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. With that, I'll pass to Eric.
Thank you. Welcome everybody. We're going to go kind of quick, but we'll have Q&A sections throughout. So, hopefully we'll be able to get to all your questions. We also had a few releases out this morning, so just giving people a heads up. One's already I think hit, the other one's coming shortly. 10 years ago, we started Tempus to solve a single problem, which is could you use AI to essentially unlock precision medicine. Okay, given our clicking capabilities are back, I'll start over. So 10 years ago, we started attempting to solve a single problem. Could we use the artificial intelligence to unlock precision medicine? In order to do that, you basically need two things. You need vast amounts of proprietary data to build models to bring the benefits of AI to healthcare 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 healthcare 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 into the US healthcare 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 one of the 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 healthcare data morphologically, typical and molecular data. So this is 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, it's methylomic 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 network effect system. Something will always not work and we'll just kind of bounce and hit it too. The ecosystem we built now exists at scale. So we're connected to about 65% or two-thirds of all academic medical centers in the US. The vast majority of oncologists in the US 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 US healthcare system. There's maybe 8,000 hospitals or so. We generate enormous amounts of data and 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 connectivity 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've been watching the growth of our database over time, it's really quite extraordinary. I mean, not long ago, we were at 50 petabytes of data. And here we are some five 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 four and a half 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. It's 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 bio arm side, in order to make this business sustainable, we've divided it into essentially two parts. We built a diagnostic business and then a data and applications business. So I want to first start with the diagnostic 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 is 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, they 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? That 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 diagnostic 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 something, 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 is you can't be a smoker. So by connecting rich molecular data or any kind of laboratory test result 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 vast amounts of multimodal data where you have an insight connected to 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 I possibly can and so the investments we've made in connectivity and AI essentially allow us or 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. If 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.
It 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 comprehensive profiling as intelligent as we could. And we made a decision early on that if we were going to be in the business of contextualizing tests, we wanted to be as comprehensive as we could be. We didn't want to just give somebody part of an answer part of the time. We 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 has just been 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 sequenced. So you need both a 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 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 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 ad 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 in a little bit. We have a series of LDT tests and those cover RNA, liquid biopsy, other areas, whole, something 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. They also have pharmacogenomic profiling of patients with neurological issues such as major depressive disorder, bipolar disorder. They 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 are ordered alongside these whether it's immunohistochemistry 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 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 US 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 two normal patients. Because as many of you may know there's many cases where we're able to capture that blood and 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 DNA. They are able to be found when we're looking at the RNA signatures. And 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 receive both a DNA result as well as 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 via 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 two assays. So we have two 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 the mutations that are again looking at those two 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 six to seven 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 adding multiple XF tests 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 to 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 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. So again additional evidence as to why clinicians are increasingly looking at both a solid tissue profile as well as a liquid biopsy profile. Thanks.
So just for a second to kind of provide some context. So when Tempus is about 10 and a half years old, give or take and when we started Foundation Medicine was the leader by far in solid tumor profiling and had a liquid offering and Guardant obviously leader in liquid and at the time people thought it'd 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 and the lab's maybe eight or nine years old over the last let's say eight plus years where we become number one or number two in both of those spaces and have unit growth that is kind of 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 or 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 site of 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 is going to talk about in a second. These algos now have an attachment rate of greater than 40%. Which essentially means a doctor's choosing to bolt on one of these algorithms more than 40% of the time they order with Tempus which means they find enough value to say I want that additional insight. Ezra, you want to talk a little bit about IPS.
Thanks Eric. And we set out, I'm Ezra Cohen. I'm the CMO of oncology and I'm a medical oncologist by background. If we can advance the slide, we set out about two and a half 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 in oncology which patients benefit from immunotherapy not in just one 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 IPS 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 an 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. Thank you.
