We're going to get started. Dim the music. Great, thank you. Welcome, everyone. Welcome to our inaugural Investor Day. Great to have you here in person, and thanks to those who are joining us virtually today. Before we begin, just a quick reminder, today's presentation will include forward-looking statements, which are subject to risks and uncertainties that are described in our SEC filings. We have a number of our speakers today that are outlined here on the slide and are joining me here on the stage that you'll be hearing from throughout our sections. Here's a quick look at the agenda. We'll be starting looking at our D iagnostics 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 quick, but we'll have Q&A sections throughout, so hopefully we'll be able to get to all your questions. We have a few releases out this morning, so just giving people a heads-up. One's already, I think, hit, and the other one's coming shortly. 10 years ago, we started Tempus AI to solve a single problem, which is could you use AI to essentially unlock precision medicine? See if my clicker works. No clicker. Technical difficulties on clicker. We could stare at this slide for the next three hours. The laptop isn't connected. It's one of those you feel like you're at your house where you're just going to move it around the room. Given that this is not our space, this is the building's space, it's hard to complain. They were nice enough to let us use it. Should we just connect a laptop? Say it again? Nope. Nope. Okay. Hold on, I'm now clicking, which is exciting. Okay, given our clicking capabilities are back, I'll start over. 10 years ago, we started Tempus to solve a single problem. Could we use 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? 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 U.S. healthcare system, which then makes people want to connect with us more and therefore feed us more data, generate more insights, deliver more applications. 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, morphologic, phenotypic, and molecular data. 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 metabolomic data, imaging data from CT scans and MRIs. It's digital pathology slides, it's clinical data in structured and unstructured format, and all this data comes together in real-time to essentially create this I think I lost my mic, this network effect system. Something will always not work, and we'll just kind of bounce. We'll 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 U.S. The vast majority of oncologists in the U.S. are now connected to Tempus in some way, shape, or form, ordering our tests. We run a large volume of tests, we generate lots of that molecular data at the beginning of that flywheel. We're connected to more than 5,000 institutions throughout the U.S., which means we're connected to a significant percentage of the U.S. healthcare system. There's maybe 8,000 hospitals or so. We generate enormous amounts of data, have built a world-class team to make sense of all this data. 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 it 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. 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 nine 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. All the totality of what you would need to basically interrogate real-world data and figure out all the insights we're going to talk about in a minute on the biopharma side. In order to make this business sustainable, we've divided it into essentially two parts. We built a diagnostic business and then a data and applications business. 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? Why am I going through all this effort to contextualize molecular reports? 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. If so, the journey we set out 10 years ago was could we essentially make diagnostics smart? Could we help contextualize them and basically wrap technology or AI around them to help physicians make really high-quality decisions and help researchers do much more efficient research? On the 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've learned something. How do I connect it to clinical data so I can essentially figure out, 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. 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. 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. That same data, that same vast amounts of multimodal data where you have an insight connected to outcome and response, is also what's needed for research. 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. 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? The investments we've made in connectivity and AI essentially allow us, and will allow us in the future, to distribute these insights at scale. Our goal is not just to distribute them to all cancer patients, but to all patients in the U.S. If Tempus is successful, over time, we'll be connected to every hospital in the U.S., or almost every hospital across all major disease areas. 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, then deliver those to every clinician in real-time so that patients are always on the right therapeutic path. Begins with we started in cancer. You have to pick a place to start, 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. 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. In cancer, that begins, if you look at the compendium, it starts with who's at risk of getting cancer? Translates into who has just been diagnosed with cancer, and how do I essentially put them on the right therapeutic path? 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. 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? 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. 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. Given that we have ADLT pricing, that's quite powerful. That was a big approval for us. 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 exome, things of that nature. Several of those are also going down this kind of FDA regulatory approval path. We have a series of pharmacogenomic assays we offer, things that have become super powerful these days, whether it's DPYD or UGT1A1. We also do pharmacogenomic profiling of patients with neurological issues such as major depressive disorder, bipolar disorder. We have a whole bunch of algos that sit on top of these diagnostics we'll talk about in a second. On a series of tests that typically are ordered alongside these, whether it's immunochemistry stains or other tests of that nature, and then we have obviously a fairly large and growing portfolio in rare disease and cardiology and obviously hereditary profiling. A significant body of assays. Essentially, to understand Tempus's strategy, it begins with what is the diagnostic that's going to be most commonly ordered in this disease area, and how do I either offer that diagnostic or partner with somebody that's offering that diagnostic, both of which are perfectly fine solutions. Generate that diagnostic data, begin to generate or consume that diagnostic data at scale in real-time across a large percentage of the U.S. market, and then begin to collect clinical data that's connected to that diagnostic so I can figure out what's happening. What drugs are patients going on? Are they responding? Are they not? 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. Just starting off at the top, one of our 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 tumor 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. 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. 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 via DNA. They are able to be found when we're looking at the RNA signatures. This is an area where we're seeing a preponderance of orders coming in both inclusive of DNA and of RNA. I'll talk about next a little bit some of the studies that we've done that show that incremental benefit that we've been able to see as a result of adding RNA onto that DNA finding. We have published a number of cases where we're looking at the incremental benefit. What are we able to find when we're looking at patients who receive both a DNA result as well as with an RNA result? 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. Not surprisingly, we're seeing a large portion of our patient population include these RNA xR orders in addition to our xT orders. Moving from the solid tissue side of the portfolio to our liquid biopsy. In those 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. 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. 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. In this case, we're in that 6 to 7-day range, which has been helpful for many clinicians in many patient cases. Importantly, what we're seeing is that these clinicians are also looking at this liquid biopsy and the evolution of tumor biology over time. This translates to having multiple xF tests that are ordered to understand what are the resistance mutations that might be happening in a given patient case. For example, with EGFR in lung cancer cases, a resistance mutation may be able to be found during the course of therapy. We're seeing in 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 found around 9% additional actionable variants are found when liquid is added into that solid tissue case. Clinicians are, in many cases, including this as part of their standard routine course of care. 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 in 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. This is data that represents what are we finding incrementally beneficial in cases like lung and in breast and in prostate and in CRC. There are these kind of incremental additive benefits. Again, additional evidence as to why clinicians are increasingly looking at both a solid tissue profile as well as a liquid biopsy profile. Thanks. Just for a second to kind of provide some context. Tempus is about 10.5 years old, give or take. When we started, Foundation Medicine was the leader by far in solid tumor profiling and had a liquid offering, and Guardant was obviously the leader in liquid. At the time, people thought it'd be very hard for us to catch up and make progress. 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, 8+ years, where we've become number one or number two in both of those spaces, and have unit growth that is a best-in-class. It really speaks to not only the comprehensive nature of these assays and how good they are. We are the gold standard, I think. We're in that top tier gold standards in both solid tumor profiling and liquid biopsy, but also the technology we wrap around these tests. We'll show you in a few minutes our main ordering system, which is called Hub. 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. What ends up happening is the ecosystem, if you're a doctor ordering our tests, it just gets 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. 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. One of the newest algos we've deployed is called our Immune Profile Score, which Ezra's going to talk about in a second. These algos now have an attachment rate of greater than 40%, which essentially means a doctor'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, do you want to talk a little bit about IPS? Yeah. Thanks, Eric. 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. 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. It gives providers that extra insight to make the decisions on whether they're going to use immunotherapy for their patient or whether they should go to a different modality. It's provided a tremendous amount of value. The feedback has been incredibly positive. That's a perfect example of what we can do with what we have built at Tempus, as Eric just read. Thanks, Eric. The benefit of these algorithms is that they're essentially providing insight on top of something else that might be previously known. 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. 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. 