10-K

UNITED STATES

SECURITIES AND EXCHANGE COMMISSION

Washington, D.C. 20549

FORM 10-K

(Mark One)

For the fiscal year ended December 31, 2025

Commission File Number 001-42130

Tempus AI, Inc.

(Exact name of Registrant as specified in its Charter)

(Address of Principal Executive Offices, Zip Code)

Registrant’s telephone number, including area code: (800) 976-5448

Securities registered pursuant to Section 12(b) of the Act:

Title of each class TradingSymbol(s) Name of each exchange on which registered

Securities registered pursuant to Section 12(g) of the Act: None

Indicate by check mark if the Registrant is a well-known seasoned issuer, as defined in Rule 405 of the Securities Act. Yes ☒ NO ☐

Indicate by check mark if the Registrant is not required to file reports pursuant to Section 13 or 15(d) of the Act. YES ☐ No ☒

Indicate by check mark whether the Registrant: (1) has filed all reports required to be filed by Section 13 or 15(d) of the Securities Exchange Act of 1934 during the preceding 12 months (or for such shorter period that the Registrant was required to file such reports), and (2) has been subject to such filing requirements for the past 90 days. Yes☒ NO ☐

Indicate by check mark whether the Registrant has submitted electronically every Interactive Data File required to be submitted pursuant to Rule 405 of Regulation S-T (§232.405 of this chapter) during the preceding 12 months (or for such shorter period that the Registrant was required to submit such files). Yes☒ NO ☐

Indicate by check mark whether the registrant is a large accelerated filer, an accelerated filer, a non-accelerated filer, smaller reporting company, or an emerging growth company. See the definitions of “large accelerated filer,” “accelerated filer,” “smaller reporting company,” and “emerging growth company” in Rule 12b-2 of the Exchange Act.

Large accelerated filer ☒ Accelerated filer ☐

Non-accelerated filer ☐ Smaller reporting company ☐

Emerging growth company ☐

If an emerging growth company, indicate by check mark if the Registrant has elected not to use the extended transition period for complying with any new or revised financial accounting standards provided pursuant to Section 13(a) of the Exchange Act. ☐

Indicate by check mark whether the Registrant has filed a report on and attestation to its management’s assessment of the effectiveness of its internal control over financial reporting under Section 404(b) of the Sarbanes-Oxley Act (15 U.S.C. 7262(b)) by the registered public accounting firm that prepared or issued its audit report. ☒

If securities are registered pursuant to Section 12(b) of the Act, indicate by check mark whether the financial statements of the Registrant included in the filing reflect the correction of an error to previously issued financial statements. ☐

Indicate by check mark whether any of those error corrections are restatements that required a recovery analysis of incentive-based compensation received by any of the Registrant’s executive officers during the relevant recovery period pursuant to §240.10D-1(b). ☐

Indicate by check mark whether the Registrant is a shell company (as defined in Rule 12b-2 of the Exchange Act). YES ☐ NO ☒

The aggregate market value of the voting and non-voting common equity held by non-affiliates of the Registrant, as of the last business day of the Registrant's most recently completed second fiscal quarter was approximately $11.0 billion (based on the closing price of the Registrant's Class A common stock on the Nasdaq Global Select Market on June 30, 2025 of $63.54 per share).

As of February 20, 2026, there were 173,729,901 shares of Class A common stock and 5,043,789 shares of Class B common stock, each with a par value of $0.0001 per share, outstanding.

DOCUMENTS INCORPORATED BY REFERENCE

Portions of the Registrant's definitive proxy statement, or the 2026 Proxy Statement, relating to its annual meeting of stockholders to be held in 2026, or the 2026 Annual Meeting, to be filed with the Securities and Exchange Commission, or the SEC, within 120 days after the end of the fiscal year to which this Annual Report on Form 10-K relates, are incorporated herein by reference where indicated. Except with respect to information specifically incorporated by reference in this Annual Report on Form 10-K, such proxy statement is not deemed to be filed a part hereof.

Table of Contents

Page

PART I

Item 1. Business 3

Item 1A. Risk Factors 45

Item 1B. Unresolved Staff Comments 108

Item 1C. Cybersecurity 108

Item 2. Properties 110

Item 3. Legal Proceedings 110

Item 4. Mine Safety Disclosures 111

PART II

Item 6. [Reserved] 113

Item 7A. Quantitative and Qualitative Disclosures About Market Risk 133

Item 8. Financial Statements and Supplementary Data 134

Item 9A. Controls and Procedures 184

Item 9B. Other Information 185

Item 9C. Disclosure Regarding Foreign Jurisdictions that Prevent Inspections 185

PART III

Item 10. Directors, Executive Officers and Corporate Governance 186

Item 11. Executive Compensation 186

Item 14. Principal Accounting Fees and Services 186

PART IV

Item 15. Exhibits, Financial Statement Schedules 187

NOTE REGARDING FORWARD-LOOKING STATEMENTS

This Annual Report on Form 10-K, including the sections titled “Business” and “Management’s Discussion and Analysis of Financial Condition and Results of Operations,” contains forward-looking statements about us and our industry that involve substantial risks and uncertainties. All statements other than statements of historical facts contained in this Annual Report on Form 10-K or incorporated herein, including statements regarding our future results of operations or financial condition, business strategy and plans and objectives of management for future operations, are forward-looking statements. In some cases, you can identify forward-looking statements because they contain words such as “anticipate,” “believe,” “contemplate,” “continue,” “could,” “estimate,” “expect,” “intend,” “may,” “plan,” “potential,” “predict,” “project,” “should,” “target,” “will” or “would” or the negative of these words or other similar terms or expressions. Within the meaning of Section 27A of the Securities Act of 1933, as amended, or the Securities Act, and Section 21E of the Securities Exchange Act of 1934, as amended, or the Exchange Act, these forward-looking statements contained in this Annual Report on Form 10-K include, but are not limited to, statements concerning the following:

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the evolving treatment paradigm for cancer, including physicians’ use of molecular data and targeted oncology therapeutics and the market size for our current and future products;

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our ability to expand our business beyond oncology into new disease areas;

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estimates of our addressable market and our expectations regarding our revenue, expenses, capital requirements and operating results;

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our ability to develop new products and services, including our goals and strategy regarding development and commercialization of our Application products;

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our ability to maintain and grow our datasets, including in new disease areas and geographies;

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any expectation that the growth of our datasets will improve the quality of our products and services and accelerate their adoption;

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our ability to capture, aggregate, analyze or otherwise utilize genomic data in new ways and in additional diagnostic modalities;

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any expectation that we will continue to commercialize de-identified records and license them to multiple customers;

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the acceptance of our publications in peer-reviewed journals or of our presentations at scientific and medical conference presentations;

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the implementation of our business model and strategic plans for our products, technologies and businesses;

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competitive companies and technologies and our industry;

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the potential of Intelligent Diagnostics to be disruptive across a broad set of disease areas and the clinical trial process;

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our ability to manage and grow our business by expanding our sales to existing customers or introducing our products to new customers;

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the impact of macroeconomic conditions, including new and potential tariffs, on us and our customers;

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third-party payer reimbursement and coverage decisions, including our strategy to increase reimbursement;

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our ability to establish and maintain intellectual property protection for our products or avoid claims of infringement;

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potential effects of evolving and/or extensive government regulation;

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the timing or likelihood of regulatory filings and approvals;

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our ability to hire and retain key personnel;

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our ability to expand internationally, including through our joint venture, SB Tempus Corp., in Japan;

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our ability to successfully acquire businesses, form joint ventures or make investments in companies or technologies, including our ability to realize the expected benefits of our acquisitions of Paige.AI, Inc., Ambry Genetics Corporation and Deep 6 AI, Inc;

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our ability to protect and enforce our intellectual property rights, including our trade secret protected proprietary rights in our platform;

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our ability to service or pay down existing or future debt obligations;

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the outcome of pending or threatened litigation;

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our anticipated cash needs and our needs for additional financing; and

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anticipated trends and challenges in our business and the markets in which we operate.

You should not rely on forward-looking statements as predictions of future events. We have based the forward-looking statements contained in this Annual Report on Form 10-K primarily on our current expectations and projections about future events and trends that we believe may affect our business, financial condition and operating results. The outcome of the events described in these forward-looking statements is subject to risks, uncertainties and other factors described in Part I, Item 1A, “Risk Factors” in this Annual Report on Form 10-K. Moreover, we operate in a very competitive and rapidly changing environment. New risks and uncertainties emerge from time to time, and it is not possible for us to predict all risks and uncertainties that could have an impact on the forward-looking statements contained in this Annual Report on Form 10-K. The results, events and circumstances reflected in the forward-looking statements may not be achieved or occur, and actual results, events or circumstances could differ materially from those described in the forward-looking statements.

In addition, statements that “we believe” and similar statements reflect our beliefs and opinions on the relevant subject. These statements are based on information available to us as of the date of this Annual Report on Form 10-K. And while we believe that information provides a reasonable basis for these statements, that information may be limited or incomplete. Our statements should not be read to indicate that we have conducted an exhaustive inquiry into, or review of, all relevant information. These statements are inherently uncertain, and investors are cautioned not to unduly rely on these statements.

The forward-looking statements made in this Annual Report on Form 10-K relate only to events as of the date on which the statements are made. We undertake no obligation to update any forward-looking statements made in this Annual Report on Form 10-K to reflect events or circumstances after the date of this Annual Report on Form 10-K or to reflect new information or the occurrence of unanticipated events, except as required by law. We may not actually achieve the plans, intentions or expectations disclosed in our forward-looking statements, and you should not place undue reliance on our forward-looking statements. Our forward-looking statements do not reflect the potential impact of any future acquisitions, mergers, dispositions, joint ventures or investments.

PART I

Item 1. Business.

Overview

We endeavor to unlock the true power of precision medicine by creating Intelligent Diagnostics through the practical application of artificial intelligence, or AI, in healthcare. Intelligent Diagnostics use AI, including generative and agentic AI, to make laboratory tests more accurate, tailored, and personal. We make tests intelligent by connecting laboratory results to a patient’s own clinical data, thereby personalizing the results. Our novel insight was realizing that all laboratory test results, genomic or otherwise, could be contextualized for a specific patient based upon that patient’s unique characteristics, and technology could therefore guide therapy selection and treatment decisions to allow each patient to progress on their own unique path. The drugs recommended, the clinical trials explored, the care pathways evaluated, and the adverse events considered—all have the potential to be refined and enhanced when test results are connected to a patient’s personal profile, enabling the right patient to be routed to the right therapy at the right time.

