Ready pre-sale to be joined by the team from Tempus . We have Eric Lefkofsky, Founder and CEO, and Jim Rogers, the CFO. Before we get started, I'm required to read you some disclosures. For important disclosures, please see the Morgan Stanley Research Disclosure website at www.morganstanley.com/researchdisclosures. Thanks guys for being here. Maybe we can just hit on Q2 first. Talk us through what drove the strength there, but then also zooming out, you know, just over a year being a public company, what are you most proud of? Maybe any of the key challenges you would probably flag thus far?
Do you want to start with Q2 and I'll pick the most proud?
Yeah, Q2 was a great quarter for us. The genomics business kind of re-accelerated our growth rate. We grew 20% year over year in Q1. That accelerated to 26% unit growth in Q2, and a lot of that was driven by sales efficiencies and really just adoption of the product. Genomics revenue was kind of north of 30% given some reimbursement tailwinds. On the data side, really just continuing to execute on a lot of the agreements that we've signed over the last couple of years. We announced the big partnership with AstraZeneca and Pathos. That project got underway in Q2, and it started to contribute revenue. Overall, great quarter. Continued our improvement from an adjusted EBITDA standpoint, about $10 million of improvement quarter -over -quarter. On track to flip positive in 2025.
I think in terms of what it's like being public and what we're most proud of, the business is performing just incredibly well on really all cylinders. It's nice that it's less about being public or not public. It's more getting to the scale at 10 years where we're getting close to $1.3 billion of revenue and you still have these two main businesses growing at roughly 30%, which is only compounded by our acquisition of Ambry, which accelerates our growth rate. It's just nice that the business is this solid, this strong, really across all the major growth levels. If you would have said to us 10 years ago, where do we want to be, we would have said to you, right here.
Fantastic. Let's kick off with genomics. Obviously, you have one of the broadest oncology portfolios out there. You give physicians that offer to have a kind of one-stop shop. How do you weigh up the pros and cons as you now think about expanding the portfolio from here? How does kind of a single vendor value prop resonate versus a more broad spread provider?
Yeah, I mean, there's just under 15,000 oncologists in the United States. There's kind of no single group of physicians that control this much spend relative to the U.S. healthcare system. These folks are incredibly busy. They have a lot going on. I suspect over time, this group will want to work with fewer vendors who can solve their problems in a more holistic manner. We have long felt like in order to win this space, you have to be in hereditary risk. You have to be in treatment selection, both in terms of solid tumor profiling and liquid biopsy, and you have to be in MRD and monitoring. I suspect if we go forward five or 10 years, the largest players in the space for MRD will be the largest players in the space for treatment selection and vice versa. We've long thought that that's how this would evolve.
No different than Amazon didn't win books in e-commerce. It won e-commerce. I think the biggest issue for us is not how do we expand our portfolio within oncology. We already have a very broad portfolio. It's really how does the portfolio expand outside of oncology. For example, Ambry is going to be doubling down. Our acquisition of Ambry will get to double down in rare as the pediatrics. You have a kind of a brand new large category that we'll be doubling down in. I suspect more categories like rare will also start to get positive reimbursement. That will be additional categories we get to go into.
I'm curious what the feedback has been from physicians on the MRD side, covering both tumor naive and tumor informed. It's early days, but you know, how's that been thus far?
I think it depends on the category, right? In certain subtypes, tumor informed, like for example, in colorectal cancer, where you have lots of tissue, it's a perfectly good solution. In the lung, where there's less tissue, people might want a liquid offering. We have long felt that you need to have both tumor informed on the solid side and tumor naive on the liquid side. You need to have solutions for both, and the market by subtype might evolve in certain ways where you need both. It is possible that over time, the naive side of this becomes so sensitive, so specific, where the limits of detection are so low that you could see significant displacement. I don't see that happening for quite some time. I think we think our approach of being in market with both, which I think Viterra now has a similar approach, is probably right.
Given Caris's recent IPO, one question we've been getting is whether doing a whole exome really adds that much diagnostic yield versus, you know, a broad but targeted DNA panel. What are your thoughts on that?
