200,000 to $500,000 then I would say, okay, well, in the next 5 years, I bet it goes from $500,000 to $3 million. Like in other words, it's -- especially with biotechs that have no money or smaller or pharma companies that are more budget conscious, that's -- they're far more conservative than a giant global pharmaceutical company that can make a $20 million bet and this isn't the end of the world if it's not a great bet.

But to a company that's got $50 million, they raised $100 million, those bets are -- so I would suspect that it goes way up. And you end up -- if we fast forward 5 years from now, you probably have, whatever, 500 clients spending $3 million to $5 million instead of 200 clients spending $500,000...

I think the surprising thing is that since we're addressing this kind of a unique question of like why aren't these patients responding to standard of care therapies, that's not just a big pharma challenge, that's a biotech challenge. And usually in biotech, their whole future relies on that question for their one trial or their one drug. And so what we've been surprised by is the number of biotechs are now signing on where data isn't a nice to have, it's an essential question to address because your future relies on it.

And so the more that we can see -- like the market can see the success of how you can apply this makes it sort of an embedded budget line item for not just a big pharma company, but also biotech. And so we expect customer numbers to grow. We're not just the top 20, but also the dollar spend is going to be the biggest in the biggest biopharma companies.

Yes. And another way to think about it, and then we'll go to the next question is like you could almost imagine a world where we're probably there now because I go to every single biotech in oncology in the United States, market cap, let's say, sub-$1 billion or sub-$500 million would be like, hey, we'll give you access to our data, just give us like 20% of your company. And I would think like almost every one of them be like great. Like it's that valuable, right?

Brad Bowers from Mizuho. A bit of a preamble and then a 2-parter here. Just Tempus doesn't really get any credit as an AI company. I think it trades at 4x sales if you pressure the genomics business, especially the drug discovery stuff and the clinical trial benefits here. We saw software bottom over the last couple of weeks. So I guess this kind of gets into my first question, and this was touched earlier, but I'm starting to see GPU counts, compute of pharma peers kind of get shared around really just within the last few days.

I think I have Lilly in the lead at about 1,000 GPUs, Recursion about 500 and then Amgen and BioNTech much lower. You talked to 1,000 H200s just for the foundation model. So I wanted to give you another opportunity. You touched on moat a bit, but just to kind of double-click on that and whether you think that the compute will start to enter the dialogue. And then also double-clicking on the Cowen question and maybe throwing it back to you, but it sounds like you talked on our models unnecessarily slowing down. It looks like the data business at 25% is a bit of a slowdown. So I'll throw it back on you why we should expect that slowdown when it sounds like those businesses are firing on all cylinders, and it feels like you kind of built an ark ahead of a flood here.

Yes. So let's talk about the ark for a second. So our compute capacity is probably equal to all of pharma combined. Like literally, we have -- forget our H200s, we have a roughly equal size cluster of GB200s, which are like 4x the H200s. So it's like just -- I mean based on the numbers you just gave, we probably have comparable compute to like all biotech and all pharma in oncology and then some because that doesn't even include all of our other compute, which is equal to or bigger than the 2 clusters we set up for foundation models.

So we have a ton of compute and we have a ton of data. And so I think that is the ark. I think the challenge for us in terms of both valuation and growth is, we have always been focused on long-term sustainable growth. We've had opportunities in the past to kind of make decisions that would accelerate our growth in the short term, but may have been hurt us in the long term.

And we just tend not to choose those. And so I think we feel very comfortable that the aggregate business will grow 25% or so. We've called out that the data business will grow faster. That could be in the 30s. That could be even faster. There could be other parts of that business that grows slower, like, for example, our CRO business which is, I think, shrinking or relatively flat.

So we have other parts of the business that are not growing because we're not investing in them. And so it all kind of -- and we don't get into all the micros of these different businesses. But at the end of the day, our 2 largest businesses, our oncology sequencing business and our data licensing and modeling business are growing faster than everything else because everything else is growing slower and you can see our growth rate.

And we don't think that's slowing down. So we're not forecasting like something to actually decelerate materially. We're just saying, hey, for 2026, expect 30-plus percent growth in the data business and the 3-year growth rate is going to be, call it, 25%, and we don't benefit by saying the 3-year growth rate is going to be 30% or 35%.

Our stock won't go up because of the first part you mentioned, which is we're caught in this middle ground where technology investors who tend to invest in and think about AI don't understand diagnostics, and diagnostic investors who are deeply steeped in next-generation sequencing don't understand the data and AI.

And so you end up in a world where somebody is always worried about the thing they don't understand intimately, and so they just don't know what to do. And so I don't know when that solves itself. It may solve itself. It may not solve itself, in which case we could do things to solve it. But at the end of the day, we've been focused this year on just kind of getting these businesses in the best spot we can, making sure that ark is as good as it can be.

And if the market doesn't ultimately recognize the value of our data and apps business, which I -- if we were -- if we took the data and apps business public tomorrow, I would suspect we'd trade higher than the entire market cap of Tempus and could trade at 2x it. So it's not -- I mean it's -- one could argue it's got negative value. So we just kind of look at it and say, eventually, that will either solve itself or we'll solve it.

It's Mark Massaro, BTIG. Maybe flipping back to the Diagnostics business. So your partner, Personalis, I think, has received about 4 Medicare coverage decisions in the last 6 or 7 months. To me, having non-small cell lung, breast and I-O monitoring really is a bit of an unlocking.

So when do you expect to sort of unlock the gate, so to speak? And just kind of -- can you give us a sense for what percentage of your reps have been promoting MRD versus when you do unlock, is that going to be a full flip? And then quickly on the rare disease portfolio, you indicated that you plan to launch a whole genome panel this summer. Can you just help us rank order your priorities, like how big of a push do you intend to make in rare disease? Why is that important to you relative to some of the other segments you're going after?

Yes, I'll cover the first. So the number of people currently focused on the MRD portfolio, I don't see Laura. I think she was here earlier, left. But I'm going to say, call it, 15 to 30, somewhere in that range. And we have about 200-plus folks in the field, I think, in just core oncology CGP. So think of it in terms of like if you were trying to understand like percentage, it's 10% to 15% or something, I don't know, somewhere in that range, maybe sub-10%.

So it's highly gated. And Personalis is public, so you could look at their financial statements. But if we sent them -- let's just say we sent them 20x the volume of orders we were sending them today, how much cash would they burn? I don't know how much cash they would burn, I don't know how much cash they have, but I don't think that math is sustainable. So it's all been tightly orchestrated to make sure that they -- we are growing as they are growing and that the whole thing works, so we don't end up either breaking their labs or breaking the financials of the business.

And so it's in a great spot. I mean what I would -- what we have said historically is the demand is way more robust than we would have thought a year ago. There doesn't seem to be a cap on it. So I'm comfortable that as we continue to invest here and expanding, we'll expand. In terms of...

On rare. So as Tom had mentioned, they had a whole genome offering that was in place. They've largely kind of deprioritized a little bit when reimbursement wasn't there. Obviously, the reimbursement landscape has changed. And so they began working on a whole genome offering just because that's obviously where the market is moving. So it's obviously not the top priority for Ambry given how large the HCT business is, but we do think we can be a player given the relationships that we have with genetic counselors and build out kind of a meaningful business there as well. So you'll hear us talking about it more, but it's still not a significant driver today.

If you also -- if you look at the investments we make, it's kind of interesting, right? You could -- if you think about the decision tree, you could say to us, "Hey, why don't you invest another $25 million and try to develop like some novel epigenetic or methylomic assay, right?" So that you -- someone might say that. But if you look at where our core strength is, it's really at the intersection of technology and diagnostics, not being first to market with something really, really, really novel.

And so when you think about rare, I can't think of a single use case that would benefit from a full understanding of the clinical case of a patient than rare. It lives at the intersection of some molecular insight and the clinical diagnostic odyssey, and we're as good as anybody at pulling in that data and making sense of it. So I would suspect long term, if it's not Tempus, it will for sure be a company that looks like Tempus that wins rare. It will not be the company that can generate a whole genome BAM file. That many companies can do quite well.

Maybe we'll do one more.

This is Paul Stewardson from Stifel here for Dan. Just wondering on the foundational model that you shared some early data, hazard ratio, that sort of thing. What's the level of need for prospective validation? You mentioned you have these retrospective, isolated cohorts that you can validate the models in.

Is there a need to kind of -- given there may be some survival bias of what models are coming out, is there a need for doing long-term prospective? And to what extent does that gate your ability to move that into a useful application? And then just briefly on the other side of the business, can you talk about the investment? There's this next-generation tissue-naive test. What kind of clinical evidence generation plans do you have that might be bigger than you had before the V2 was being talked about?

I'll take the first. And James, you, can take the second. There Is no -- it's a 2-part answer. There is no gate needed, and we, for sure, will run those studies. There's no gate needed because physicians are free to make decisions based on the data you present them, and we don't get paid for these insights. So if I was -- like if this was Oncotype DX, for example, and I wanted to get paid, I'd have to run a very large study to then convince somebody to pay me and put me in a guideline.

But in my case, if I can predict EGFR response or ALK response, I don't get paid for it. And so -- and doctors can make a decision based on what I published as to whether or not they believe that, that is predictive or prognostic and whether they want to pay attention to it. So I think there's no gate. These things will come at scale, but I suspect all of them will turn into -- and they'll have to be analytically and clinically validated before they get on the report. And I suspect all of them will turn into really cool studies over time to figure out how good are they. And eventually, they'll weave their way into guidelines. But the river will come, and we will not gate it. And I think it will be material in terms of how people have to react to it.

And then, Kate, I know you're over there on the V2 kind of studies. I know you briefly hit on it, but...

Yes, we're making those investments now in terms of -- we showed the one slide over the next few years. We've got about 5,000, a little bit more patients in studies today. We continue to enroll. We're enrolling across all major indications, so really pan-cancer, and we're sort of building the technology to be able to -- once we make it through analytical validation, then we'll just be able to start hitting clinical validations across all of those different indications.

I think the way to think about this space is the -- first of all, a significant percentage of the market is CRC. And in colorectal cancer, you have lots of tissue. So it's not a great offering to say, hey, switch from a tumor-informed assay that's really sensitive and come to a tumor-naive assay that's less sensitive when you've got a lot of tissue. So we have all along suspected tumor-informed would win the day in CRC.

In other areas, like, for example, lung cancer, where tissue is far more scant, you think, okay, this is a great assay for tumor naive. The problem is those assays haven't performed that well. So tumor-informed is winning the day there as well. So I think we're in this kind of weird zone where -- including our own, which -- whereas the tumor-naive assays aren't performing well enough to win the market at scale and the tumor-naive assays need to go through this consistent R&D process, getting from 500 ppm to 300 ppm to 200 ppm to 100 ppm to 50 ppm.

So there's some zone. I don't think you need to get to like 10 or 2, but there is a zone where you need enough sensitivity and specificity and a low enough Illumina detection that you actually like, okay, this thing is -- can play against the market-leading assays like Signatera. We are getting very close to there now. And so we're migrating from Version 1 of our naive assay to Version 2, and now we're going to kind of roll that out at scale in terms of these different studies.

Maybe one more comment to make there, just like something that differentiates us in this space. We've talked a lot about this kind of multimodal data. And what we start to see not just in Tempus, but in the field at large is when you add additional modalities of data, you're able to increase signal-to-noise. And so as we think about the really broad data set that we have that we're continuing to generate, part of the belief is that we will be able to then layer in these other modalities much faster and be able to then improve upon the assays and the technologies that we have. So lung cancer or whatever the indication, we'll be able to leverage imaging and all of the other things to add to that signature.

Yes. I will say one last thing on MRD. I'm first of all a big believer in MRD. I think it's an awesome space we want to play in it. It is unclear to me how this whole thing shakes out. Remember, I think you have to really understand the adoption, right? You turn this test on and then you turn it on in bundles. You get doctors to basically order it in bundles on a recurring basis at scale. And so all of a sudden, you go from like not a lot of volume to what looks like a lot of volume.

And if you get even some minimal level of reimbursement, $500 a test, it's like not small. But I don't really know how that all shakes out. Like I feel very good that comprehensive genomic profiling has shooken out. But I think on the MRD side, we have to let this thing play out for another couple of years to figure out what's the cadence, what's the cadence doctors are going to want to order, what's the cadence that's going to get paid for?

How does it really work? It will be a great space. But I don't think you can just look at the trend lines today and be like, oh, they're going to continue for the next 10 years. I think there's going to be some movement there to figure out how to rationalize what's going on. You've got some doctors ordering these things every month and some doctors never ordering them. And that's a unique paradigm relative to CGP. There, you just have people that believed or didn't believe in the genome. Like I didn't know -- I don't know think it matters. But once they're like, oh, it matters, the ordering patterns were pretty normalized. On that note, thanks for joining us. I think we're going to do some tours.

Yes, what are the logistics on that?

Please stick around for some refreshments. We're going to do 3 separate groups for lab tours for those who can stay. So we'll collect those in the back. Thanks for joining us.

Thanks, everyone.

Thank you, everybody.

Ladies and gentlemen, thank you for standing by at this time, I would like to welcome everyone to the Tempus AI First Quarter 2026 Financial Results Conference Call. [Operator Instructions] I will now turn the conference over to Liz Krutoholow. You may begin.

Thank you. Good afternoon, and welcome to Tempus AI First Quarter 2026 Conference Call. This afternoon, Tempus released results for the quarter ended March 31, 2026. The press release and overview of the quarter and our latest presentation are available on our IR website. Joining me today from Census are Eric Lefkofsky, Founder and CEO of Tempus; and Jim Roger, CFO. Before we begin, I would like to remind you that during this call, management may make forward-looking statements that are subject to risks and uncertainties that could cause actual results to differ materially.

For a discussion of these risks, please refer to our 10-K and other subsequent filings with the SEC. During the call, we will discuss non-GAAP financial measures, which are not prepared in accordance with generally accepted accounting principles. Definitions of these non-GAAP financial measures, along with reconciliations to the most directly comparable GAAP financial measures are included in our earnings release, which is available on our IR page. I would now like to turn the call over to Eric.

Thank you, and welcome, everybody. We had a great quarter. Revenue was $348.1 million, up a little over 36% year-over-year. Our diagnostic revenue was $261.1 million, representing almost 35% growth, driven by particular strength in our oncology business, which had unit growth of about 28% it was strong across the board with our solid tumor and liquid biopsies performing well and our MRD volume performing even better. Hereditary slowed down a bit, which was to be expected given that we're lapping some extreme growth rates from a year ago. We expect that business to return to mid-teens in the second half of the year.

Our data business data and applications business did extraordinarily well, $87 million of revenue, representing 40.5% year-over-year growth with particular strength in our data licensing and modeling business insights, which grew over 44%. We had our third straight quarter of bookings north of $100 million with TCV rising and visibility in the best place it's been for data and apps business in quite some time. So all in, the business is doing extremely well. Our main businesses are performing at or above plan. We're on track for a great year, and as a result, increased our guidance to now a range of $1.5 billion to $1.6 billion for the year with adjusted EBITDA of about $65 million.

With that, happy to take questions.

[Operator Instructions] Your first question comes from the line of Kallum Titchmarsh with Morgan Stanley.

Eric, I wanted to start with insights just given some of the recent update. Can you maybe just talk about how discussions with large pharma customers have been trending so far this year, particularly as interest in AI appears to be evolving. And I'm curious where the identified data is sitting in the hierarchy of needs. And investors are obviously cognizant of contract closing and renewal dates for your larger agreements. So really looking for your latest thoughts on longevity and extension potential here?

Yes. I mean, so I would say that all of our core big data relationships are very strong. We have a long history of renewing these agreements at or above where they historically stood. We feel great about that trend continuing. And I think equally important, if not more important, is that we've now -- we're adding just some really big new names to that prestigious group. This quarter alone, we added Merck, who signed a very large strategic collaboration with us. We expanded our relationship with Gilead. We're in late stages on others. So we just have a really strong and robust pipeline.

And as I called out in the letter, it's -- first of all, I don't think anyone thought our data business and modeling business would get to the scale would be growing as quickly at this scale or would be disterable. But one of the -- to me, one of the most amazing parts about it is to get to these very large levels where people are signing $100 million-plus agreements with you to license your identified data over multiple years. it would be, I think, pretty impressive to do that with 1 pharma, even more impressive with 2. But we now have almost half a dozen folks at that level where people are signing these very large strategic agreements with more coming.

And I would suspect over time that becomes the vast majority of all big biotech and big pharma. And we're seeing this migration where people aren't just licensing our data more and more, they're actually building a model with us, whether those are foundation models as is the case with AstraZeneca or they're building smaller models, leveraging our data. But we have a very large database now in excess of 500 petabytes of data, it's all connected to this analytics and model-building platform. That's now connected to not just CPUs, but GPUs and people are increasingly building proprietary models to get smarter about their own internal R&D programs. And that trend seems to be up and to the right.

Your next question comes from the line of Ryan MacDonald with Needham.

This is Matt Shea on for Ryan. Eric, maybe just jumping off on that last question. Is there anything in terms of either the recent Gilead or Merck deal that you would call out in terms of size or scope that's maybe different from some of your other strategic collaborations or just anything notable to call out with those 2 wins in particular? And then, Jim, as we layer the Merck and Gilead wins on top of the $350 million of TCV. That was our earmark for revenue in 2026. How much visibility and confidence do you have in hitting the implied $410 million of data revenue guidance? And what are the potential levers for upside there?

Yes, I can start. So Merck was a very large strategic data and modeling collaboration, we have very large collaborations with people like AstraZeneca, GSK and BMS. And obviously, another very large collaboration of that magnitude. So it's unique in that there's only so many of these that we have. But it's, as I mentioned a minute ago, far more than others. And so it's nice to see people getting to that size and scale where they're really leaning into a strategic level with dedicated teams, lots of data and broad access and AI model building and all the great stuff that you want to see for a long-term sticky relationship.

Gilead is a bit -- it's a bit different. It's quite large, smaller than Merck, but quite large, but what's cool about it is it represents a very significant step up from their historic levels. And so we're monitoring here 2 things, right? We want to obviously get to a point where we've got $100-plus million agreements with as many big pharmas we can, be biotech, but we also want to see the growth of these accounts because we don't -- typically don't get to that strategic level upfront. It takes us time. We have to -- as we've talked about historically, we tend to start with 1 project, maybe in 1 subtype and then we're doing a few subtypes and a few different projects.

Eventually, people realize they can use our data to be far more intelligent in terms of which compounds they actually want to interrogate, how they design Phase I and Phase II trials for the greatest life of success, how they ultimately enroll patients and make sure their product is fit for commercial viability. So they're using our data across that entire spectrum, and it takes time to get people comfortable and so it's nice to see someone like Gilead stepping up in such a big way from their historic levels.

And then in terms of the visibility, obviously, we mentioned the year-end call that we had about $350 million of TCV that was related to 2026. So that gave us a tremendous amount of visibility into kind of the guide and then on top of that, we had visibility into a very strong pipeline. And so Merck and Gilead are obviously part of that pipeline that closed in the first quarter that increases the level of visibility and the pipeline remains strong as well as Eric noted. So the Insight business is really performing incredibly well at this stage. We've never been at this point in the year with this level of visibility into kind of the overall number.

And it's exciting for 2026, but also for 2027 and beyond. As Eric noted, our TCV actually increased in the first quarter, which is incredibly impressive, considering you're delivering $80-plus million of revenue that you're still growing kind of that backlog that will contribute to revenue for the balance of the year and over the next several years to come.

Your next question comes from the line of Subbu Nambi with Guggenheim.

One for Jim, are there any updates on your excess FDA submission? I know it was reiterated that it was submitted, but any realistic time line for an ADLT pricing update on this test. And then second, could you break down for us what percentage of your data licensing comes primarily from oncology? And what has come from other areas like rare disease, cardiovascular longer term, where do you see that mix shaking out?

Yes. Thanks, Subbu. I'll start with the FDA submission and then Eric can take the question on kind of the data breakdown. So kind of, I'd say, no update on the XF submission that was made earlier this year, and so we're awaiting feedback there. As we've previously noted, we don't expect that to have any impact on pricing or ASPs in 2026. And -- the other thing that we called out in our letter relates to an amendment that we're making to our FDA-approved assay or the submissions we made an amendment there that will cover tumor only, so cases where we no longer get or we don't get a normal sample. That will allow us to kind of accelerate the migration over to the ADLT version of the assay we're expecting a decision there kind of imminently. And so those are the 2 big updates or 1 big update from an FDA standpoint.

Eric?

Yes. In addition to driving ASP higher on the diagnostics side, on the data side, the vast majority of our data licensing today is oncology and almost entirely, if not entirely, comes from our therapy selection business. We we basically, we built a de-identified data business off of the combination of matched clinical molecular data, predominantly from therapy selection or liquid biopsy test our solution profiling test and that database, which sits in over 500 petabytes drives the vast majority of our data business. It has been nice to watch some recent wins in neurology and in particular, we were just engaged to begin building a multimodal model in Alzheimer's disease that was a multimillion dollar project that we're in the middle of right now that we'll finish up middle of this year.

So we do have people that are starting to tap the database in other areas. But I think for us, that represents really significant long-term growth drivers. We can see the data business -- especially the data and modeling business in the U.S., getting to multibillion dollars. And then I would suspect as we get into other disease areas, there's all kinds of opportunity there just in the U.S. alone, but alone international. So lots of thing run.

Next question comes from the line of Dan Brennan with TD Cohen.

Maybe just one on cash flow in the quarter. Just how do we think about cash flow from operations was down about $70 million plus or minus. I think you guys said free cash would kind of approximate EBITDA, which was, I think, a $3 million loss. How do we think about the progression of kind of free cash as we go through the year? And then maybe just 1 related or actually related to orient but you've got XT FDA approved, you're going to seek to get XR FDA-approved, does that change at all the ability to build both of those separately to your local MAC, if whatever reads jurisdiction changes and you have put to those FDA approved, like how do we think about the door body of that going forward?

Yes, I'll take the first one, and then Eric can take the second one. So in terms of free cash flow, it was a little bit elevated in Q1, which is pretty typical for us over the last couple of years. Few things kind of going on. One is just timing of payables plus kind of bonuses get played out in Q1. As we noted in the letter, we would anticipate kind of a significant improvement in Q2, driven by one normalization of those payables, but then two, a number of our large insights contracts that kind of had prepayments or deferred revenue that we're burning down kind of flip over to quarterly payments.

And so again, we would anticipate significant improvement in the second quarter. And then from there, just continued improvements as adjusted UDA improve. So -- with that, alternators for the second.

We're in a good spot in that, given that we're expecting to generate about $65 million of positive EBITDA with every quarter, significantly improving performance, we now -- I don't even know, 5, 6, 7, 8 quarters in a row of every quarter, improving EBITDA pretty dramatically on a year basis. We feel great about our cash position. We don't need more cash. We don't need to do anything. So for us at this point, the quarterly fluctuations of cash flow aren't that critical.