To accomplish this, we built the Tempus Platform, which comprises both a technology platform to free healthcare data from silos and an operating system to make the resulting data useful. Our proprietary technology has allowed us to amass what we consider to be one of the largest libraries of clinical and molecular oncology data in the world. Our goal is to embed AI, including generative AI, throughout every aspect of diagnostics to enable physicians and researchers to make personalized, data-driven decisions that improve patient care.

The ability to deploy AI in precision medicine at scale has only recently become possible. Advances in cloud computing, imaging technologies, large language models and low-cost molecular profiling, along with the digitization of vast amounts of healthcare data, have created a landscape that we believe is finally ripe for AI. However, despite an increase in the availability of healthcare data, physicians and researchers are largely unable today to leverage this data to improve patient care. The vast majority of healthcare data remains disconnected and lacks harmonization and structure. Traditional diagnostic tests are typically based only on a single data modality, such as a blood-based biomarker or a genomic mutation, and do not connect and integrate other forms of relevant clinical data, such as outcomes, or adverse events, or pathology results, which are essential for many clinical decisions.

In order to bring AI to healthcare at scale, we believe the foundation of how data flows throughout the ecosystem needs to be rebuilt. We established new data pipes, going to and from providers, to allow for the free exchange of data between physicians, who interpret data, and diagnostic and life science companies, who provide data, integrating relevant clinical data, such as outcomes, or adverse events, which are essential for many clinical decisions. Without this capability, we believe that data would continue to accumulate without impacting patient care. To accomplish this, we built both a technology platform to free healthcare data from silos and an operating system to make this data useful, the combination of which we refer to as our Platform. Our Platform connects multiple stakeholders within the larger healthcare ecosystem, often in near real time, to assemble and integrate the data we collect, thereby providing an opportunity for physicians to make data-driven decisions in the clinic and for researchers to discover and develop therapeutics. We aim to help physicians find the best therapies for their patients, help pharmaceutical and biotechnology companies make the best drugs possible, and enable patient access to emerging therapies and clinical trials when appropriate.

Tempus is a technology company focused on healthcare that straddles two converging worlds. We strive to combine deep healthcare expertise, providing next-generation diagnostics across multiple disease areas, with leading technology capabilities, harnessing the power of data and analytics to help personalize medicine. Unlike traditional diagnostic labs, we can incorporate unique patient information, such as clinical, molecular, and imaging data, with the goal of making our tests more intelligent and our results more insightful. Unlike other technology companies, we are deeply rooted in clinical care delivery as one of the largest sequencers of cancer patients, and patients with other diseases, in the United States. Straddling both worlds is advantageous as we believe Intelligent Diagnostics represent the future of precision medicine, informing more personalized and data-driven therapy selection and development. We believe their adoption could empower physicians to deliver better care and researchers to develop more precise therapies, with the potential to save millions of lives.

Our Platform includes proprietary software and dedicated data pipelines that create a network of healthcare institutions through more than 700 unique data connections, many of which supply us with complex multimodal data in near real time, across more than 5,000 healthcare institution sites that order our products and services. Healthcare institutions supply us with this data in our capacity as a covered entity (for example, when we provide Next Generation Sequencing, or NGS, services on behalf of a patient), or as a business associate (for example, when we provide clinical trial matching services or data de-identification and

structuring services). In addition to the data we receive in these capacities, we currently have a limited number of paid license agreements through which we acquire de-identified data directly from healthcare associations or institutions, and in certain circumstances we cover the actual direct costs associated with the technical integrations needed to create a data connection. We then integrate this data into a unified multimodal database through which we offer numerous analytical and decision support capabilities to our customers. We establish dedicated and integrated data connections with healthcare institutions to enhance the information we provide in our clinical reports, to increase the effectiveness of our clinical trial matching services, and to enable our Data and applications product line, which we believe has the ability to transform healthcare.

We have developed multiple products—each based on our Platform—that have allowed us to invest in structuring and harmonizing multimodal data, which is a necessary precursor for deploying AI at scale. Our products are organized under two product lines, Diagnostics, which comprises our Oncology and Hereditary testing businesses, among others, and Data and applications, which includes, our Insights, Next, Trials and Algos businesses, among others. Each product line is designed to enable and enhance the other, thereby creating network effects in each of the markets in which we operate. Our business model allows pharmaceutical and biotechnology companies to unlock value from the data we collect, and allows us to monetize a de-identified copy of that data, in different ways across our different product lines. We believe these network effects provide a unique advantage to our business as the compounding value of each data record in our database serves to enhance our competitive moat. The more data we collect, the smarter our tests become, the more applications we launch, the more physicians join our network, further growing our database, making our tests more precise for clinicians and our database more valuable for researchers.

Our Diagnostics product line leverages our laboratories to provide NGS diagnostics, PCR profiling, and other anatomic and molecular pathology testing to healthcare providers, life sciences companies, researchers, and other third parties. However, unlike other laboratory diagnostic testing providers, many of our tests are connected to clinical data in some manner, which allows our suite of tests to be self-learning and become more accurate with each new test that we run.