And the benefit of these algorithms is 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. These are kind of coarse 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 when 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, other disease areas. You generate some kind of diagnostic insight laboratory test result. You connect it to rich outcome and response state 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 profiling 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 come out recently that estimates the percentage of doctors ordering these tests is 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 ordered. That's not a small percentage of cases and so we suspect the whole market will grow and since we're going 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 Nixon. 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 into stage two and stage one depending on the disease area going to be profiled. So that's another benefit. The third benefit is that there's been a significant migration to more comprehensive profiling at 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 aren't ordering that will. You have stage expansion into earlier stage and you have this trend of being more comprehensive ordering more tests. So we suspect that 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 five 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 when 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 earned the right to go to liquid that was a conscious decision we made we then began doing hereditary profiling and realized 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 space 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 tumor informed profiling or tumor naive? So and that is still I think up for debate. And the next question is how was 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 two so this space was growing very very quickly getting to scale, burning lots of money and yet the space hadn't yet really kind of landed in the place it's going to land long term. So our approach was to be kind of derisk that by having multiple irons in the fire. And so we made a decision to partner with whom we thought was the best in class on the tumor informed side Personalis who 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 and I'll bring up 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 can, we've got enormous data points and can learn and refine over time. The other is that we're 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, I'll bring it 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 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 then and follow and track that patient's variants that are contributing to the disease. So this really results in ultra sensitivity 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 have 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 decisions, how will that translate into survival and other types of clinical benefits? As great as tumor informed assays are and the fact that they can get to this really low ultra sensitivity 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 Tempus. We've launched a first assay in CRC where we're able to now just from a liquid test 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 and pancreatic and head and neck and all of the indications where a patient luckily may need to have an ultra sensitive 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 informed 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 unit economics continue to improve on their side. They started like a year ago had zero approved. They now have three. So and there's more coming. It also I think speaks on the tumor informed 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 has a platform where you have apps that they make money off of. If you look at the unit economics of MRD and tumor informed, it speaks to that. We essentially generate the kind of profits at the present moment from that test that we would generate if we landed 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 EVA 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's moving so quickly that the limits of detection 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 a 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. Tumor 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 with Tom and then we'll come back. Okay, 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 familiar with the marketplace, there's three large buckets of testing orderers. 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 hereditary cancer testing is quite immense for a few different reasons. One is there's established NCCN guidelines. There's a robust reimbursement model and every commercial payer as well as Medicare covers this testing for patients who meet 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 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 two years ago, which tells me our strategy is 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. This 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 their risk profile based on NCCN criteria. We do pre-test education for those patients and then we flag them for our clinicians. So 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 to the physician, but 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 is really prime for significant growth. This year we're adding enhancements to CARE. I won't get into all of them but on the front end we are 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 rapid 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 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 umbrella around that. Make sure we identify those patients for our clinicians and then send them back a flag 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 are CancerNext, a pan-cancer test, CancerNext Expanded and then BRCAplus. The advantage for BRCAplus mostly for breast surgeons from the time we receive a result to the time we get a 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 RNA insight. RNA is an 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 that other labs don't. And we can classify mutations that 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 hereditary cancer testing and in 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 a million RNA patients. To date, we'll probably do another 400 plus thousand this year. With regards to rare disease, Ambry's 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 microarray, 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 are with 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 advance of 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 a third of these patients being identified, it'll 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 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 liaison work with them to answer any questions that they have. This impacts roughly one in 20 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 Ambry/Tempus.
Thank you. So just really quickly, if you'll notice, 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 more intelligent platform that can grow. And so that's just our playbook area by area.
You want to come? So really, I just want to cover one thing. We're going to actually give you a quick demo of Hub for a second. We haven't demoed our two main systems. Hub is our main system that physicians use, and Lens is the main system that researchers use on the biopharma sites. 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 different data modalities. Again, phenotypic data, morphologic data, some kind of text or image or model, 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 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. And we have built a ton of agents. I don't know, 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, Claude, Google's models through Gemini, ChatGPT, these models were not trained on healthcare data. They were trained on internet data. So they just don't work perfectly with digitized pathology slides or DICOM files from CT scans or rich molecular data. If you dump in trillions of A's and B's and T's and C's, you don't get much out of a large language model. So sitting inside these products is our brain, One. And with that, Laura is going to give you a quick demo.