I think at the present moment, therapeutically, on the DNA side, there's only really several hundred biomarkers that are clinically relevant. Whether you do whole exome or whole genome, most of that information is being compiled for research use only. It doesn't really have any clinical significance. There's no drugs tied to it. There's no therapies tied to it. We have long felt like doing whole transcriptome was probably generating more insights because you get to see things downstream from a genetic mutation. That said, I do think the market, and we've discussed this at JPMorgan, we launched our whole genome-based heme assay, which will come out later this year. We do expect over time to migrate our solid tumor portfolio to whole genome, less because the data is so relevant today, more because it's an easier workflow. You now can generate rich data at lower cost.
I suspect it's just going to be nice to move to one chassis where you don't have to run these disposable panels.
The number of paid oncology tests has seen a nice steady improvement. What's the latest progress on securing reimbursement among the commercial payers?
We're on the same journey as everybody else. Obviously, our lab is a little bit younger than some of our competitors. We see positive trends, although the commercial payer landscape for us is very fragmented. It doesn't necessarily always show up in the numbers in any given quarter. I think generally we've seen more coverage than what we saw five years ago, and we'll continue to kind of chip away at that on the commercial payer side.
Are there any assays that are lagging others on the commercial coverage side?
Obviously, MRD for us is the big one because we don't have any reimbursement today. As we get that kind of in the tail end of this year, first through CMS, we'll kind of approach the commercial payers. I would imagine that will be the same case for MRD.
Got it. I want to spend some time on Ambry as well. I think you recently called out long-term growth rates there could be maybe higher than originally expected. The market on the hereditary cancer side has seen as maybe more established than some other areas. I'm curious, why could you see this reinvigoration of growth?
Yeah, I think there was this belief, I think, based upon the performance of a bunch of companies in the space that the growth rates in hereditary profiling had kind of capped out and it was becoming a commoditized space. I don't really know how that narrative evolved, but we thought it was an inaccurate narrative. I think the unit volume is kind of playing that out. I mean, there are far more people that are at risk of getting disease than have disease. We benefit, Tempus and others like us benefit from the collective R&D efforts of the entire ecosystem. Every academic medical center researcher, every biopharma company, everyone doing work to find some molecular biomarker that's connected to disease turns into something you have to watch.
Whether that's for various types of cancer where markers beyond BRCA will become equally relevant, or whether it's outside of cancer, early onset dementia, type 2 diabetes, pick a risk. I would not be surprised if companies like Ambry end up sequencing 10x or 20x the amount of patients companies like Tempus sequence in oncology alone, just because I think there are far fewer people that have disease than are at risk. It does feel like a space where ASPs have normalized, where the unit growth would be much higher than people anticipated. We're optimistic. That said, we've only owned it for two quarters. We told the world, like, let's wait and see how the next few quarters go.
Yeah, that I think north of 30% growth for Ambry in Q2, how sustainable is that for the rest of the year?
Yeah, we talked at the Q2 earnings call that about half of that growth came from share gains from competitors, the other half from kind of organic growth within their accounts. The share gains obviously can't continue forever, so we would anticipate those ticking down over time. They're performing ahead of where we anticipated when we bought them.
What does the mix look like between hereditary, rare disorders, and pediatric for Ambry specifically?
We don't disclose. Obviously, the majority of the business is still hereditary oncology, and then a smaller component is rare.
What are some of the investments you're making on those other two areas for Ambry specifically?
Most of the investments in rare have been made. We have quite a good portfolio there, both on the exome side and the whole genome side, and a whole platform around tracking these patients over time. I think we're well positioned. We're one of the largest players in the market today. Obviously, GeneDx is a large part of the market, and it's really us at Baylor. I suspect, given the fact that we just now are ramping that up, we'll grow quite quickly. Historically, for Ambry, reimbursement wasn't there, and they put more of their energy on the cancer side. Each one of these areas where all of a sudden we demonstrate enough clinical utility for reimbursement to normalize, you get the opportunity to invest.
Okay, I want to shift onto data now. What are some of the challenges you had to overcome to build the data infrastructure you have today? Why is it that someone else couldn't come in and emulate it?