Our Data and applications product line facilitates drug discovery and development for life sciences companies through multiple products, including, among other things, Insights, Trials, Next and Algos. Through our Insights product, we license de-identified libraries of linked clinical, molecular, and imaging data and provide a suite of analytic and cloud-and-compute tools to

pharmaceutical and biotechnology companies. Our Trialsproduct leverages the broad network of physicians we work with in oncology to provide clinical trial support for pharmaceutical companies that are looking to reach hard-to-find and underserved patient populations.

Next is an AI platform that leverages machine learning to apply an “intelligent layer” onto routinely generated data to proactively identify and minimize care gaps for oncology and cardiology patients. As this product gains adoption, we intend to leverage large language models, generative AI algorithms, and our vast database of de-identified data to develop algorithmic diagnostics designed to identify these patients earlier in their disease progression, when treatments are most effective.

Our Algos product is focused on developing and providing diagnostics that are algorithmic in nature. We currently offer a suite of Algos in oncology, including our TO, HRD, DPYD and Tempus Purist Algos, as defined and described below, among others.

Industry Background

The Limitations of Employing Technology, Data, and AI in Healthcare and Precision Medicine

Technology has had a significant impact on almost every sector of our global economy. From the way we shop online, access information on the internet, or use GPS to navigate the world. We benefit from, and depend on, technology, data, and the vast computational and connective ecosystem that surrounds us. Yet healthcare has seemingly lagged other industries in embracing the power of technology and leveraging the ensuing computational revolution.

We believe this is changing. Recent technological advancements have facilitated the deployment of modern computational methods, such as AI and machine learning, to improve healthcare. Breakthroughs in cloud computing, imaging technologies, large language models, and low-cost molecular profiling have made it easier and more cost effective to digitize, structure, harmonize, and store healthcare data, and analyze the resulting datasets at an unprecedented rate. These developments are expediting the adoption of AI, which we believe will impact all aspects of healthcare, from clinical diagnostic testing to the discovery and development of therapeutics, to healthcare delivery more broadly.

Despite the accumulation of healthcare data, we believe the healthcare system still lacks the integrated networks and modern analytical tools necessary to facilitate data-driven care at scale. The vast majority of healthcare data created today remains locked in silos and lacks harmonization due to decentralized institutions using non-standardized methods for collecting data, in addition to a large percentage of the data being in unstructured formats like free text (such as physician progress notes) and non-digitized images (such as pathology slides). Clinical outcomes data, to the extent it even exists, often remains disconnected from diagnostic data, and traditional laboratory tests provide results that are often based only on a single data modality that lack patient context. In addition, clinical and research decisions are too often made based on small sample sizes of historic data.

In order to bring AI to healthcare at scale, we began by rebuilding the foundation of how data flows in and out of healthcare institutions, which we refer to as the Tempus Platform. We have established data pipes, going to and from providers, which allow for the free exchange of data between physicians, who interpret data, and diagnostic and therapeutic companies, who provide data. Harnessing the power of this data at scale required a Platform that could break down data silos, collect vast amounts of multimodal data, structure and harmonize it, and deploy AI to make it useful for physicians and researchers to make data-driven decisions in the clinic or at the lab bench, thereby advancing precision medicine. Our access to broad and diverse data serves as the basis for our ability to train generative AI models, and we believe our relationships with healthcare institutions provide us with proprietary data to deliver on the promise of AI in healthcare. Without this Platform, we believe the data would continue to pile up at an increasing rate without improving patient care. We have built a version of this Platform and are now deploying it at scale in oncology in the United States, with other disease areas following.

Importance of Multimodal Healthcare Data

Technology is enabling the healthcare industry to collect data at an unprecedented scale, yet most datasets continue to be fractured or narrowly focused by disease type or data modality; almost none are comprehensive enough to provide a full picture of the patient and their clinically relevant characteristics. We set out to solve that problem by building a platform that collects broad datasets in near real time and at scale. Our Platform is differentiated in several ways. First, we collect data from multiple diagnostic modalities, including NGS, anatomic pathology slides, radiology images, and other laboratory tests. Second, the data we collect is often connected to EHR data, such as key phenotypic characteristics, therapeutic data, and clinical outcome and response data. Third, our Platform is multi-disease, spanning oncology, neurology, and cardiology. Our Platform is purpose built to deploy AI at scale, using multimodal datasets, across disease areas. We believe these differentiators have the potential to transform healthcare.

A New Industry: Intelligent Diagnostics to Advance Precision Medicine

While AI has the potential to broadly impact healthcare, we believe it will transform diagnostics first. Diagnostics, broadly defined, is the process of determining by examination or assessment the nature and circumstance of disease. Physicians use diagnostics all the time; they order blood tests, biopsies, scans, genomic tests, and others. Physicians rely on diagnostic results to make the vast majority of their treatment decisions. Researchers rely on diagnostic tests to better understand disease and make better decisions throughout their discovery processes.

The ability to leverage generative AI on top of large, harmonized, multimodal datasets provides the opportunity to make diagnostic tests more personalized, and therefore more intelligent. Intelligent Diagnostics incorporate an individual patient’s longitudinal phenotypic, morphologic, and molecular data, including outcome data from the patient’s EHR, to give laboratory test results clinical context. In doing so, Intelligent Diagnostics can leverage generative AI to make laboratory tests more accurate, tailored, and personal. The test result itself is designed to be specific to each patient and their own unique patient journey. The result is also informed by our large dataset that enables association of clinical outcomes and therapeutic response for patients who are similar to the patient being treated.