We're going to generate cash, we're going to be EBITDA positive. We don't need alternative financings in terms of like funding the business. So at this point, we're in a pretty good spot. As it relates to XT XR and XF, I would suspect that over time, all of our main assays are FDA approved. We have 1 approved today, which we're expanding, as Jim mentioned, in solid tumor profiling. We have another that's in front of the FDA now in liquid biopsy. We'll take RNA to them as well. I don't think these things will impact how the tests are ultimately order to build, they're ordered in build on an individual basis.

And based on medical necessity and when they're order to bill, they paid for how they get paid for. I do -- we do believe that ASPs are likely to rise years by virtue back that our current ASP sits at around 1740 or something like that, somewhere in that range, 720. And we would suspect there's about $500 worth of incremental ASP lift over the next year or 2 as we get all these things FDA approved. So remember thing, we can see nothing about the current trend is anything but significantly positive.

Next question comes from the line of Bradley Bowers with Mizuho.

Just maybe I want to get to oncology genomic trends. I feel like we see maybe some more news headlines from competitors just on companion diagnostic status wins I just wanted to think about the impact of Tempus kind of as therapy selection gets more widely formally included into labels as companions to pharmaceuticals. So maybe just an update on what inning of adoption we're in therapy selection? And is there still rising tides for all companies? Or do you maybe need some more formal partnerships to keep driving that 20% volume growth on that business? I appreciate it.

Yes. I mean we have seen no -- I mean CDxs have been part of therapy selection for years. There are many of them. They've had no impact on physician ordering in the U.S. at least by virtue of both how drugs are paid for in the U.S. and how a diagnostic tests are ordered. In other markets where you can't get the drug without that particular companion being ordered it may have an impact. But in the U.S., we don't have a system that is set up that way. And in fact, the migration has been the other way, where people have been kind of looking to move away from companions as a precursor to ordering.

So I would suspect that whether we win more CDxs or not, regardless of who wins CDx is, it won't have any impact on the amalgamation of companies that represent the vast majority of external sequencing I would suspect will all be just fine. The differential in growth rates, the fact that we're growing faster than others or and most others in therapy selection, is predominantly related to the technology platform we built, which is comprehensive and allows physicians to kind of do their job well. And we see no sign of that slowing down. And I think CDX won't have an impact on it.

In terms of where we are, it still feels to us like we're, I don't know, maybe early to the middle of the game in terms of therapy selection. There's still a significant. There's been some papers published recently that there's a significant volume of physicians that still aren't ordering a comprehensive genomic profiling when they're treating cancer patients. There's lots of patients historically that haven't been profiled, so I would suspect there's pretty decent unit volume growth for the industry over the next, let's say, 3 to 5 years. I think we'll be faster because of all the advantages we've built into our platform, but it does feel like it's a healthy space in terms of solid tumor profiling, liquid biopsy and then even healthier on the MRD side, given that it's still fairly new.

Next question comes from the line of Kyle Mikson with Canaccord Genuity.

Can you talk about how important rare is going to be to do the hereditary testing business kind of remaining or getting back to the mid-teens. And now, can you just elaborate on the growth profile of XG that grew 50% year-over-year in Q1, I think.

Yes. The -- I mean, the German assay for us, the percentages are meaningful, right, but it's small. I mean our MRD assay grew 500%, but it was only 6,500 a test. So it's awesome, but the percentages can be a bit misleading. The vast, vast majority of our HCT volume is obviously on the Ambry side given the amount of volume they do in hereditary. We suspect -- and because the units are so high, really nothing rare can do can move the unit volume metric, right? It moves the revenue metric because you get reimbursed significantly more per test, but hard to move the units.

So the fact that we expect to get back to mid-teens is a function of the -- we've long called out or at least have called out for some time, we expected that business to kind of be a mid-teens grower, it's lapping periods of much higher growth last year when that assay was growing at like 40%. So it's just -- it's a bit lumpy their growth has been lumpy. And so when you're lapping periods of lumpy growth, it's still lumpy. But I suspect as we get into the back half of the year, the growth rates will return to kind of mid-teens. I think RARE will also do well -- we had a slower start to the first half of this year.

I think as GeneDx called out, they've migrated a bunch of volume to whole genome, which has some ASP impact for them. we were a bit later to actually get that product in market. And so for us, it's been more of a volume issue. We haven't been selling a bunch of tests. As our product enters market, we expect to have some of the volumes pick up and obviously, even a $3,000 or whatever is ASP, it's still going to be ASP accretive to us. So I would say the back half of the year looks much better for our business as we are lapping slower periods of growth last year as rare starts to really take hold. And so I would bet that by the end of the year, that business is feeling pretty good.

Next question comes from the line of Mark Schappel with Loop Capital Markets.

Eric, it was highlighted in the prepared remarks that roughly -- you have a 40% attach rate for your algos, I think it was on your solid tumor assays in oncology. I was wondering if you could just break down a little bit further which products are driving the higher attach rates there? And maybe what gives you confidence of even expanding that within the next 12 months or so?

Yes. We have a variety of algorithms that we've built over the years. Some of them, for example, are her homologous combination deficiency algorithm our tumor origin algorithm where about 5% of cancer patients. We don't know the cytoprimary diagnosis. We -- off of our transcriptomic assay RNA assay, we can actually predict that with super high fidelity we have an immune profile score that basically -- typically, you'd have a lipid test like tumor mutational burden. And if you were TMB-high, you'd get a checkpoint inhibitor if you weren't, you wouldn't that test is not perfect. And so our immune profile score actually refines that test.

It turns out that there's a significant population of people that would actually do well on an immunotherapy that don't get it. And likewise, a population that looks like they're going to do and they would respond to immunotherapy that doesn't, so we can predict that. And as these algorithms basically get more and more pervasively ordered, they're just another tool in this overall bag of kind of technology-enabled assets that our physicians increasingly rely on.

They can just do all kinds of things that they can't do with others. And so when you look at -- we called this out years ago, we said to people like we will experience significant growth rates over time and I think a couple of years ago that people were like, I see it, but now it's like years in the rearview mirror. And as much as we called out years ago, technology was going to drive a bunch of ordering behavior. I think we've just demonstrated that. Physicians are overworked. They're seeing a ton of patients. They don't have time in the day to do their job.

And those companies that can help them make decisions, analyze real-time data, get to the right answer, so on and so forth, they're just going to flock to that platform, no different than you and I flock to Amazon. If it's convenient and easy and I get everything I need, that's going to drive my behavior. And so we don't see that trend slowing down. In fact, one of the reasons that we entered the MRD space -- and one of the reasons we entered the hereditary space is we actually believe that, that will hold true across all major assays in oncology, from is my patient at risk, how should they treat them when they get disease, how do I monitor them post treatment.

And so we want to be comprehensive. We want to be embedded within the workflow, and we want to help physicians make real-time data room decisions, all of which is driving our growth.

Next question comes from the line of Casey Woodring with JPMorgan.

Maybe a related one to Dan's earlier question, but you're guiding to adjusted EBITDA to hit $65 million this year. This quarter, it was negative $3 million. Maybe just walk us through how you see EBITDA progressing over the course of the year and the cadence of gross margins and operating expenses. And then secondly, on MRD, just I would be curious to hear your latest thoughts on when you really expect that to start ramping up in terms of volumes.

I'll start on the adjusted EBITDA, and then Eric can take the MRD question. So similar to last year, the phasing will be kind of growing throughout the year. So we had about 1-plus million of improvement year-over-year in Q1, and we would expect similar trends in Q2 and then obviously, the back half of the year is a bigger period for data, which leads to kind of expanded margins and more of that drops down to the bottom line. So as we kind of highlighted at the beginning of the year, we are fortunate that we're generating a lot of gross profit dollars that allow us to make many of the investments that allow us to generate long-term growth, but we want to continue to show improvement in operating leverage, and we feel like we're set up to do so.

In terms of MRD, the growth is really, really robust. I mean, as we called out, I think, last quarter, we're generating these kind of results with a very small sales force dedicated to MRD. We have not unleashed this to our entire sales machine, which is hundreds of people. And in part, it's because the unit economics until reimbursement is better and roughly 97% of our tests are tumor informed. So the personals is really carrying the burden of that reimbursement. They have a few indications approved but they're in the midst of getting many, many more.

And as they get a more rounded reimbursement package that looks and smells deals a bit closer to enter it's very hard to kind of unshackle all that volume because we would just be generating massive loss for them. Like if we dialed it up 10x their cash burn would go up a lot. So we have to meter it, which we're doing, which we're doing in close coordination with them. And as reimbursement improves over time, you'll see us continue to roll that out more aggressively. And I would suspect if you can just -- you can kind of do the math, right? I mean, if we really put a bunch of wood behind this we would be a very, very formidable MRD player in the United States.

Next question comes from the line of Dan Arias with Stifel.

Eric or Jim, I'm just looking at your slide deck here, and you have one in there that has a slide that talks about expecting 25% top line growth over the next 3 years. You also have a slide in there though that talks about ASPs potentially being 30% higher. So I know it's illustrative, and I think the point is really to emphasize the EBITDA trend. But what is either an underlying volume trend or just a revenue trend that kind of takes into account some of these ASP items that we should think of? Is that 25% that you're talking about inclusive of some ASP increase.

Yes. I mean we always have puts and takes. I mean 1 of the things that I think great our business is -- and if you look at -- for those that have been tracking us now for 3 or 4 years. I know it's like whatever now we're going to do with, I think, our guidance is around $1.6 billion. But like it's hard to -- it shouldn't be lost on people that 3 or 4 years ago, we were doing 300 million. We're quite small. So we've had significant growth that we've been able to manage. And for a long time, we've called out our guidance. Now we have a small range, historically, just a number.

And the reason that we can be, I think, relatively precise in this is we have a highly durable business with lots of levers that we can control. Some go our way, some don't go our way. Jim and I have never -- we've been at this for a long time. We've never had a quarter where everything goes our way. Something always doesn't go our way. But the good news is in the aggregate, more things are up in to the right than aren't. And that's the benefit of having a diversified business where you've got lots of different growth engines and growth levers.

And so I think that for us, we felt comfortable enough to say to the world we expect 25% growth not just in 1 year, but over 3 years. And at our scale, that's not a small number. I mean I haven't done the math, but $1.5 billion goes to like $2 billion and $2.5 billion or $3 billion and it becomes a pretty big number. No, there will be ASP lift. There will be unit and volume lift. Some things will go our way, some things won't. There will be trends. It will be weather, there will be this, there'll be that. But in the aggregate, we built a business that's durable enough across a comprehensive portfolio in diagnostics that touches lots of different areas of hereditary to therapy selection to MRD to other disease areas like rare and so on and so forth to a very robust data and applications business.

And so we're just fortunate that regardless of what happens, we feel pretty good that we can sustain good growth.

And then the only thing I would add, Dan, is there's nothing implied by -- given the upside that we do have in reimbursement, there's no implication on a volume perspective. Obviously, the increases in reimbursement are difficult to pinpoint exactly when they will occur. -- our volume trends continue to be very strong. So there's nothing implied by those 2 statements in the deck.

There are no further questions at this time. I will now turn the call back over to Liz Krutoholow for closing remarks.

Thank you all for joining us today. We look forward to speaking with you again in a few weeks at our Investor Day. Have a great day.

Ladies and gentlemen, that concludes today's call. Thank you all for joining, and you may now disconnect.

Hello, everyone, and welcome to this next session of the 25th Annual Needham Virtual Healthcare Conference. I'm Ryan MacDonald, and I lead Needham's Health Tech research efforts. And in this session, I'm pleased to be joined by Tempus AI CFO, Jim Rogers. Jim, thanks for joining me today.

Yes. Thanks for having me, Ryan.

So thanks for those for everybody who is joining us today. For those listening in, we've got about 40 minutes to go through a list of questions on a fireside chat here. But if you do have questions for Jim, feel free to put them into the chat box, and we'll make sure to get those asked and answered over the last 5 minutes or so of the fireside chat.

But with that, let's jump right in. So Jim, for those who are less familiar with Tempus, how about a brief overview of the business?

Yes, of course. So at Tempus, we've been spending the last 10 years kind of focused on building a platform that allows us to bring the power of data and AI to health care to positively impact patients, starting in oncology. And so we've focused on kind of 3 different categories to date. One is kind of an AI-enabled diagnostics for providers. And so we run a very large sequencing laboratory or several of them across the United States, where we process samples on behalf of ordering physicians for their oncology patients and kind of contextualize the results even further through some optical integrations that allow us to access clinical data.

We also partner with life sciences companies to improve their drug discovery efforts through the licensing of the identified data. And we also developed kind of a suite of AI application tools that can do things such as match patients to clinical trials, close care gaps or some of which even become kind of algorithmic diagnostics of their own, such as our cardiology efforts, which we can get into later on.

Awesome. Yes. And you touched on it a little bit there. But in my view, the tech backbone of Tempus is a real differentiator for the business. you talk a bit more about how Tempus' combination of data, AI and diagnostics is really unmatched in the industry? And how difficult would it be for another company to replicate what Tempus has today? And how does AI play into the company's moat?

Yes. So we've been a data -- we very much view ourselves as a technology company. And so we've been focused on data from day 1. And the original idea was how do we aggregate all of the siloed data to say something insightful to a physician or to a life sciences company.

And so at the same time that we are kind of building out our wet lab capabilities to generate genomic information, we went to hospitals and had conversations with them about kind of building these EMR integrations that would give us access to clinical information. So molecular information is obviously incredibly powerful in its own right. However, when you pair it with clinical information, that allows you to do 2 things.

One, as I mentioned before, you can contextualize the results for the physician that is ordering the test. Give them additional insight that you can't lean from just the molecular information. That also -- that data is identified and kind of leveraged in drug discovery allow you to kind of design more effective trials and really take a different approach than what life sciences companies have historically done because they didn't have access to this multimodal kind of longitudinally updating information. And so that was core to what we built from the beginning. We're now connected to over 5,500 institutions.

And so we have a pretty broad coverage in terms of integration, and that's allowed us to amass a very large data set that really is kind of the driver of the flywheel that we have.

The more data that we generate and the more data that we collect allows us to generate and garner even more insights that we can embed back into the test or with our life sciences partners. And the challenge is that a lot of people have access to data. There's other sequencers that have obviously a tremendous amount of molecular data.

You have some EMR companies that have access to clinical information, but it's really the marrying of those 2 that kind of sets Tempus apart and why it's important to not only get access to data, but build a suite of tools that allows you to kind of structure, harmonize that data and also tools that our partners can leverage to kind of interrogate the data itself.

So we have a physician-facing portal called Lens -- or sorry, a research-based portal called Lens, where our life sciences partners can go in, view the data, build models right within the environment. And that's really incredibly important for them to glean the insights that we provide.

That's really helpful context there. So as we think about the sort of 2 revenue segments of the business, you've got the diagnostics, which is sort of testing business and then obviously, the data business. Maybe just starting with diagnostics, given sort of how large it is as a percentage of revenue.

For those getting up to speed, could you just cover sort of the tests within your genomics or diagnostics segment and maybe how you think about the growth prospects across those tests?

Yes. So within diagnostics, we kind of have 2 offerings. We are kind of 2 call points. We sell to oncologists through our kind of therapy selection and MRD offering, and then we sell to genetic counselors through our hereditary offering. And the hereditary offering, largely the legacy Ambry business that we acquired back in 2025. Within oncology, we have a very robust portfolio where we do DNA, RNA, liquid, solid, germline. We have a tumor-informed offering and MRD through our partnership with Personalis and then a tumor-naive test that we've developed internally. And so we can really serve as kind of a one-stop shop for oncologists to meet all of their genetic testing kind of needs.

In the hereditary space, the majority of that business is in oncology today, although there is a small [ rare ] diagnose disorder business that is growing. And so different call points.

The growth trajectories for the businesses are different. Obviously, in oncology and therapy selection, the industry continues to grow. All of our peers are experiencing strong growth as well. Our oncology growth rates in Q4 were 29%, so very strong kind of volume growth in oncology.

On the hereditary side, in 2025, we benefited from some competitor disruption. And so we had outsized growth for 2025. That began to moderate in Q4 and will continue to moderate in Q1 as we've highlighted for folks. The hereditary space is more mature in oncology. Rare is still relatively untapped. And so that represents an opportunity for that growth rate to kind of pick up. But that's kind of a quick overview of kind of the breakdown of our assays and kind of the anticipated growth for each.

Very helpful. And before we kind of dive into the growth drivers within diagnostics specifically, I wanted to ask you about some recent news. Late last week, the Diagnostics Group of Stocks and Tempus included were pressured by some updates around the Crush RFI and announced efforts just more broadly from CMS to sort of eliminate wasteful spend.

Can you provide a bit of color on the Crush RFI and what potential impacts it could have on the diagnostics business over time?

Yes. So the CRUSH RFI, as you just described, was an effort aimed at kind of minimizing wasteful spending within CMS. And we -- for us, the reimbursement landscape has constantly evolved since we've started sequencing patients, and we've always taken approach of having kind of multiple mitigating strategies to hedging against any reimbursement risk.

We have multiple labs in different kind of MAC jurisdictions. We've been bringing assays to the FDA to kind of seek coverage under the national coverage determinations, and those efforts will continue. So we believe we're well positioned to kind of adapt to any potential changes that could come in the future. And that's always been our strategy from day 1.

Got it. Got it. All right. And so as we think about sort of the diagnostics business, obviously, it's P x Q. So volume -- we'll talk about volume and pricing from a growth perspective. But if we start with volumes, can you talk about the drivers of growth -- volume growth for Tempus across oncology and hereditary?

How much of success in oncology is sort of market share gains versus sort of a simpler broader market growth and sort of more patients actually just getting their cancer sequence?

Yes. So certainly, there's -- as I mentioned, we're all benefiting from the fact that the market as a whole continues to grow. We used to say that we thought about 1/3 of cancer patients were receiving this type of sequencing. We believe that number is probably about 55% today. So there's still room for growth of the overall market, but everyone is benefiting from the fact that these are -- this type of testing is being ordered more frequently.

For us, the advantage has always been one of the insights that we can provide. And so we certainly have experienced over the last several years, market share gains as a result of kind of our differentiated offering. As I mentioned, we grew 29% in Q4. That has accelerated over the course of 2025, largely due to some sales force realignments we did in the previous year that has caused some disruption. By the time we got to Q2 of last year, we had started to kind of see efficiency kind of tick up in the sales force, and that continued over the balance of the year.

On the hereditary side, as I mentioned, the hereditary cancer market more mature. However, we still sequence -- if you look at the number of people that should receive hereditary cancer screening, it's still a relatively untapped market, but you have to be able to kind of see that pull-through to kind of drive higher growth rates there.

Rare is a little bit different just because that's relatively new. Reimbursement only kicked in over the last couple of years. So our competitors in that space are also growing quite nicely. And so we would anticipate that growing faster than the oncology hereditary.

Makes sense. And as you think about sort of the oncology segment, you mentioned 55% probably of the market is -- or patients are getting sequenced today. Within the existing portfolio, how much of that sort of existing addressable market can you serve?

And then how does that expand as you get more tests approved across sort of solid tumor, liquid and MRD, the categories you talked about?

Yes. I mean I think expanding the 55% largely comes from changes in guidelines, right, and doctors' adoption of this type of testing. You've seen some of that over the last couple of months where there's been expansions in things like pancreatic cancer to cover earlier stages and things.

So as you look at that 55%, I'd say less so tied to new assays coming to market, more so that, again, these doctors are now leveraging these types of tests in different circumstances where they may have previously not done so.

So then as we shift to sort of ASPs within the oncology segment there, you've talked about there's a potential year to expand ASPs by about over $500 over the next several years here. If we look at sort of the categories of that, you talked about an incremental $200 of ASP growth that could come from just the XT to XT-CDx transition. Can you remind investors sort of what percent of tests have been migrated to CDx thus far? Sort of what's inhibited the transition, if at all? And then when do you think this migration is going to be complete?

Yes. So there's kind of 3 initiatives that we have on the ASP front. The first being that we got XTD was FDA approved, got ADLT status, and we began migrating volume in 2025. We ended the year between 30% and 40% of kind of volume that had been migrated.

We're confident that we'll end the year in '26 with the vast majority of the remaining volume kind of migrated over to the ADLT version of the assay.

There's a variety of factors that lead to kind of the timing of that. But again, we're confident that we'll be able to exit the year with the majority of that being on the ADLT version. The second initiative relates to our liquid biopsy portfolio xF, which we submitted to the FDA earlier this year won't have a '26 impact on rates.

But as we get to '27 and get approval, that will also lead to additional upside in reimbursement for xF. And then the last initiative relates to kind of commercial payers. We're largely still an out-of-network lab and kind of payments for these -- for our assays varies greatly amongst kind of commercial payers.

We do think that over time, we will continue to kind of make incremental gains with those payers. Obviously, as I just mentioned, like changes in guidelines that recommend the testing obviously lead to coverage in certain instances. When we get FDA approval of assays, that also helps with some of those conversations with commercial payers.

So we'll continue to crack at the number of times that we receive very little or no payments from commercial payers, and we think that adds $100-plus over the next couple of years.

4Yes. And how much -- as you get sort of assays approved across solid, liquid and then also with MRD, like how big of a factor is having sort of tests in each of those categories into, let's call it, the broader commercial reimbursement and where you could start to see that final 150, I think you talked about that factors into ASP.

Yes, exact. I mean I think certainly, again, it's payer by payer. They all have their own policies and kind of what they cover or don't. Some may only cover solid tumors, some may only cover FDA-approved assays. So it's very fragmented is kind of how you chip away at it.

But we're doing all of the things that all of our peers are in terms of trying to drive up that ASP with commercial payers, which has lagged kind of where Medicare and Advantage have been at.

That makes sense. Okay. And as we shift to the hereditary segment, you mentioned, obviously, nice market share gains due to some disruptions at a competitor last year, starting to see that market sort of moderate in terms of the growth rate there. How should we think about sort of the blend of volume growth versus ASP expansion within that segment as we think about 2026?

Yes. So the ASP is obviously more mature in the hereditary screening space than they are in kind of therapy selection. And so the only kind of anticipated expansion we would see was as the rare business grows and becomes a larger piece of the pie, obviously, the reimbursement for that testing is higher than what you have for hereditary oncology. And so that would lead to some increase over time.

But we're not anticipating kind of significant changes in reimbursement for the hereditary space for -- until that occurs.

Yes. And then while not directly comparable, like do you see any knock-on effects from sort of the news out of GRAIL with some of the concerns around their tests? Or does it change or shift sort of consumer perception of how sort of hereditary screening or sort of pre-cancer screening is viewed by consumers in the marketplace in your view?

We don't believe so. I mean that is obviously kind of an early detection test versus the testing that we're doing, which is identifying whether you may be a carrier of a mutation that a loved one has is the same mutation they develop cancer. So it's a different type of test and one that plays an important role in people's care. So we don't think that there'll be much impact there.