Yeah, I mean, as a tech company, we just have a different orientation than most of the big labs we compete with. We have something like 700 software engineers and folks in that world. We make enormous investments in cloud and compute on top of our investments in engineering talent. We've been making these investments for a very long time. We've built up a very large and very mature technology stack that allows you to make sense of all this disparate multimodal healthcare data so that clients, especially R&D clients, can get real benefit from it. When we first started licensing de-identified data to biopharma clients years ago, it didn't go well. They didn't like the data. They couldn't generate insights. We had to really invest heavily in building products that would make the data useful.
I suspect if you fast forward today and look at our data business relative to others, one of the reasons our data business is so much larger than anybody else is because we've made those investments. It's not just that we have more data than other people or that it's real-time in nature based on only thousands of connections. It's also the amount we've invested in software products and tools that make that data useful. You said this during the IPO. I kept using this example. It's like mowing 3,000 lawns. It's not that somebody can't do it. It takes enormous effort, and you can't cheat it. There's no way to snap your finger and say, "Oh, my 3,000 lawns are mowed." You have to mow them.
What is in it for the healthcare institutions to provide you with the healthcare data, essentially for no payment? I guess, what is in it for them? Could they, and I guess, have they ever changed their minds?
I don't believe we've ever had anybody that turned off the data. I mean, for them, they see value by getting kind of a more intelligent diagnostic result. By sharing clinical data with us, we're able to contextualize the results for the individual patient for which the test was ordered, recommend trials that they're actually eligible for based on the inclusion and exclusion criteria, removing therapies that they've already received in a prior line and failed, and giving access to the broader database for physicians to sort through and see how other patients were treated. It's really by sharing the data, they're getting more insights and actionable insights back from the diagnostic test.
On insights, the vetting and trial period with potential customers, how does that process work? What are the typical studies you work through with them? On the insights specifically, what's that kind of trial vetting period like? Maybe talk us through how that works.
I mean, typically what happens, we have hundreds of biotech clients and then we work with most of the big pharma and oncology. Often the way it works is somebody will license a very small amount of data, you know, several hundred thousand dollars, whatever, a small amount of data, maybe $1 million. They, in one subtype, answer one set of questions. Maybe a year or two later, they realize it's adding real value and they'll expand that into multiple subtypes. At some point, once they realize that data is helpful across their entire oncology portfolio, you begin having these conversations about, okay, if I'm going to license lots of this data, what's the best price I can get?
The way our pricing for data works, which is also when you think about the fact that we've got kind of a total contract value north of $1 billion, meaning people have signed up for data to be delivered in the future at that significant rate. What's kind of wild about that is you can license our data, you can license one file. It's not like you have to sign a massive deal to get our data. The only difference between one file and 10,000 files or 20,000 files is price. It's a bit like the way AWS or GCP or Azure prices their cloud products, where you can use it in very small denominations, but you're going to pay kind of retail. If you want to make a longer-term commitment, multi-year commitment with a certain dollar amount, you get a discount.
I think the fact that so many people are signing long-term agreements means that the data is obviously adding a ton of value.
Obviously, a lot of agreements are in play. Can you give us some examples of pharma use cases of your data, sort of how they've used it to better outcomes?
Yeah, I mean, AZ's kind of published on this, so you could read about it. They had published a year or two ago that they saw a roughly 5% PTRS lift, probability of technical and regulatory success lift, across big parts of their oncology portfolio. If you accelerate, it's this simple. If I increase the probability of success by 5%, or if I increase the time for a drug to get to market by 12 months, either one of those two produces something like $90 million of NPV per asset.
If you've got 10, 20, 30, 40 drugs in your portfolio, and you can use our data to build a synthetic cohort against a single arm phase two, or figure out that a shelved asset should be unshelved, or attach a biomarker to a drug that didn't have a biomarker, or remove an exclusion criteria that you really don't need because in the real world, it's no longer there. Any one of those, these things are like massive. I would find it kind of hard to believe that 10 years from now, every major oncology company isn't licensing significant amounts of data from us or someone like us. I just would find that hard to believe.
It's helpful. How does the data piece aid the genomics part of the business model? Are there particular aspects of the genomics offering that you could point to that make sure clinical tests are more differentiated versus those by competitors, thanks to the data?