The process for making a diagnostic “intelligent” improves upon the process for performing genomic testing, by leveraging technology and data to add clinical context and therapeutic insights. An Intelligent Diagnostic requires the following: (i) perform a laboratory test or ingest results from a laboratory test; (ii) review the test results on a stand-alone basis; (iii) combine the stand-alone results with other forms of relevant clinical data from that patient’s medical records; (iv) contextualize or reconfigure the stand-alone laboratory results to the extent necessary with the insight derived from that patient’s clinical history; (v) include the outcome and response data of patients who are similarly situated to the patient for whom the test was ordered; and (vi) use generative AI to derive analytical and clinically relevant insights and provide those to the physician and patient. See below for an illustration comparing an Intelligent Diagnostic to a standard genomic test:

We believe the adoption and deployment of Intelligent Diagnostics will have a substantial impact on patient care. In oncology, for example, Intelligent Diagnostics have the potential to eventually incorporate insights using data from molecular and anatomic pathology, bioinformatics, genomic variant analysis, inherited cancer risk, computational biology, drug label data, noted adverse events, clinical trial data, research publications, investigational studies, care pathways, real world evidentiary studies, and phenotypic and morphologic data. We already have the ability to incorporate many of these data elements today.

The consequence of incorporating multimodal data is to make precision medicine “personalized” as opposed to “targeted.” A targeted diagnostic test might find a specific condition or characteristic of a patient that is relevant to a particular therapy. For example, in cancer, a targeted diagnostic test may identify a genomic biomarker that could inform therapy selection, such as identifying a HER2 amplification that would allow a HER2 inhibitor to be prescribed to a breast cancer patient. The standard test to determine whether a HER2 amplification is present (other than at Tempus) is typically not designed to assess factors such as

whether the patient is male or female, old or young, or has diabetes or a heart condition. Nor does the standard test consider the medication the patient has taken or is currently taking, or the adverse events the patient has experienced.

An Intelligent Diagnostic test, by contrast, might recommend specific therapies based not just on a singular characteristic, but on the comprehensive profile of the patient who will receive the proposed therapy. For example, an Intelligent Diagnostic might highlight that the breast cancer patient should consider immunotherapy before taking the HER2 inhibitor, or might highlight a series of adverse events the physician should be aware of based on other phenotypic characteristics for that patient, such as if the patient had a heart condition and therefore an elevated risk of a cardiac adverse event from taking the HER2 inhibitor. By linking multimodal data regarding both the disease, such as cancer or diabetes, and the host, our tests can provide a more comprehensive and holistic view of the patient and reconfigure results based in part on the clinical data we collect and the aggregate information in our database.

Intelligent Diagnostics also have the potential to disrupt the clinical trial process. Today new therapies are typically approved based on randomized clinical trials that apply to broad populations and demonstrate incremental improvements over the existing standard of care. The current process suffers from several inherent flaws. First, clinical trials are generally expensive and slow to complete. Second, if and when therapeutics are approved, they can have less of an impact on the larger population than the trial population, given an inherent bias on who has access to academic medical centers and emerging studies. Third, many new therapies are only effective on a subset of patients that enter clinical trials.

We believe Intelligent Diagnostics, AI, and technology broadly can help solve these problems. We believe our ability to contextualize test results to individual patients, to incorporate real world evidence at scale, to identify patterns across similarly situated patients, will help physicians make better, data-driven decisions— which drug to prescribe, which trial to consider, and so on.

The Tempus Platform

Tempus set out to build proprietary technology to implement Intelligent Diagnostics and to facilitate access to, and use of, the resulting datasets. The Tempus Platform connects multiple stakeholders within the larger healthcare ecosystem and provides both the technical infrastructure for what we consider to be one of the world’s largest libraries of matched clinical and molecular data, and an operating system to make that information useful. Our Platform is end-to-end and vertically integrated. It allows us to ingest data from providers, perform diagnostic testing upon request, generate results leveraging our multimodal database, and provide clinical context for a specific patient. Below is a graphic illustrating our Platform’s core functionality.

We believe our AI-enabled Platform can provide unique value whenever two conditions exist: a heterogeneous diseased population and a variety of therapeutics or therapeutic pathways, which are often prescribed based on trial and error. For example, in oncology, there is a diverse population diagnosed with cancer, and each subtype has different characteristics. The combination of unique patient characteristics and different cancer subtypes results in a variety of phenotypic attributes (old, young, male, female, black, white, etc.). In addition, there are hundreds of possible therapeutic paths to consider in cancer (surgery, radiotherapy, chemotherapy, targeted therapy, immunotherapy, etc.). These conditions create an ideal backdrop for the benefits of big data and AI.

The same is true in neuropsychiatry. A heterogeneous population suffers from numerous neurological disorder subtypes, such as depression, anxiety, bipolar disorder, and other psychiatric conditions. Like oncology, there is a diverse patient population and a number of prescribed antidepressants, often based on trial and error. Further, the complexity of oncology, neuropsychiatry, and many other major causes of morbidity necessitate a multimodal data approach, as any single modality (e.g., DNA-only) is unlikely to provide enough information to differentiate meaningful patient subgroups. We believe technology and AI should facilitate data associations and substantially reduce the guesswork associated with which drug to prescribe, in what amount, and in which order.