Yes, absolutely. Okay. So if we shift over to the data business, we saw total contract value climb to north of $1.1 billion from $940 million. Net revenue retention about...

And what drives -- what are some of the key drivers that drives like future strength in the data segment?

Yes. So we have always said that over time, kind of TCV growth in NRR should largely align with your revenue growth. There's obviously -- for us, when we sign kind of some large chunkier deals that has a larger impact on TCV in the short term. What we did say at year-end was of the greater than $1.1 billion of TCV, $350 million of that relates to 2026.

By way of context, we did about $370 million of data and apps revenue in 2025. And so that backlog provides us a level of visibility as we enter 2026 that is really strong. And so we do think that the way that these relationships are kind of established are that they are set up to kind of grow over time, meaning that they typically start small where someone is licensing kind of one cohort of data, they see value from that.

And so they expand it to kind of multiple indications or assets that they're working on internally. And ultimately, they arise to kind of some of these strategic collaborations of which we've disclosed a handful of those over the years.

So we work with 19 of the top 20 big pharma companies, a couple of hundred biotechs. So very good or strong customer footprint today. The good news is that many of them are still kind of in that small or mid-tier in terms of the number of cohorts that they're licensing.

And so there's a tremendous opportunity for us to march all of them up to kind of the strategic collaborations or the majority up to those strategic collaborations that will kind of fuel the growth over the next couple of years.

Yes. You've had a strong start to the year, frankly, within the data business, and it's proven through sort of the announced deal with Merck, which I think is a new strategic collaboration partner. You just announced an expansion with Gilead. Can you just talk about sort of the environment for the data business right now? What kind of got Merck over the hump and sort of some context around what's involved in that deal?

Yes. So we mentioned that at kind of year-end on the earnings call was that we've had really strong engagement from our biopharma partners. Obviously, that's continued into the quarter with the announcement of several deals, Merck being kind of the headline deal.

The thing, as I just mentioned is these relationships kind of evolve over a multiyear period. And so these are folks that we have been working with previously on a smaller scale. They've obviously seen the value that our data can bring to their drug discovery efforts and have decided to expand kind of those relationships more broadly across kind of multiple indications.

So this is not a type of business where things can just kind of come up out of nowhere, they're relationships that we're cultivating over kind of multiple years, making sure that they see value in the data that they're licensing before they kind of expand. So it's really exciting to see Merck kind of join that group.

Great to see the Gilead expansion kind of as well, but one that we're not surprised by given, obviously, the engagement that we've seen from biopharma over the last 6, 9 months.

Yes. And maybe just as a clarifying point because I think it's something that's interesting that as we've done meetings together that I think is really underappreciated within the investment community is sort of the opportunity for expansion within -- even within these large strategic collaborations with customers.

Can you just talk about sort of what -- sort of in a typical deal like one of these collaborations, like what percent of the data set they're actually using and how there's opportunities to expand into additional cohorts over time? These aren't sort of just deals for the entire data platform from my understanding, is it...

Yes. So I mean I think that the reason why people kind of end up committing to kind of these multiyear strategic deals is really one of scale and discount, right? You can license file from us or 10 files from us or you're just paying a higher rate than if you're committing to spend.

And so what typically happens is that once organizations determine that they want to embed the data across their entire portfolio, they commit to those higher levels of spend to secure a higher discount. That said, it doesn't mean that you can download all of the records for these strategic collaborations. There's obviously limits in terms of what people can access.

And it's a relatively small percentage of the overall database that they can download. So lot of let's get in the door, let's expand this across the entire portfolio. That's not a cap on what someone could spend in a given year.

They may still come back and have 3 other projects that they need to work on, but it gives them the ability to kind of leverage the amount of spend that they have to secure a larger discount.

Okay. One of the most notable deals you signed last year was sort of the foundation model deal with AstraZeneca. Can you just provide us sort of an update on sort of where we are in sort of the development process of that deal? And how is that factoring into the pipeline of incremental opportunities for similar type foundation model deals?

Yes. So we announced the kind of AZ Pathos foundation model deal last spring. As we talked about kind of on the earnings call, development and training of that model has gone well. We've delivered the first version of that to AstraZeneca. And so we feel like that is going as well as we've expected.

I think that in terms of pipeline, that's obviously a very large deal, and so it's difficult to predict when another one of those. We've certainly seen an uptick from other folks that want to build smaller models, maybe indications specific. And so there's always ongoing discussions related to those types of deals.

I don't think long term, because we need to retain a copy of the model and leverage it to make our data products and our diagnostics more intelligent, like that's not going to kick in at the first version of the model. But you start to identify some of these insights that we can either turn into enhancements or insights in our diagnostics or leveraging the model to improve the effectiveness of our data products, that work is ongoing.

So we're super excited about, obviously, the work that we've done over the last 12 months to kind of get a first version of the model. It will be ongoing to continue to kind of tweak and further train the model. But certainly, we'll start talking more and more about the insights and things that we're seeing.

And as we've been sort of checks in the marketplace around sort of the competitive differentiation of Tempus' data relative to peers in the group, it really sort of seems to boil down to sort of uniqueness of the proprietary data sets that you own and then sort of data visualization or ease of use and surfacing insights.

So when you think about sort of where things stand today and the investments you're making on the data side of the business, what are the prioritization in terms of remaining competitive and differentiated there? Is it filling sort of gaps within the data set that you're looking at? Or is it working to sort of make surfacing insights a lot easier or making the platform easier to use for your life sciences customers?

Yes. I mean I think we're always evaluating new data sets and how they can play a role in kind of the data offering. That said, what we have today allows us to kind of fuel the needs of our customers today. And so there's not kind of big gaps in the data. The interesting thing about the data set is it's constantly updating, right? And so every time there's a new therapy or a new guideline, like you start to see that kind of flow through the data and our customers need access to kind of the latest data.

So it's kind of maintaining that continuous flow of data is obviously important. And we certainly have made tremendous investments in the tool that I mentioned earlier called Lens, which is what researchers are kind of leveraging to access a lot of the data and build their models.

And we've embedded things like Tempus One there, where before you have to go in and kind of manually identify kind of to build your own cohort, you have to understand the data model. Now you can just talk to it and say, build me a cohort with these things and it can automatically.

So that ease of use is incredibly powerful, especially when you're dealing with the amount of data that we're leveraging with our life sciences partners that they're not spending all this time trying to just understand how to get to what they want, have that be readily available for them so that they can spend their time modeling versus trying to build some of these cohorts.

Makes sense. When you acquired Ambry, I know it was noted that they didn't really have much of a data business at all, but there was an opportunity as you integrate it to sort of build products and leverage that data sort of in the future. What's the product road map look like in this regard? And how near term could you start to leverage some of the data from Ambry in that data business overall?

Yes. So we've owned Ambry for about a year now, and they've operated largely independently. What we think as Tempus' role in kind of the ecosystem is how do you positively impact patients. That comes in the forms of intelligent diagnostics to providers and also leveraging the identified data sets for the purposes of drug discovery.

And so we're in the process of kind of evaluating the nature of that data and how it gets incorporated into the data offering. And based on that, that will kind of dictate how quickly or how long it takes us to kind of incorporate it into our life sciences partnership.

So that is ongoing. That will continue to be ongoing in '26 and into '27, but certainly an area that we think the platform can be very applicable to given what we've built in oncology.

Yes, that makes sense. And then with the current sort of forecast, as you think about sort of the growth in the data business, it's not really dependent on integration of Ambry at all, I would imagine, within into that -- into the data center or any sort of starting of monetization there? Is there?

Yes. The vast majority of our data revenues today are within oncology -- kind of late-stage oncology. And so that is not something that we're having an impact in '26 or '27.

I want to shift on the -- and touch at least on the AI applications business as well because I think this is where there's some really interesting sort of long-term future opportunity for Tempus. You continue to validate and gain reimbursement for a number of applications.

Obviously, you have the ECGAF algo out there that's gotten some reimbursement on there. How do you view the sort of the framework developing for additional approvals in this portfolio?

And what do we need to see to sort of hit an inflection point where the AI application business can sort of be a driver of growth and profitability over time?

Yes. And so when you talk about kind of that flywheel that I mentioned earlier, one of the exciting things is as you start amassing all this data, you can start identifying very interesting insights that kind of get turned into algorithms of their own or algorithm diagnostics. We have a suite of those in cardiology that you mentioned, 2 of which have FDA approval, have limited reimbursements.

We have some FDA-approved offerings in radiology. We have DigiPath through the Paige acquisition. So we have a number of these efforts that are ongoing that have been deployed and are reaching some scale. But to your point, there isn't kind of a strong reimbursement framework.

Unfortunately, there's not a great answer on kind of timing of when that changes. But our viewpoint is that it would be very odd for us that the health care system wouldn't want to pay for things that can have a positive ROI on kind of overall spend.

So if you can identify a patient that's at a higher risk for something early on and prevent a catastrophic event, the math probably works in your favor to reimburse those. Our approach has been to deploy these things. So to develop them, deploy them, start kind of having them run in the clinic and kind of start building, obviously, the evidence of kind of their impact while simultaneously having discussions with folks in the government about why these types of things should be reimbursed.

For us, this is not something that we're building in. We've said it's going to be a multiyear effort. So it's not in our '26 guide or we don't anticipate a significant impact in '27. But the really exciting thing is that they can scale incredibly quickly because we're leveraging a lot of the integrations that we've already built with hospitals. So we can deploy these things they're relatively inexpensive for us to run today.

But when reimbursement ultimately does come, which we hoped and believe that it will, then they can scale very quickly. And so because we sit on top of this very unique data set, that affords us the ability to kind of identify these insights, turn them into algorithm diagnostics and deploy them while simultaneously kind of trying to push or move the football on the field in terms of reimbursement has been our approach today.

Yes. And obviously, a lot of work to do on the reimbursement side and sort of getting FDA approvals as you continue to build the portfolio of algorithms. But where is sort of private payer thinking on this right now? Is it on their radar? How much education are you having to spend and do right now with the private payers?

Yes. I mean I think the private payers are similar to some of the government conversations of -- there is a recognition that these things can have or add enormous value to the health care system, but it's how do we prove that out before we're willing to reimburse it.

So I think those are the conversations you're having of, okay, show me how exactly this plays out in reality such that we see the value that we think we're going to see.

And sort of short of AI applications sort of obviously being a material revenue driver or margin driver in the business. Obviously, there's a lot of sort of provider education right now and sort of the opportunity to sort of attach the AI applications on to existing tests. Where does that stand today in terms of what do attach rates look like? And how does that maybe even just create a retention or differentiator relative to the other diagnostics tests in the market?

Yes. So we have a handful of algorithms within oncology today that are basically add-ons to some of the wet lab procedures that someone may order. Today, it's about 40% of our orders come in with one of the algorithms selected, which gets back to kind of my earlier point of where we win within oncology therapy selection is one of ease of use and additional insight that we can provide.

And I think demonstrated by the fact that we've got a large percentage of our orders coming in with these algorithms just highlights that doctors obviously see tremendous value.

Makes sense. One of the questions we got in from the audience is around sort of where you see the value and incremental investment within the diagnostics business in terms of sort of investing in sort of new assays or refinement of assays versus sort of spending more of your investment on sort of the data side. How do you think about sort of the differentiation relative to the more traditional diagnostics vendors here?

Yes. I mean if you think if you look at the investments that we've made historically, we've taken a slightly different approach than some of the other diagnostic players that are kind of always bringing the first assay to market and kind of first mover where we've taken a slightly different approach where we've been focused on being a very comprehensive sequencer, but then having these additional kind of insights.

And so that's not to say that we don't invest, obviously, in new assays. We're obviously building out our tumor-naive MRD portfolio. We continue to iterate on versions of our core assays as well to add additional features. But we just don't make all of our investments in that space.

We also make investments on kind of the data and platform side and on these integrations that allow us to kind of provide that additional kind of insights. And so there's not a right or wrong answer as to kind of where people choose to make those investments.

But it just gets back to kind of at our core, we view ourselves as kind of a data technology company and less so kind of a traditional diagnostic company that's always worried about kind of being the first mover because there's only kind of one transaction, which is what you get paid for the test and what it gets cost, right? Where have kind of multiple shots on goal through kind of the various offerings that we have.

Makes sense. Okay. Another question we got from the audience is around sort of -- some of these regulatory proposals around potential for nationalizing MolDX and what sort of the implications or change could be going from moving from a regional system to more national. I guess how concerning is that? Or what sort of process would that sort of result for Tempus to do? What kind of disruption could it create?

Yes. So this is similar to the CRUSH RFI discussion kind of earlier. Our view has always been the reimbursement landscape continues to evolve. It's evolved over the last 10 years that we've been operating the lab. And we always have kind of mitigating strategies to minimize any impact. And that's why we've been bringing things to the FDA to get approval and get under the NCD to the event that there's a change in any of the MAC kind of dynamics. But again, I think we're well positioned to minimize any impact that may come.

Awesome. Good questions coming in. If those who have incremental ones, please let me know. We can get those asked and answered. Jim, I want to shift to, obviously, we'll give you a few CFO questions. Why not? Talk about -- there's obviously a very large growth opportunity ahead of Tempus, both in the diagnostics business, also in the data business and the AI applications, plenty of ways to sort of allocate capital.

Can you just sort of provide some color on sort of the framework of how you're thinking about growth versus profitability and sort of how you're thinking about sort of reinvestment of gross profit dollars over the next couple of years and then sort of beyond that?

Yes. So we've told people that if you assume a 25% growth over the next couple of years, our plan is to reinvest 2/3 of the incremental gross profit dollars back into the business, let 1/3 drop down to adjusted EBITDA. We think it's important to demonstrate that there's obviously continued -- we've demonstrated leverage, but that there's continued leverage in the business. And so there's obviously -- we're operating at a scale now where that's a fair amount of discretionary spend that we're choosing where to invest.

And so as we kind of look across the various offerings, you have diagnostics, which is growing at a healthy rate. We have MRD still to come. So that's exciting. And so that's an area that's definitely worth doubling down on.

And on the data side, as you mentioned, we've been able to establish a business that operates at scale, but it's still relatively early days with most of our customers there. And so we want to continue to invest so they continue to see that value that leads to kind of that expansion of those relationships.

And then the last category is kind of the AI applications, that obviously isn't a near-term revenue driver, but we do think is a worthwhile investment because that will afford us the ability to continue kind of growth long term.

After about 3 years, we think we can reverse that trend of reinvesting 2/3 and that can drop down to 1/3 because at that point, you're generating a lot of gross profit dollars such that your investments you think that you can sufficiently invest while letting more drop down to the bottom line.

So while we're mindful of profitability and kind of continued leverage in the business, we do think it's important given where we're at in our life cycle to continue to invest in these initiatives.

Makes sense. And then as you think about -- you talked about the 25% growth, but trying to sort of work towards multiple years of growing at that consistent rate. How do you think about sort of where to invest to meter that growth in times when, say, last year, you had hereditary that was growing much faster than what you initially expected.

This year, data is off to a really hot start. How do you think about sort of balancing and sort of shifting those investments as you're seeing sort of the changes in the end market activity on a year-over-year basis?

Yes. I mean I think there's always going to be periods -- you're going to have periods of time where you're going to have a reimbursement uplift, right? And that's going to lead to outsized growth. And our view is, okay, let's see what is working and where can we double down on those investments. We're very thoughtful in that when we enter a new disease area, for example, we do so very small to medium. We don't kind of get ahead of our skis. We want to prove it out before we kind of double down.

But then when you're running kind of ahead of plan in a certain area, then you want to identify those areas that are going well and kind of double down on those efforts. And so it's something that we look at on a quarterly basis to identify what are those areas that we should be looking at and then making sure, obviously, that they end up seeing that ROI down.

Makes sense. Makes sense. And how are you thinking about sort of M&A as a capital allocation strategy moving forward? Obviously, historically been acquisitive over recent years, makes sense with Ambry to sort of build out the portfolio. Paige is a nice interesting asset to add to sort of give more context when maybe sequencing doesn't -- isn't effective. But how are you thinking about sort of incremental M&A to supplement organic initiatives?

Yes. So we think that on the diagnostics side, we have a pretty well-rounded portfolio at this point. Obviously, Ambry was an outsized acquisition relative to the one that we typically do. Many of the companies that we look at are kind of smaller tuck-in acquisitions where somebody has built something very interesting from a technology or integration standpoint or in Paige's example, a digital pathology kind of offering that we think can be complementary to the current business, but also has some future growth potential as well.

And so we evaluate kind of all these the same way of how does it fit into the offering? Is it something that ultimately will improve patient care? And if it checks and doesn't divert us from our kind of continued leverage in the business, then we look at kind of tucking some of those folks in.

Awesome. I'm not seeing any other questions from the audience. So we are going to leave it there. Jim, thank you so much for taking the time today, and thanks for everyone who joined us and for all the interactive questions from the audience. Have a good day, everyone.

Thanks, Ryan. Really appreciate it.

Awesome. Well, I appreciate everyone being here. I'm Matt Strom, Morgan Stanley Investment Banking. I lead the health care data and AI practice. Great to see you all and great to have Eric with Tempus here with us again this year. And maybe just before I start quickly, for important disclosures, please see the Morgan Stanley research disclosure website at morganstanley.com/research disclosures. If you have any questions, please reach out to your Morgan Stanley sales representative.

So with that out of the way, I think we want to jump right in. And today, we're going to really focus on a unique sort of health care data and AI story with Tempus and want to dig right in with Eric. So maybe, Eric, just off the start, I think a lot of people think of Tempus in some ways as a genomics company. It's obviously still a large portion of your revenue. But if you zoom out, you've built a really large multimodal longitudinal clinically annotated data set and now you're sort of layering this AI on top of it. Maybe for the audience, just start with, is Tempus a diagnostic company? Is it an AI company that happened to start in oncology? Maybe just start with that framing.

I think it's -- at our root, we have always been a technology company. 10 years ago, you really couldn't be an AI company other than in theory, today, if you're a technology company, you're probably an AI company at least in some way, shape or form. So today, we are absolutely an AI company. One of the challenges we've always had is that we don't come at this in terms of like, oh, we're going to build models, and that's our core business. We really come at it by saying we're going to garner access to proprietary data, use that data, whether it's enhanced by our own models or other people's models to generate insights and deploy those insights back into the clinic.

And so that's our -- has always been our core business. In order to get the data, we had to basically open up a lab and start sequencing patients because that was the data that we needed in oncology to kind of generate these insights. And so that business over the last 10 years has -- we're now maybe 8 or 9 years ago, has become the biggest part of our revenues, about 3/4 of our revenues, give or take. And so we live in both of these worlds where we are both a NGS company sequencing patients and generating proprietary data and a data AI company that takes that data and generates insights, licenses the data, licenses the models, all that.

So we really have these 2 different businesses going, which makes it complicated because diagnostic investors are always kind of afraid of our data business because they don't get it, they don't invest in AI or tech. AI investors are scared of the diagnostic business because they're like, I don't do diagnostics. And so we've had to straddle both of those worlds as other companies like us, like Tesla and Amazon, other people have straddle both of those worlds.

Maybe just double-click into where your data actually comes from. You mentioned the genomic side, but there's a lot of people that produce genomic data. But where does it come from? And sort of what had to be true operationally as well as culturally to ingest that data, get the trust of your customers and be able to actually use that data?

Yes. The first thing we had to do, which was unique is when we began -- because we're a tech company that began sequencing patients, from our earliest inception, we would go to people and say, "Hey, we'll sequence your patients, but you have to give us the clinical data for these patients because we're not just interested in sequencing your patients. We're interested in understanding whether or not the insights we produce from these -- from the sequencing is working. Like if we found a mutation and we recommended a drug, did your patient go on that drug and how did they respond?

And so the big hurdle that is both cultural and logistical and administrative was saying to people, like we'll sequence your patients, but you got to give us all this data. And we -- not only is it give us this data, we have to be able to de-identify the data and use it for any lawful purpose we want because not only are we interested to generate insights that make our reports better, but we want to generate insights that make drug companies better. And this might sound like reasonable today, but 10 years ago, this was like heresy. Like you'd mentioned the word drug company and to most providers, they would be like, I'm never going to talk to you again.

But we would go into these meetings and say to people, unless we're missing something, you people don't make drugs. And so why would we not want to make drug companies smarter. So I think culturally, from our earliest inception, we weren't just sequencing patients, we were collecting clinical data for those patients longitudinally over time. And so very quickly, we ended up amassing a very large data set of rich molecular data, DNA, RNA, other insights, connected to rich outcome and response data. And it's the combination of that data set that was -- that is and was so valuable.

However, once we began amassing huge amounts of data, and you're talking hundreds of petabytes of data, we realized, okay, it's now time to start to license this data to biopharma. But when we just handed them a bunch of data, they couldn't find real value in it. So we had to build a whole array of tools around that data that make it useful, not just harmonizing and structuring the data, but really allowing people to interrogate the data, build cohorts of interest, refine those cohorts, unpack the data, unpack insights.

And so if you look at our financials relative to other people in our space, especially on the diagnostics side, the most glaring standout is we have a very -- and have always had a very large technology team, I think like 700 software engineers and product folks, people like that, and enormous investments in cloud. 5 or 10x other people in our space. So we've always invested a lot in making the data useful. And that's -- we announced -- we may get to it. But if we look at our recent deal we announced with Merck, which is another very large strategic collaboration for us, you just don't have people like AstraZeneca and GSK and BMS and Merck and others signing these $100 million-plus deals unless the data is both incredibly useful and they can generate real insights from it.

And so how does that differ from -- so take the Merck example, you guys just announced a deepened collaboration with them today. It strikes me that those are, again, gearing more towards real deep collaboration relationships rather than, as you said, just access to data or something. But how does that sort of -- the relationships that you've got with your pharma customers differ on the data side from what they could go -- theoretically go find in other parts of the market, whether it's rollout data, rollout evidence, et cetera?

Yes. I mean I said this at JPMorgan a few months ago, we -- sorry, I'm just choking for a second. But we saw a few years ago, we saw people entering the data market, especially our competitors talking about how they were going to launch data businesses. And so we had some of that noise. And today, that noise has really dampened. I mean we just -- when we're working with big pharma, they're either licensing our data at scale where they're really just not licensing this kind of data. At the present moment, we just have a unique product.

And so we're never in a situation where -- or at least if I think about the last year or 2, we're never in a situation where someone says, "Hey, we want to license your data, but we're also looking at somebody else, another big sequencing provider in the space, whether that's Caris or Guardant or whoever, and we're kind of -- this is their price and this is your price. Like that never happens. They either want the kind of data. They either believe that the kind of data we have can be transformative to their oncology programs, what assets to pursue in early R&D, how to design a more intelligent Phase II, how to manage site selection to ensure you're enrolling the right patients, all that.