Yeah, one of the advantages, and this kind of leads into the AstraZeneca-Pathos agreement, is, you know, by structuring all of this data for purposes of the data licensing business, it also gives us a really robust data set to kind of train models on, identify insights, and embed those back in the genomics or in our diagnostic offering. The AZ-Pathos agreement, where we're building this foundation model on the entire data set, or training it on the entire data set, we're confident is going to yield those types of results that are going to differentiate our genomics. We often have talked about this flywheel where genomics is kind of the data provider for the data business. We mine it for insights, and we embed those back, kind of giving us an advantage. These businesses are definitely interconnected.
We said this during the, no one's ever run this kind of foundation model in oncology. You're talking, you know, north of 350 PB of data being moved into a cluster of essentially 1,800 GPUs that are going to be running for like three years on that data set. With all the tools we built, it's something like 1,200 proprietary agents that make sense of multiple healthcare data. This is like a non-small effort. We don't really know what's going to come out of that. We're finishing pre-training now. We'll run compute the end of Q4. I suspect what will come out of it, if you look at our growth rate, which even at our scale, I think we ran 212,000 tests last quarter, like growing units at 26% year -over -year at that growth rate is pretty extreme.
The main driver of that is that our tests are just more personalized, more contextualized than others. Physicians like them. What they're really trying to figure out is, okay, in light of this molecular insight, whatever it is, this RNA expression level, this DNA mutation, what do I do? What drug do I give? How do I change therapy? I think what I'm hoping comes from the foundation model is a plethora of insights that we couldn't see until we ran compute at this scale. Associations, for example, where you can look at non-small cell lung cancer patients where frontline therapy might be an EGFR inhibitor if you're EGFR mutated, but we can see, oh, wait a minute, here's 20% of the population that never responds. You as a physician need to do something different because this patient moved back in three months.
I think it could be transformative in terms of those level of insights, but we'll have to see it till it's done.
How should we think about total contract value from here? Obviously, now peaking over $1 billion, how much fluctuation can we expect there in the coming years?
Yeah, we get this question, I think, a lot. If you go back several years, that number was $300 million. You look at kind of on an annual basis, it has grown kind of steadily over the last four or five years. Within any given quarter, if you sign a $200 million deal, obviously there's big fluctuation. There may be a quarter where less is signed, and it comes down a little bit. Largely, when you look over multi-years, it should be kind of growing at similar rates to the growth rate of the revenue over that same time period.
How hard is it for a company? You have these long-term contracts, they've used your data for a while. Surely they don't want to let that go, right? They want to keep using it and expanding beyond that.
We only had two. Many of these contracts are with a kind of, you know, four, five, six years in duration, the bigger ones. They haven't come up a lot. We had a few. We had one of them, which we announced, which is Merck KGaA, came up for renewal after a very large three-year contract, and they renewed for another three. That was one of the few. AZ agreed to do this foundation model. They had a few years left on their old deal. This new foundation model is a really big investment by them into leveraging our data. That is another great proof point that the data is adding a ton of value, because otherwise it was [$300 million]. I suspect as other contracts come up, I have no reason to believe they all won't do that.
Yeah. Who would own the foundation model once it's completed? I guess, how do you anticipate using this in other pharma partnerships?
In this particular case, I think each one of these things might be slightly different if we do other deals. In this deal, each party gets a copy of the foundation model. We get to own it for diagnostic and data purposes. Pathos and AZ each get a copy of the model that they get to use for their own internal drug discovery efforts. Pathos is a small biotech, AZ is a global pharma, but they can use it for their internal R&D work.
On the Q2 call, you noted a flaw in the U.S. healthcare system when there's no kind of mechanism for reimbursement for AI and algos. Is there anything you can do to further accelerate that evolution or drive awareness of the potential benefits?
I don't know. I mean, we're in the middle of a lot of those conversations now. The system has to fundamentally change. You can't not pay for AI in healthcare when you spend as much as we spend as a system and produce the results we produce. It's just not sustainable. The only solution I can think of is technology and AI that in theory could produce better outcomes. We have to find a way to pay for that. That said, I don't have any, there's no like, this is coming next month, it's going to be game changing. I think the system, at least with this administration in particular at HHS and CMS, I think you have people that recognize they got to do something different.
Have you been engaging with the FDA on this?