Facilitated by our relationships with many leading hospitals across the healthcare system in the United States, we believe we are well positioned to introduce precision medicine at scale across multiple disease categories and drive adoption of our Platform and novel AI solutions. We are leveraging our ability to collect, structure and harmonize data, and deploy AI on large datasets to facilitate precision medicine broadly. Below is a timeline of our Platform’s evolution, both within oncology and into different disease categories:

Core Elements of our Platform

The Tempus Platform combines multiple elements into a vertically integrated infrastructure that enables us to ingest data from providers, structure and harmonize the data into a common database, provide laboratory diagnostic testing, and deliver personalized results that provide clinical context by leveraging our data. We offer closed-loop, full- stack, bi-directional integrations between a clinician’s desktop and our laboratory diagnostic capabilities, analytics platform, and repository of multimodal data. Our scaled, interconnected provider network covers more than 55% of U.S. oncologists and provides us with broad data rights, including the rights to longitudinally updated data from time to time. The combination of our Platform and vast provider network yields a powerful flywheel that continues to become more accurate and precise as more patients are added, thereby compounding the network effects of our offering. We believe each of these elements is difficult for competitors to

replicate, and together represent a significant competitive advantage. The following diagram represents the different elements of our Platform.

Ingestion and Generation of Data

We ingest healthcare data in near real time and at scale, including molecular, clinical, and imaging data. Between our sequencing and data collection efforts, we are connected in some way to more than 55% of all oncologists practicing in the United States, along with a growing number of patients in neuropsychiatry and cardiology. Our methods for collecting and creating data include the following:

Ingesting data through our relationships and partnerships with healthcare providers. We have developed proprietary tools to establish approximately 700 direct data connections, across approximately 5,000 hospitals, many of which are bi-directional. We have established relationships with hundreds of provider networks, including more than 65% of all academic medical centers in the United States. To obtain data from these sources, we use a variety of near real-time connections (e.g., HL7, FHIR) and batch data exchanges. Healthcare institutions supply us with this data in our capacity as a covered entity (for example, when we provide NGS services on behalf of a patient), or as a business associate (for example, when we provide clinical trial matching services or data de-identification and structuring services). We ingest and structure data using optical character recognition, or OCR, natural language processing, or NLP, and proprietary workflow tools along with manual data curation. Our proprietary tools connect to a provider’s EHR system, data warehouse, or third-party data provider to pull out relevant structured and unstructured data that the provider has agreed to provide to Tempus, including longitudinal follow-up data to the extent the provider has made such data available. To facilitate these data- sharing relationships, we have developed software products and services that align to our customers’ interests by helping providers use our software tools to improve patient care. In certain circumstances, we cover the actual direct costs associated with the technical integrations needed to create a data connection. We cover these costs to help facilitate providers’ contribution of data and their corresponding use of our products, which then makes our tests more intelligent and helps them to facilitate the delivery of better care. We generally retain the rights we acquire in de-identified data even if our contractual obligations expire or are terminated.

Relationships with industry associations. In addition to healthcare providers, we work with numerous industry associations in the United States, through which we structure and distribute oncology data. We also have agreements in place with large integrated community practices. While our relationships in oncology are widespread, we are making inroads in other disease areas. For example, we have a relationship with a large hospital network to train algorithmic models based on a de-identified subset of more than 3.5 million electrocardiograms, or ECGs, across more than 800,000 patients, with decades of longitudinal clinical data, including outcome and response data. We also have agreements with numerous other institutions through both our sequencing and data efforts to collect and structure multimodal data, and have entered into a variety of partnerships and collaborations across neuropsychiatry, diabetes, and cardiology giving us access to additional clinical data.

Laboratory diagnostics. In addition to our dedicated data pipelines, we generate data for our Platform from our five high-throughput diagnostic testing labs in Chicago, Atlanta, Raleigh, Aliso Viejo and Minneapolis. Our labs offer a range of anatomical and molecular NGS tests, including a broad portfolio of solid tumor, liquid biopsy, and hereditary cancer tests. Our laboratory offerings enable us to populate our database with connected and comprehensive molecular, clinical, and morphologic data that has been de-identified. We also make available an unrestricted copy of the raw files containing the rich data we generate in the laboratory, along with any clinical data we curate, to the providers who order our tests, to further enable their own research efforts. In February 2025, we acquired Ambry Genetics Corporation, or Ambry, a leader in hereditary cancer screening and the supplier of our germline sequencing (Tempus|xG) for hereditary cancer risk. Ambry’s offerings span multiple disease areas, enabling us to expand beyond oncology into new categories, such as pediatrics, rare disease, cardiology, reproductive health and immunology. Additionally, Ambry’s significant laboratory capabilities on the west coast will continue to help increase our overall footprint in the country.

We ingest and generate a variety of different types of data from different sources. The following represents selected data modalities that we collect and aggregate into our database.