They either believe that data is transformative or they don't. If they do, they -- we're the partner of choice. And then what ends up happening is these deals all kind of start small. Merck is a great example. They all start relatively small. Somebody licenses whatever, $1 million of data, and they want to solve -- answer 1 or 2 questions and then they want to answer more questions. And then at some point, they realize they want to answer lots of questions.

And if you look at our data business, any biopharma can license one file for a few thousand bucks. Like so there's no -- we don't mandate that you have to license lots of our data over multiple years. So when a client signs up for -- and in the case of Merck, it's a 5-year agreement but 4 years are committed. So when someone signs up for like 4 years of locking into lots of data, all they're getting is access and a discount, right? They're essentially getting access to our tools at a discount on the data.

And so our pricing works very similar to AWS or GCP or Azure, where you can buy a little bit or a lot and all that varies is really price. And so I think it speaks to the fact that as people -- they might start small, but pretty soon, they realize like I'm going to need a ton of this data and it's integral to my programs, and I want the best price I can get. And so I'm happy to sign up for a multiyear commitment.

It strikes me that the data business for you all, maybe partially because of the more health care-focused investor base has always been a debate. The debate when I first started working with you guys was, oh, you can't produce revenue out of this, pharma is not going to pay for data. I think at least part of the debate in the market now is what's facing a lot of tech companies, which is the data is going to come from somewhere else or you can Vibecode your way into some sort of solution that's going to work, which seems ridiculous in the pharma context, but so be it.

I'm just sort of curious, as you guys look at the data today, is it the scale? Is it the density? Like is it the size of the asset that makes the difference? Is it the tools you built around it? Like what are some of the moats that you feel like you're building up with your customer base besides the uniqueness of the product itself?

Yes. I mean, I think -- look, the -- I think if you think about the existential threat these days more and more is that the large foundation models are going to get so smart that they can do a lot of things other people can do. This is the whole like AI eating software. One of the challenges those models have by their own admission is that at some point, they run out of kind of free public data to train the models on. And there's varying estimates of when they run out of that data.

But I think there's pretty good consensus in like '27, '28, they're hitting the ends of that. So more and more of those companies are coming to people like us saying, what data do you have? And I think the next frontier of fun is going to be the big frontier modelers trying to garner access to more and more proprietary data like the kind of data Tempus has to train their models. In our case, the data we have is really hard to replicate. First, you have to go to, in our case, I think, 5,500 of the roughly 8,000 hospitals in the United States and convince them they should give you their data, which is not quick.

Then you have to get through legal, which is even slower. And then you have to get through IT, which is even slower because these people have Epic or Cerner or these large systems, they have an enormous road map of work that have to get done. And in order for us to get the data, we typically have to integrate at scale. And it has to be longitudinal. You can't just get one time point. You got to get multiple time points and not just one kind of data, you need structured data, you need unstructured data, physician progress notes, you typically need other forms of data.

So we built up this really large data set. It's approaching 500 petabytes. It's connected to lots of hospitals. And so I think -- and it also, by the way, is connected to our own proprietary sequencing. So even if somebody could get their hands on the clinical data, they can't get their hands on the VCFs and BAM files, all that rich molecular data that a company like ours has unless you partner with some company like ours and try to marry it all up. And one of the flaws of other people that I think have tried to compete with us is you've had people who have lots of molecular data trying to cobble together clinical data or people with clinical data trying to cobble together molecular data, and it just doesn't work. So -- or it hasn't worked up until now.

So I think we're in a unique spot. And I would suspect that it's only a matter of time. I'm running a blog post on this, so I want to give that away. But we're in regular contact with the world's largest modelers. And I would say -- and technology. And I would say their interest in this kind of data on a scale of 1 to 10 was a 1. I would say it's now like a 5. And interestingly, every one of these companies that we're engaged with, again, this is coming out in a week or 2, is asking the exact same question. They want longitudinal patient histories at scale.

And if you think about it, the reason they want longitudinal patient history is not to digress is, these models are very good at predicting the next likely word. They're so good at predicting it that you can ask almost any question and they give you incredible insights, right? They become that good. And it's just because they've been trained to predict the next likely word, see spot and the next likely word is run.

I think these folks believe as we do that with enough data, like the kind of data we have, you can predict -- instead of predicting the next likely word, you can predict the next likely drug or the next likely therapy that would work for a patient. And my guess is that we're relatively close to being able to train these very large models that could be truly predictive. That can start to say, like if you're on 5 milligrams of statin, should you be on 10 or if you're on this antidepressant and this hypertension medicine, is it bad for you, not for the whole world, but for you.

Individualized.

Individualized. And so I think at that point, that use case, I think, for these folks is very compelling because if you're paying $20 a month to like write an assay or to write an e-mail or like whatever and something else comes up that's nearly as good, you might stop paying $20 a month. But if you're paying $20 a month to figure out like how you're -- what drugs you should be taking and how to protect your health, it's a pretty durable use case.

It strikes me in the example you just gave, though, that the model is being tuned and trained with your data, you could see that use case. But -- and so in that case, your data is very valuable. But conversely, if you're going to go back to trying to impact the patient at the point of care, your pipes in and out of the hospitals are very valuable, too. It's sort of a go-to-market partner essentially.

And I also think -- yes, I also think we very much view it as our data is going to be central, not just in oncology, but we've got large data sets in cardio and neuro. Our data is going to be invaluable to build models and generate insights. On the consumer side, I suspect those models will be delivered by the big consumer companies. Like we have no aspiration to be that company. So they'll be delivered by Apple and Google and OpenAI and Anthropic or whatever.

On the provider side -- on the pharma side and provider side, I suspect those insights will be delivered by companies like ours, both because in order to connect to the U.S. health care system, you have to be a covered entity. It's complicated. There's all kinds of logistical issues. So I think at the end of the day, we have a moat. And then in terms of pharma, they're not just interested in like asking, at least at present, asking like superficial questions. They're interested in asking incredibly detailed questions that are influenced, and this is the key part, by their own data. And in our case, we are a trusted provider, both to 8,500 oncologists in the United States and most of big pharma. And we have their data and data is moving back and forth. And I just don't see a world anywhere in the near term where the biggest pharmaceutical companies are uploading their critical clinical trial data to OpenAI or Google or whoever. I just think it's too invaluable. So I suspect we've got a pretty good moat on both sides.

Maybe we could move from the data level to the sort of intelligence or AI level for a second. You guys have had some announcements around foundational models in the space. What does that actually mean in health care? What are you referring to when you're talking about building those for -- in partnership with your customers?

Yes. So I think I'll give you the most tangible example because I think it's relatively close to being at a point where this is public as well. So like if you think about it, the foundation model we're building, and we're building 2. We're building one with AstraZeneca and Pathos. We're building a second that's pan disease on our own, 2 different compute clusters that we've established. One is about 1,000 H200s, one is roughly that size, but GB200s. And what's happening is we're building these models so we can generate multimodal insights that you just can't see unless you have enormous amounts of data. So let's just take one of those insights.

So if I'm a non-small cell lung cancer patient, the standard of care is that I would be profiled for 2 particular biomarkers, EGFR and ALK. And if I'm EGFR positive, I would go on an EGFR inhibitor. That would be my guideline therapy. And like most drugs in cancer and like most drugs in many other disease areas like diabetes and cardiac patients, these drugs tend to work in episodic in different ranges. So some percentage of the population, the drug doesn't really work at all. You'd go on the drug and within 3 months, you need to go off the drug because it's not working.

Some percentage of the population, you're going to be on that drug for 5 years and to have an incredible response. And then there's a big part that's in the middle. So it's very hard to take all the different clinical characteristics of patients and build models that are predictive because as you can imagine, patients that get non-small cell lung cancer are quite varied, a ton of heterogeneity. So -- but when you have a large model like we have, you can begin to train the models to look for those outliers and build predictions. And so I think we're not far away from -- on our tests, unlike other tests, not just saying this patient is EGFR positive, but also providing context. This patient is EGFR positive, high, EGFR positive, mid, EGFR positive low.

And that means do X? Is that the...

High would mean something like this patient is going to -- we predict this patient will be on -- will do very well, taking an EGFR inhibitor, whereas EGFR low would be we predict this patient will not do well. Like if you give the patient an EGFR inhibitor and tell them to come back a year later, don't be surprised they had metastatic disease. So -- and I think you will see that like we're about to open that Pandora's box. And I think it just is the beginning of an entirely new era of precision medicine, where you can collect vast amounts of data, train very big models and be unbelievably predictive so that you start to have this end of one contextualization of every drug instead of the way we are today, which is, oh, your cholesterol is high, go on 5 milligrams of a statin.

Like really, should be 5, 10, 20, this, should I come in and have a calcium score? Should I whatever stress test or an [indiscernible]. And you don't know because we can't -- we're not good at stratifying risk. But these models can stratify risk. And so I would suspect that, that -- and so that I would think will be highly catalytic to our diagnostic business because we're just -- our tests are smarter and more personal than others and also highly catalytic to our data business because every pharma company over time is going to need to know where does the drug work and where does it not work because physicians are going to know that and ultimately, patients are going to know that.

Does that change the -- is there a regulatory infrastructure that needs to change for you to deploy those specific insights, the EGFR example and a reimbursement regime that needs to change? Or does that fit into the current sort of world?

I think it fits into the current world of oncology because in the current world of oncology, we give oncologists a great deal of latitude to make decisions as to how to treat these patients because that's just the world of oncology. Other disease areas are far more rigid. And also because most of these tests are LDTs, they're not FDA-approved tests. We have an FDA-approved test as a few others do, but the vast majority of tests in the market are just non-FDA approved, there's a different regulatory structure to modify those.

If you want to append a medical device that's FDA approved, you have to go back to the FDA. So like our ECG algorithms, we have to get FDA approval because GE got FDA approval for its electrocardiogram. But for laboratory diagnostics, you can say all kinds of insightful things on top of that because these tests go through an alternative pathway. And they have to be reviewed by a physician in order to take action.

So I think there's a pretty wide amount of latitude. I would suspect, though, over time, our competitors on the diagnostic side will want or need similar tools that help them quantify their tests. We have a test out in the market now called Immune Profile Score, which basically modifies another test called Tumor Mutational Burden, which is wrong about 20% of the time on both ends, meaning it misses patients that should get an immunotherapy and it captures patients that shouldn't. And other -- we have other competitors that have similar algorithms, and I suspect more and more coming.

Just on the model side, maybe one last question. So I think you've talked today and you've certainly talked a lot publicly in the past around the sort of integration of a lot of different types of data, genomics, pathology, clinical notes, et cetera. Are there sort of other large data sets or forms of data you need to either produce yourself or get your hands on to improve these models and improve what you guys can sort of deliver in the future?

Yes. I mean I think at the present moment, no. But in the near term, I think, yes. And we -- in our most recent letter that Jim and I wrote, we called out the fact that we were fortunate that the business was generating more gross margin because we're running at whatever, I think our growth last year was like 33% or something, but we're growing at around 30% and the cost we need to run the business are much less. And so we're generating lots of leverage in the core business. And so we made a decision to not just handle that EBITDA -- incremental EBITDA gain to the bottom line, but to hold back some of it to invest in sustaining that growth.

One of those buckets of investment is new data sets, both outside of oncology in areas like immunology, but also in new data modalities in oncology, in particular, single-cell sequencing, spatial transcriptomics, epigenetic data at larger scale. So I think there's other data sets that will become important proteomics beyond base level proteomics. But right now in oncology, we have an enormous amount of data and still even with people like Merck coming on board, which is amazing, joining the ranks of some of our other large strategic partnerships, there's still -- I don't know what the total number is, but we still have well more than 50% of the biggest oncology companies, top 20 that aren't strategic clients of Tempus. Maybe we have 5 and there's 15 to go. And so I suspect over time, all those folks will also sign up.

If you think about...

At that level, they're all clients just now at that level.

Right. Expansion of that opportunity. Right. If you think about health care AI, where do you see the long-term value accruing? There's all this debate right now. Is it the data layer, the model layer, is it the application, the workflow layer. Where do you sort of see it accruing in health care as you play out the next sort of phase here?

I think we're still at the part of the curve where the data is the scarcest asset to train the models that change both patient and physician behavior. So we're at the part of the curve where those who have access to the data at scale likely have the proprietary asset. Over time, we'll move to what you do with the data becomes more important. I think there's a fork in the road, as I mentioned. There will be consumer companies that dominate one side of it, and then there will be enterprise companies that dominate the other side. I tend to think they'll be different. And so our focus is on dealing with providers of biopharma.

There's -- and that's just -- so we've always thought of our business in kind of 3 buckets. There's a data generation part of our business, we're very lucky that the data generation side of our business is both high growth and generates really high margins, like 65% margin. So that's a healthy business in and of itself. And then that provides all this data that has an even higher margin, 75% and is growing even quicker. And we think both of those businesses are kind of multibillion-dollar businesses over the next, whatever, several years.

And then I think the real interesting part of the story, which I think you're getting at is, look, when there's -- when data is pervasive and there's all these models out there, whether Tempus is the leader in that or one of the leaders, you're going to be generating all kinds of insights and how do we pay for those insights. And I don't have an answer for that. I think it's through like AI-enabled applications or some kind of algorithmic diagnostic that's paid for, but I can't tell you that for sure because I don't know. But it feels to me like that business eventually is the really big business.

Like if these are big businesses, that's the mega big business because -- and I just use our ECG algorithm as one example. Like we have this ECG algorithm that predicts undiagnosed AFib and undiagnosed low EF, about 70% of heart attack and stroke are one of those 2 in terms of normal ECGs. So in theory, we can predict about 2% of the total error of ECGs in the United States just off our 2 FDA-approved algorithms. We run 200 million, 300 million ECGs a year in this country. That algorithm currently has partial reimbursement for a subset of that at about $128 an algorithm.

But like at some point, you could imagine there being universal reimbursement at $50 to $100 for that algorithm. If somebody runs $100 million, it's a big number. And I think that is going to be repeated over and over again where we just have these algorithms that will predict mistakes that are made at scale, which type 2 diabetes medications you go on? Are you -- should you be on a statin and ACE inhibitor or some other cardiac -- pick an algorithm that -- where you've got people on the wrong drug, the wrong time, wrong dose. And so I think algos is a big business down the road.

Maybe just 2 last questions to close. I think you've talked a lot about in the short term, AI and health care is probably overhyped in the long term, it's probably underhyped. You may have played a little bit of that vision out today, but what is it that you think investors and maybe especially technology investors who don't spend as much time in health care are maybe sort of misunderstanding or could understand better about that paradigm?

Yes. And I think -- by the way, I think it's interesting because when I said that, there was -- and I think it was probably a year ago or I don't know, 7, 8 months ago, there was all this kind of euphoria around just AI more broadly. And that seems to have dissipated, at least in our case, it seems to have dissipated quite a bit. I think there's still a bunch of that private euphoria as it relates to maybe Anthropic and OpenAI. We'll see how they trade in the public market. There's certainly still a bunch of euphoria around NVIDIA. But some of the -- like everything with the word AI in it a year ago trading high, I think that has certainly gone away.

And one could argue, I think probably -- I don't know if that has anything with Bitcoin, but you had some of these asset classes that felt like they were kind of risky and retail-driven that were trading high about a year ago that have all come way down. So now someone said to me, is AI overhyped in health care in the short term, I would say, no, if anything, I think we've actually crossed that chasm where the opportunity of AI in the near term is probably underappreciated. It's way underappreciated in the long term, but it's probably also now underappreciated in the near term or the short term.

And I think it's because we're about to start to see -- and I suspect in '26, I believe in '26, you will start to see very tangible evidence that AI is going to impact health care at incredible scale, both from companies like ours on the provider and pharma side and companies like OpenAI or Anthropic or Google or whoever or Apple on the consumer side.

So if you played out the next -- Tempus is, I think, around 10 years old right now, a little over. If you play out the next 5 years from the company, it strikes me that there's sort of a big transformation ahead of you. So if you guys execute well on that next 5-year journey, what does the company look like at that time? Where do you think the real drivers of the business sort of sit at that point?

Yes. We've projected 25% growth for the next 3 years. But if things go well, I would suspect or I would hope we beat that pretty materially, especially on the data side. It's harder to predict the diagnostics side only because now I'm getting into like long-term trends of NGS. But I think the data long-term prediction and the AI long prediction is much higher than 25%. So I think if things go well, the business is just significantly larger. If you compound something at around 30% for 5 years, it's a much bigger number. We're starting on a base of about $1.6 billion. And so just kind of you can do the math.

And so we're focused on that. We're focused on building a business that is growing rapidly, that generates lots of leverage that allows us to reinvest in ways other people can't to compound our leverage so that 20 years from now, not 5 or 2, but 20, that's how you -- I think that's how you build a very big company, right? When you look at companies like Amazon or whatever, they've just been compounding for a long, long, long time. And that's what we want to build.

Awesome. Well, thanks a lot for coming, Eric. I appreciate you being here, and we'll look forward to what you guys do in '26.

Thank you. Thanks for having me.

Good day, and thank you for standing by. My name is Tina, and I will be your conference operator today. At this time, I would like to welcome everyone to the Tempus AI Fourth Quarter 2025 Fiscal Results Conference Call. [Operator Instructions]. It is now my pleasure to turn today's call over to Liz Krutoholow, Vice President of Investor Relations. Please go ahead.

Thank you, Tina. Good afternoon, and welcome to Tempus Fourth Quarter 2025 and Full Year 2025 Conference Call. This afternoon, Tempus released results for the quarter and year ended December 31, 2025. The press release and overview of the quarter and our latest presentation are available on our IR website.

Joining me today from Tempus are Eric Lefkofsky, Founder and CEO of Tempus, and Jim Rogers, CFO.

Before we begin, I would like to remind you that during this call, management may make forward-looking statements that are subject to risks and uncertainties that could cause actual results to differ materially. For a discussion of these risks, please refer to our 10-K and other subsequent filings with the SEC. During the call, we will discuss non-GAAP financial measures, which are not prepared in accordance with generally accepted accounting principles. Definitions of these non-GAAP financial measures, along with reconciliations to the most directly comparable GAAP financial measures are included in our earnings release, which is available on our IR page. I would now like to turn the call over to Eric.

Thanks, Liz. 2025 was an exceptional year for Tempus with both of our businesses growing rapidly and performing well above expectation. Total revenue of our core business was up over 33%, when you factor in the acquisition of Ambry, obviously much higher. If you look at our 2 main businesses, I'll start with Diagnostics and Oncology. We had unit growth of 29%, which was and has been accelerating throughout the year. We called out that our MRD growth rate was actually 56% quarter-over-quarter, which is extraordinary. Hereditary held up well with 23% unit growth. So all in our Diagnostic business is accelerating and performing above expectation.

In terms of Data, that business is growing even faster it's made up of really 2 product lines, our Licensing business and our Applications. Our Licensing business or Insights was up 69% in the quarter when you factor in the onetime impact of the AstraZeneca warrant and we're projecting roughly 40% growth this quarter. Total contract value was greater than $1.1 billion, and most importantly, has been rising faster than revenue over the past several quarters, and net revenue retention was 126%, which is a super strong, all things considered.

We guided to $1.59 billion, which is in line with our 25% long-term growth expectations and approximately $65 million of positive adjusted EBITDA. Our balance sheet is in great shape. Our products are resonating our AI advantages are continuing to take hold. So all in, we're poised for a phenomenal 2026. With that, happy to take questions.

[Operator Instructions] And our first question comes from Kallum Titchmarsh with Morgan Stanley.

Eric, I wanted to zoom out from the financials. I'm sure that will be covered and go a bit broader. The markets are a bit anxious around AI and how value is scaling distributed within that ecosystem. And we're now obviously seeing kind of traditional AI players push into the health care sphere. So I'm curious how you feel your position is protected on the Data side? And I guess to that point, why you expect the large pharma companies you work with, particularly within Insights to keep coming back for more? The Q1 guide is obviously strong, but I'm kind of looking on that.

And then finally, just on that point, I'm interested whether the feedback you're receiving from these customers suggests that they're getting better at what they do because of what you're providing them with? Just any sense of success stories and what you're hearing on the ground would be appreciated.

Yes. So I mean I think the most interesting business models, I believe, surrounding AI, in particular, large language or large multimodal models, really center around access to proprietary data to train models and proprietary distribution once you have a model that generates insight. So as we've talked about historically, Tempus is uniquely positioned in that we have both of those at scale. We have over 450 petabytes of connected multimodal data which flows from our Diagnostic business, which has real-time insights, real-time connection to outcome and response, is able to track patients longitudinally, rich molecular data, rich imaging data. So we have this really unique proprietary data set that you can use to train AI to train models.

And then once we generate insights or some kind of contextualization that we want to put in the hands of a doctor because we're connected to more than 5,500 hospitals because we have more than 8,500 regularly ordering oncologists and thousands of other physicians in other areas. We can deliver these insights in real time as part of routine clinical care. So that's what makes us unique. If somebody wanted to replicate our data business, they'd have to go reproduce all that real-time data, which is quite hard to do, you have to enter into contracts with providers. You have to get the data, you have to work through IT issues. You have to structure the data. You have to build technology to make the data useful, it's an enormous lift that we've been on over the last 10 years. And I think because of that, because of the work we've done to build the data pipes to harmonize and structure the data to build technology that wraps around the data, we just have a unique offering and other people have been unable to replicate it.

And so if you look at our -- the scale of our Data business, I mean, a few years ago, people thought we couldn't hit $100 million of Data revenue, and we're now 4x that and projecting to grow 40% even at this scale. We have 126% net revenue retention, which means, on average, our clients are ordering significantly more year after year than they ordered the year before and all kinds of proof points. Our largest clients continue to re-up, those contracts get extended. We've announced a bunch of them over the last several years. And it's because we're demonstrating real -- our data is demonstrating real value where these clients are able to use our data and our technology to be more intelligent about what assets to go forward with, refine their early-stage discovery projects, design more intelligent, Phase IIs and Phase IIIs, recruit the right populations at the right time and get their drugs approved and in market faster. And that is why the Data business is just kind of having a moment and the growth is actually accelerating.

Your next question comes from the line of Subbu Nambi with Guggenheim.

Earlier in 1Q, you launched Paige Predict. Given you previously discussed that you don't expect this to contribute meaningfully to revenue this year, can you discuss the strategic value of the added capability when samples are QNS. How often is this case with HD and xR?

And a quick one for Jim. Jim, ASPs are expected to reach over 2,200 in the coming years, but what are you expecting in '26 guidance?

Yes. So I'll start with Paige Predict. So we have -- and I think I'd encourage you to read the letter, we tried to spell some of this out in the letter we published. But we've long talked about how there's enormous benefits to the contextualization of these diagnostics and the technology we wrap around them, and it's not just about having the next version of an assay or running a study that produces some kind of wet lab improvement like that only means so much, if you look at our growth and the fact that our growth is this strong at this scale, it's in large part driven by the fact that we just have a technology advantage that makes physicians want to order our products because they get greater insights from those products.