So hi, Laura Ster, Chief Commercial Officer at Tempus. So 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, and DNA, RNA, a handful of IHCs, as well as some of our algorithms. So the liquid biopsy on day five 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 ask something like, '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 citing, you know, including the citations. So here I might have mistyped there. So you can see we can include the citations here as to where we're getting the information from. And then if you go to next, then at this point now the provider needs to decide what to do for the patient. And so they're going to think about putting them on a combo chemo-immunotherapy. So if you look at the RNA results here, we can see that the patient has a rare NRG1 fusion. And so this is something that often wouldn't be surfaced on DNA alone. And in fact, we've co-published that 40% of these rare fusions are found through RNA. So at this point, the patient starts on the combo chemo-immunotherapy. This patient also had the Immune Phenotype 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 to that chemo-immunotherapy combination.' And so because of that, the provider might think to order MRD testing to now monitor how the patient's doing. So you see here we've got this timeline. 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 in a demo environment because we hand you the main production environment or someone shows up to five guns or whatever. 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 summary of their current therapeutic regimen or whatever's going on. Off to the side, 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 de-identified it, and the note is there. And then the system is just consistently, as you make decisions of 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, these cases are going to get very complex and being able to summarize what happened, being able to ask questions becomes pretty powerful.
So in this case, they're tracking the MRD and it becomes significantly 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 timeline that you saw on the right, this patient is having significant weight loss and we're questioning is that cachexia, 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 this 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 weaves them together and pulls it, you know, using the technology and the testing together.
Cool. So just at a really high level, this technology just doesn't really exist other places. So other people have ordering systems where you can track an order, 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 attack, stroke, cancer, later stages, they're complex. The comorbidities, the amount of things you have to consider, just complicated. And so as an oncologist or a treating physician, you think you're treating one disease, but pretty quickly you're treating another disease 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 this, that Agent One that sits inside the system that allows you to basically bring all these different disparate healthcare 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 Pathos. AstraZeneca was providing the majority of the funding. They 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 H200s. So it's a fairly large compute cluster. We loaded in an enormous amount of de-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, 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 know, you've heard this morning already about the algorithms that we develop and put on top of our diagnostic test. 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 HR data, the molecular data that we have, both DNA, RNA, and images, and to combine that. And instead of developing algorithms, you heard about it and some of the amazing tools that are already out there today, those were developed by traditional computational data science schemes really combing through the mass of 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, KRAS, 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 those standard biomarkers. Our model is now able to do that in addition across many different clinically relevant biomarkers. 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 TP53, 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 an AI physician to really be able to get a more global and deep understanding of the patient that they're looking at.
Yeah. So thank you. So really quickly, and then I'll bring Ezra back up. So this is our strategy: comprehensive tests, add on a bunch of algorithmic 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 six months or year, generate an insight a week. That's just things that people didn't know. Is a patient going to respond to an EGFR inhibitor? Are they going to respond to an ALK inhibitor? Does the NRG1 fusion matter? Are they going to respond to immunotherapy? What are the adverse events most likely? How long are they going to be on this therapy? 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 Page or 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 the doctor can't get that information. So I'll pass it off to Israel.
Thanks. Thanks, Eric. And so here we have, as Eric was saying, two examples of how we've leveraged digital pathology, digital pathology 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's 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 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 digital pathology 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 an 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 there really are 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 digital pathology. 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 digital pathology 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 digital pathology, 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, two 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 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 of diagnostics.
Thanks, Eric. So I think that gave 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 on 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 pick up over time. We've talked previously about this path to kind of achieving $500 in incremental with the announcement this morning of XT to CEX 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 pick 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 hereditary side, we saw growth rates moderate in Q1 which was anticipated given some of the large 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're done kind of lapping some of those share gains. Then lastly, we've talked previously about kind of this 25% growth rate over the next three years. That would put us about $1.9 billion just to give you kind of the size and scale of the diagnostic 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 three-year period, there's going to be periods where ASP may outpace and you may be growing them faster than 25%. But what we really wanted 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 three-year period. So with that, I think we're going to do some Q&A. I know that they're going to grab some mics to walk around.
Yes, Dave Westonberg 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 statistical models that are predicting survival. You demonstrated this across three cohorts of famous studies. I believe they were on lung cancer. Two questions. First of all, all three of these 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 multi-million dollar trials? Thank you.
You want to start with the first couple?
Yeah. We're going to talk more about this actually in the data section and the science, but that's okay. You jumped in. Yeah, so first of all just in terms of how this model compares to other more traditional methods that you're mentioning and leading 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. And 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 prospective. And so the future state will be taking some of these models, asking questions, and then working on validation in a lot of those different spaces.