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, but other disease areas. You generate some kind of diagnostic insight, laboratory test result, you connect it to rich outcome response data over time, you track what's really going on, you refine the diagnostic, it becomes smarter, collect data, track, refine, so on and so forth. Before we leave therapy selection or comprehensive genomic profiling and go to MRD, I want to talk a little bit about the growth drivers of CGP. We believe that the unit growth rates we're experiencing, which are pretty extraordinary, are sustainable for a long period of time for a few reasons. One, CGP is still not saturated. There have been a significant number of reports come out recently that estimates the percentage of doctors ordering these tests is in the kind of 40%-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. We suspect the whole market will grow. Since we're growing faster than the market, that'll accrue to our benefit. The second is that we're the beneficiary of 40+ 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. If you've looked at the trend, we've been sequencing patients earlier and earlier in their diagnosis. We suspect over time the vast majority of patients, even into Stage II and Stage I, depending on the disease area, are going to be profiled. 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. 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. We suspect that trend will continue. 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. We suspect the top companies in therapy selection or CGP will continue to do 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. 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. In each area, we have to think strategically. We got into therapy selection. That's where we began. We began in solid tumor profiling, had to earn the right to go to liquid. That was a conscious decision we made. We 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 assay. With MRD, it was a bit trickier. It was an emerging space. It was a new space. We had to make a series of decisions. One is, what did we think was going to win long term? Was it going to be a tumor-informed profiling or tumor-naive? That is still, I think, up for debate. 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? 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. This space was growing very, very quickly, getting to scale, generating lots of money, and yet the space hadn't yet really kind of landed in the place it's going to land long term. Our approach was to kind of de-risk that by having multiple irons in the fire. 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. I want to say one thing and then I'll bring up Kate. We have a distinct advantage in this area. One is we generate enormous amounts of data. 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'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 U.S., 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. There are advantages, as you mentioned, to both tumor-informed and tumor-naive. We like to think that we have the best of both worlds. 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. That's because of the technology that they use. Personalis and ImmunoID NeXT leverages whole genome sequencing from tumor tissue and then actually looks at up to 1,800 different variants, kind of leveraging that whole genome information to be able to sort of personalize then and follow and track that patient's variants that are contributing to the disease. 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 has been in the game for a little while. They've been investing heavily alongside us in different clinical studies to be able to show the validity and utility of this assay. Of course, with MRD, one of the challenges we all have in the field is that you really need to tie this to outcomes. 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. With different treatment modalities or with different clinical decision-making, how will that translate into survival and other types of clinical benefit? As great as tumor-informed assays are and the fact that they can get to this really low 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. If you want to cover all solutions for all patients, you also need a tumor-naive assay or a liquid approach. 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. We started in CRC, but we are looking at lung and breast and panc and head and neck and all of the indications where a patient luckily may need to have an ultrasensitive assay or an assay to monitor for therapy response. This is good news for patients at large because it means that therapies are able to achieve really deep responses, and now we actually need these diagnostic tools to be able to understand still within patients who've had great responses, who might ultimately relapse, why are they relapsing, and what therapy do they need next? 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. Before we get to hereditary, I want to just cover a few things. 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 the unit economics continue to improve on their side. They started, like a year ago, had zero approved. They now have three, 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. 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 on tumor-informed, it speaks to that. We essentially generate the kind of profit at the present moment from that test that we would generate if we ran it at scale on our own. We're a bit agnostic. From a profit perspective, we're agnostic as to whether or not we partner with somebody and generate the EBITDA we would love to generate or whether we run the test and eventually generate that. 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 in PPM you have to hit is just much lower. We were out there with an assay at, I don't know, 500 or 1,000 PPM, and the market was at 100. We began working on a next generation of our tumor-naive assay about a year ago, I think. We are already 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. We have to kind of skip a bunch of that and really go from redoing CRC to focusing on pan- cancer. That's our strategy in naive. Given that it represents 2% or 3% of our volume, it just isn't material today, but eventually, hopefully, it will be. On that note, let me hit hereditary for a second. I'm going to turn it to Tom, and then we'll come back. Okay. It's all yours. Thanks, Eric. 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 a emerging and growing footprint in rare disease. I'll talk briefly about both of those. Those of you familiar with the marketplace, there's three large buckets of testing orders, the biggest being the genetic counselor space. They order roughly 50%-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 OBGYN. 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 U.S. who meet NCCN criteria for hereditary cancer testing, and they're just going undetected. Roughly 1.5 million-2 million tests per year is what's happening right now. There's a huge opportunity. Tempus is very uniquely set up to capture the unaffected patient population. Actually, about two years ago, which tells me our strategy is working, we finally surpassed where we're doing more unaffected patients than we are affected patients. Things are moving in the right direction. There's a huge opportunity here. We have a automated high-risk platform called Care. There's roughly about 250 sites and growing across the U.S. right now. 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 pretest education for those patients, we flag them for our clinicians, 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, we can also send those directly to the patient if they desire and do the post-test counseling. This automated platform allows us to access that unaffected patient population, which we see is really primed for significant growth. This year, we're adding enhancements to Care. I won't go into all of them. On the front end, we are going to be seamlessly integrating into EMRs, starting with Epic. That happens this summer. On the back end, we have what I call a safety net wrapped around our patients. Patients going through the health systems create lots of care gaps just because of the workflow and the complexity of it. We're launching a product in the summertime for starting with breast cancer and then moving to other cancers that have guidelines for germline testing. Just to help with the scope of how big the opportunity is, one of our pilot sites looked at breast cancer patients alone for the past 60 days that are active care throughout that health system, identified 5,000 patients, breast cancer patients, that should have had germline testing that didn't get it done. The Care platform will be able to wrap a safety net or umbrella around that and make sure we identify those patients for our clinicians and then send them back a flag to get tested. 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, pan-cancer tests, 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 three to five days. 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. 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 40,000-plus RNA patients. It increased our diagnostic yield by almost 9%. This is the first time in hereditary cancer testing in a decade where you can actually prove that you have a better test on the marketplace. We crossed over well over a million RNA patients to date. We'll probably do another 400,000-plus this year. With regards to rare disease, Ambry had actually been in rare disease for quite some time. We launched the first commercially available Exome test way back in 2011. Our product portfolio currently, we have MicroArray, we have Exome. In the fall of 2024, we launched a test called Exome Reveal, 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. 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%-10% of the U.S., and about 80% of those are genetic, 50% of those are with children. These folks go through this diagnostic odyssey. It takes typically five to seven years to identify the genetic disorder for these children. Unfortunately, with the advancements in technology, diagnostic yield is increasing. Exome Reveal, 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. Instead of a third of these patients being identified, it'll be a little over 40%. 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. 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 liaisons work with them to answer any questions that they have. This impacts roughly one in 20 of the patients, so about 5% of our total patient population, which is a pretty significant increase in diagnostic yield and identification for our patients, and this is very unique to Ambry/Tempus. Thank you. 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 really more intelligent platform that can grow. That's just our playbook area by area. You want to come on up? 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 sides. We'll demo both those today for a second. All this comes together, right, in this giant connected ecosystem. Every part of this company is working on this platform that essentially generates rich diagnostic data, typically molecular data, connected to other form, different data modalities. Again, phenotypic data, morphologic data, some kind of text or image or 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. This is all happening at scale. 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. With that, let's talk a little bit about our main platform. The platform essentially, you're going to see this both today in Hub and in Lens. Hub is essentially an ordering tool that we use for physicians to connect with. You can go to it directly on your iPhone or iPad. It's integrated in with most major EHRs in some way, shape, or form. Sitting inside Hub is this brain, which we call Tempus One or One. This is essentially all the benefit of the agents we have built live inside One. We have built a ton of agents. Thousands of agents that essentially take disparate, miserable, siloed, multimodal data and make sense of it. 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. 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 As and Bs and Ts and Cs, you don't get much out of a large language model. Sitting inside these products is our Tempus One, and with that, Laura's going to give you a quick demo. Hi, Laura Elster, Chief Commercial Officer at Tempus. I'm going to walk you through the demo of Hub. There we go. Great. 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. I'm going to go down to a fictitious patient, Christina Collins. You see that Christina Collins here, her provider ordered comprehensive testing. You see our tissue test, our liquid test. We have DNA, RNA, a handful of IHCs, as well as some of our algorithms. 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. 