And those insights, we like -- if you look at just a few that we called out, One is Paige Predict and others what we call our Immune Profile Score. And there are dozens of others. But if you look at Paige Predict, here's an example of we've built technology that at scale allows us to digitize pathology slides and generate insights from those pathology slides by which we can predict mutations that exist, that will show up when we do the next-generation sequencing. The benefit of having a system where you're sequencing tons of patients and following mutations and you're digitizing pathology slides and tracking those as you can begin to correlate these things. All next-generation sequencing has some amount of error. It's just inherent to the process of using [indiscernible] or other sequencing companies where you don't get 100% output when you sequence a patient.

So some percentage of the time. It's a small percent but some percentage of the time you have results that can't be returned to a physician. Being able to digitize the pathology slide, and render insights even when sequencing fails, just makes our tests a little better than somebody else. The fact that we can also render those results in hours makes us a little faster to deliver those insights. So it's the same thing if you look at our immune profile score, we're able to look at lots of different multimodal data we generate, could be digitized pathology slides, could be transcryptomic data from RNA, it could be DNA data and refine what have historically been traditional biomarkers like tumor mutational burden or others. And each of these insights that we can generate, again, not that one of them alone is the reason that a physician would use us, but they just keep stacking up.

And if you look at the foundation model efforts that we're engaged in that's going to do nothing we think but accelerate that dramatically. And so to the extent today that we are x percent better than somebody else because we can generate y percent more insights, you should expect that to grow quite a bit over the next several years.

And then in response to the ASP question. So ASPs in Q4 were around $1,640, that was up about $40 quarter-over-quarter. As you noted in our investor deck and kind of what was discussed at JPM is we think that there's about $500 or greater than $500 of upside to ASP based on the current mix, driven by a couple of factors. One is the continued migration of xT CDx from the LDT version to the FDA-approved version as we previously stated that we planned by the end of 2026 to be exiting with the vast majority of volume on that FDA-approved version. So that's kind of the biggest impact from an ASP perspective.

We also announced that we've submitted our xF, which is our liquid biopsy to the FDA that will unlikely to have much of a '26 impact from ASP, but as we get into '27, we'll start to contribute. And then lastly, there's still some upside from commercial payers as we kind of chip away at those So that's kind of the build -- those three initiatives kind of get you upside of $500 with xT CDx being the biggest driver and that will play out over the course of 2026.

Our next question is from the line of Ryan MacDonald with Needham & Company.

So my question for Eric .You just sort of talked about the foundation model and how that can sort of exponentially sort of sort of help the stacking initiative and sort of development of additional algorithms, different additional modules over time. Can you just give us an update on sort of where things sort of stand on sort of the development of that foundation model? I think you mentioned last quarter, you're hoping to have the first version of the model ready in the first quarter at '26 here. And so just curious if you -- if that's launched yet, how it's performing relative to expectations?

And then for Jim, just curious in terms of -- you talked about ASPs just now, but just curious how you're thinking about sort of underlying assumptions for volume growth across Oncology, in particular, but Hereditary as well in the '26 guide?

Yes, I'll start. So the foundation model had a deliverable in Q1 where we had to hit certain benchmarks that AstraZeneca had established. We think we've hit all these benchmarks. We've submitted all that to AZ. They're testing the model now, but we feel great about the model's performance, and so those efforts will go on.

We've also -- that was a particular cluster we set up of about a little over 1,000, H200s dedicated to that Oncology foundation model. We've also procured a second cluster of more than 500, GB200. So in terms of actual compute power, it's greater than the first cluster and we're running additional models internal, not just in Oncology, but across all of our data because we have [indiscernible] amounts of radiology data and pathology data and cardiology data [ neuropsyche ] and so on and so forth. So we're incredibly long on the value that these models are going to deliver, and we're doubling down on those efforts. We think they'll be catalytic, both to our diagnostic business as we keep dropping insights into our tests that make our tests smarter and better than others. And so that should be an accelerant to growth. And then we also intend to propagate these insights through our Data business so that our clients get more value.

Yes. And then in terms of the kind of underlying volume assumptions in the Diagnostics business. Oncology, as Eric mentioned, had 29% growth in Q4. The first quarter is off to a good start, and so we're not seeing kind of that pace slow down. On the Hereditary side, volume growth was 23% in Q4. As we've talked about previously, what we highlighted in the letter is we do anticipate that continuing to moderate as we lap some of the share gains that they had. There likely will also be some lumpiness in the Hereditary growth rates in 2026. And so in the letter, we've called out kind of this high teens longer-term growth rates. It might be a little bit lower in Q1 and then kind of pick up throughout the year, there will probably will be some lumpiness what we think that, that high teens is still achievable.

So Oncology, again, we feel really strong about where the Oncology business is at. And on the Hereditary side, again, it was -- we were anticipating some of the slowdown giving lapping of some of the share gains.

Our next question comes from the line of Mark Massaro with BTIG.

Congrats on a strong year. So I think in your letter, you talked about MRD volumes came in around 4,700 tests in the quarter. And I think you made a reference that level could have been 20x higher if the entire sales force had been selling it. So am I understanding it correctly that approximately 5% of your sales force was selling MRD in Q4? And is the right way to interpret this that if everyone sold it, it could be 20x higher in the Q4? Or is the 20x higher or more of an aspirational longer-term outlook?

Yes. I mean -- so the -- obviously, it's just -- it's a hypothetical, so it's impossible to say what could have happened in Q1. We were simply highlighting the really unbelievable strength of our MRD offering on the main vector being we ran 4,700 tests. It's 56% quarter-over-quarter, not year-over-year, quarter-over-quarter growth, and we are highly containing this effort. And so yes, we have a very small percentage of our cumulative sales force that is currently selling MRD. And if we were to let everyone sell it and completely unblock it, it could be 20x higher.

So now whether it would be, obviously, you only know that when you unblock it. But our point is, the growth is just really amazingly strong with a highly constrained sales effort. Now we will eventually unblock that sales effort. It's a function of reimbursement. It's a function of the appropriate timing but we intend to, over time, un-gate this and be in market fully. It took other companies that have really strong reimbursement like, for example, [ Natera ], it took them quite some time to establish coverage in a broad enough way that this made financial sense. We're fortunate that we can kind of un-gate this in an intelligent, appropriate manner, and not kind of re-financial habit.

But what we are calling out is when you look at our Diagnostic platform, which is obviously broadly connected for Hereditary profiling, broadly connected for therapy selection, both in solid tumor profiling and liquid biopsy when you look at the number of EHR connections we have and the number of integrations we have and the number of feet we have on the street deeply embedded in the workflows of such a large percentage of the U.S. oncology market. I would suspect when we un-gate this, we will become a very large MRD supplier.

Your next question comes from the line of Kyle Nixon with Canaccord Genuity.

It's [indiscernible] on for Kyle Nixon. So just taking a step back in oncology, I was wondering if you could provide a bit of more clarity on your tests. You've recently touched on ASP and volume dynamics, but could you just kind of walk through what each main growth drivers will be for xT, xR, xS xH, and xE in 2026 and beyond?

Not to answer that question. It's a fairly broad question. But I would say in the letter, we call out, I think our main platform advantage that provide -- that has been kind of fueling our growth. That platform advantage -- the fact that we can textualize diagnostics, the fact that we're marrying clinical data with molecular data, the fact that we have such a comprehensive profile, the insights we can generate by virtue of that, that advantage exists in xT which is our DNA profiling. It exists in xR, which is our RNA profiling. It exists in xF, which are our liquid biopsies. It exists in xH, which is our heme offering, which, by the way, we have a whole genome heme offering that goes live this year, and it exists in xE, which are [indiscernible] offering. So it's across our entire platform.

So it's not as if we've got like one driver driving DNA and another driver driving RNA, our core technology advantage drives the growth of all five of those assays.

The other thing I would add is the market itself is also growing. And so amongst our peers, everyone is experiencing kind of healthy growth rate. So clearly, sequencing is becoming more prevalent amongst our ordering physicians and ultimately, patients. And as Eric noted, kind of that data advantage is what allows us to kind of capture additional market share.

Yes. With us, obviously, in solid tumor growing. It looks like at this point faster than others.

Our next question comes from the line of Casey Woodring with JPMorgan.

Can you walk us through what the guidance embeds for Data and Services revenue in 2026? I know that you pointed to $350 million of current TCV being tied to '26. So can you just talk about the visibility in your bookings to get to that guide and the timing around that?

And then maybe as just a follow-up, can you split out the guide of the 40% growth you're assuming in Data in 1Q? Maybe just talk through how that will shake out across Insights and Trials? And any contribution embedded from the current foundation model with Pathos and AstraZeneca?

So the -- as we called out, I think, during the JPM conference, the bookings have been so strong that we start the year with greater visibility into the 2026 revenue build than we've ever had by a long shot. So we called out, again, at JPM that it is normal for us to generate about $100 million of revenue within a given year from bookings in the year, meaning we -- somebody wanted data and we delivered it within the year. So the fact that we have such a high percentage of our revenue already committed for 2026 means that we expect to be doing our best to manage the growth of the Data business because it has just such systemic growth drivers going into 2026. And that's directly a correlation between bookings and revenue. We just have greater than $1.1 billion in the tank. A bunch of that applies to 2026.

And so we just are starting the year super strong, and we just have got crazy amounts of demand, for our Data products, feels like we are in a unique spot in that we're just we're pulling further and further away from the competition. So that business just is having a moment. And as it relates to like how the rest of it stacks up the vast majority of our Data and Apps is Data Licensing. It represents the biggest chunk of it. And so everything else is kind of relatively small, but it all adds up to the other piece, and that's our clinical trial matching business time, our care gap product called Next and a few of the ancillary products, that are connected to that.

Our next question comes from the line of Douglas Schenkel with Wolfe Research.

This is Colleen on for Doug. So you delivered high 20s clinical Oncology volume growth exiting 2025. How should we think about the durability of that volume growth into this year and next? And how should we be thinking about liquid versus tissue CGP growth throughout this year? Any color you can share on repeat testing with xF and if we should continue to think about xF being about 25% of total clinical Oncology volume going forward?

Yes. So I'll start and then, Eric, you can chime in. So in terms of the 29% growth and how that stacks up, again, as I mentioned before, kind of we're off to a good start in first quarter here. And so we don't see a massive slowdown in the Oncology growth rates. Obviously, you're getting to larger and larger scale. So there -- it's tough to continue growing at the same rate, but we're off to a good start.

In terms of the breakdown, outside of MRD growing dramatically faster than the rest of the portfolio, we see strong growth across both xE and xF. xF may be growing slightly faster than solid but not -- there's not a dramatic variation there. And we don't see kind of that trend -- that's a trend that we've seen for quite some time. So we don't see a lot of variation in kind of the product mix as we like in '26.

Yes. And I mean I'll just add to that, like, so we -- our guidance implies 25% growth year-over-year. So we've called out that our Hereditary business is growing slower. It's kind of like for the year, we think kind of high-teens, mid-to-high teens or on that range. And we have a few other businesses that we historically have called out are also not growing. Like, for example, we deemphasized -- we have a relatively small, but maybe $20 million [indiscernible] business that we don't spend time on, things of that nature. So you have a little bit of revenue -- with a decent of revenue, that's growing kind of significantly less than 25%, which means that our Data business and our core Oncology Diagnostic business are growing 30-plus percent, right?

And just as we told the world, we expect it to grow at 30% or so last year in our core business and ended up growing at 33%, I suspect something similar this year, right? You can -- if you do the math, we'll be growing in the roughly 30% range plus in those businesses. So they're super healthy. Liquid is growing a little faster than solid. So that's been a long-term trend for us. We don't disclose breakdown of each and so on and so forth, but liquid is growing a little faster. Both are super healthy. And there's just no one driver of the growth. It's not as if like -- it's not as if repetitive testing or concurrent testing or this kind of testing or that kind of testing are having an outsized impact. We're just seeing really good solid growth. We're seeing really good liquid growth slightly better, but that's been a long-term trend. And more and more people want the benefits of tumor normal profiling. More and more people want the benefits of DNA and RNA. More and more people want our connected platform that's intelligent. So our unit growth in Oncology is really strong and showing no signs of slowing down.

Next question comes from the line of Andrew Brackmann with William Blair.

Eric, it's sort of been just over a year since you closed on Ambry. And if we sort of go back to when that acquisition was announced, sort of a big part of that thesis was really around the data that you would be able to sort of generate across the entire sort of Oncology testing spectrum. So can you maybe just sort of talk about the acquisition in that lens now that it's sort of been a part of the company for some time. Just sort of what you're seeing in that regard and how that's led to growth in the Data business throughout?

Yes. I think we called out multiple reasons to acquire Ambry. The first by far was to broaden the comprehensive nature of our testing compendium. So if someone said to me, why did you buy Ambry, I wouldn't say data. I would say the #1 reason with Ambry was, they had a very strong hereditary profile. More and more of our clients want a comprehensive solution. I think we said this historically, I very much believe that over the long term, when it comes to treating cancer patients, it's going to be like e-commerce shoppers going online. I don't go to one e-commerce site for books and another for clothes and another for consumer electronics and so on and so forth. I go to Amazon because they have kind of everything I need in one place. And I believe it's going to be the case as it relates to sequencing.

So more and more providers want somebody who can help them manage risk, help them treat patients once they've been diagnosed and help them monitor those patients post treatment. And so we want to have a broad menu. That said, we're also seeing a trend of more and more of our provider partners and certainly, obviously, to the benefit of patients wanting to contribute the identified data to platforms like ours to be used to accelerate research and to accelerate drug discovery and development. This is a very big trend that I don't see stopping. We still have 600,000 people here that are diagnosed of cancer. We're not making nearly enough progress as it relates to eradicating that. And so I think you're seeing a movement among institutions that are saying we need to help stop the waste to make sure these patients get better drugs and get them in market faster. And so we see that as a constant movement and a benefit of the kind of data we collect and then on the identified basis take market.

Our next question comes from the line of Dan Brennan with TD Cohen.

Sorry about that, Eric. Just maybe one on MRD and on Insights. Just on MRD. Any update on the first-gen CRC assay? I know it's sitting at [indiscernible]. Just any color there?

And on the next-gen tumor-naive assay, have you guys discussed kind of what type of performance advantage you would expect to get out of that? Obviously, you're filing for 2 tumor types this year, and then you have another 2 that you mentioned in the letter. Just wondering what kind of performance enhancements do you think that could offer versus the existing tumor [indiscernible] landscape?

Yes. So we're back and forth with [indiscernible] now. We didn't call out when that gets resolved, I mean -- because I don't control [indiscernible]. I mean, so it's possible that we have reimbursement in the month, and it's possible it takes longer. I don't have any idea. We also didn't call it out because it's just not that relevant to our current MRD offering, which is, I don't know, like 95% tumor informed, because we're largely in market with a tumor-informed product in partnership with Personalis, it just represents the vast majority of our current market penetration.

And this particular product, which I do expect to be reimbursed by [ MolDx ] is just not going to be a needle mover because -- what the goalposts keep moving, and so what's happening is tumor-naive products have to keep getting better and better to really compete.

In CRC, I think it's going to be quite some time before you have a significant amount of the volume moving away from tumor-informed to tumor-naive. So I think the naive market is either episodic or it's for those patients where, for whatever reason, they can't get tissue, but tissue is pretty pervasive in colorectal cancer. And so I think tumor-informed wins the day in that subtype for a while. There are other subtypes, like, for example, lung, where tissue is more spars, where I think tumor-naive products can do quite well. We realized that we just weren't getting the performance off the first version of our assay. So instead of like -- we're running a ton of studies to collect samples, I mean a ton. And so we decided to kind of pivot and begin working on the second generation of assay instead of like burning those very precious study samples on an old version, we wanted to move to the new version. So we kind of pivoted and we were fortunate. Everything we do take into consideration this notion of like a comprehensive portfolio. So we were fortunate that we had a tumor-naive product that was just kind of doing super well in the market, more than giving us for volume that we candidly want or need. And so we didn't have to kind of overly push on the accelerator for tumor naive. But the second version is coming along well, and I suspect at some point, we'll have a really nice assay in market.

Next question comes from the line of Mark Schappel with Loop Capital Markets.

Eric, the start of the year is typically when companies adjust their sales organizations and their go-to-market strategies. First, if you could just give us an idea whether you've implemented any meaningful changes to the sales org this year?

And then as a follow-up, maybe you could just sketch out what you believe are the firm's kind of key investment priorities for the coming year?

In terms of the sales force, we've made no big moves to reorg sales force. We did that, obviously, early 205, and we announced the impact of that. And so the good news is we've -- we're long lapping that. And in terms of our priorities, they remain the same to bring the benefits of technology and AI broadly to Diagnostics and make sure that every decision -- whether that's a decision in clinic or a decision for research is data-driven. And I would expect us, given that we're growing so rapidly and our business accelerating, I would expect us to stay the course.

Our final question comes from the line of Bradley Bowers with Mizuho.

Just wanted to get into some of the ASP outlook and maybe the gross margin implication. Obviously, a lot of upside here with the 2,200 test outlook. Just wanted to kind of hear about what the expectations should be for gross margin. It assumes a big lift. I mean, if you assume that the costs hold and do some math, it kind of gets towards gross margin in the genomics side of 70% plus. What could you say about that progression? And is that one-to-one with ASP? Or are there some offsets we should be considering?

Yes. I think -- obviously, any time ASP increases that would lead to an increase in gross margin. I think our -- we've long kind of taken the approach that as ASP kind of increase or as cost of sequencing come down to reassess kind of how much you kind of increase the size of panels to generate more data, obviously, that's great for patients and great for doctors and all that.

And so that's something that we do on kind of on an annual basis. We're not -- given the fact that we have kind of these 2 businesses, Diagnostics and Data, we're less reliant on maximizing gross profit within the Diagnostics kind of product line as some of our peers may be. That said, as ASPs increase, we would anticipate seeing some increases in gross profit, but always balancing to make sure that we're bringing to the market the broadest panels possible because it obviously has a bunch of downstream implications.

There are no further questions in queue. I will now hand the call back over to Liz Krutoholow for closing remarks.

Thank you all for joining us today. We look forward to updating you again next quarter. Have a great day.

Thank you again for joining us today. This does conclude today's conference call. You may now disconnect.

All right. Great. Welcome, everybody. I'm Casey Woodring from the Life Science Tools and Diagnostics team here at JPMorgan. Welcome to our conference. Pleased to be joined by Tempus AI, CEO, Eric Lefkofsky. We'll go through the corporate presentation, then leave time for the Q&A afterwards. Eric, all yours.

Thank you. Welcome. There's seats up here -- for people in the back, but by all means, feel free to walk up. So I'll try to give you a little context of what we've been building at Tempus and a little bit about where we're at now.

So 10 years ago, we started Tempus to solve a single problem, could AI-enabled diagnostics unlock precision medicine. Essentially, could we use diagnostics as a vehicle to make precision medicine as opposed to targeted medicine a reality. In order to do so, you really need two things. You need access to vast amounts of proprietary data to train models and uncover insights and you need a distribution system to deliver those insights to the hands of physicians and patients. Most people have failed historically at having one or both of those.

We have spent the last 10 years building both. We've built up a dataset that is now quite substantial. And equally importantly, we built up distribution capabilities to take those insights and distribute them to tens of thousands of physicians across the country, reaching millions of patients to advance therapy selection, what therapeutic path should my patient be on; to advance clinical trial matching, is there a trial that they're eligible for and ultimately to advance research and drug discovery.

The idea we had 10 years ago was that in order to bring AI to health care, you had to start somewhere, and we thought you should start with diagnostics. It's the only external data modality that physicians interact with all day long. So you either -- if you want to bring AI to the health care system, you either ask doctors to go somewhere, you take systems like Epic and make them AI-enabled, or you have to find some alternative pathway given that physicians order laboratory test results and routine diagnostics when they make almost every major decision, we thought if you could wrap AI around the diagnostic itself, you could kind of infuse the benefits of technology into health care.

And so the platform we built is now broadly connected to about 5,000 -- more than 5,000 providers across the United States. To put that in perspective, it's probably 2/3 or so of the United States who are connected to Tempus in some way, shape or form. They're either ordering our tests or using our technology to match their patients to clinical trials or close care gaps, things of that nature.

The scale of the platform is unique in that we touch more than 2/3 of all academic medical centers, more than 55% of all oncologists, 7,000 of which are regularly ordering our tests, interacting with our products. And the dataset we've amassed as a result of that is more than 400 petabytes of rich, multimodal health care data.

When I say multimodal, what I'm referring to is phenotypic, morphologic and molecular data. So think of it as text-image in molecules at scale. So who's this patient? What drugs are they taking? How are they responding? And what's their molecular composition? Understanding all of those and critically understanding all of those is necessary to advance AI. Everything else is just a point solution. It will solve part of the problem but not solve all the problem.

We've been focused on amassing this dataset. We had a theory that it was crazy that people would try to do research, 10 years ago when I started Tempus, building these very small and expensive datasets, writing grants, raising money, trying to procure samples in small denominations, sending them off to places like the Broad to be sequenced. And as a result, you amass 50 or 100 patients worth of data over a year or 2.

And it was crazy to me that at the time people were sequencing patients at scale, but that data was lost. It either wasn't being handed back to hospitals by the companies that were sequencing, or it wasn't being matched with other critical data, like what drug is that patient on and how are they responding? What's their longitudinal journey? It wasn't being matched with other key datasets like pathology slides or radiology scans. And as a result, it couldn't be used to advance research.

We started focusing on building these large datasets that could be de-identified and used broadly. And as we sit here today, some 10 years later, we've amassed a dataset that's truly unique. It spans over 45 million patients, over 8 million have digitized imaging records that are annotated, we've sequenced over 4 million samples. And at the very bottom of this funnel, there's more than 350,000 records that have rich molecular data, rich genomic data, rich transcriptomic data, rich imaging data, rich clinical data and that dataset really powers the majority of our data business today, which operates at scale.

The idea of marrying these two concepts of taking clinical diagnostics and allowing them to power research datasets is still in place today. The network effects that result from these two is pretty profound. The more patients we sequence, the more data we collect, which allows us to provide additional insights, further enhancing our businesses and allowing us to collect more data. That flywheel that began to kind of turn 6 or 7 years ago is now in full flight. And as you can see, we're not just collecting some data, we're really collecting the totality of data that's necessary to figure out if a patient is on the right drug or the right trial or how I would advance drug discovery development.

If you think about the problem that we have been solving for the past 10 years, it really exists on two levels. The first is you have to acquire enormous amounts of data. We have this dataset that now is unique and yet it still is not at appropriate scale. So we will continue to amass a lot of data over the next several years as we really try to get a significant percentage of the U.S. market data in our hands.

The second is you have to build a platform that allows you to make sense of the data. Otherwise, you just have exabytes of data. And to make sense of the data, you need a platform that is sustainable. You have to be able to invest in harmonizing and structuring and making sense of this data and generating insights.