So good tee-up for what's going to come later. We'll talk about this in a minute, but at a super high level, we generate an insight from these foundation models. But one main, very 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 data sets and validate it. Diagnostic insights will essentially for the near-term live on our diagnostic tests because that's where they'll 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 that have access to one or more of these models that are using those insights to interrogate their R&D portfolio, design more intelligent Phase 2s, so on and so forth. So they're already being used and we'll talk about that in this section.
You want to call people?
You want to get the microphone?
Thanks. Kyle from Panacord. Thanks for the day, guys. Great stuff. Want to ask a multi-part question. So first, on the LRP, I know you've been talking about the 25% for a while, but treat that like 20% revenue behavior for diagnostics. So maybe just talk about what we're missing in terms of ASP and volumes, MRD, and AI and RNA and stuff. And then secondly, maybe just talk about the diagnostics M&A kind of strategy. You haven't done one since Ambry, but you have, you know, Personalis, scale 15% or so. Maybe just talk about how you're viewing dilution versus growth in that segment in terms of things.
We can both cover it. I mean, I'll start. I don't follow the street model. Some of the models I've looked at essentially have like high growth rate in '26. They had high growth in '24, low growth rate in '26, '27. Then they had high growth rate in '25, low growth rate in '27, '28. Now they have high growth rate '26, low growth rate 2029. 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 what that logic is. We look out at our portfolio. If we're going to tell the world we think we're going to grow at 25%, we don't want to look stupid. So you have to believe that 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 had kind of two big drivers of that. Roughly half of that gain showed up this morning when we had FDA approval for TTO. That's $200 of lift across a massive number of tests. I don't know the number, but it's probably $75 to $100 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 the other things are happening. There's always pluses and minuses. Everything doesn't grow equally up and to 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% year growth rates. In terms of MRD, we chose Personalis 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 even 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. I know it sounds a little crazy to diagnostic investors, but I promise you, Amazon doesn't make every single product. And nor does Apple make every single app. There's going to have to be technology platforms that take the US healthcare 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, but figuring out ways to make money partnering. And as I mentioned earlier, our net unit economics today are as good with Personalis as they would be if we owned the company. So if we at one day own the company, all you get is revenue gain. You get no net income gain.
Great. Thanks. Casey Woodring from JP Morgan 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? Thank you.
Yeah. So in terms of the attachment rate, it hasn't changed over time. I think we've published previously it was around 25%. 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 sets that don't get reimbursed but we're still going to see a net add to the overall reimbursement since we're not at par with our peers. So we think we're well positioned to continue to win in the space. It is another thing that arms doctors with additional information that is incredibly useful and that's why we offer it.
Yeah. It's also worth noting when we went public two years ago, people were like, 'Oh, you're running DNA and RNA and this is not going to get paid for and it felt kind of like you guys were on the edge.' If we were on the edge two years when we went public, we're now in the middle of the bus, like maybe getting the back of the bus. I mean, you've got reimbursement rates from our competitors that are two times 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. It just will keep showing up forever.' So we are not cutting edge in terms of like, 'Hey, order a bunch of stuff and is it going to get paid for?' 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 physicians to be able to logistically make a decision and order things in an administratively intelligent way, but we never want them ordering tests they don't want and we never want to bundle in five tests like the next year when they don't really want that. So I think to the extent we were on one end, we're not. In fact, I think, Kate, you don't know who that 9% is prior. 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 23% 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 at this point certainly on the RNA side, that train has left the station. We could argue whether or not there will be rules over time about how often you can order an MRD test, how often they're going to get paid for, how often treatment response monitoring is going to get paid for. I do think that space is going to be, as I mentioned earlier on, I don't think all the puzzle pieces are in the right spot. So I don't know where that's going to land, but the RNA train's left the station. It just matters way too often there. We're in the middle of a bunch of CDx. Our competitors in the middle of a bunch. You come back five years from now, there will be dozens and dozens of RNA-based therapies that you will need RNA expression for. We'll do one more. We have more Q&A, so we'll do it in rounds.
Thanks. Callum Tish 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 this kind of new adds? And then I guess, have you seen any examples of physicians switching to Tempus tests as a result of the technology infrastructure behind it, like HARMONIC and Page Predict?