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? They can ask a question like that, and in seconds, Tempus One will surface a result. What you'll find is we're including the citations. Here, I might have mistyped there. You can see we can include the citations here as to where we're getting the information from. If you go to next, then at this point now the provider needs to decide what to do for the patient. They're going to think about putting them on a combo chemotherapy and immunotherapy. If you look at the RNA results here, we can see that the patient finds a rare NRG1 fusion. This is something that often wouldn't be surfaced on DNA alone. In fact, we've co-published that 40% of these rare fusions are found through RNA. At this point, the patient starts on the combo chemo immunotherapy and chemotherapy. This patient also had the Immune Profile Score, which you heard the team talk about, and the result was an IPS low in this case. 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. Because of that, the provider might think to order MRD testing to now monitor how the patient's doing. You see here, we've got this timeline. We show all the results over time. 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 at time points, we start to see significant elevation. Let me stop you for one second. Yeah. Just to provide some context. Sorry. You're in a demo environment because we can't let you in the. Yeah. Fictitious. production environment or someone shows up with guns or whatever. Essentially, what you can see here is that we have all this technology wrapped around the report itself. We have the ability to take the clinical data we're connecting and have the system automatically generate a summary of that patient, or a summary of their current therapeutic regimen, or whatever'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've pulled the note out, we've de-identified it, and the note is there. 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. 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. Yeah. In this case, they're tracking the MRD, and it becomes significantly elevated, so that's when the provider might go order imaging. In this case, the CT confirmed progression. 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? You see it quickly surfaced here now, information with linking out and sending you to more information. 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 using the technology and the testing together. Cool. Just at a really high level, this technology just doesn't really exist other places. Other people have ordering systems where you can track an order or get a result, but here it's all brought together allowing you to contextualize it. The challenge with cancer cases, and I think most of the things that kill us, heart attack, stroke, cancer at later stages, they're complex. The comorbidities, the amount of things you have to consider, just complicated. 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. 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? What does that mean? How should I think about it? Maybe I'm going to bring somebody back sooner or dose them differently or be careful. 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. 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 a decision to build a large-scale foundation model in partnership with AstraZeneca and Pathos AI. AstraZeneca was providing the majority of the funding. I think they invested about $200 million to build this model. Then we began building it. The model is quite significant. It sits inside a cluster of about 1,008 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 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. Yeah. As Eric mentioned, we're really excited now to enter this next era. You've heard this morning already about the algorithms that we develop and put on top of our diagnostic test. Part of the future promise here is that we can start to do that at a really broad, amazing scale by using foundation models. One example that we've been working on is just starting to take this multimodal data that you've heard about all morning, the clinical notes, the EHR data, the molecular data that we have, both DNA, RNA, and images, and to combine that and instead of developing algorithms, you heard about IPS and some of the amazing tools that are already out there today. Those were developed by traditional computational data science teams, really combing through the massive data and coming up with those algorithms and then validating them. Future state is that we're going to have models that will be able to surface those insights very rapidly in a more automated fashion, and then we'll be able to validate them quickly. We've started by just looking at very traditional biomarkers. We know that we already have good biomarkers like EGFR, ALK, ROS1. We can name a whole laundry list. We ask the question: Could we go even further and contextualize those patients? 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. 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 standard of care therapies. We'll be able to then also look at other biomarkers. We looked at things that were already well known about EGFR patients, P53, other co-mutations, other comorbidities. The model's actually able to pick up beyond those standard biomarkers other signs that a patient may or may not respond to standard of care therapies. 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 a physician to really be able to get a more global and deep understanding of the patient that they're looking at. Yeah. Thank you. Really quickly, and then I'm going to bring Ezra back up. 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. The next level of that would be run large foundation model at scale, have the system, instead of generating one insight every six months or a year, generate an insight a week 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 this NTRK fusion matter? Are they going to respond to immunotherapy? What adverse event is 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, 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, over here you can, 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 Tempus Preview, which is essentially leveraging our digital pathology library to generate a whole bunch of insights. Some of those insights are making calls early, some of them are making calls when a doctor can't get that information. I'll pass it off to Ezra. Thanks. Thanks, Eric. Here we have, as Eric was saying, two examples of how we've leveraged DigPath AI into the diagnostics. The first I'll talk to you about is Tempus Preview. 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. 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. 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. How have we addressed that problem? We've addressed it through DigPath. 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. 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. 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, there really is very few methodologies that can get us below that 7% threshold. We decided that we would address this problem in a different way, again, bring in the capability of DigPath. Here, this is called Paige Predict, I'll show you, this is a real-world example, obviously, the patient's name is different, where we can use the DigPath 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, 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. With the DigPath, 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. 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. On that note, I'm going to turn it over to Jim to talk a little bit about the financials of diagnostics. Thanks, Eric. I think that gave you a good overview 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 accelerating growth rate throughout 2025. We have favorable ASP tailwinds that have led to the revenue growth. We'll hit that in a slide in a second. 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 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. Again, we've seen ASPs tick up over time. We've talked previously about this path to achieving $500 of incremental. With the announcement this morning of xT CDx being approved, that allows us to capture that $200 at the beginning of 2027. 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. There's also commercial coverage over time continues to tick up. That's not 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, 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 lapping some of those share gains. Lastly, we've talked previously about this 25% growth rate over the next three years. That would put us at about $1.9 billion, just to give you the size and scale of the diagnostic business. We provided a rough breakdown of where that's coming from, primarily in clinical oncology, hereditary, obviously moderating back to the mid-teens that we had talked about previously. Within that three-year period, there's going to be periods where ASP may outpace and you may be growing faster than 25%, what we really want the takeaway to be is that this business is really set up for durable long-term growth. On the right is the list of initiatives, both near term, which are all being executed on today, also the longer-term growth drivers that will allow us to move beyond that three-year period. 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. Start with Dave. Can you state your name? Yes. Dave Westenberg from Piper Sandler. I wanted to talk about the trajectory model. The AI model predicts how patients will do over time, significantly outpacing the statistical models at predicting survival. You've demonstrated this across three cohorts of famous studies. I believe they were all in lung cancer. Two questions. First, 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. Do you want to start with the first and I'll cover the second? Yeah. We're going to talk more about this actually in the data section in life science. That's okay, you jumped in. Yeah. First of all, just in terms of how this model compares to other more traditional methods that you're mentioning and reading from the press release. This model, we have many different models. We'll talk more about that, but the model that we're highlighting there is what we call a patient trajectory model. It's actually able to look at patients over time, which is one of the benefits that we think we're really excited about. One of the things we were highlighting is that it's very good at predicting outcomes. For that particular model, it's able to look at outcomes, in particular survival. 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. 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. It's also a space where we can then start to validate against 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. 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. 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. The future state will be taking some of these models, asking questions, and then working on validation in a lot of those different spaces. Good tee up for what's going to come later. We'll talk about it in a minute, but at a super high level, we generate an insight from these foundation models. One 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. Once that's true, you know you have something that's working, then you go seek to basically go to third-party datasets and validate it. Diagnostic insights will essentially, for the near term, live on our diagnostic tests because That's where they'll live. 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 II, so on and so forth. They're already being used, and we'll talk about that in a second. Shoot. You want to call people out? Liz. You want to hand the microphone off right there? Yeah. Come mic. Thanks. Kyle Mikson from Canaccord. Thanks for the day, guys. Great stuff. I want to ask a bit of a multi-part question. First, on the LRP, I guess, I know you've been talking about the 25% for a while, but the street's at 20% revenue CAGR for diagnostics. Maybe just talk about what we're missing in terms of ASP volumes, MRD, and AI and rare and stuff. Secondly, maybe just talk about the diagnostics M&A strategy. You haven't done one since Ambry, but you have Personalis, about 15% or so. Maybe just talk about how you're viewing dilution versus growth in that segment in terms of M&A. Thanks. We can both cover it. I'll start. I don't follow the street model. Some of the models I've looked at essentially they have high growth rate in 2026. They had high growth in 2024, low growth rate in 2026, 2027. They had high growth rate in 2025, low growth rate in 2027, 2028. Now they have high growth rate 2026, low growth rate 2029. They're just nonsensical. They're essentially saying, "You're growing really fast, but one day you won't." 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. We have to believe that we really think that's going to happen. 