So we effectively had to clear two hurdles. We had to find a way to amass lots of data and find a way to do so without burning tons of cash. And what we're most proud over the last 10 years is not only that we built this dataset and touched so many patients, but that we now are completely self-sustaining. We generate positive EBITDA -- adjusted EBITDA. We have great momentum in terms of our financial performance. And so the machine is both humming and sustainable.

To think about these two businesses and their integration, you have to understand the two businesses in isolation. So we'll start with diagnostics and then get to what we call data and applications. Our diagnostic business consists of us running tests broadly in oncology and other disease areas and billing insurance. We are effectively a provider similar to companies like Labcorp or Quest or similar to the other sequencing companies that we work with or that we compete against, namely Guardant and Caris and Natera and folks like that. We run these tests, we bill insurance, we get paid, but in our case, we're connected to those hospital systems, bringing in data and so our tests are getting smarter. The system is getting smarter. So we're contextualizing these tests, and we're learning and we're producing as an offshoot of those diagnostic tests vast amounts of data. That data gets de-identified, and we license it pretty broadly.

So let's start with diagnostics. Our diagnostic business today is made up of really two main components: a genomics business and a genetics business. We essentially span the entire cycle from understanding hereditary risk, who's going to get cancer or who's going to get a particular disease to how do I treat that person once they've been diagnosed with the disease, whether that's through solid tumor profiling or liquid biopsy. And then ultimately, how do I monitor that patient post-treatment to see if their disease has come back. So Tempus is unique in that it spans the entire spectrum. We are just as good in almost every part of that continuum.

In terms of hereditary risk, we acquired a company called Ambry, which is where we do the majority of our germline testing, and we operate at significant scale. I think we're the largest player in that market. In treatment selection, we're unique in that we are just as good at solid tumor profiling as we are at liquid biopsy. And both of those businesses continue to grow and growth is accelerating. We do a variety of other testing, in particular, a whole litany of targeted tests or algorithmic tests. And then we recently launched both a tumor-naive and tumor-informed MRD product that's also gaining traction.

Behind -- the backbone behind all of this is something we don't spend a lot of time talking about. We don't tend to overly emphasize studies we're running or readouts that will occur, but we have significant technical capabilities. We have about 700 software engineers, about 400 PhDs, 50 to 100 MDs across the entire organization. So it's a fairly large technical team that allows us to do best-in-class research, publish that research and ultimately, protect the intellectual property. We operate at a scale that's pretty extraordinary as it relates to how science is applied across our platform.

This has resulted in us being kind of first to market with a whole litany of insights. We wrote seminal papers in terms of the benefits of concurrent testing, seminal papers in terms of how incidental germline findings influence care and seminal papers on the benefits of transcriptomic profiling, RNA profiling and how it enhances fusion detection. These papers were first and -- often first in the market and led to a change of behavior across the field, and we're proud of that fact. And we intend to continue that as we move forward. It's one of the reasons that our platform is not just growing but growing faster than most others.

If you look at the output of that growth, we run over 800,000 clinical tests in 2025 and had 28% growth rate in Q4. Our oncology business is growing quite rapidly and has actually accelerated in growth over the last 3 quarters. And our hereditary business maintains really strong growth rates despite some concern that, that business wouldn't -- would not bring such high growth.

In addition to our unit growth being best-in-class, we also have rising ASPs. And our ASPs from our perspective, we have really good tailwind that we expect to materialize over the next several years. In particular, as we migrate from our LDT version of our solid tumor assay to our FDA-approved version, it comes with enhanced reimbursement through our ADLT offering. We're in the midst of getting approval for our liquid biopsy assay from the FDA, that will come with enhanced reimbursement. Commercial coverage continues to go up every quarter as more and more people cover our tests. And as a result, we expect our ASP to rise from $1,630, which is what it was this quarter, to somewhere in the $2,200 range over the next several years.

One of the best parts of Tempus is the strength of our business. Even with ASP that's dramatically lower than our competitors, that will rise over time as a function of reimbursement just getting better, we still operate with margins in the mid-60s, I think, across the business, in large part because we're highly efficient. So our genomic business still has high margins, and our data business has even better margins. So we have significant leverage in the business, high growth and leverage even with lower ASPs than others, which means we're well protected and likely to get significant upside.

Switching to the data business for a minute. We have a large and growing data business. When we started Tempus, most people thought our data business would never get to $10 million in revenue. I think we did $316 million last year with the business growing at about 30%. And so you do the math, we're operating at significant scale. And the data -- our data business is really both accelerating and pulling ahead of others. We just have never seen greater strength in this business. We've never been better positioned. And in large part, it's because people are more and more realizing how instrumental the kind of datasets we can build are to their entire discovery and development platform.

Biopharma uses our data across the board. They use it for trial design, for figuring out how to enroll patients, figuring out what companion diagnostics they should consider, which targets they should go after, what kind of evidence they need to generate to get regulatory approval, how to design Phase IIs and so on and so forth. And I suspect that this will kind of have these moments where as we begin licensing data to companies, typically, they'll start with a small amount of data and then license a bit more data then maybe more for several years. And at some point, they realized that this data is just too instrumental to their programs to not have within their environment at scale. And so that's typically we move from a 1- or 2- or 3-year deal to a much larger multiyear agreement.

What people don't realize is you can license on file from Tempus. There's no minimum. So the fact that people sign $100 million multiyear deals is only a function of the fact that they want access and they want a discount. Otherwise, they could license one file at a time.

Our data business, I mentioned operates at scale. We work with 19 of the 20 largest pharmaceutical companies, over 250 biotechs. We've signed licensing agreements for data that are north of $2 billion over the last several years, which is pretty extraordinary. We had $316 million of data revenue last year, 31% growth. We have partnerships with numerous companies, AZ, GSK, BMS, Pfizer, Novartis, Merck, so on and so forth. And we've delivered over 8 million de-identified patient records to biopharma to advance drug discovery and development. When you think about people running trials with 50 patients, 100 patients, 300 patients, 500 patients, the fact that we've delivered over 8 million de-identified patient records to advance discovery and development is pretty extraordinary.

We announced last night that we have over $1.1 billion of total contract value in place. So this essentially is the -- I think most people think of this like a bookings metric. It's the total value of the data licenses we have under contract to deliver in future periods. There's a small amount of this that's opt-ins, but the majority of it is just contracted revenue where people have -- they're just licensing our data and have to fulfill their contract terms. We don't include milestones or bio bucks or any weird number in this. This is just real money, real cash.

We also disclosed that our net revenue retention, which is the second metric we look at related to our data business, was 126%. The way to think about this is like same-store sales. So this is effectively if a client was licensing $10 million of data last year, how much are they licensing this year. That went up by -- to 126%. So in this case, they will be licensing $12.6 million. So it speaks to the strength of the -- the enduring strength of the data business as people consistently year after year license more data and renew contracts.

We also tried to give a breakdown of how this TCV relates to our forward bookings. As I mentioned, we've never been in a better spot in terms of visibility to our performance next year. We have $350 million of TCV related to 2026, which is an extraordinarily -- amount given that it's not atypical for us to generate about 1/3 of our data revenue within the year. We have 100 salespeople selling these products. There's needs all the time. People come to us and say, "I'm looking for this data or this analytic product." And as a result, that gets recognized within the year. So our -- one of our biggest challenges going into 2026 is how do we control the growth of this business that is in just an incredibly strong spot.

Switching for a second to algos, which is our third product line, the -- one business is, we call it diagnostics; the other, we call and applications, applications, which is the one is made up of a series of algorithmic diagnostics or algos that help route patients to the right therapy, route patients to the right trial or make some kind of prediction. And that's our applications business. It operates at scale in that we have algos deployed across a significant population, but it operates at very low revenue because essentially, there's minimal reimbursement in this category.

So even though we have these AI algorithms clinically at scale, revenues are quite small. So we think long term, that changes and this becomes quite a large business, but today, it's operating in a small state.

As I mentioned, it's made up of predominantly three product lines. Matching patients to trials, closing care gaps or producing algorithmic insights. Our trial-matching business, we call TIME. This is essentially, we're scanning clinical data in real time, identifying patients who are a perfect fit for a trial based on inclusion and exclusion criteria and then matching them to that trial. Again, operates at significant scale. We find and enroll a huge number of patients, small revenue.

Our NEXT product closes care gaps. Same thing, it's reading clinical data in real time. It's finding patients that have fallen through some very clear care gap. They're a non-small cell lung cancer patient, it's NCCN guideline, they be tested for EGFR. They weren't tested for EGFR. The provider thought they were or made some kind of mistake, we want to catch that and ensure that no patient falls behind, and so we close that care gap. Operates at significant scale, very low revenue.

Same thing in our algos business. This is us essentially running an algorithm maybe on a pathology slide, a radiology scan and producing some kind of insight, typically unpaid for.

We expect that over time, AI will get paid for. We can't -- I can't think of any other way that we avoid cataclysm in this country besides bringing AI to health care, given the rising costs. And so as it gets paid for, companies like ours that happen to have broad distribution of AI, in theory, should be beneficiaries of that. So we tried to show in this investor deck one use case of that, which is our -- in addition to oncology, we operate in other disease areas, radiology, pathology, neuropsych and cardiology, we showed one use case for cardiology, which is essentially we developed a series of algorithms based on 12-lead electrocardiograms. We developed one to predict undiagnosed atrial fibrillation and one to predict undiagnosed low ejection fraction. We have several others in flight with the FDA now, and we expect to have a whole suite of these things in market.

These algorithms are deployed, again, as I mentioned, at scale. So we can -- we could have 50 different providers that have these algorithms deployed, touching thousands of physicians and millions of patients. So the impact is large, but the revenue at the present moment is small. One of these algorithms, these -- our ECG algorithms recently got reimbursement from CMS. It got a CPT code and approved reimbursement at $128 per algorithm run.

So we've just begun. We're in the early stages of rolling this out with provider partners. We announced Northwestern Medicine, whose leadership happens to be 6-feet in front of me a few minutes -- a few weeks ago. And this is just one example of rolling these algorithms out to world-class providers who are starting to say, "Hey, it's not okay that I have 3% of patients who get an ECG, are told they're fine and yet have a heart attack or stroke within a year. If I can use technology to predict that, I want to predict it." And so as these things get rolled out, we expect they will scale, including financially given the reimbursement codes that now exist.

To put this in perspective, just this one algorithm, this one ECG-based AFib algorithm, if it were rolled out at scale, would produce hundreds of millions of dollars of revenue. And I would suspect over time, some company, I think it will be Tempus, but some companies will have dozens or hundreds of these things operating, and you can kind of do the math.

Even though that product line is small, AI is at the center of everything we do. It literally touches every product we have. It's instrumental to every product that we have built. It's integrated in our diagnostic pipeline. People consistently ask, how are you growing so fast? How is it possible that you have best-in-class unit growth rates? And the answer is -- we've been saying for a long time is we have the most comprehensive and technology-advanced platform. It helps doctors make decisions, and so they use it. They use it regularly. And AI is embedded into that entire product suite. Plain and simple, we make sure patients are on the right therapy and the right trial more often than others. And as a result, physicians increasingly are coming to our platform.

In addition to that, AI is embedded throughout our data business, and in particular, we announced about 7 or 8 months ago, we've begun building the largest multimodal foundation model in oncology in partnership with AstraZeneca and Pathos. This is a large multimodal model. We procured a compute cluster of 1,000 H200s, which is being leveraged for this model. We've begun building a second model using a cluster of 524 GB200s. So for those that are thinking about AI, these are very large compute infrastructures now leveraging our 400-plus petabytes of data to build very large multimodal models, which we assume will be catalytic both to our diagnostic business and to our data business.

And this is largely just happening in oncology in the United States. We are in these other disease areas, neuropsych, cardio, radiology, rare disease and digital path, but they're still quite small by percent of revenue, I don't know the number, but it's unbelievably high, the percent of our revenue that's U.S.-based and oncology-based. And I would suspect over time that won't be true. If we fast forward a decade from now, I would imagine that Tempus will have meaningful revenue in cardiology, in I&I, in neuro, so on and so forth and in other markets outside of the United States.

Really quickly, just in terms of updates. We did $1.27 billion last year. It was ahead of our guidance. The growth rate was fairly extraordinary given the acquisition of Ambry, but even without Ambry, it's about a 30% growth rate of our core business, again, best-in-class. Our data business grew even faster in Q4. It was 68% growth, I think when you take out the onetime AZ warrant in Q4 of 2025. So diagnostic business growing well, data business growing well. And as a result, that puts us in a great position for our long-term guidance. We've told the world we expect to generate a 25% growth over the next 3 years. That would equate to about $1.59 billion this year.

And even though we could generate lots of EBITDA, given the growth of our gross profit dollars, given that we're early in our growth cycle, we're investing 2/3 of those back in the business, so we should generate about $65 million of adjusted EBITDA this year, which is an improvement of maybe $100 million, $150 million. So not small.

On that note, [ we'll move ] to the questions.

All right. Great. Yes, I guess maybe to start the Q&A session. I have a few on the preannouncement. So you preannounced the top line beat, driven really by diagnostics. So can you just walk through the performance in that business in the quarter? How should we think about volume growth across xT, xR and xF? I think historically, xF has been growing a little faster than xR and xT. So just curious if that was the case in the quarter.

Do I have to push this thing or does it work?

I think you're good.

I'm good?

Yes.

The -- we had significant growth across all of our therapy selection assays. Our liquid product did well, our solid product did well. We didn't have one particular area that carried the majority of the growth. It was really quite diffuse.

Liquid is growing slightly faster than solid, I think, across the board for a variety of reasons. But at the end of the day, we're lucky that we have kind of the gold standard of solid tumor profiling. We have a liquid product that people love. And so both are growing.

Of our 28% growth or whatever it was in Q4, a couple of points was related to our MRD product line, but the majority is non-MRD products, which just speaks to the strength of our therapy selection assays.

And oncology ASPs came in above our expectations. Can you talk about what drove the strength there? Was this largely from the xT CDx migration? And then I guess on the latter point, where are you in terms of the portion of xT tests that are being converted to the CDx version relative to your 40% goal by the end of the year?

Yes. We haven't disclosed the exact percentage. I mean, obviously, we put out kind of flash numbers for JPMorgan and the full numbers come out when we file the 10-K. And -- but there wasn't -- we continue to be up and to the right in terms of adoption of our xT CDx assay, but nothing material. We're fortunate that we have a business that has, as I showed in that slide, really significant ASP tailwinds.

And if we -- our concern in trying to jam those through too quickly is that all it does is create accelerated growth that you have to lap at some future period, same with our data business, which is like on fire. So we think a lot about sustained growth over long periods of time as opposed to like accelerated growth that you have to lap. So -- whereas other companies might be trying to like grow even faster and then have these like very wonky 30% growth down to 15%, we believe it's more prudent to just be slow. So I suspect ASPs will be in our favor for quite some time. I suspect you'll see significant improvement over the next several years, but we want that to be every quarter up and to the right instead of lumpy.

No, that makes sense. Maybe moving on to data and services. So 4Q is typically a seasonally strong quarter, and you did see a nice step up there relative to 3Q. Maybe walk us through customer demand trends, including any bookings color, if possible. And what the setup looks like for data in 2026? During the presentation, you noted you have $350 million of the current TCV that relates to 2026. So maybe just talk about what percentage of that you think is locked and loaded and noncancelable, and how should we think about additional bookings on top of that in 2026?

Yes. It's all noncancelable. I mean we don't -- we have opt-ins that are like way out in '28, '29, whatever it is, but the $350 million is essentially the contracts we have that we'll deliver in next year. So I think we have -- we're just at a moment where our data business is kind of firing on all cylinders. And as I mentioned on the podium, our biggest challenge is going to be containing growth.

One of the challenges we have in this space is that a bunch of folks who understand diagnostics don't understand data. And so they just -- they don't know how to think about it or value it and so it can have no value. But in reality, the data business is in many ways, far more durable than the diagnostic business. It's just that people understand the diagnostic business. The data business is made up of an enormous number of clients. You have 19 of the 20 largest pharma companies licensing our data, 250 biotechs. These are lots of contracts across lots of people who have our data embedded across their entire portfolio.

And when I say embedded, what that means is that people have downloaded our data. They've brought that data into their environment, their data warehouse. They have it as a part of multiple tools that they're using internally to make critical R&D decisions. It's likely a part of multiple regulatory filings. And so it's embedded. And that's one of the reasons that people want to sign up for multiyear access and make sure they have discounts because they realize they're going to need this data. They can't just turn it off.

And so we just see our data clients licensing more, buying more, signing up for longer periods of time, making bigger commitments, again, even though they don't have to. And we saw that trend pick up at the end of last year. It continues to pick up. And so the data business is just crazy strong.

Yes. Maybe we can dig into the data business a little bit. A lot of us are health care folks here. So maybe help us understand, first of all, the moving pieces of Tempus' -- it's now greater than $1.1 billion TCV. So there are around $300 million of future opt-ins, as you've talked about. As of the 3Q filing, there was around $360 million of noncancelable performance obligations related to multiyear contracts. So that leaves around $440 million of the remainder, call it. Just help us understand that last piece, whether those bookings are cancelable, your level of visibility into those and what those customers are saying about eventually committing to datasets?

Yes. So the -- as we've disclosed historically, the only amount of our TCV that's cancelable is what we call opt-ins. So we'll say it and people will be like, "Ehh." But like we keep saying it. There's only two things that matter. There's bookings and there's revenue. So at the end of the day, like you can get as lost as you want to get, but in the world, we have, like I sold something and I delivered it and nothing else. So everything ultimately has to basically flush itself out in that. So if our bookings are a certain amount and our revenue is a certain amount and our bookings are a certain amount and our revenue is certain amount, you can kind of back into the strength of both.

So we have $1.1 billion of effectively bookings. There's a certain amount that's opt-in, which we've disclosed, which means the rest of it is contractual and it will deliver.

Even the opt-ins by the way, like we call out that they're opt-ins, but I don't know how they would kind of go away. Like in other words, people might say, "I'm signing up for 5 years of data, and I'm going to pay $100 million, but I want the right to continue in year 6 and year 7." So now I get to the end of 5 years and they have our data embedded across 100 people in their company, 100 tools, 100 projects. If the contract ends, they have to delete all that data, remove it from every server, take it out of their systems to retrain everybody. Like how does that happen?

So we haven't seen anyone ever like leave us, and I don't know how easy it would be to even do so. Like it's not simple. So if you're buying $0.5 million of data, it's easy to say, "I'm done with Tempus." But once you get to the level that we're talking about, these strategic partnerships where people have us embedded across their ecosystem, very hard. Like I don't even know how it would happen. So if you said to me, place a bet on the likelihood these opt-ins become real revenue, I'm like 100:1 any amount of money you want to bet.

Okay. Fair enough. Maybe one more on data. We have -- we've seen other diagnostics companies start focusing on building out data licensing offerings. Are you starting to run into other players in the market in data?

And beyond that, maybe the size of Tempus' data, is the direct data pipeline with hospital is a key advantage for you guys relative to some of the other players that are coming into the space? And do diagnostic peers that run larger panels have an advantage just given the added data that's collected per patient?

Yes. I mean you can see -- so the good news is that -- so the companies we compete against in diagnostics, the good news is that all but one are public. The other one is also public, which is part of Roche. So Caris is public and Guardant is public and Natera is public and we're public, and so you can see it. And so you can see the fact that at least at present, nobody has a real data business, like all -- and if you go back and look 3, 4 years ago of some of the thoughts people had about building a data business, they thought it would be much larger. They thought it would be much larger. I thought it would be much larger.

If you would have said to me 3 years ago how good are your diagnostic competitors going to be in data? I would have said very good. And I would have said it's going to be -- and I think I actually did say this, if someone go back and look at like old [indiscernible], I think it's going to be like we're going to be like AWS, but they're going to be like GCP and Azure. It has not turned into that. It's like we're AWS, GCP and Azure in one company, and they're like half of Oracle or like 1/3 of Oracle. So it's that extreme.

And as a result, if you look at like last year, I don't think I ever heard any one of our sales reps or leadership come to us -- come to me at any point in time ever and say, "We didn't get a data deal, somebody else got it." Zero. So that's extraordinary. And I don't think great. I would much prefer they had better data businesses so we could collectively educate the market on how valuable this date is. But at the present moment, probably because we're a tech company at heart and a diagnostic company second, we've invested enormous amounts of money north -- well north of $1 billion in amassing data and building tools that make that data useful.

And so when somebody gets our data and our tools and somebody else's data in their tools, they're like, "Oh, that's horrible and this I like." And as a result, even people who've signed with other people come to us afterwards and say, "I need your data." In fact, if you look at everyone who has been announced to our competitors over the last 3 or 4 years, all of them are licensing our data. Every one of them. So I think it just speaks to the fact that we've invested in the technology and tools.

Okay. Got you. That's pretty helpful. I guess moving over to genomics in the last few minutes we have here, I have a question just on MRD. When looking at xM's test performance relative to peers, the longitudinal specificity is notably lower at around 90%. Peers are closer to 98%. Has that come up in conversations with MolDX at all? And what gives you confidence that physicians won't prefer to use competing tests with higher specificity and comparable sensitivity?

So it has not come up with MolDX even in the back and forth, but 100% -- I don't have any confidence that they're not going to prefer to use other people's tests. They do, they will, and that is happening today. We have two products in MRD in market. We have a tumor-naive xM test largely in CRC, and we have a tumor-informed product in partnership with Personalis in a bunch of other subtypes. This is directionally right. The vast, vast, vast majority of our volume is tumor-informed. In CRC, it's even more pronounced because you have -- you have enormous amounts of tissue. So why not do tumor-informed.

So I'm not worried that in the back and forth, we could eventually get MolDX reimbursement. I am worried that, that product is not taking off relative to our informed product or other informed products. We could see this like 6 months ago, and I think we talked about it in our last earnings, we began to realize that like even though we could get this thing reimbursed at some point, we're not going to get adoption. And so we worked on a second version of the naive product, which we talked about, I think, last quarter. And we have studies running in many disease types at the present moment, first being non-small cell lung cancer, we'll bring it to CRC quickly now.

That is a better version of that assay. It has significantly improved sensitivity and specificity, and it does compete on the numbers, at least at present more appropriately with assays like Signatera. And I think like reimbursements are relevant. Market adoption, we're going to have to have a better naive product to win the market as I think are others or it's going to be an informed market. So we're investing in both of those.

So we're lucky that our economics relative to our informed product are really good. We make a bunch of money by selling those tests instead of lose a bunch of money. And we're getting adoption. We're seeding the market. We're getting share. And eventually, we'll have to figure out the right portfolio to maximize. But in the near term, it's going to be largely informed.

Okay. Maybe last couple of minutes here. Just looking ahead to 2026, you're pointing to $1.59 billion in revenue. Maybe walk us through the moving pieces there in terms of what you're expecting from contribution from diagnostics versus data and algos? And where would you see the most upside in 2026 between the businesses?