What's Mike? Any thoughts on the new versus existing? Yeah, 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 ordered with us or physicians that have previously gone stale and perhaps had an order with us within a 12-month period and they've kind of come back. And that number continues to at the very least hold steady and we've actually seen some modest growth in terms of that new ordering physician base. So there's really kind of two 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 was just going to add one quick thing there. There's only 14,000 oncologists in the US and so when Eric talked about we think it's about 50% of oncologists 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 Page Preview or Tempus Preview, you know, I think we just are deploying these things now. We acquired Page I think maybe six months ago or nine 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, there is just 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 the next-generation 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 one or two days and we will be the first people that can offer that at scale. And so that's also pretty 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, three, six, nine months, twelve 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, two 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 unfamiliar. So we're going to spend some time trying to walk through 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 healthcare, the answer is like 100% yes. It can't not happen. Every industry who ever has said it's not going to happen has been washed away by technology. And just giving you one example, 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 never be replaced by technology and it's all been replaced. So that's just the unstoppable nature of technology and healthcare 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 US healthcare system at scale? How do you combine it with something else that makes it super interesting like molecular data? How do you produce an insight and then how do you package up that insight? If you think about it, we got two 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. Packaging it up for a biopharma company is far more complex. They're designing trials. It could be novel discovery. It's a lot going on. So you have to give them the tools to interrogate this data. And in that regard we stand alone. If people are wondering, when we started the IPO process two years before we went public and people, I think if I would have asked nine out of ten investors they would have never thought our data business would be this big and they all would have thought we'd have massive competition. Both of those 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've built that wrap around this data, the connected platform, the analytic capabilities, the ability to deliver data at scale, interrogate at 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 CareGap 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. Many, many ways to get data which allows the data set to be real, contemporaneous, and useful. It's also part of this network effect that fuels our diagnostic 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 licensing it, which allows us to invest in those tools, and it becomes this really positive virtuous cycle. This is used across the entire...
Compendium of decision-making. What I think a lot of people don't understand is why are people licensing your data. And Ryan's 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 lifecycle. Do I have the right target? Am I going after the right indication? Do I need a biomarker? How do I design my trial? These kind of questions aren't worth a million dollars or 5 million to a big pharmaceutical company or big biotech; they're worth hundreds of millions. You get it wrong, you've got a billion-dollar 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. Our products touch really anywhere you could use data or AI or modeling to help make more intelligent decisions. We're certainly not in oncology at scale and will be in other disease areas over time. Ryan, you want to jump in?
Yeah. 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've been in the business of licensing multimodal records for longer than anyone in the industry. 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. This is a 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. 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 non-coders is essential to turn data into insight. This is the platform that is the front door of our data set. 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 I'm giving you an early preview that we're announcing in a few days about the new launch of Lens with these AI tools. For every step in your journey as a user of this system, we have Tempus One as a co-pilot or co-scientist to really help you on your journey to generating insight. 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 built fit for purpose. 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, things around particular template 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. 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 it quickly identifies 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. What you're seeing now on this left-hand side is that my co-pilot helped 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. 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, are there patients in the real world that I need to better understand? Again, with this combination of DNA, RNA, treatments, and outcomes, 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. 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. 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. You can start to see that not just in terms of prevalence, but you can also start to run feasibility on what 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. 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. 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. One of the things you can quickly do in this system by just clicking a few steps here, I can start to compare cohorts over time. I can start to look at other data sets that I may have added. I can start to look at 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 that only Tempus can provide, something like co-expression analyses as well. I can start to dig a little bit deeper. We allow our users to get to this type of insight in literally a second or in days. Looking at things beyond EGFR and KRAS that we know today, but looking at novel biomarkers that are coming in the future like MTAP deletions is essential for our drug developers. This is a detailed breakdown of that 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 as specific as MTAP biomarkers and what that is doing to their various behaviors in the real world. I can look at the distribution of gene expression. I can look at the correlation or the pair-wise expressions between not just one marker but two markers at a time. Looking at MET versus EGFR, MET versus KRAS, MET versus MTAP, these are the various iterations of questions that our users are going through and they can get that insight all within this tool. 