Proprietary Data Processing

Once data is ingested, we deploy proprietary clinical data abstraction tools, including natural language processing, optical character recognition, and our abstraction software, to structure, harmonize, and de-identify the data we collect. We have developed various software tools, including algorithmic agents that leverage large language models, to organize millions of records into a common format that spans a variety of data types. For example, we organize clinical data from unstructured documents and structured EHR fields, and typically digitize whole-slide pathology images as part of our clinical workflow. We then combine this data with the molecular data that we generate in our labs or process from third parties, giving us a more comprehensive profile of patients. Unstructured data housed in physician notes and other documents is processed using OCR and NLP, mapped to Tempus’ Medical Ontology, and routed to data abstractors for further curation and quality control. Typically we receive identified data, either in our capacity as a covered entity under the Health Insurance Portability and Accountability Act, or HIPAA, or to the extent we have a business associate agreement with the provider. Following abstraction and structuring, we de-identify data and only retain the resulting de-identified dataset, other than through our obligations to retain selected identified data as a covered entity providing laboratory tests to clinicians. Many clinicians who order Tempus tests clinically are also involved in research related activities. By making this organized and structured data available to the clinicians (along with raw files associated with the testing we perform) we serve, those clinicians can use the data to further their own research efforts to help patients.

Our Proprietary Multimodal Database

We believe most healthcare databases lack real-time functionality, depth among data types, and the scale of matched clinical and molecular records needed to meaningfully improve therapeutic research and development. Tempus is attempting to solve this problem by democratizing the use of near-real time molecular, clinical, and imaging data by embedding our solution into the clinical care of patients. As our testing volume has grown, and as our dedicated data pipelines have expanded, the size of our database has increased exponentially. Since we launched our Platform in 2016, Tempus has amassed approximately 1.4 billion documents, across more than 8.6 million de-identified patient records, including over 1.6 billion pages of rich clinical text that we use to train our large language models. The database also includes over 8 million records with imaging data, more than 1,500,000 with matched clinical records linked with genomic information, and more than 360,000 with full transcriptomic profiles. Within oncology specifically, we believe this represents one of the largest and most comprehensive molecular libraries of cancer patients in the world. The breadth of our database, the quality and diversity of our data, as well as its regularly updating nature, allow us to offer a variety of AI-enabled solutions to the market. We believe our unique data set enables us to bring the benefits of generative AI and large language models to healthcare, as our curated, multimodal database can be used as a proprietary training set to build a variety of AI based applications, which we intend to deploy through our existing network and distribution platform. We also retain the rights to broadly commercialize de-identified data. As the amount of data in our cloud environment continues to grow from its current size of more than 450 petabytes, we believe new applications and opportunities will emerge that are only possible with scale, driving innovations in patient treatment that were previously unattainable. The following diagram represents the growth of our database over time.

Another valuable attribute of our dataset is the number of different data modalities represented. We believe multimodal data is a necessary predicate to successfully build and deploy AI-based applications given the complexity of disease and the various attributes across different forms of data (e.g., text, images, molecules, etc.). As of December 31, 2025, our database included the following types of data, among others:

Footnote: Our clinical data typically includes the following information to the extent provided and abstracted by Tempus: unique identifier; age; sex; race/ethnicity; histology; stage of disease; sample type (primary vs. metastatic); anatomical site of sample and method of procurement; cancer treatment history, including therapies administered; timing of relapse and timing of treatments, including cancer-related treatments and surgery; genomic profiling results (e.g., internal, external providers); tumor response; progression free survival; RECIST or equivalent; ECOG/Karnofsky scores, or equivalent; and adverse events.

Proprietary Software Tools and Solutions

We have developed numerous software tools and applications to help make our services accessible to multiple constituencies within the healthcare ecosystem and support our various product lines. We believe this system architecture, which employs AI techniques such as neural networks, deep learning, large language models, and other statistical learning techniques to generate patient-specific insights. We are able to not only train and validate some of these AI models for research use, but we can also develop them into clinical-grade algorithmic tests, or Algos, and deploy them clinically as part of routine care. As our data advantage and system architecture continue to improve, we believe our existing Intelligent Diagnostics will gain further adoption thereby accelerating our ability to deploy technologies, including Applications, in the clinical setting.

We are both a healthcare company and a technology company, which we believe allows us to more quickly and effectively develop and deploy AI techniques within our proprietary software systems. To do so, we rely on employees with expertise spanning multiple disciplines, including those with PhDs and other advanced degrees in the scientific fields of machine learning, data science and computational biology, as well as Medical Doctors practicing disciplines such as pathology and oncology. In addition to our diverse employee base, we are able to train AI models using our proprietary and expansive multimodal, de-identified dataset. We leverage our varied expertise and extensive resources to continuously monitor and review the statistical performance of the models used across our Platform to ensure performance and prevent degradation.

We describe below some of the core software applications that form part of our Platform, including examples where we have developed and deployed AI techniques.

External Facing Applications

We have two primary software applications that serve as interfaces for different markets and allow our customers to interact with our Platform. Hub is our clinical application for physicians and other healthcare providers and is used primarily in our Diagnostics product line as an end-to-end application for healthcare providers who use our NGS tests. Lens is our application for life sciences customers and other healthcare researchers, launched in May 2021. Lens is aligned with Insights, one of our products within Data and applications, and allows users to identify, license, and ultimately analyze cohorts of data for research purposes. We typically enable our customers to access free or charge certain software applications (like Hub) and certain features of other applications (like Lens). However, in some cases we may charge for access to Lens when a customer is interested in some form of customization or access to Lens’ full suite of capabilities.