I don't have the breakdown of the exact percentage of diagnostics to date, and we haven't given that yet. We'll give some color, I'm sure, when we announce full earnings. Both businesses are performing super well. We've got really strong unit growth in diagnostics with rising ASP and our data business is crushing it.

I mean I think to the extent there's significant overperformance, I mean our goal that meeting -- like outside of diagnostics, like in the normal IR world, you want to give guidance and slightly beat and slightly raise for a variety of reasons. And so we think that's the right approach. We don't want to give guidance that might be sandbagged and have like massive beats based on cash you collect that you probably knew you were going to collect from 3 quarters ago. We don't do that [ game. ] We try to give guidance that's like relatively intelligent, that's why we give people a number. And in theory, we want to beat and raise that number but in appropriate amounts.

If I give a number and beat it by 20%, it means I'm not great at forecasting. To the extent that happens, and we have a significant beat in '26, it likely will come from either the data business, which we're having a hard time controlling the growth because it's growing that fast or one of these ASP levers, just -- we just can't stop it. Like it just happens, and it creates really a one step-up function in ASP. I would much rather have 25% growth for 3 years then have that happen, but it is possible that we can't contain the growth. But we're very focused on predicting our business conservatively and intelligently and being in a position to beat but not beat in crazy ways.

All right. Well, looks like we'll have to leave it there. Eric, thank you and the Tempus team. Thank you.

Thank you.

Enjoy the rest of the conference, everybody. Thanks for coming.

I'm David Westenberg, the life science tools and diagnostics analyst at Piper. With me today is Tempus AI. I'm happy to have the CFO, Jim Rogers. Liz is in the audience as well, if anyone -- any investor here has any questions here.

So let's just kick it off with the quarter here. Tempus achieved its first positive adjusted EBITDA in Q3. How do you envision profitability trajectory evolving in '26? And what operating levers will be prioritized to sustain the profitability, but while still -- you got to be a growth company too. So...

Yes. Well, first, I appreciate you having us, David, and everybody for joining. Yes. So achieving adjusted EBITDA positive was kind of a long-term goal for us. We said by the time we were 10, we wanted to kind of achieve that, which we did in Q3. And on a go-forward basis, we don't intend on obviously taking left turn and going back to being negative from an adjusted EBITDA standpoint.

So we said in our Q3 letter, the way that we're kind of viewing the world on a go-forward basis is assuming we're growing about 25% a year top line over the next 3 years that we would reinvest about 2/3 of that -- 2/3 of the incremental gross profit dollars back in the business and let 1/3 drop down to adjusted EBITDA. After kind of that 3-year period, that probably flips to 1/3 being reinvested in the business and 2/3 dropping down. But given kind of where we're at in our life cycle of many of our products, we feel that it wouldn't be prudent to kind of maximize adjusted EBITDA in the short term, but we do want to demonstrate continued discipline and improvement in the metric.

Sounds great. Let me maybe talk about your data differentiation relative to competitors. And if you can go at this angle, not just the kind of the AI component but the software business component.

Yes. So from very early on and when we were building out our wet lab capabilities, we felt that it was very important to kind of build these connections into the EHR systems to pull out clinical data on the patients that we were sequencing. We felt like we could provide additional value to physicians by contextualizing the results for those patients. And that now we're connected to over 5,000 institutions. We have over 45 million patient records, and so a very large database.

And so we de-identify that information, and we license that broadly to biopharma. We work with 19 of the to 20 big pharma companies and a couple of hundred biotechs to leverage their data for drug development purposes. So these businesses are very much interconnected. But we think that not only do we want to impact patient care by providing information to physicians, leveraging this data to impact kind of drug development and improving patient care in the future is equally as important.

Got it. Maybe you could talk about that, the biopharma/software data business in a little bit more detail. I think the average investor doesn't have any difficulty in understanding what you do on the CGP front, they have Guardant, they have Caris. I mean, it's -- foundation's been around for years and it used to be public. I do think that they probably have a little bit less understanding on the data services business and how you're differentiated. Can you talk about what you do differently than other CGP companies for pharma in a little bit more detail?

Yes. And so those relationships are typically kind of multiyear subscriptions where they're licensing cohorts of data, raw molecular information tied to longitudinally-updating clinical information for the cohort of data. So our data business actually doesn't really compete with the other big CGP players. You can obviously see their financials and see kind of how large their data businesses are. And so we kind of set ourselves apart and have a fundamentally different business model than many of them do.

And the use cases for this data are; how do you improve clinical trial design and set yourself up for success by leveraging real-world data, again, raw molecular data tied to this longitudinally-updating clinical information, so you know who these patients were, how they were treated and how they responded to those treatments is immensely valuable to pharma.

Got you. Well -- and that makes a lot of sense and particularly in somatic oncology now, germline hereditary cancer is a little bit less established. Can you talk about the acquisition of Ambry and how you integrate the Ambry data into the overall Tempus platform for finding additional data insights?

Yes. So the Ambry transaction, which we announced last year and closed in February of this year, we did that strategically for a couple of different reasons. One is they were a vendor of ours. We actually outsourced our hereditary screening to them. And so we knew the team quite well, and were quite impressed with the product offering that they had. From a data perspective, it allows us to, one, start interacting with patients earlier on in their cancer journey, either newly-diagnosed patients or at-risk patients, where most of our database originally comes with metastatic or later-stage cancer patients. So getting a more longitudinal perspective of these patients from a data perspective is interesting.

In terms of the insights that ultimately will be derived, we're in that process now. We closed the transaction in February. Embedding the hereditary data with ours will be a kind of multiyear effort. So it's not a near-term driver, but one that we are excited about. The other reasons for the deal were they have an offering in rare and undiagnosed disorder. That allows us to kind of move into that space from a testing perspective, but also build out interesting data sets, leveraging our data connections, kind of expanding beyond oncology, both in testing and data.

And then lastly, the asset had been cleaned up. So they were already EBITDA positive. So back to our conversation around kind of path to profitability, for us to do an acquisition that large, we wanted to make sure it didn't result in us kind of taking that left turn that we were trying to avoid.

Yes. Got you. And can you talk about some of the different products that can come out of the germline testing and the Ambry integration? Eric mentioned on the earnings call, a little bit more interest in rare disease. Is there any -- can you talk about all the different growth contributions that could come out of maybe some of the adjacencies of what Ambry does?

Yes. We think the hereditary screening market is going to have a period of growth. And Eric has mentioned this previously as well. People have kind of viewed this market as being very mature and saturated. And while it's probably more mature in areas like oncology, it's less mature in other disease areas. And the Tempus platform can really be applicable to any disease area where there's multiple kind of therapeutic options and a diagnostic test that can be useful. So where genetic sequencing is important, which we believe it will become more and more important in other areas. So they have other offerings in cardiology as well, meaning Ambry does. And so we'll look to continue to expand those as well.

Got you. Well, maybe we can talk about the AstraZeneca and Pathos AI deal. This is a multiyear deal with expected revenue recognition scheduling over multi-year period. Can you talk about both the coming together of that deal and how you expect to recognize revenue over the next few years?

Yes. So AstraZeneca has been kind of one of our longest-standing customers on the data front. So they signed a $320 million deal back in 2021 and then also signed this $200 million deal with Pathos involved this year. And this is kind of the culmination of many of our data collection efforts of -- obviously, AstraZeneca has seen tremendous value in leveraging the data in their portfolio. And so we started having conversations around what if we looked at all of the data, they're still licensing a relatively small subset of our overall database, and could we train a model on the 400 petabytes of data that we've collected over the last 10 years.

Pathos was a company that we obviously have a relationship with but as does AstraZeneca as well. And so it's a $200 million kind of data license to build this model. Ultimately, at the end of it, we all get a copy of the model for use in our respective areas. So AZ and Pathos can use it for drug discovery. We can embed the insights to make our genomic test smarter and also improve our data offering to biopharma. In terms of revenue recognition, it's largely -- the $200 million is largely ratable over the next 3 years.

Got it. Okay. So can you talk about the commercial strategy for Tempus Pixel and the ECG algorithm? What are the projected revenue contributions and margin profile for 2026 from this offering?

Yes. So Pixel and some of the ECG offerings, we have a number of these kind of algorithmic diagnostics that we've created over the years. As we've talked about, there's not really a reimbursement mechanism for many of them. There are some early signs, the cardiology algorithms being one of them, where there are reimbursement in select areas, but it's still very early days. And so when we think about kind of the guide and the contribution for next year, we don't have anything baked in associated with those businesses.

Long term, though, we don't see a reason why these types of technologies would not be reimbursed. They have clinical utility. They're very useful for doctors. And then many times, they're cheaper than the alternatives in terms of either the alternative testing or the adverse events that come without identifying these patients earlier on.

So we don't envision a world where as you zoom out, whatever, 7, 10 years that these things would not be reimbursed. We're fortunate that we have a very strong core business in genomics and data that have allowed us to achieve breakeven from an adjusted EBITDA standpoint, while still making very large investments in these spaces. So while we're doing all of the same things that others are in terms of convincing people that these things should be paid for, we're fortunate that our business model doesn't hinge on them being paid for in 2026. But as we zoom out kind of longer term, we think this will be a significant portion of the business.

Got you. Can you talk about the launch of xT CDx? You've got ADLT status, 14 -- sorry, $4,500 reimbursement, what's been the market uptake since its launch? What's the strategy to drive further adoption in '26 and beyond?

Yes. So xT is our solid tumor DNA test. We got FDA approval and ADLT status, and then we kind of nationally launched the test earlier this year. By the end of Q3, we had about 30% of our solid tumor DNA testing volume had been migrated over to that version. And again, for context, that's a $4,500 price point with Medicare versus the roughly $3,000 we get for the non-ADLT version. And we have plans in 2026 to get the vast, vast majority of that remaining 70% migrated over. So as we exit '26, we would envision, again, most of it being on the ADLT version.

Got it. It's interesting with the reimbursement rates across the industry. I mean we're seeing CGP tests in the high thousands and MRD tests being frequently reimbursed. So I think there's some -- a little bit concern on investors about when exactly CMS could come in and maybe cut prices. Can you just remind investors about the value provided by these tests in oncology and how they can necessarily save the system money and better outcomes for patients, et cetera?

Yes. I mean within therapy selection, obviously, the -- many of these cancer treatments can run in the hundreds of thousands of dollars. And so several thousand dollars for ensuring that we're getting the patient on the right treatment at the right time makes a ton of sense financially. And then with respect to monitoring and MRD, obviously, the sooner that you can identify recurrence and get some beyond treatments relative to the adverse events of not identifying that early on, we think that, that makes a ton of sense.

In terms of kind of overall reimbursement, our average reimbursement is about $1,600. Obviously, David, you know our competitors are significantly higher than that. And so we don't envision a world where the long-term reimbursement trends aren't positive to where we sit here today.

Yes. Very fair point. So one of the interesting things is you're not just oncology, and you have broader ambitions here. So can you talk about some of the newer disease categories in which you could use your data advantage and what the strategy is for expanding into these areas?

Yes. So again, it's kind of disease area by disease area. And we've long said we don't have to run every diagnostic test. So in cardiology, for example, we obviously are looking at the output of of ECGs; in radiology, we're looking at scans; in other areas such as rare, we may actually run the diagnostics. So we look at it disease by disease area.

We also are very thoughtful on the investments that we make in new disease areas. So these aren't $100 million investments. We start these very small to prove out that, one, there's a need that we can provide insights to physicians, but then also that there is a data business that can be -- that can further support kind of additional investment in the area. So we will continue to expand in other disease areas. But again, it's all with that kind of profitability framework that I discussed earlier.

Got you. And then what are the key milestones in '26 for new product launches? What are some of the enhancements that you're working on, both genomics and in the data segment?

Yes. So in genomics, obviously, we said in the Q3 earnings release as well, we're submitting xF, our liquid biopsy test, to the FDA here shortly. We're awaiting reimbursement for xM, our tumor-naive MRD test. And then there's always iterations of assays that occur during the year. And ramping rare within Ambry is another kind of area of focus for us.

On the data side, we've said that the first iteration of the foundation model that we're building would take about a year. So that obviously will come during the year. And then just continuing to build out those capabilities and advance those partnerships with biopharma because, again, it's still very early innings on that side of the house.

Got it. Well, we do have to talk about the things that you ask as CFO. So can you talk about margin progression, GM, whether or not you have still like a NovaSeq X upgrade that you could be doing, whether or not just volume going through the system is going to help you with some of the margin progression? I mean, anything you can give us in terms of reductions in cost of goods sold that we should expect over the next couple of years?

Yes. So within genomics on the -- excluding Ambry, margin expansion comes from increases in ASP, kind of going down that ADLT path that we talked about. We still run the 6000 from Illumina, so we're not on the X at this stage. Obviously, as we launch new versions of the assay, we'll look to migrate over to that. We do have a fleet of those in-house today.

We've long taken the approach of, as costs come down, kind of reinvesting those back into larger panels, eventually getting the whole genome. And so unlike a traditional diagnostic company that is hyper focused on what the genomics margin is, while we should see some expansion, we're okay with it being probably less so than what you would otherwise see if you were only focused on that. If it's generating more data, that leads to improvements on the data or continued growth on the data side.

Data margins are pretty stable. You'll have given quarters where if a new project starts, margins may dip or vice versa. So they've been in the kind of mid-70s range, and we don't see any kind of shift there other than if the AI applications business takes off as we think that it will at some point, that operates at a very high margin. So expansion there would be kind of longer term as that AI application business grows.

Yes. Maybe you could -- that brings up kind of an interesting point because like we hear from investors talking about the justification of price, but you mentioned kind of here, where the cost of goods sold savings just goes into expanding the assays. So I mean, can you just maybe think about that in terms of how ASPs could progress and actually be stable in the fact that it's continuous development. Kind of answered that myself but...

Yes. I mean I think that the ASPs will land where they are, and obviously, you adapt the assays depending on that to maintain a healthy margin. So we're -- our overall margin, we think, is in a healthy spot today, and we continue to evaluate all kind of the moving parts to determine what that long-term margin profile would look like.

Got you. Could you talk about some of the expenses on the OpEx side, priorities for you? Is it -- where are you going to work at? Sales force expansion, R&D? I think you're probably fairly decent on the G&A front, but maybe you have some sort of international expansion or something that you would consider?

Yes, most of the investments are going to come on -- so we have kind of two R&D lines in our P&L, traditional R&D, which will be kind of your wet lab R&D akin to most of our diagnostic peers. And then we have a technology line, which is all of the R&D that kind of goes into the tech platform. Those are the two biggest areas of investment for us. The sales force, it grows as we continue to kind of bring in more volume and you split territories, but there's not a need to kind of have a massive increase in the overall sales force. Almost all the investment is going into those two R&D lines.

Got it. Can you talk about where you see the business in a few years from now in terms of -- just because you brought up the long-term software margins are quite attractive in this kind of business, can you talk about the revenue mix you could see in the future between software and say, wet lab and dry lab?

Yes. I mean I think you have periods where one may grow quicker because you get ASP tailwinds or whatever it is, and that leads to outsized growth maybe in genomics. So you may sign a big data deal and that results in greater growth within the data segment in any given period. We think both genomics and data can be very large businesses. AI application is the one that is kind of the unknown, right? Because that may grow -- it could go from 0 to something very large overnight if reimbursement kicked in for some of these algorithms. But again, as we think about guides and all that, how we think about the world, we don't include these things until we have a clear line of sight of them landing.

So we're doing all the work in the background to have them land. But you could envision a world where if you zoom out 10, 15 years that the AI business is the largest of the three of them. But again, until we have a clear line of sight of that, we don't bake that in.

Got you. Can you talk about the relationship that you have with Personalis, and where you see the mix the market is going between tumor-informed and tumor-naive?

Yes. So we structured an agreement with Personalis last year to be the exclusive distributor of their tumor-informed assay in breast, lung and IO. They just received coverage in breast a few weeks ago. And so we're starting to kind of scale the commercialization of that effort.

The MRD market, we think, is going to play out pretty similarly to how therapy selection played out. You're going to have some doctors that really prefer the sensitivity and specificity of a test like Personalis, and you're going to have other doctors that really prefer the operational ease of a tumor-naive assay. And at some point, the performance of these tests are going to kind of reach some level of parity, no different than they did in therapy selection.

So we felt it was important to have both offerings. Our view has long been to be kind of a one-stop shop and offer everything to physicians because we do think that physicians don't want to be ordering from three or four different providers. They would prefer to have one that's integrated, that is very easy to use, that provides additional insights. And again, we don't have to always run that diagnostic, we can embed that into our platform, such as what we did with Personalis.

And so the relationship has been growing great. We're excited that they finally received coverage in breast, and excited about what that leads to in '26.

Got you. Well, you do have the sales force and the strategic reach that a lot of companies don't have. Do you see yourself having more of these kind of relationships like you do with Personalis to kind of expand the overall offering? And do you think the market is migrating to a kind of a one-stop shop in different oncology tests?

Yes. I mean, ultimately, we think the MRD market will be a natural extension of the therapy selection market. It would be very odd for us to believe that somebody is going to want to use, again, multiple kind of providers. And so we think over time that we'll have a sizable kind of MRD offering.

In terms of other tests, we're constantly evaluating what we think that doctors need by listening to them and having them tell us what they need and see if we can find things that can augment the portfolio. We're also cautious though to not overload our sales force with too many different things to disrupt kind of the core offering. So it's that balance of making sure that we're meeting doctors' needs but maintaining that customer experience that they're using.

Yes. Brings up the point of the apps. Can you talk about some of the different applications that you're seeing in your therapy selection business and how that's been contributing to growth and essentially how you can balance, talking about just therapy selection, but you know what -- knowing tumor -- tumor of unknown primary. I don't know, I think you guys have that one, but...

Yes. I mean ultimately, the same data that our biopharma customers leverage, we leverage internally to see can we identify insights, again, that can make our test smarter. Some of these get -- turn into what we call algos, which are basically add-ons to a genomic sequencing test that somebody can order. And oftentimes are built on some combination of molecular and clinical data. So we have a handful of these in market today. You mentioned a tumor of unknown origin. We have another one called IPS, which is immuno-profiling score.

Again, doctors are overwhelmed with the amount of information that they're being provided. They're obviously seeing a bunch of patients. And so if we have access to this information to the extent that we can provide insight by having these available to doctors, then we make them available. And the uptick has been great. It's not an insignificant amount of orders that are adding these things on because doctors obviously see value in what they're providing, and we'll continue to bring more and more of these to the market.

Got it. Lastly, in terms of capital deployment strategy, how are you thinking about either acquisitions or deploying more of the capital back into the business? And how far are we away from an idea like share repurchases? I'm guessing still 3 to 5 years away from something like that. But anyway, longer term.

I think on the M&A front, we've obviously been fairly acquisitive over the last couple of years, Ambry being kind of outsized relative to some of the other deals that we've done, we did Paige in the digi-path space; Deep 6, which has kind of a connectivity platform. There's a lot of very interesting companies out there, especially in the data and AI front. We feel like the genomic portfolio is pretty well rounded out, but there's a lot of companies that create really interesting technologies, but kind of couldn't get to commercialization. And to the extent that it makes sense, again, within kind of our profitability framework to bring those in, then we may do so. But that's more of a build-versus-buy discussion than anything else.

Thank you very much for your time.

Thank you. Thank you, everybody.

Okay. Welcome back, everyone, to the 2025 Stifel Healthcare Conference. We are back on the Life Sciences and Diagnostics track. I'm Dan Arias. I'm the Life Sciences and Diagnostics analyst here at the firm.

We're happy to have Tempus AI with us. Speaking for Tempus AI is CFO, Jim Rogers. Jim, thanks a bunch for agreeing to join us here today.

Yes. Thanks for having us.

Yes, my pleasure.

What I have been doing is sort of starting with the quarter for a lot of these sessions. But for this one, what I want to do is actually just start with sort of a high-level question about AI because we get a lot of questions about AI. As you can imagine, Tempus is one of the companies that like fits the bill as an AI company. So can you just maybe talk to us about how you use AI on the diagnostics side and then also on the biopharma side? What those capabilities really do bring to you?

Yes. So we get this question quite often, as you can imagine. And AI is obviously a very broad term. But we've been focused from day 1 on how do you leverage data and technology to really impact patient care. And so AI is really embedded in everything that we do. A few examples. So on the diagnostics side, we have a platform called Hub that our physicians can log into. There's a feature in there called Tempus One that allows them to interact with the diagnostic test. So they can ask it questions, what does this mutation mean, show me the guidelines when this mutation exists, what are the side effects of this therapy. It really allows them to interact directly with the diagnostic, and that's a patent we filed, I believe, in 2019. So it's something that we've been working on for a number of years. Obviously, we're the beneficiaries of some of these advancements in technology.

On the data side or data licensing side, the same thing is true. So we have a software tool called Lens that our researchers can use to kind of build cohorts. Again, they can talk to the database, ask it, how many patients have this mutation, build this cohort for me, again, embedded in kind of the core business.

The next step in that is since we have this multi-model database, you can kind of create these algorithmic diagnostics or AI-enabled diagnostics that are purely just algorithms that look at different data modalities and can be predictive in nature. We have a number of those that are in market today.

And then lastly, we announced earlier this year, building a foundation model with AstraZeneca and Pathos. There, we're leveraging the entire database and really building a model to identify insights. So you really can't see if you're not looking at massive amounts of structured data, multimodal structured data. We believe over time, that will be catalytic to both of those businesses because you're going to find insights that we can embed back into our core products.

Okay. And so on the diagnostics side, it's fair to say that the clinician gets information that is AI informed. Because one of the questions I get is, okay, so late-stage cancer, lung cancer patient comes in the door. You want to know whether his or her cancer has an EGFR mutation. You could get that information from several different panels that are available in the market, but it sounds like the oncologist is more well informed in certain cases, maybe not in the specific EGFR case, but maybe cell. I don't know. I'm just sort of like thinking about how it is that your information that's provided to a clinician would be AI informed and differentiated.

Yes. So from a sequencing standpoint, there's a number of companies that are obviously great sequencers. We all use Illumina equipment. We all can make those types of calls. For us, it was always that is one piece of the puzzle, and you need to connect into the institutions, into the EHRs to pull out other data for that patient because then you can contextualize the result. So that means if we recommend a clinical trial to a doctor for specific patient, we've looked at the inclusion/exclusion criteria that others would have a difficult time doing so due to the lack of those data connections.

Okay. Very helpful. So now let's talk about the quarter. Third quarter was a 28% organic growth quarter for you guys. It was high 20s on genomics. 33% on Ambry, I believe. And then mid-20s on the data side, margins expanded. EBITDA was positive, a couple of million below Street, but positive, which is an inflection that we'll talk about. How would you characterize where you are with the business and what drove the 3Q results? I'll ask you a bunch of specific questions that you can take as you decide to, but at a high level, what would you say about where we are now with the business?