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 R and Jupyter notebooks to be able to go even further for those that actually want to code. Again, we've embedded Lens as a co-pilot here to be able to help people ask certain questions around, how do I create an oncoplot looking at the top 10 most frequently altered genes. I can quickly do that, figure out which 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 analysis in my R environment. Again, we're giving the user the control here. The code is populated, but they can actually edit this code. No different than what they would do in an R environment. The most important thing is that I can quickly get to insight. I can refine this and I can have my files generated instantaneously. These types of outputs are the things that really drive our business today. Maybe if we flip back to the slides, being able to go through those iterations very quickly, get to these types of outputs is really the first step in a multi-step 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 the three steps: querying, building data sets through natural language, using agentic workflows to be able to analyze this data in much more granular ways. 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 a compute infrastructure to help our customers build and fine-tune models as well. One of the things that we do is not just helping understand different co-mutations and helping early development. We also are helping clinical development and late-stage development for those high-risk, high-reward types of decisions like a phase three global trial. An even 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. I wanted to spend most of the time maybe addressing that question head-on around what are our customers doing and what are they getting out of this type of unique data set. I'll walk you through three examples. The first one is a global biopharma company that was interested in advancing their immunotherapy franchise, but they really needed to think through could they uncover additional novel biomarkers in a particular patient population. For this particular project, we were able to assemble a data set that didn't exist in the world, a 5,000-patient data set where we had biopsy samples and DNA and RNA sequencing pre-treatment and post-treatment. 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. This data set didn't exist before, but what it allowed us to do is uncover four different novel targets that were able to advance their drug discovery pipeline. They spent time and effort and money investing in these types of projects. One project alone, and you can just think about 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 50x of what they spent. This measurement of value is the common motion for us in our collaborations so that we can not 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 significant investment decision that these companies are making is a go/no-go decision and refining the trial for a phase 3 global study. Here was a different global pharma company that was faced with this critical decision. 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. 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 heterogeneity or surprises in our phase three study. In colorectal cancer this is essential and so we're able to not just deliver this type of insight with our biopharma company but it is essential to be able to de-risk a decision and ultimately creates a net present value for our customers of somewhere north of $500 million. You can start to see each project starts to stack up and 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 3 study but do I go first line, do I stay in second line. That type of decision is a high-risk, high-reward type of play. Again, we start to look at what data sets do we have, what multimodal data sets that 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 decision for that particular drug was going to go up against that type of landscape. We built the data set, we worked with the teams and we were able to de-risk a number of the decisions by building these patient subgroups, but also the go decision was made to ultimately allow that customer to proceed. We talk a lot about the 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. That makes it not just a perceived benefit, but actual ROI metric that ultimately leads to why our business has grown over time. So with those three, pass over to Eric.
Great. Thank you. Okay. So, at a high level, I just want to cover the kind of scale and scope of our data business. We're connected to, we're working with something like 19 of the 20 largest pharmaceutical companies in the United States. That metric is 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 have now large partnerships in place with not one or two big pharma companies but lots and this number continues to grow as we sign more and more of these kind of multi-year, $10 to $20 or $30 million a year engagements with folks. We also deliver an enormous amount of data. As Ryan talked about, the Lens platform, these capabilities exist at scale not just to generate data, structure, harmonize, clean it, analyze it but also deliver those insights including the raw underlying de-identified data to biopharma. 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, if they're licensing $20 million of data, are they generating $60 million or $100 million of gain? 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 roping in biz dev, they're roping in other teams to validate the return on this data licensing. 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. Very rarely does somebody say, 'Hey, I'd like to sign a $100 million deal and license $25 million a year of data for the next four years.' More often than not, they start with, 'I'll do a $250,000 project or a half-million-dollar project or a million-dollar 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 and R&D teams and they interrogate this data and try to figure out is it good, is it clean, can they use it, is it 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, 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 a billion dollars 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 multi-year, very large agreements with Tempus, the only thing they're getting is a discounted price, meaning they value the data so much they're willing to make a multi-year commitment because they want to save that money. I think it just speaks to the value of the product we built. Obviously there's a great example of that. Our first strategic partnership with a big pharma. We have several strategic partnerships with big biotech but with AstraZeneca that was signed I think in 2021. Obviously that relationship is going strong and they're funding our foundation model and that project runs for the next several years and so we've got a longstanding relationship. 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 pharma 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 just the overall relationships and concentration, we work with about 240 companies in 2025. That number should be up in 2026. We worked 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 five 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. 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 they actually want models. You could 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. I'm going to bring Kate up to talk a little bit about the benefits of the foundation model on the biopharma side. Again, this is a huge cluster, 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.