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. At the present moment, we believe we're going to grow at 25% roughly. Our volumes are pretty healthy right now. We're growing in the low 20s, that's awesome. Some of it's going to be ASP lift. You had two big drivers of that. Roughly half of that gain showed up this morning when we got FDA approval for xT CDx. That's $200 of lift across a massive number of tests. I don't know the number, it's probably $7500 of gain. That's a real number. xF, when that's approved, is another big piece. Part of it's ASP lift, part of it's volume lift. You're launching new tests and other things are happening. There's always pluses and minuses. Everything doesn't grow equally up and to the right. 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% three-year growth rate. 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. 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. 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. Nor does Apple make every single app. There's going to have to be technology platforms that take the U.S. 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. We've always been focused on not just running tests well, but figuring out ways to make money partnering. As I mentioned earlier, our net unit economics today are as good with Personalis as they would be if we owned the company. If we one day own the company, all you get is revenue gain. You get no net income gain. Great, thanks. Casey Woodring from JP Morgan, 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% 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. 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. In terms of the attachment rate, it hasn't changed over time. I think we've published previously it was around 25%. It largely has stayed intact over the years in terms of the number of physicians that are ordering it. 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. There will be some tests that don't get reimbursed, but we're still going to see a net add to the overall reimbursement since we're not at parity with our peers. We think we're well-positioned to continue to win the space. It's 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 it's not going to get paid for. It felt like, you guys are on the edge. If we were on the edge two years ago when we went public, we're now in the middle of the bus, maybe getting toward the back of the bus. 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. They just will keep showing up forever. We are not cutting edge in terms of- 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. I think to the extent we were on one end, we're not. If I could make a comment, Eric. Yeah. Please. You don't know who that 9% is a priori. That is the other thing to keep in mind when you are 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 is going to fall into the 23% that is only going to be surfaced by RNA, so I have to order both. Logically, it makes sense to reimburse. 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 it's fully all the puzzle pieces are in the right spot, so I don't know where that's going to land. The RNA train's left the station. It just matters way too often. We're in the middle of a bunch of CDXs. Our competitors are in the middle of a bunch. You come back five years from now, there will be dozens and dozens of dozens of RNA-based therapies that you will need RNA expression data for. We'll do one more. We have more Q&A. We'll do it in rounds. Go Kallum. Thanks. Kallum Titchmarsh with Morgan Stanley. Maybe just on the oncology business and the volume growth you're seeing today, could you just break out that growth between the existing account penetration versus new adds? Then, I guess, have you seen any examples of physicians switching to Tempus's tests as a result of the technology infrastructure behind it, like Hub and Paige Predict? We'll start with you, Mike. Any thoughts on the new versus existing? Yeah. We've held pretty steady in terms of addition of new clinicians and new ordering systems. We monitor this really closely, so we look at what is the reorder rates over time, and then how do we actually add net new physicians that have never ordered with Tempus or physicians that have previously gone stale, perhaps hadn't ordered with us within a 12-month period, and they've come back. 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. There's really two vectors that we're seeing this growth come from. One is in this 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's only 14,000 oncologists in the U.S. Eric talked about we think it's about 50% of oncologists actually order that. It's always a combination of continuing to have the physicians identify more patients that should receive this type of testing and then tapping into that untapped market of folks that aren't ordering at all. In terms of some of the new products like Paige Preview or Tempus Preview, I think we just are deploying these things now. We acquired Paige, I think maybe six months ago or nine months ago or something. 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. It's just that there's no way to solve that problem. We have that problem. All of our competitors have that problem. You just occasionally don't have enough tissue. Occasionally, the Illumina sequencing process just doesn't yield the results you want. 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. 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, 12 months. Let's jump to the data business, and then we will come back to Q&A, if you don't mind. Okay. 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. 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 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. I just give you one example, you can go back to 1960, 1970, when people were trading on the New York Stock Exchange, orange futures, and would've bet you their life that this could never be replaced by technology, and it's all been replaced. That's just the unstoppable nature of technology, and healthcare will be a beneficiary of that. It is coming. In our case, we have spent the last 10 years really building the piping to generate a healthy and sustainable data business. That piping is all about how do you pull data out of the U.S. 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? 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. It's typically not a simple answer. They're designing trials. It could be novel discovery. There's a lot going on. You have to give them the tools to interrogate this data, and in that regard, we stand alone. We started the IPO process two years before we went public. I think if I would have asked nine out of 10 investors, they would have never thought our data business would be this big, and they all would have thought we'd have met massive competition. Both of those have proven the opposite. Our data business is big and growing, and we have almost no competition. 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 the scale, that's unique. This data comes from many sources. You can't just be a sequencer that generates DNA and RNA data and be like, I'm in the data business. We have data coming from our care gap products, our clinical trial matching products, our real-time clinical trial matching products, our AI tools and technology, our diagnostic business, our radiology products, our pathology products, our cardiology. We have many, many ways to get data, which allows the data set to be real, contemporaneous, and useful. It's 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. 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? Ryan's going to get into some use cases and the tools around it in a second. It's really the entire R&D, the discovery and development life cycle. 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 $1 million or $5 million to a big pharmaceutical company or a 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, where they're certainly not in oncology at scale and will be in other disease areas over time. Ryan, you want to jump in? Yeah. This is a quick snapshot of the platform. Like Eric was mentioning, we've been licensing data to our biopharma customers for several years now, and we have been in the business of licensing multimodal records for longer than anyone in the industry. 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 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 here 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 a 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 either Tempus 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. Right? They start with a population of interest. Then they're trying to understand for the particular population that I'm going after for my trial, is there patients in the real world that I need to better understand? Again, with this combination of DNA, RNA treatments, and outcomes. Really the essential step is that you have to be operating at scale to get to the bottom of the funnel that is significantly powered, thousands of patients. 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. 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. Now, one of the things that our customers are doing all the time, 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, right? One of the things you can quickly do in this system, even by 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 around, things that only Tempus can provide. Something like co-expression analyses as well. I can start to dig a little bit deeper and we allow our users to get to this type of insight in literally in a second or in days. Looking at things like beyond EGFR and KRAS that we know of today, but looking at novel biomarkers that are coming in the future, like MTAP deletions, is essential for our drug developers. 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 pairwise 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 kind of 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, 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, right, and that actually have that capability. Again, we've embedded a copilot here from Lens to be able to help people ask certain questions around, how do I create an oncoplot? You're looking at the top 10 most frequently altered genes. I can quickly do that, look at which tools that I need to call, but I can also start to see the actual code that was written. I can pull that up here. It starts to run the actual sort of analyses in my R environment. Again, we're giving the user the control here. The code is populated, 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 have that 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. If we can switch back to the slides, I can kind of then cover some of the other aspects. All right. I already walked you through the three steps, the query, 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 III global trial. Even a 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 really was interested in advancing their immunotherapy franchise. They really needed to think through could they uncover additional novel biomarkers in a particular patient population. Here, for this particular project, we were able to assemble a data set that really didn't exist in the world. A 5,000-patient data set where we had biopsied 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. Again, 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, but one project alone, and if you 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 30x-50x of what they spent. This measurement of value is the common, I would say, 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 in refining the trial for a phase III global study. Here was a different global pharma company that was faced with this kind of critical decision. Here we were actually trying to better understand what was going to be the comparator arm. Can we actually understand standard of care and make sure we establish a good baseline for what those patients are facing and how their outcomes are performing today, but also to make sure we're stress-testing the inclusion and exclusion criteria, 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 III study? Again, in colorectal cancer, this is essential. We were 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, in how we build over time. The last piece is really another global biopharma company that was faced with a slightly different decision, which is really around not just investing in a global phase III study, but do I go first line? Do I stay in second line? That type of a 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 we can build so that we can analyze the molecular distributions of patients that have high PD-L1 versus low PD-L1, because this ADC sort of decision for that particular drug was going to go up against that type of landscape. 