Yes. At a high level, it was a great quarter for us within -- so our genomics business is broken down into oncology and hereditary. Oncology volumes grew accelerated in terms of growth again to 27%. We're still seeing obviously some ASP tailwind, so revenue growth was slightly higher than that. And then the hereditary -- Ambry has been the beneficiary of some disruption, obviously, in the hereditary screening space. So they had a really strong quarter. We've highlighted that, that will moderate as we get into Q4 as those share gains are compressed.

And then on the data side, the data licensing business is performing really, really strong, about 37% growth in the quarter. We do see some headwinds in our CRO business, which we highlighted, and I'm sure we'll get into. So the total data and services growth rate was kind of mid-20s. But the one that drives the majority of that business is data licensing, which is growing higher than that. So we were thrilled.

And then lastly, on the adjusted EBITDA side, we long said that our goal of ours was by the time we turn 10 to be adjusted EBITDA positive. We have turned that corner even with adding in some additional kind of Paige expenses with the Paige acquisition. So we're really happy with where we are.

Okay. Helpful overview. Let's dive into therapy selection a little bit, which is kind of like the bread and butter for a lot of these companies that are doing either tissue or liquid. It's a very established market, reimbursement is good. There are several elements of the business that you have. And I'd love to just see whether there are some things that we can kind of point to say, "Okay, here is why volumes are accelerating." There is a tissue assay. There is a blood assay. First question is, are you seeing some of those start to be run concurrently in a way that they haven't? There's tumor normal sequencing. You offer an assay that does that. Are you starting to see that be something that's more frequently run in your case? There is a large panel, there's a small panel. Could you talk about whether or not there's a shift there or something going on that is helpful for you. So essentially, what I'm asking for is like are there things in the therapy selection portfolio that maybe don't rise to the level of obviousness, but that are like sort of underpinning this across the space, really obvious acceleration in therapy selection usage.

Yes. So I'd start by saying you've seen all the -- our public comps have had really strong quarters as well from a volume perspective. So clearly, the entire market is expanding, and we're a beneficiary of that. We haven't seen any significant shifts within ordering habits or kind of mix within our therapy selection assays. All of them grew at similar rates to what we've seen previously. Certainly, when we introduce a new product, for example, when we launched the larger liquid panel a few years ago, there was a shift from kind of smaller to larger just because the doctors tend to like more information, but nothing within the quarter.

For us, a lot of it came down to kind of sales force execution. We hired a lot of reps in kind of the beginning of 2024. They're now kind of hitting their stride, which we noted in Q2 and that's continuing in Q3. But no significant shifts in terms of ordering habits or increased concurrent testing or anything like that, really strong growth kind of across the entire portfolio.

Okay. On the blood side, you have the 100 gene panel, 100-ish and then 500-ish. I think it's like 523. The larger panels tend to be the pharma-focused ones. Is that the case here? Or is it a mix of oncology use and biopharma use?

It's definitely a mix. I think that when we originally brought the larger panel to market, it represented a smaller percentage than it does today, but you do have oncologists that migrate to those larger panels just given the additional information that's available.

Okay. About hospital connectivity. I mean when we first started doing our work, it became very clear that, that was going to be an advantage for you just in the sense that you are touching quite a few institutions in a meaningful way. Is that a number that's stable, that's growing? And how important do you see that as a driver going forward? How much greenfield do you think there is when it comes to just sort of like reaching out and touching them, but also getting yourself really integrated into their EMR systems?

Yes. So the number of connected institutions has grown. It's over 5,000 today. Back several years ago, it was probably a few thousand. So we continue to enhance those connections each quarter. We've reached a scale, we're obviously touching a large percentage of oncology patients in hospitals broadly. But for really people to get the true value out of the Tempus platform, those connections are important. So it's obviously important to us because we get the data, but that also allows us to make these intelligence or make these diagnostics intelligent and return those results. So that's why these things kind of get built is. The hospitals and institutions really see the value of doing those connections, and that also just makes the platform more sticky.

To what extent when you sort of survey the landscape of what it is that you're competing against, to what extent do you see EMR integration and just the ease of ordering and sort of streamlined behavior for the oncologist? How much of a decision-maker is that or decision point is that for oncologists? And would you say that that's something that kind of sticks out for you guys?

Yes. I'd say for us, we think oncologists -- you have to cross a bar with the performance of these assays, right? And all the big sequencers obviously are good at doing the sequencing. Where we win is really ease-of-use, connectivity being a one-stop shop, so kind of having a very broad offering that meets all their needs. And then what additional insights are you able to provide as a result of that connectivity.

So when we kind of bump up against competitors, that's where we end up kind of winning share. And I think for doctors, they're incredibly busy individuals. And so they're really looking for who can give me results very quickly, very easily and provide additional insights, and that's why we've kind of designed the platform as well.

Okay. Let's touch on ASPs. I know we're bouncing around a little bit, but I've got a whole list here, I got to try and 19 minutes work through as much as possible. ASPs have been on their way up. They're right around 1,600 now. I think we started the year closer to 1,500. You've got the ADLT reimbursement trend for you on the xT assay. I think you've said 40% of total xT volumes should be ported over to the ADL price by the end of the year. Is that still the target? And then the follow-on is that or the follow-on question is when you say the majority should be shifted over in 2026, am I thinking more like 55%? Or am I thinking more like 85%?

Yes. So the reimbursement landscape obviously is always changing, but the long-term reimbursement trends are certainly going to be in our favor. So for xT, we have about 30% today. Target was 40%. We'll see where that ends up, but we're on that track. And then by majority, we mean the vast majority. So closer to that 85% by the end of '26, not 55%. So within solid -- the tissue DNA test, we have ADLT status. We're going to submit the liquid biopsy, the larger liquid biopsy panel to the FDA shortly here. And so that will be another catalyst as we get into '27 and then followed by kind of the RNA panel after that. So if you look at our reimbursement relative to our peers, we certainly are still lagging, but we're doing all the things that they did. We're just kind of earlier on in that journey.

Yes. The broader trend is certainly up across the space. But to your point, I mean, as far as shorter-term mix goes the xM assay and the xE assay will be things that sort of I don't want to say headwinds, but they will tamp down any increase that you're getting elsewhere?

Yes. So for xM, we have that in front of MolDx right now in CRC, hoping for reimbursement shortly here. But as you scale, there certainly are some headwinds there. But again, longer term, as you kind of look across the entire portfolio, we're certainly behind our peers and looking to close that gap.

Have you ever thought about renaming these assays in a way that makes sense?

I stay out of the naming of the assays.

Humble opinion from analysts would be anything you could do to make this easier would be great.

Fair enough.

Okay. Maybe on MRD, the early traction for you guys has been good. This is a really highly competitive space. It's going to get more competitive as we go along. And I'm always asking you about data generation or at least you guys about data generation because from where we sit, it seems like it will become increasingly important to put something in front of a clinician that's now got 10 MRD options, and try and convince him or her that this is the assay that you should use. How do you see -- what is your philosophy or what is the firm philosophy on data generation, clinical evidence? I say this knowing that your MRD assay is doing well out of the gate. And you've got a couple of studies, but it doesn't -- it's not voluminous the way that some of the other players are in the market. Do you think that, that's something that you need to do more of going forward? Or is what you have subset analysis of CIRCULATE study? Is that going to sort of carry the weight going forward as this field evolves?

Yes. I mean our view on studies, and we've had this conversation in the past is that we certainly do studies to prove clinical -- or validate the assays, clinical utility and certainly the study is necessary to secure reimbursement. We take a slightly different approach than the market in terms of kind of running these very large studies. We choose to make our investments kind of elsewhere on the technology and data side than on these large studies. But we're the beneficiaries of kind of the work at large across all of these companies is demonstrating the validity and the utility of these types of tests. And we often point to therapy selection. The early movers and therapy selection had to run very large studies to convince payers to pay for these tests and then to be included in guidelines and all those things.

And the companies that were later on didn't have to do the same amount of investment to secure reimbursement. And we think the same is going to be true in MRD. And when we started in therapy selection, there were very large established players in the space that we were able to displace by leveraging our data connections and the insights and ease of use. And we really think that the MRD market will play out similar.

It would be odd for us to be -- as we're sitting here a few years down the road to believe that somebody would choose somebody for therapy selection and somebody different for MRD. We think that those are going to be very related decisions and one that we should be able to carve out a piece of the market.

Okay. So the idea is draft behind the work that has been done in order to sort of get the field where it needs to be. And then to your point earlier, rely on sort of the comprehensiveness of your portfolio.

Yes. And I would say that we don't run -- I mean, like we publish a lot, but we just take a slightly different approach with these very large studies.

Yes. There will be some studies coming out. I don't know if it's 2026, whether you've said when we should expect data. But you have an ultra-high sensitivity assay or a higher sensitivity assay that you're working on, tumor-naive for MRD. It did sound like there was a study that we would -- that we hear about at some point. Is there a timing that you would kind of orient us on in terms of when we could start to hear more about that?

Yes. So we're working on a second version of our xM assay. First indication will be non-small cell lung cancer. There will be data coming out in '26. We haven't specified exactly the timing there. And then that will follow with breast and IO, so -- which would be in '27. So a lot of work going on in MRD. We obviously are with CRC to start, but highlighting for folks that there are more things coming, and we're excited with where those assays are turning.

Okay. The next assay, the Personalis assay that you're kind of aligned at the hip with those guys on is focused on lung cancer. Is that a situation where there needs to be anything different that happens internally when you have 2 assays pointed at the same cancer type in the same application?

So we don't think that we have to kind of worry about that. There's going to be kind of a bifurcation within the market where certain physicians prefer tissue, and they want kind of that ultrasensitive test that -- of which Personalis is one. And then you're going to have other doctors that just prefer give me a result very quickly. And so again, similar to therapy selection, we think liquid and solid tumor naive are both going to have a large market going forward.

Okay. I'll ask some number of questions. I'll say the clinical details for another conversation. But I do want to touch on Ambry because the growth is really good. I mean I think if I go back to our model in 3Q, most of the outperformance came on the Ambry side. When you did the deal, the idea was that, that would be a high-teens grower. And I think in the quarter, you were more like low 30s, if I quoted the numbers right early on. Some of that is due to market share change, that's my belief based on what you guys have kind of talked about. But some of it isn't, can you just sort of take apart those 2 things and talk about, a, how you get comfortable with what is versus what isn't market share change? And then b, where the rest of the volume acceleration is coming from?

Yes. So obviously, parsing out market share versus organic growth is a little bit difficult, but we think it's about 50% organic, 50% coming from some of the competitors in the space. Obviously, there's been some disruption with some of those competitors and Ambry has benefited from that. So we've told people that is going to moderate those share gains can't continue forever. But Ambry has a best-in-class kind of hereditary offering. So I think naturally, as some of that disruption occurred, people kind of migrated to the Ambry platform.

So the hereditary space performed, and we think will continue to perform well. One thing we've noted is a good opportunity for them is in rare and undiagnosed disorders. Still a relatively small percentage of their testing volume today, but one that we'll look to double down on in '26.

Yes. Okay. And so if you take out the market share piece, are you able to sort of give us a thought on the long-term growth rate on Ambry?

Yes. We said at the quarter kind of low to mid-20s. Obviously, the hereditary kind of market within cancer might be on the lower end. But as you kind of layer on some of these other disease areas that improves a little bit.

Okay. Have you put a number on the rare disease and peds portion of that?

We haven't disclosed that publicly yet.

Okay. Maybe moving on to the data side. You grew the total contract value that you had booked thus far by $150 million in the quarter. That gets a lot of focus because up until the point that you do that, it's kind of like a low visibility exercise to understand what's going on there. Can you talk about how that bucket of revenue or future revenue grows, where it's coming from? And then how we should think about sort of the frequency of step-ups from here just so that when investors do start to think about where things are going or are not going, they can gain comfort with that -- that bucket of revenue that's sizable, but low visibility?

Yes. So I mean on the data side, we disclosed two metrics. One is total remaining contract value and one is net revenue retention. The total remaining contract value we highlighted in Q2 was north of $1 billion. Net revenue retention in 2024 was 140%. Those are annual metrics because, as we've noted before, there's always some fluctuation in terms of when these deals get signed. But we now have two very strong quarters back-to-back. We signed the $200 million deal in Q2 with AstraZeneca and Pathos. Over $150 million of contracts signed after Q2. And the nice thing about Q3's number is it was a combination of different factors. We had a new biotech customer that signed a $66 million deal, a couple of existing customers that expanded their relationships, both biotechs. And then also a association effectively that is licensing data as well. So expanding beyond kind of just the traditional pharma and biotech side.

We also noted at the quarter that the pipeline remains very strong. Q4 is always the largest quarter for us, typically, from a revenue perspective, certainly, but typically a strong bookings quarter as well. And so we are continuing to see an uptick in engagement both across big pharma and biotech, such that we're excited about where that business is at.

Okay. There is a re-up decision on the part of -- GSK and AstraZeneca are two big partners for you. They've been sort of cornerstones on the data side. There's a re-up decision that's on its way, not imminently, but in the next couple of years. Is there any reason that you could see why they wouldn't decide to re-up on a contract?

Yes. So AZ and GSK have been two of our longest-standing customers. We don't have any reason to believe that they would not re-up. These strategic collaborations are interesting. Because typically, what happens is someone starts by kind of licensing one cohort of data, and that expands over time. And these examples have kind of reached where they've embedded it across their entire portfolio. And the reason why they kind of sign up for these strategic collaborations is really that they get a larger discount. So the pricing is similar to some of the cloud providers. So it would be odd for us to believe that somebody who is spending a significant amount of money suddenly would not spend that on a go-forward basis. And both of these relationships are really strong. We'd point to AZ that even kind of double down in the interim with a separate deal as a proof point though.

Yes. That certainly makes sense. Has there ever been a large customer that has not done that? I mean I get this conversation on just, again, visibility on the data business, what do you think will happen with the existing customers? How will you grow new customers? Has there ever been a case where you haven't had someone resign a license after the initial, however, many years?

The only time -- we've quoted that we work with 19 of the top 20 big pharma companies, a couple under biotechs, and that was probably the same list that we talked about over the last couple of years. So the only kind of churn that we typically see is, if a biotech has one asset and they choose not to move forward, they may be a onetime purchaser and then move away. But most of the time, these relationships expand over time. And we're still relatively early days. We've been able to establish a business of some scale. But for the vast majority of those customers, they're still only licensing kind of 1 or 2 cohorts. So as we continue to kind of march them up over time, there's a tremendous opportunity for growth there.

Yes. It always feels like companies are working with 19 out of 20. I have to find out who this one ops in pharma company as it finds a way to not round out the group for several types of companies. Okay. So the data side feels good. You've talked about your strategy around growth that relates to data, but also just bigger picture, at least in our conversations. I'd love to just have you and maybe a public forum, talk about how you think about long-term growth versus short-term growth. Because one of the things that I'll ask you about is, for instance, a 3Q to 4Q transition and a step down that looks like for someone, it might be meaningful but for you is not because you think in longer-term time frame. So how is your revenue recognition philosophy relative to some of these shorter thought -- shorter time frame thoughts just in the sense that like the priority is, if in one quarter, you need to recognize a little bit less revenue, but it sets you up better for the long term, that's what you will end up doing. And I don't know whether I've perfectly captured that, but I know that like the way in which you think about safeguarding growth over time and keeping that consistent is not always the way that some investors think about 90-day time frames.

Yes. Yes. We want this business to grow at the 25% to 30% range for a long time. And that's always how we've thought about the business. And so we don't try to jam as much revenue in any given quarter because we don't think that, that sets you up for kind of healthy long-term growth. And so the data business is a good example of could you go out and sign more data in any given quarter? Yes, probably, but are you really establishing the value for biopharma such that they're going to come back and re-up those contracts and extend them. So we spend a lot of time making sure that customers are seeing value on the data side and really building these long-term relationships that will fuel that growth over many years versus trying to maximize any revenue in any given quarter.

Yes. Okay. So the point is there could be some quarterly fluctuation and that is not something that you feel is overly meaningful.

Yes. I think on quarterly, you're always lapping something, there could be a project that started or stopped and so there's some noise in there. And so the annual growth rate is what we're kind of focused on.

Okay. Okay. A couple of minutes left. I want to make sure I do touch on the CRO business, which is seeing some headwinds. What are the prospects for a rebound there? And then does this ARPA deal that you announced recently, does that change the trajectory at all?

Yes. So we acquired a CRO back in 2022, a small CRO that primarily worked with biotechs. At the time we did it because we were launching kind of this clinical trial matching, and we really wanted to understand or had folks that were familiar with the clinical trial process. And they certainly started hitting some headwinds last year, like a lot of the other CROs did as studies kind of dried up. It's not a huge area of focus for us, meaning like we're not investing a bunch there to try to get it to grow. But as kind of funding generally for CROs opens up, we might see some benefit there. The ARPA deal is a good example. That's a little bit interesting because some of that will go to the CRO kind of line item. There's also a sequencing direct bill sequencing component of that. So it will be a little bit split revenue in terms of where it comes through on the P&L. But it's not -- we're starting to see things stabilize, but we don't think that it's something going to return to be a high growth catalyst for us.

Yes. Okay. Maybe just moving down the P&L and thinking about investments. How do you think the investment needs will change over the next couple of years as this expansion mission that you're on evolves and presumably some of this trial work at whatever level it takes place gets going? I have you at 60% of revenues as an OpEx total for the year. That's down from 80% last year, so it's coming down, but I also have you flat as a percentage next year. Is there a justification for that? Or is the idea to slowly as you grow the business, start working that number down?

Yes. I mean I think our view is that it's still very early days across all of our kind of offerings. And so while we've been able to achieve adjusted EBITDA breakeven, we don't think it's prudent to try to maximize adjusted EBITDA in the short term. And so what we've told people is over the next, call it, 3 years, if the business grows 25% top line, we'll reinvest about 2/3 of the incremental gross profit dollars back in the business. And then in the fourth year, that probably drops to a 1/3. So we will continue to see improvement in adjusted EBITDA over that time period, obviously, as top line grows, but there's a tremendous opportunity for us here, again, across all of our offerings that we think that, that's kind of a healthy level of investment over that time frame.

Okay. How about on the gross margin line? How optimized are you on the infrastructure that you're working on -- working with when it comes to just sequencing platforms, efficiencies derived from the sequencers? Are you running the xs at this point? Do you see any gross margin benefit that can be had from just sort of production initiatives going forward?

Yes. So on the genomics side, I think we've long taken the approach that as costs come down, you reinvest some amount of that into kind of broader panels. And so that trend certainly will likely continue. We're not running anything clinically on the x today. Certainly, that will present some savings when we migrate those things. But long term, we think kind of the low 60s margins for the genomics business are kind of a healthy spot to be. So short term, as ASPs go up, you'll likely see an increase. But long term, we think kind of low 60s makes a lot of sense.

On the data side, that obviously operates at a higher margin. Q3 was a little bit lower because we had some kind of start-up costs associated with the foundation model. We think that kind of rebounds into the mid-70s in Q4. So quarterly growth rates in data can be a little bit more volatile just given the starting and stopping of projects. But if you look at kind of for the year, that will operate at a mid-70s margin compared to genomics.

And then the last piece I should actually add on margins is some of these AI applications operate at a very high margin. They represent a very small amount of revenue today. But if you go out long term, to the extent that those scale, which we believe that they will, that would skew margins more positively.

Yes. What is the time frame for which we should think about those scaling?

I mean, given that there's not really a reimbursement framework for a lot of those today, we haven't disclosed what we think at that time frame could be, but it's not going to be a '26 event.

Okay. All right. We're at time here. Jim, I do appreciate you come in. Good to see you.

Yes. Thanks, Dan. I appreciate.

Ladies and gentlemen, thank you for standing by. At this time, I would like to welcome everyone to the Tempus AI Third Quarter 2025 Financial Results Conference Call. [Operator Instructions]

I would now like to turn the conference over to Liz Krutoholow, Vice President, Investor Relations. You may begin.

Thank you. Good afternoon, and welcome to Tempus Third Quarter 2025 Conference Call. This afternoon, Tempus released results for the quarter ended September 30, 2025. The press release and overview of the quarter and our latest presentation are available on our IR website. Joining me today from Tempest are Eric Lefkofsky, Founder and CEO of Tempus and Jim Rogers, CFO.

Before we begin, I would like to remind you that during this call, management may make forward-looking statements that are subject to risks and uncertainties that could cause actual results to differ materially. For a discussion of these risks, please refer to our 10-K and other subsequent filings with the SEC. During the call, we will discuss non-GAAP financial measures, which are not prepared in accordance with generally accepted [indiscernible]. Definitions of these non-GAAP financial measures, along with reconciliations of -- directly comparable GAAP financial measures are included in our earnings release and is available on our IR page.

I would now like to turn the call over to Eric.

Thank you. Q3 was a great quarter all around. Our genomics volume came in super strong with 33% overall growth with oncology growing at 27% and Hereditary growing at 37%. We expect Hereditary growth will moderate a bit, although we now expect growth to be in the low to mid-20s as opposed to our previous guide of mid- to high teens. Our genomic growth was across the board. Really all of our assays did exceptionally well. And with MRD reimbursement on track and our planned regulatory filing of our liquid biopsy xF later this year we expect additional tailwind in that business, both from a unit perspective and revenue.

Our data licensing or insights business grew 38% in the quarter with an additional $150 million in total contract value, which was a super strong bookings quarter for us across multiple contracts that we highlighted in our letter. This is on top of the multi-hundred million dollar foundation model deal we struck earlier this year. So from a bookings perspective, our data licensing business is just really performing exceptionally well.

The combination of growth in genomics and growth in our data business allowed us to generate positive adjusted EBITDA for the first time this quarter, which has been a 10-year goal of ours and a key milestone. This was inclusive of several million dollars worth of additional expense from Paige, which an acquisition we made a mid-quarter. And even with that, we generated a positive EBITDA and would have been close to $4 million in adjusted EBITDA without Paige. So the business is doing exactly what we had hoped. We now expect for the year to be slightly positive adjusted EBITDA, and that's even with additional several million dollars of drag from Paige.

So all in, the business is performing well. We're growing at a rapid pace, and we're managing our cost to generate leverage in the business, which is exactly where we want to be.

With that, we'll take some questions.

[Operator Instructions] Our first question comes from Ryan MacDonald from Needham.

Congrats on a great quarter. Maybe, Eric, to start just on the genomics business, obviously, oncology portfolio continuing to perform very well and a great increase in sort of testing volumes there. Can you just talk sort of click -- double-click a little bit on sort of to what you attribute that the great strength in the volume growth here? Are we starting to see sort of a broader market and industry shift to more NGS testing that's sort of just helping see more patients that are just getting sequenced? Or would you say that you're really starting to see a benefit from the execution changes and sort of the sales coverage here with the broader portfolio? Just maybe what sort of Tempus controlled success, if you will, versus sort of broader industry and market tailwinds?