We've been engaged, we've engaged with the FDA for quite some time. The FDA is not, what's interesting is the FDA is not the problem, which is, which is most people think they are. We have, I don't even know, a dozen more FDA-approved AI-based algorithms. FDA-approved, meaning the FDA has approved an algorithm for Tempus to look at a 12-lead ECG. From a normal 12-lead ECG run by GE or others, we can basically say that result is wrong and this person has undiagnosed AFib or this person has undiagnosed low ejection fraction. More are coming. We have the same thing in DigPath. We have the same thing in radiology. We can detect infantile pulmonary nodules. The FDA is not the problem. If you're willing to go through their process, they're willing to give you approval if the bar is met.
The problem is once you get approval, you get no money. That's the problem. The problem is we don't bundle FDA approval of these tests with reimbursement.
Understood.
That's what has to change.
You've got a number of analytical tools to support researchers, use those insights from your data. Loop and Lens are a couple of them. Maybe just tell us a bit more about these solutions and any recent traction you've seen.
With Loop and Lens?
Loop and Lens, yeah.
Yeah. I think, as I mentioned, one of the tools we built that is differentiated and drives our data business is this application called Lens. Lens is basically an analytic tool that we built that allows you to build cohorts of interest, interrogate those cohorts, run your own models on our technology stack. It allows you to kind of move around a lot of data and interrogate the data at high fidelity and low cost. That product is starting to get some real traction. On the modeling side, we also, when we started sequencing patients, started thinking a lot about the kind of data you would, the kind of multimodal data you would need to generate these insights. We used to talk about this notion of phenotypic morphological molecular data, or basically text, images, and molecules.
We also were cognizant that no matter how much data we had, there would be a certain amount of data we didn't have and that we would want to generate on our own. We built a modeling infrastructure, in our case, based on organoids, where we began bringing in basically frozen or fresh tissue that we would then cryopreserve and build these organoids and do drug screening on these kind of mini tumors across really every epithelial cell category. That bank has now gotten quite large. In addition to our normal data business, we also have an emerging kind of synthetic data business where we're able to interrogate all these different drug combinations across these little mini tumors. That has had some really nice wins where clients have come in and said, I want to do a data licensing deal and in part bring in that capability.
We call that Loop. I just highlighted, I think, in one of our calls, these are just two of the kinds of products that we built that add some fortitude to our data business.
On the physician apps, Tempus One, Tempus Next, and Hub, maybe just give us a brief overview on those and how they differ from maybe what's out there, if anything is out there.
Yeah, I mean, Hub is kind of as I mentioned before. Physicians can go in there, they can kind of view the broader database, they can filter down for similarly situated patients, either from a genomic standpoint or phenotypic, see how those patients were treated and how they responded. Tempus One is embedded both in Lens and in Hub, so physicians can actually talk to the diagnostic test. They can ask, what does this link me out to the guidelines? What are the side effects of this therapy? All those things are kind of built right in Hub. Again, getting back to the point of, in addition to providing the diagnostic, we want to provide tools and technology that make physicians' life easier. That's why we embed all these things into their workflows.
Paige AI recently announced, maybe again, overview on that. What do you think it brings to the business?
The Paige acquisition was for us really interesting on a few levels. One is we think digital pathology is an exciting space, and over time, one of the main cornerstones of bringing AI to diagnostics will be through digital pathology. They had a whole bunch of capabilities in that space. They had built a viewer. They had a series of FDA-approved or independent algorithms to make predictions clinically. They had built a foundation model called Virtuo, which got into some real scale. They had an incredible team that was good at manipulating digital pathology data and building these models. They also had a unique relationship with Memorial Sloan Kettering that gave them access to all of MSK's digital pathology data connected to a certain amount of clinical insight. We wanted all that. At various moments in time, we had talked to the company and price didn't align.
As we got a few months ago, price did align and we were able to bring them on board.
Following July's convert, you're pretty well capitalized now, organically investing or also through M&A. How are you thinking about capital allocations at this point?