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. Also, the go decision was made to ultimately allow that customer to proceed. We talk a lot about probability of success, but we've been in these collaborations for long enough where our customers have actually seen success. We've seen approvals for the programs that we've supported, and that makes it not just a perceived benefit, but an actual ROI metric that ultimately leads to why our business has grown over time. With those three, pass it over to Eric. Great. Thank you. At a high level, I just want to cover the scale and scope of our data business. We're working with something like 19 of the 20 largest pharmaceutical companies in the U.S. That metric has held pretty constant over the past several years. The good news being, we're still working with all these folks. We work with over 250 biotechs. We've signed in excess of $2 billion worth of data licensing deals. Our revenue last quarter on the data licensing side was $87 million. We have now large partnerships in place with not one or two big pharma companies, but lots. This number continues to grow as we sign more and more of these multi-year, $10 million-$20 million or $30 million a year engagements with folks. We also deliver an enormous amount of data. I think as one of these, as you talk about this Lens platform, these capabilities is they 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. When you think about delivering petabytes of data, it's just not a small task. Ryan got into a bit of the ROI that we are used to measure. 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 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 $0.5 Million project or a $1 million project." They get the data. They have to try it and test it. Multiple teams are involved. They have internal computational biology resources and bioinformatics resources and biostatistical resources 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? 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. They're happy to have you as a client, or you can spend $1 billion a year. The only thing that you gain by making a longer-term commitment, both in terms of years and dollars, is a reduced price. 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, here's a great example of that. Our first strategic partnership with a big pharma, we have several strategic partnerships with big biotech, but it was AstraZeneca. That was signed, I think, in 2021. Obviously, that relationship is going strong, they're funding our foundation model, and that project runs for the next, I don't know, several years. We've got a long-standing relationship with AZ. GSK was another large partner that came on board. There's a few years left in that agreement. 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. 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. We'll get into some of the metrics of the business, but in terms of overall relationships and concentration, we work with about 240 companies in 2025, and that number should be up in 2026. We worked with 35 in 2020. Over a five-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 on a free fall down. The good news is the business is diversifying itself over time. Increasingly, people don't just want our data, they also want models. We call it data and applications, but in reality, it's not a great name, because what's happened to us over the last year is it's rare that people just want our data. More and more and more, they actually want models. You can almost call it modeling and applications. Data in and of itself is interesting, but in a world of large multimodal models, which all of our companies have some exposure to, they want to know how they can use this data to build their own proprietary models or take their proprietary models and hyperscale them with more data and actually figure out how to build something that's proprietary and advantageous to us. Almost every conversation we have now is a blend of license some data and use our capabilities to build models. Build those models on the Lens platform. We have both CPUs and GPUs connected to that platform at scale, or we'll partner with you to build models in some way, shape, or form. 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. Kate will cover that. Yeah. We talked earlier about a little bit more detail about our model. Actually, when we say model, we're really moving quickly towards many models. 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, we have things there. We mentioned the co-scientist type of approach. 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 tops of tools. The same thing is true here for our biopharma partners. The ecosystem we have today already includes multiple models. 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. Rather than having to run sequencing, they can now use an H&E image to understand which patient that they should enroll and to pull those in. In a similar way, we're actually able to take all of our clinical data that you've heard about this morning and to be able to leverage that and pull that into a model. 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. Yes, we can have a genomics model that combines DNA, RNA, TCR sequencing, BCR sequencing. We can also then combine that with the clinical model we just talked about in terms of patient trajectory, thinking about patients over time and what's happening to them in the clinical space. 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. 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 standard machine learning and data science approaches. Future state 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. 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 I/E 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. Again, I think the release this morning covers some of this. There's 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 an open 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 which they had trained a very specific model. The question was both could this large-scale model replicate trial outcome data that's publicly available and privately available, both where there's no model that's predictive and a highly tuned model that's predictive? That was the hurdle we had to get over. 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 0.1 versus ChatGPT 1 versus whatever 5 point whatever we're on now. 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 Diagnostics side of the business and the Data side, but also the Application side. How do you take these applications or algorithms and distribute them broadly? Given that we have this connected ecosystem to 5,000+ hospitals, we're in a unique position to be able to distribute AI into the U.S. healthcare system at scale in ways other people can't. There's all kinds of questions that people are answering every day. What critical biomarkers should I target? What are the therapeutic options I have? What clinical trials is my patient eligible for? Did I overlook something? 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 kind of 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 kind of 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? 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 care gap program we call Next, and both of these things are operating at scale. They're operating at scale. They just don't generate lots of money, which I've said many times. They operate at real scale. It's not like, oh, we've got a few people using these things. We have many, many providers using them, multiple care gaps deployed, millions of patients being screened. We are enrolling lots of patients in trials. We are closing lots of care gaps in real-time. These things operate at scale. If they were paid for like I suspect they will one day be paid for, this would already be a large business. We're fortunate that our two main businesses, Diagnostics and Data, generate enough gross profit and 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+ 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 if the number is 80 or 70. It's some very large number of providers that spans 1,000+, 2,000+ oncologists. 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 space of kind of we've been over the last three, four years proving that it works, and now all the conversations are about enterprise engagements where big pharma's like, I want to give you 15, 20 trials. We now are in a position where we're like, it's too many. We're now fortunate that we're able to say to people like, we can't take your 15 or 20 trials, which is creating a really unique market condition. All of these technologies that allow you to scan clinical and other forms of data in real-time and then generate an insight essentially are part of this future where there will be ambient AI-enabled copilots that exist in the wild that essentially make good doctors great doctors, and great doctors superhuman. 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. 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 doctor through Epic or through other EHRs that they're using. All of these technologies are necessary to kind of pull off this, how do you listen in the background, find an insight, and deploy it? In addition to our applications business that is growing rapidly, we also have an algorithmic business, which we call algos. We believe that in the future, these algos will be pervasive. We've talked about that historically. We are now not alone in that conversation. We have over the last several months, I think, been dragged to Washington several times. People have been here. I think the entire world now is thinking about how are we going to manage a world where we don't just generate wet lab diagnostics, but dry lab diagnostics. How do we make sure these things can get validated and paid for? How do they get ordered? All of that. They can't be stopped. There's going to be more of them. Patients are going to want them, and the U.S. healthcare system will have to adapt. 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 are ECG-based atrial fibrillation or AFib algo, and our ECG-based low ejection fraction or low EF 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've talked about a little bit historically, which is the ECG-based use case, because I think people kind of understand maybe oncology or pathology, but this is 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. 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. 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. 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+ hospitals, touching millions of patients. Not small, 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 U.S. 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 a part of the population. The code will be expanded to, we think, the entire population. You can imagine as these things get to scale, that one hospital alone could generate a $few million of revenue and multiply that by lots of hospitals and lots of ECGs, and you get to a $several hundred million business just on our AFib predictor. Low EF is even bigger. There's other algorithms coming. 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 of revenue just from that product alone. That same thing's going to happen with echos and CAT scans and MRIs and mammographies and digital pathology slides. Each one of these data modalities that's being generated is a algorithmic diagnostic opportunity that's being missed today. Even at small dollars, $50 or $100 per algorithm, the impact you get is enormous. Even if you spend $1 billion generating an algorithmic insight, you're likely going to save the U.S. healthcare system $50 billion or $100 billion of mistake because as these patients don't get caught, as they don't get found, they show up with complex disease and the most money we spend is in the last 90 days of life. 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? Yeah. I'll hit them quick, and we can jump back into Q&A. 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. 41% growth in Q1 of this year. The insights business growing even faster, kind of partially offset by some of the smaller businesses. We announced expanded collaborations with Merck, Gilead, BMS, over the last couple of months. As we've said, the pipeline with biopharma remains very strong and engagement with our customers, given all the value that Ryan described, is in a good spot. In terms of the financial metrics, these 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. Again, in a very healthy spot in terms of that forward-looking visibility of revenue in the future. We've previously 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. Again, as these relationships expand and we 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. 