Yeah. 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 Lens and so we have things there we mentioned the co-scientist 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. The ecosystem we have today already includes multiple models. So with the acquisition of Paige 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 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. 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. When others talk about multimodal, they often are thinking about one or two 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. The future state that we'll move towards is 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, PCR 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. 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 approaches. Future state will be 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. 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. 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 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? We're really excited about we've really reached a moment where all of these things come together. They are helpful on the diagnostic side for providers, but they are also helpful for our life science partners.
And again, the release this morning covers some of this. There's also papers coming out that go into much greater detail about how these models predict what they predict and you can take a look at it. 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 two criteria we had to meet. One was a C-index score for a trial that was out in the public while the other was a trial that they had data for we didn't have data for for which they had trained a very specific model. The question was 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 that hurdle was not seen as being 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. These models just get much better over time. Think about ChatGPT 1 versus whatever 5 we're 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 performing at scale, you end up saying to 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. We've long been focused not just on the diagnostic side of the business and the data side but also of the application side, how do you take these applications or algorithms and distribute them broadly. Given that we have this connected ecosystem, the 5,000 plus hospitals, we're in a unique position to be able to distribute AI into the US healthcare system at scale in ways other people can't. There's all kinds of questions that people are answering every day. What critical biomarker should I target? What are the therapeutic options I have? What clinical trials is 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 a care gap is being broken. We built technology once we had these connected rails and we had data flowing in and out of all these hospitals and we had the ability to in real time take that data in, generate insights and put the insight back into the hands of a provider. We chose two starting places to focus on. One is could we use this technology to match patients to clinical trials and could we use this technology to close care gaps. The third is could we use this technology to develop entirely novel algorithm diagnostics and distribute those. In the first two, clinical trial matching we call Time and our CareGap program we call Next and both of these things are 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 we've got a few people using these things. We have 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. We're fortunate that our two main businesses, diagnostics and data, generate enough gross profit and enough investment, enough 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. In radiology, we've got a few in market today including our IPN module that operates at scale. 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 a thousand 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. This program is starting to really scale as we are moving from the stage 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 co-pilots that exist in the wild that essentially make good doctors great doctors and great doctors superhuman. 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 in the hands of people. Not only does this require a connected ecosystem, which we have, which we've invested enormous amounts of time and money in building, but it 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 healthcare 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-lead ECG it sits in a different wave file from GE, it's not even in Epic. So we can basically with our Edge server pull all these different data forms in, structure that data and then Air is our technology platform that allows us to basically take the insights and put them back in the hands of the doctor through Epic or through other EHRs 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 ALOS. 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 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 US healthcare 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. Two which are FDA approved, our ECG-based atrial fibrillation or AFib algo and our ECG-based low ejection fraction or LowEF algo. Many more in flight, many more coming. We envision a world where Tempus 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 let's just go to a new area. Obviously heart attack is the number one 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 we're just telling people something is wrong. We're saying 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. We've taken millions of ECGs, connected them to outcome response data and other critical diagnostic data like echocardiograms and the like. We just built a really powerful portfolio of ECG-based algorithms that are either FDA approved or in the middle of being FDA approved. These things are also deployed at scale. We have lots of algorithms deployed at 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. So you could 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 several hundred million dollar business just on our AFib predictor. LowEF 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 a billion or $2 billion dollars of revenue just from that product alone. And that same thing is going to happen with echoes 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 a billion dollars generating an algorithmic insight, you're likely going to save the US healthcare system 50 or 100 billion dollars 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. Just to summarize, the data and applications business is growing rapidly. The bellwether of that business is our data licensing and modeling business which is having a moment and growing quickly and we're excited about the future of apps. On that note, you want to hit the financials?
Yep. Quick and we can jump back in the Q&A. So as Eric mentioned, we've seen strong growth in data applications largely driven by the insights business, which is 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 months. As we 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. Not, you know, these aren't 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 previously got 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 that breakdown of TCV, $350 million of that $1.1 billion related to 2026. 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 both into this year and next year. As we keep adding them that visibility continues to grow which is great. And then the three-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 years. 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 set.
Hey guys, thank you for hosting us today and thank you for taking my questions. I have two questions. As you develop more AI agents and applications, are there examples you would highlight as practice-changing for physicians today? One, and second, Tempus has the largest multimodal oncology data set. Is the Tempus data set comprehensive enough or do pharma companies still look to sign additional data deals with other companies? Thank you.