Yeah, I mean, I think, as we said, we historically bought smaller things that kind of solve some kind of problem for us. For example, we want to increase our digital pathology data set. We want to double down our capabilities there. We can make a relatively small acquisition that allows us to kind of move that chess piece forward. We don't make big acquisitions unless we can find a company that we think is, you know, relatively speaking, as good as us. We operate at significant scale. We have a highly diversified business. All main parts of the business are growing rapidly, kind of 30% growth. We trade at some multiple. If we were going to buy something big, we would want it to fit into that paradigm. It's hard to find things that fit into that paradigm. We tend to be way more cautious there.
A small raise after Q2, I think, adjusted EBITDA was kept the same. Just maybe walk us through the philosophy underpinning the guidance and any color on what you've seen in the last couple of months, I think, through Q3.
Yeah, so I mean, I think from a guidance standpoint, it was very important for us to be self-sustaining, as we just turned 10 about a month ago or a couple of weeks ago. For us, it was very important to be adjusted EBITDA positive for 2025, and we were very focused on that. I think, you know, we raised revenue a little bit. We kept the adjusted EBITDA the same. You know, we've always said that the opportunity in front of us is still very, very large. We're not in a position where we want to just be harvesting profits. If we're running ahead of track in any given year, we may reinvest some of it back into the business. That's why the adjusted EBITDA remained the same.
Anything more recently, any color? Again, it's two months, so.
Yeah, not the one purchased by Ambry.
Yeah.
September 9th. The business is doing in the aggregate great. We're fortunate, as you mentioned, that we're able to keep reinvesting in that long-term growth trajectory. Bringing AI to healthcare, despite the fact that we operate in some scale, we're still in the very earliest part of the cycle. We don't want to win 2026 and lose 2036. We want to make sure that we're kind of appropriately aligning. That said, we thought it was important to be EBITDA and free cash flow positive. We're knocking on that door and we'll get there in some nice positive data.
Yeah, maybe on that, Jim, can you talk about just bridge us from where we are today to some longer-term targets on the profitability side?
Yeah, we haven't commented on kind of long-term profitability for the exact reasons on my previous response. Each year we're going to assess, okay, the business, each of the businesses are growing x percent. That's generating this increase in gross profit dollars. What's the appropriate amount to drop down to the bottom line versus reinvest in the business? Each year there's a laundry list of things that we go through to say, is this something that we're doubling down on, or is this something that we maybe need to be putting aside? As we approach kind of 2026, we'll provide more and more color on our thinking there. As we sit here today, it's a year-by-year effort.
I think we've got a couple of minutes left. As we think about incorporating AI into the healthcare industry more broadly, I think AI to some investors is met with some skepticism, maybe to some degree because of the way of accountability it has for patients at times. Do you think that AI will serve a big role in healthcare, maybe, you know, relative to other industries? Just how are you kind of thinking about the impact that it could have?
It's interesting. Obviously we're the leaders in bringing AI to diagnostics, yet we actually talk very little about the impacts of that financially, because I think they're very small right now. I think we're fortunate that our main diagnostic business and our main data business are big and growing, and those are tangible. You can see it. There's no doubt that AI will come to healthcare. I think it comes through diagnostics first, but either way, it will come to healthcare, and it will have an enormous impact. It's very hard to see that today. Anything we were to say on that topic would be highly speculative, and it could just as easily be completely wrong.
I tend to, the way I think about it is, given that AI is coming to healthcare at some point, and given how massive the healthcare space is, this is a space that probably has $1 trillion or $2 trillion worth of movable free cash flow based just on inefficiency. It's massive. You just want to be in the game. You want to have a broad portfolio of AI products that you can bring to market when there's actually money to be made by bringing them to market. I think that's how we think about AI, which is we're making all those investments, and I think one day they'll yield really positive results. For right now, that's kind of off into the future.
I think we have time for one more. What's something you wish investors asked you more often?
I don't think it's necessarily that. We had this conversation. If you look at the people who have kind of made the most money investing in Tempus , they tend to be more thesis-oriented. They believe that AI is coming to healthcare. They believe that Tempus AI has as good an approach as anyone, and they're long that thesis. They have invested in us, and there are other things that look like us. They're just true believers. I think they will likely probably do well. No different than if you were investing in e-commerce or search 20 years ago, you would have done well. You may have invested in five other things, but Google would have been one of them, and you'd have done well.
Got it. Okay. Eric, Jim, thank you so much.
Thank you.