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 of 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. With that, more than happy to take any questions on the data set. Oh, sorry. 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. Second, Tempus AI has the largest multimodal oncology data set. Is the Tempus AI data set comprehensive enough, or do pharma companies still look to sign additional data deals with other companies? Thank you. I'll take the first. Ryan, you can take the second. Look, I think where this is going, we talked a little bit about it a minute ago. I'm convinced where this is going is over the next several years, we will begin to layer real-world data insights on top of every biomarker that is really therapeutically relevant. It will no longer be, I sequence a non-small cell lung cancer patient to see if they have EGFR/ALK. It will be, once I know someone has EGFR/ALK, what does it mean? Are they going to be in the one-quarter of patients that have almost no response, the one-quarter of patients that'll be on that drug for five years, or the 50% of patients that'll be somewhere in between? I will want to know that because that will determine what I do next. I know my patient's very unlikely to respond, I want to bring them in right away. On the other hand, if I know they're likely to be on this drug for a long time, different path. Yeah, Eric, if I could add one more. The Care Gap, the Tempus Next program, immediately practice-changing. We are actually going to present that at the ASCO meeting in a poster. We looked at early-stage non-small cell lung cancer and the frequency of EGFR mutational testing. In the face of osimertinib improving survival in that population by 80%, only a third of patients were being tested. We piloted it in five large healthcare networks across the country. Within six months, it was 100%, and that retained over the next 12 months. That's immediately practice-changing and has a tremendous impact on patient lives. Yeah, the second question, not all data is created equal. There are many different facets of that data market where there are existing players that have been selling data for decades, right? Those groups are actually addressing a different type of question, are really around what is happening in the real world, right? It's very descriptive of looking to see what happened, playing back the news. The reason why our business has grown and what we see is we don't see any competitors in our space, is that we're addressing of why is this patient not responding to this existing standard of care? That question, that why question, is what's at stake for when they're designing that clinical trial. When people are making a phase III global investment decision, again, $200 million-$500 million is in that decision. That's at stake. I really need to understand why are these patients not responding, and my hope is that my drug is going to address that. Really, we're providing data to those types of customers and those use cases and others. That ROI, delivering that type of value and helping those biopharma companies increase the success rate of those investment decisions is why our data business has grown to a scale that hasn't really been seen in our space. That maybe addresses what we see in the broader market. I've said this earlier I think provocatively said, there will come a time when no phase IIIs ever fail. Some company like Tempus will be responsible for that. If you think about it, I know it sounds crazy, but from a tech perspective, of course, it's going to happen. If a large phase III fails, you either didn't understand the mechanism of action that drives your drug, or what you saw in a phase II is not being seen in a phase III. There's no other reason. Literally, it's like that. Both of those are solvable with real-world data at scale. You can understand what drives people to respond to your drug, and then you can look at and interrogate at a comprehensive level the population that was in your phase I and phase II, and then look at the real world to see is that representative of what I'm going to see, and then track it as you start to enroll patients in your phase III. In almost every instance, when you have big phase IIIs that fail, we've gone back and looked at several massive phase IIIs that have failed, and we basically said ahead of time, we could've predicted this failure." Here you can see it. I could've predicted it from just digitized H&Es, let alone more complex data. I think the R&D spends are going to get very efficient as AI becomes pervasive in R&D. Hi, guys. Over here. Brendan Smith, TD Cowen. Thanks for all the great info today. Appreciate the color on the monetization of data and applications here. I wanted to maybe double-click a little bit on that and just can you speak to how you're thinking about evolving the actual monetization of the data business and even the foundation model itself, just within the biopharma customer end market? I guess, you mentioned deal size has grown per customer over year. Some are bigger than others. You're integrating new data. You've got all these agents now moving forward. I guess, how should we think about actual value switches over the coming quarters, and I guess also is there any differences in terms of revenues to Tempus on whether a customer expands within early discovery, clinical, commercial, just cadence over the next few years? Yeah, I can start. I think there's no big seismic change that we see coming. The big seismic change, I think, in terms of just a general business was, we sold this very large foundation model deal to AstraZeneca and Pathos, and that came with both a very large data license and some compute, and the compute is at a lower margin, right? That's like, oh, wait, that feels a little different than what is normally there." There was a time when I thought people would sign a bunch of these very large deals. The way the market has evolved is they'll sign lots of smaller deals, but not, I think, these giant big deals. You're seeing it now. People are saying, I want to build a lung cancer model. I want to build a prostate cancer model. I want to build a digital pathology model. I want to work with you to find a new biomarker using scan data, whatever. I think it's just moving so fast that that's the way it seems to be moving from lots of teams across these big pharmas. I think what you're going to see is the margin profile of our data business will look similar at what it is today, if not better. I think you'll see a blend between models and data that looks, at some point, almost you won't be able to tell what's what. Are you paying me $2 million bucks to build a model or licensing $2 million of data? It won't matter. Most deals will have some of both. I think the most sophisticated biopharma companies that we see that are really embedding AI into those critical decisions are actually using the data now at a higher level, which means they're just going to consume more, right? The AI systems and the use cases you deploy within a pharma company will need that, especially if you're seeing high ROI use cases for not just one phase III, but you want to see it for every phase III and every phase II or every trial. That's what we've seen for the most sophisticated. I think for a majority of the market, though, they're not all as sophisticated as the leading early adopters. Many of those use cases are really just using AI to go a little bit faster, to build a little bit of a faster car, but aren't addressing how do I increase my success rate, right? I could go faster, which is still valuable. When you think about the inefficiency of drug development, where do we waste all our money? It's in the failures. If I can increase my success rate, now the ROI can quickly flip on one trial. This is what we've seen across that expansion, like Eric was mentioning. Once you see a successful trial readout and you saw what you did differently, now you're asking the question internally, well, why aren't we always doing that? Right? That's what we see is it's just a natural consumption of more data, not just more in terms of volume, but more in the sense of having real time data of not three years ago, but literally of last year. Similar to the big compute guys, you don't go to Azure and say to Azure or GCP, let me see your menu. How much of this is storage? How much of it's compute? How much of it's large cluster compute, small cluster compute? How much of it's ingress? How much of it's egress? You're like: I don't care. Just give me the number. Okay? Because the margin profile is all pretty similar. Same thing with us. We're doing a deal right now that we're just licensing embeddings, licensing modeling embeddings. No files are moving, just the insights from those files that exist from a model we built on top of those files. It looks and smells and feels like a data license, but it's really just giving somebody something they can use to build a model. These things are going to merge together. Hey, Eric, just a quick follow-up. Dan Brennan, excuse me, also from TD Cowen. There's no doubt the interest from pharma appears to have really surged in AI over the last quarter or two. You listen to the public commentary from them. I'm just wondering, you've discussed coming into the year a really strong TCV and a high conversion rate, so you've been enthusiastic about the data growth. How would you characterize the interest today? It appears it's gone up dramatically. Are you seeing that? Will that translate in coming quarter bookings? Any way to contextualize that just in terms of this real increase that we are seeing right now? Yeah, I think a piece of it you've already seen, which is we've had three quarters in a row of $100 million+ bookings, some significantly way higher than that, and TCV growth. TCV's just going up and up and up. Even at our scale where we're licensing a lot of data. I think that certainly is a part of it. I think it's nothing compared to what's going to come. I think the first step is you have these CEOs saying, this really matters.. For that to translate into a signed contract takes time, and we're in the middle of that world. I suspect over the next one to three, four, five quarters, you're going to see a lot of these folks that are realizing they have to jump in with two feet, jump in with two feet. What I would say to you is a year ago, we had a pool with one or two people thinking about diving in. Now we have a pool with 20 people thinking of diving in. The question is, how many are going to dive? Hey, guys, Andrew Brackmann from William Blair. I wanted to ask on the algos business, recognize it's not a near-term driver until probably after 2028, maybe can you just talk about the distribution system that you're putting in place here? In oncology, I get it. You have the lab, you have the report, you're giving that molecular information and those contextualized results. I guess, how do you distribute these in those non-oncology settings? What's the hook look like to these institutions? Thanks. Yeah. Let's just start here. Take Northwestern. We have spent an enormous amount of time building pipes between us and Northwestern, building an infrastructure where that data can flow freely, deploying these algorithms at scale. By the way, which took years and years of effort. Legal, IT, not small. Once you deploy these algorithms, when you bring AI into the healthcare system, you inevitably break some part of it. These systems weren't designed for AI. We began running our ECG algorithm at M, and all of a sudden, we were producing an enormous number of patients that needed an echo or needed to wear a patch. They don't have doctors lying around. You're like, Whoa, whoa, wait a minute, what's the change management side of this? You have to go through that. That's why it's going to take time for these businesses to actually really scale. You have to lay the pipes or do all that kind of foundational work for years, then as the revenue starts to come, it really can be like a river. The failure of almost every AI company in our space is that the revenue is always way further than you think and the cost is always way higher than you think. If we just had 100 AI companies and looked at their decks, you'd be like, oh my god, every one of them was too optimistic and failed. Most go out of business. They can't keep investing in that horizon that's always further out. We're just super lucky that we have a business that allows us to make those investments and still generate incremental EBITDA improvement. That's just compounding. Our CDx approval this morning adds a ton of additional revenue that we either drop to the bottom line or choose to invest. If you look at the kind of EBITDA generation of the business today and what's coming in 2027, we just have a lot of money that we're able to invest and still be EBITDA positive and cash flow positive, where most of our competitors that also make a lot of investments are just burning money. I think just to add there, a lot of the work that we did to build the integrations for oncology can transfer over to the other disease areas. We've become a trusted partner within these hospital systems. It is easier than being a new company that shows up that tries to connect with somebody like Northwestern. We've been a trusted partner with them for a long time. That makes it easier as well. Ryan MacDonald with Needham. Eric, Jim, Ryan, thanks for hosting this today. I thought it was really helpful that you laid out the use cases in terms of how your pharma clients are using Tempus, was kind of curious to understand, as you think about the data business today across the early stage, clinical stage, and then the commercial use cases, where does the majority of that revenue lie? Where are you seeing the most demand this year in terms of the use case, and where is the greatest white space for you to go after in applying the platform? Yeah, I can take it. Really, early discovery and clinical development are kind of merged together under the R&D budgets of these pharma companies. That's the bulk of our business. Again, we're addressing the why aren't certain patients responding to the existing therapies. Every discovery team needs to know that. Every development team needs to know that. That's the lion's share of the budget that's being allocated to our contracts. That being said, we have customers that are also working with us in commercial, but that's a growth area for us. Our data can be used there as well, but again, no one has been able to address the why aren't these patients responding to therapies, and that's why we haven't really had to compete with others and why we've been so focused on that. Even though we're working with 19 of the top 20, we're not working at the same level for all 19 of those companies. Our growth expansion still has opportunities just within R&D, and then you have expanded customer segments that can grow even beyond just the R&D teams. Yeah, I'll say one thing, then we'll jump to financials, then we'll do Q&A again in a minute, if you have questions. I think people have not historically understood our data business, and they don't understand the moat around it. Here's a great example. We have been one of the largest abstractors of cancer patient data for the last decade. Literally, when ASCO decided to partner with somebody, we were one of their two partners. We've been doing this for a long time at scale. We also have invested, I don't know, a few billion dollars in technology. We have 600 or 700 software engineers that have been focusing on this problem. Just assume we're a massive abstractor and assume we build unbelievable technology and we only know oncology. That's if you think of our last 10 years. Okay. Now the world of large language model shows up and you're like, can I just do this in an automated way? Why do I have abstractors? That journey, and we're now crossing that journey in two of the largest indications. That journey has taken us all this time to be in a world where we have the ability now to take 100% of our lung cancer patients, breast cancer patients, and do automated abstraction at scale, which we're doing right now. All of a sudden, I, as a data client, can now access 100% of all the notes that exist across these millions of patients. That's taken a decade of unbelievable time and energy just to get there. Even then, you still need humans to ensure that it's right. It becomes another powerful tool as we leave early-stage R&D and move deeper into development and commercialization. Should we hit the financials for a second? Yep. Be quick and then we'll flip back. I think we've covered the economic model over the course of the session today, but it's obviously a framework for durable growth, operating leverage, which we've demonstrated over the last eight-plus quarters, and long-term value creation. It starts with diagnostics, where all the data's being generated, but it obviously serves a very important use case for physicians. We have improving margins in a scaled infrastructure that allows us to get that leverage. We move over to the data and application side, where we've got a very good backlog of things through our TCV. We have a very broad customer base, as Ryan just noted, still relatively early on in its days. We have a history of high retention and expansion. Well, plus all the wiring. It's too much. Got some commentary back there. Looking at 2026, we've given guidance of $1.59 billion to $1.6 billion, which represents about 25% year-over-year growth, $65 million of adjusted EBITDA. The drivers are exactly what we talked about today. Within diagnostics, we have very strong clinical oncology growth, improvements in ASPs. We have hereditary in the back half normalizing after lapping some of the share gains. In data and applications, it's really just executing on the agreements that we have in place, as well as expanding some of those relationships to give us more visibility into 2027 and beyond. One other note from a balance sheet perspective, we did the convert a few weeks ago to take out the remaining term loan that we put in place. That gives us about $30 million of annual savings, allowing us to achieve positive free cash flow around the end of the year. Again, if we expect a 25% top-line growth over the next three years, the way that we view the world is that for the incremental gross profit dollars that are generated, we'll reinvest about two-thirds of those back on the business with a third dropping down to the bottom line. After that third year, probably flipping that so a third is reinvested in two-thirds, because at that point, you're generating enough gross profit dollars that we can maintain the level of investment that allow us to capture all the things that we think play out over the next decade or so, but still demonstrating operating leverage and significant free cash flow. You want to take this one or you want me to take it? Yeah, we can go back and forth. Just to wrap up really quickly, and we're happy to take questions. I think just a summary of hopefully what you've gathered from today. We have a diagnostic business that's strong. The integrated nature of our technology platform is driving higher growth than most of the other folks in the space, and it's sustainable. The trends we saw in 2025, the trends we saw in Q1 are continuing into Q2, so it seems to be a long-term pattern of us taking share from other folks. In terms of MRD, we have a comprehensive portfolio of both tumor-naive and tumor-informed products. On the informed side, that assay is doing super well. We've got great market traction, and as we unleash more demand, because it's currently gated, we expect it to grow pretty dramatically, and we continue to invest in R&D. We've got something like, again, 5,500 patients in studies right now. I think we don't get a ton of credit because we don't spend most of our time focused on the readouts of these interim studies. At the end of the day, I think at ASCO we have something like 35 posters and papers and things of that nature. The scientific rigor of what goes on here is pretty extreme, and I would suspect that we're able to build a tumor-naive product that's quite good over time. The data we're generating is compounding. The moat around our data business is growing. Our AI applications are really taking hold. The foundation model seems to be performing at or above our expectation. We'll certainly, over the next three, six, nine months, have far more that hits the market in terms of insights that flow from that model. Ultimately, the company just has a really good financial profile, which was just made materially better by this FDA approval this morning. We're just in a great spot. We're growing at a good clip. Generating leverage and reinvesting it in forward growth. On that note. Yeah, shoot. Yeah, I think I've got it over here. Catherine Schulte with Baird. Great. I had one on data, but we can maybe loop it into financials as well. If we look at some of the comments you made on customer growth and customer concentration in the data business, if we look at value per customer outside of your top five customers, it went from a little under $200,000 in 2020 to a little over half a million dollars per customer in 2025. I was just curious, as we think about if we sit here five years from now for your data business, how much of that growth is from extracting value from your per-customer basis versus that customer growth? I would suspect it's actually on this kind of a curve. In other words, first, I didn't know that. If you said it went from $200,000 to $500,000, I would say, okay, well, in the next five years, I bet it goes from $500,000 to $3 million. In other words, especially with biotechs that have no money or smaller or pharma companies that are more budget-conscious, they're far more conservative than a giant global pharmaceutical company that can make a $20 million bet and it isn't the end of the world if it's not a great bet. To a company that's got $50 million, they've raised $100 million, I would suspect it goes way up and if we fast-forward five years from now, you probably have, whatever, 500 clients spending $3 million-$5 million instead of 200 clients spending $500,000. Yeah, I think that the surprising thing there is that since we're addressing this kind of a unique question of why aren't these patients responding to standard of care therapies, that's not just a big pharma challenge, that's a biotech challenge, and usually in biotech, their whole future relies on that question for their one trial or their one drug. What we've been surprised by is the number of biotechs are now signing on, where data isn't a nice-to-have, it's an essential question to address because your future relies on it. The more that the market can see the success of how you can apply this, makes it sort of an embedded budget line item for not just a big pharma company, but also biotechs. We expect customer numbers to grow, where it's not just the top 20, but also the dollar spend is going to be the biggest in the biggest bio-pharma companies. Yeah. Another way to think about it, we'll go to the next question, is you could almost imagine a world where we're probably there now, or certainly very close. I go to every single biotech and oncology in the U.S., market cap, let's say sub $1 billion or sub $500 million and be like, "Hey, we'll give you access to our data, just give us 20% of your company." I would think almost everyone would be like, "Great." It's that valuable, right? Thanks for the questions. Brad Bowers, Mizuho. A bit of a preamble and then a two-parter here. Tempus doesn't really get any credit as an AI company. It trades at 4x sales if you pressure the genomics business, especially the drug discovery stuff and the clinical trial benefits here. We saw software bottom over the last couple of weeks. I guess this kind of gets into my first question. This was touched earlier, but I'm starting to see GPU counts, compute of pharma peers get shared around really just within the last few days. I have Lilly in the lead at about 1,000 GPUs, Recursion about 500, and then Amgen and BioNTech much lower. You talked to 1,008 H200s just for the foundation model. Wanted to give you another opportunity. You touched on moat a bit, but just to double-click on that and whether you think that the compute will start to enter the dialogue, and then also double-clicking on the Cowen question and maybe throwing it back to you, but it sounds like you talked on our models unnecessarily slowing down. I'll throw it back on you why we should expect that slowdown when it sounds like those businesses are firing on all cylinders and it feels like you kind of built an ark ahead of a flood here. Yeah. Let's talk about the Ark for a second. Our compute capacity is probably equal to all of pharma combined. Literally, forget our H200s, we have a roughly equal size cluster of GB200s, which are like 4X the H200. It's based on the numbers you just gave, we probably have this comparable compute to all biotech and all pharma and oncology and then some. Because that doesn't even include all of our other compute, which is equal to or bigger than the two clusters we set up for foundation models. We have a ton of compute and we have a ton of data, I think that is the Ark. I think the challenge for us in terms of both valuation and growth is we have always been focused on long-term sustainable growth. We've had opportunities in the past to kind of make decisions that would accelerate our growth in the short term, but may have then hurt us in the long term, and we just tend not to choose those. I think we feel very comfortable that the aggregate business will grow 25% or so. We've called out that the data business will grow faster. That could be in the 30s. That could be even faster. There could be other parts of that business that grow slower. Like for example, our CRO business is, I think, shrinking or relatively flat. We have other parts of the business that are not growing because we're not investing in them. We don't get into all the micros of these different businesses. At the end of the day, our two largest businesses, our oncology sequencing business and our data licensing and modeling business, are growing faster than everything else. Everything else is growing slower, and you can see our growth rate. We don't think that's slowing down. We're not forecasting something to actually decelerate materially, we're just saying, hey, for 2026, expect 30%+ growth in the data business. The three-year growth rate's going to be called 25%, and we don't benefit by saying the three-year growth rate's going to be 30% or 35%. Our stock won't go up because of the first part you mentioned, which is we're caught in this middle ground where technology investors who tend to invest in and think about AI don't understand diagnostics, and diagnostic investors who are deeply steeped in next-generation sequencing don't understand the data in AI. You end up in a world where somebody's always worried about the thing they don't understand intimately, and so they just don't know what to do. I don't know when that solves itself. It may solve itself. It may not solve itself, in which case we could do things to solve it. At the end of the day, we've been focused this year on just getting these businesses in the best spot we can, making sure that Air is as good as it can be, and if the market doesn't ultimately recognize the value of our data and apps business, which if we took the data and apps business public tomorrow, I would suspect it would trade higher than the entire market cap of Tempus AI and could trade at 2x. One could argue it's got negative value. We just look at it and say, eventually that'll either solve itself or we'll solve it. Thanks, guys. It's Mark Massaro, BTIG. Flipping back to the diagnostics business. Your partner, Personalis, I think has received about four Medicare coverage decisions in the last six or seven months. To me, having non-small cell lung, breast, and IO monitoring really is a bit of an unlocking. When do you expect to unlock the gate, so to speak, and can you give us a sense for what percentage of your reps have been promoting MRD versus when you do unlock, is that going to be a full flip? Quickly on the rare disease portfolio, you indicated that you plan to launch a whole genome panel this summer. Can you just help us rank order your priorities? How big of a push do you intend to make in rare disease? Why is that important to you relative to some of the other segments you're going after? Yeah, I'll cover the first. The number of people currently focused on the MRD portfolio, I don't see Laura, I think she was here earlier and left, but I'm going to say, call it 15-30, somewhere in that range. We have about 200+ folks in the field, I think, in just core oncology CGP. Think of it in terms of if you were trying to understand percentage, it's 10%-15% or something, somewhere in that range, maybe sub-10. It's highly gated. Personalis is public, so you could look at their financial statements. Let's just say we sent them 20 x the volume of orders we were sending them today. How much cash would they burn? I don't know how much cash they would burn. I don't know how much cash they have, but I don't think that math is sustainable. It's all been tightly orchestrated to make sure that we are growing as they are growing and that the whole thing works so we don't end up either breaking their labs or breaking the financials of their business. It's in a great spot. What we have said historically is the demand is way more robust than we would've thought a year ago. There doesn't seem to be a cap on it. I'm comfortable that as we continue to invest here in expanding, we'll expand. In terms of whole genome- The second one's on rare. Yeah. As Tom had mentioned, they had a whole genome offering that was in place. They had largely deprioritized a little bit when reimbursement wasn't there. Obviously, the reimbursement landscape has changed, they began working on a whole genome offering just because that's obviously where the market is moving. It's obviously not the top priority for Ambry, given how large the HCT business is. We do think we can be a player given the relationships that we have with genetic counselors and build out a meaningful business there as well. You'll hear us talking about it more, but it's still not a significant driver today. If you look at the investments we make, it's kind of interesting. If you think about the decision tree, you could say to us, hey, why don't you invest another $25 million and try to develop some novel epigenetic or methylomic assay? Someone might say that. If you look at where our core strength is, it's really at the intersection of technology and diagnostics, not being first to market with something really novel. When you think about rare, I can't think of a single use case that would benefit from a full understanding of the clinical case of a patient than rare. It lives at the intersection of some molecular insight and the clinical diagnostic odyssey, and we're as good as anybody at pulling in that data and making sense of it. I would suspect long-term, if it's not Tempus, it will for sure be a company that looks like Tempus that wins rare. It will not be the company that can generate a whole genome BAM file. That, many companies can do quite well. Maybe we'll do one more. Thanks for the questions. It's Paul Stewardson from Stifel. I'm here for Dan. Just wondering on the f oundational model that you shared some early data, hazard ratio, that sort of thing. What's the level of need for prospective validation? You mentioned you have these retrospective isolated cohorts that you can validate the models in. Given there may be some survival bias of what models are coming out, is there a need for doing long-term prospective, and to what extent does that gate your ability to move that into a useful application? Then just briefly on the other side of the business, can you talk about the investment? There's this next generation tissue-naive test. What kind of clinical evidence generation plans do you have that might be bigger than you had before the V2 was being talked about? Thank you. I'll take the first. You can take the second. Yep. It's a two-part answer. There is no gate needed, and we for sure will run those studies. There's no gate needed because physicians are free to make decisions based on the data you present them, and we don't get paid for these insights. If this was Oncotype DX, for example, and I wanted to get paid, I'd have to run a very large study to then convince somebody to pay me and put me in a guideline. In my case, if I can predict EGFR response or ALK response, I don't get paid for it. Doctors can make a decision based on what I published as to whether or not they believe that is predictive or prognostic and whether they want to pay attention to it. I think there's no gate. These things will come at scale. They'll have to be analytical and clinically validated before they get on the report. I suspect all of them will turn into really cool studies over time to figure out how good are they, and eventually they'll weave their way into guidelines. The river will come and we will not gate it. I think it will be material in terms of how people have to react to it. Kate, I know you're over there on the V2 studies. Yeah. We're making those investments now in terms of, we showed the one slide over the next few years. We've got about 5,000, a little bit more, patients in studies today. We continue to enroll. We're enrolling across all major indications, so really pan-cancer. We're building the technology to be able to, once we make it through analytical validation, then we'll just be able to start hitting clinical validations across all of those different indications. I think the way you have to think about this space is, first of all, a significant percentage of the market is CRC, and in colorectal cancer you have lots of tissue. It's not a great offering to say, hey, switch from a tumor-informed assay that's really sensitive and come to a tumor-naive assay that's less sensitive when you've got a lot of tissue. We've all long suspected tumor-informed would win the day in CRC. In other areas, like for example, lung cancer, where tissue is far more scant, you would think, okay, this is a great assay for tumor-naive. The problem is those assays haven't performed that well. Tumor-informed is winning the day there as well. I think we're in this weird zone where, including our own, whereas the tumor-naive assays aren't performing well enough to win the market at scale, and the tumor-naive assays need to go through this consistent R&D process, getting from 500 ppm to 300 ppm to 200 ppm to 100 ppm to 50 ppm. There's some zone, I don't think you need to get to 10 or two, but there is a zone where you need enough sensitivity and specificity and a low enough limit of detection that you're actually like, okay, this thing can play against the market leading assays like Signatera. We are getting very close to there now. We're migrating from version one of our naive assay to version two. Now we're going to roll that out at scale in terms of these different studies. Maybe one more comment- Yeah to make there of just something that differentiates us in this space. We've talked a lot about this kind of multimodal data, what we start to see, not just at Tempus, but in the field at large, is when you add additional modalities of data, you're able to increase signal to noise. As we think about the really broad data set that we have that we're continuing to generate, part of the belief is that we will be able to then layer in these other modalities much faster and be able to then improve upon the assays and the technologies that we have. Lung cancer or whatever the indication, we'll be able to leverage imaging and all of the other things to add to that signature. Yeah. I will say one last thing on MRD. I'm, first of all, a big believer in MRD. I think it's an awesome space. We want to play in it. It is unclear to me how this whole thing shakes out. Remember, I think you have to really understand the adoption, right? You turn this test on, then you turn it on in bundles. You get doctors to basically order it in bundles on a recurring basis at scale. All of a sudden you go from not a lot of volume to what looks like a lot of volume. If you get even some minimal level of reimbursement, $500 a test, it's not small. I don't really know how that all shakes out. I feel very good that comprehensive genomic profiling has shaken out. I think on the MRD side, we have to let this thing play out for another couple of years to figure out what's the cadence. What's the cadence doctors are going to want to order? What's the cadence that's going to get paid for? How does it really work? It will be a great space, but I don't think you can just look at the trend lines today and be like, oh, they're going to continue for the next 10 years. I think there's going to be some movement there to figure out how to rationalize what's going on. You've got some doctors ordering these things every month and some doctors never ordering them, and that's a unique paradigm relative to CGP. There, you just had people that believed or didn't believe in the genome. I don't think it matters. Once they're like, oh, it matters, the ordering patterns were pretty normalized. On that note, thanks for joining us. I think we're going to do some tours? Yeah, what are the logistics on that? Sorry, you don't have a mic. Please stick around for some refreshments. We're going to do three separate groups for lab tours for those who can stay. We'll collect those in the back. Thanks for joining us. Thanks, everyone. Thank you, everybody.