Hub

Hub can be accessed on the web or through our mobile applications. Hub enables physicians and other providers to interact with our Platform, place orders for our laboratory tests, track them through the sequencing process, view results, and develop treatment plans using the other information Tempus makes available. Hub streamlines and automates what previously required a significant investment of both time and resources for those ordering and delivering genomic reports.

A physician’s experience, through Hub, typically begins with our online ordering feature, which presents providers with Tempus’ various test options and guides users through the ordering process. Once Tempus has processed an order and sequenced a specimen, Hub synthesizes information across our various tests, orders, and patients, and presents the information in a consumer-friendly interface. For example, Order Summary synthesizes information from various clinical orders, test results, and other information relevant to a patient’s course of treatment. A typical patient might have multiple sequencing events over time. Hub visually presents all of a patient’s results side-by-side, so a treating physician can comprehensively view how a patient’s disease has changed over time, including in response to therapy. Hub also provides care teams a robust set of search and filtering tools so they can navigate our Platform. Physicians can use Hub to identify similarly situated patients or patient sub-groups, including by specific molecular alteration. Physicians can also export and download the resulting dataset for further analysis.

Hub offers additional functionality that goes beyond ordering and presenting clinical results. Our clinical trial system, for example, handles the complexities of matching patients to clinical trials, by synthesizing clinical and molecular data matched against inclusion and exclusion criteria for the trial. It even allows physicians to activate their point of care as a clinical trial site, if approved by the trial sponsor, in order to easily enroll patients who would otherwise not have access to experimental therapies. The proprietary features within Hub put powerful analytics in the hands of physicians, allowing them to pursue research opportunities using accessible molecular data, and explore immune insights such as HLA type, immune infiltrates and neoantigens. Finally, Time on Therapy provides physicians a view into the Tempus Precision Medicine Library, which includes the treatment paths of patients within our de-identified database who display similar molecular or phenotypic profiles to their own patients. These tools enable new patients to potentially benefit from the experience of those that came before.

One example of an AI model whose results are available within Hub, and which illustrates a typical development and validation process for our AI models, is our tumor origin, or TO, algorithm. Our TO algorithm predicts the site of origin for cancer patients whose primary tumor site is unknown using machine learning models trained on tumor RNA expression results from our de-identified multimodal database. We began developing our TO algorithm in 2019, and it was first deployed in a clinical setting in 2021. We developed and trained the TO algorithm, like other machine learning models, by adopting current best practices for AI model development. For example, in developing the TO algorithm, we explored distinct model architectures (logistic regression, random forests and neural networks) and feature selection methods, and we utilized multiple cross validation techniques using both our own and independent third-party datasets. After its launch, we continue to monitor the performance of the TO algorithm by using advanced statistical methods to detect potential model drift or degradation over time. Each TO prediction is reviewed by our board certified pathologists for consistency with underlying data, and the distribution of expected cases is reviewed and assessed against the expected distribution of diagnoses.

Lens

Lens is our software application for life sciences and advanced precision research. We designed Lens to expose our multimodal, de-identified dataset to two main constituencies: (i) clinicians interested in exploring data related both to their own patients and to similarly situated patients from the broader Tempus dataset, and (ii) pharmaceutical and biotechnology clients that are focused on drug discovery and development and want to explore our dataset and/or supplement their own analytics with our tools and data.

For clinicians, Lens helps users filter our multimodal database to identify groups of patients that meet their research requirements. It allows browsing, segmenting, selecting, and analyzing cohorts of patients using a variety of clinical, molecular, and demographic characteristics. We generally make these aspects of Lens available to our customers without charge because such access helps our customers identify data cohorts of interest and facilitates data licensing opportunities.

In addition to this basic functionality, Lens allows advanced computational users to perform robust analytics using our cloud-and-compute infrastructure and modeling tool set. We launched certain of these advanced features in May 2021, one of which is called Notebooks, a proprietary tool that allows users to run their own AI models within our cloud-and-compute environment, taking advantage of fast and streamlined access to our data and computational infrastructure, and saving researchers time and money. Over time, we intend to enter into separate subscription agreements, and charge separately, for expanded access to Lens and the increased functionality we intend to provide to our users.

We believe that as Lens evolves, it has the potential to redefine life sciences research as investigators can both use our tools for their computational needs and instantly download the data they need for their analysis. We are not aware of any other application in oncology, or any other major disease area, that allows researchers to build large multimodal cohorts, utilize advanced analytics capabilities to explore the data, and download data for deeper analysis in near real time.

Our Two Product Lines

Our products are organized under two product lines, with each designed to enable and enhance the other, thereby creating network effects in the markets in which we operate. Our Diagnostics product line provides a broad range of diagnostic testing services to healthcare providers. Our Data and applications product line monetizes the de-identified data that we collect, leverages artificial intelligence to identify and close care gaps, and facilitates enrollment in clinical trials, which at scale has allowed us to provide a series of data related services to our life sciences customers, such as clinical trial matching. Our Data and applications product line also includes our Next product, an AI platform which leverages our database to provide products and services that help route patients to the optimal therapy and advance research and patient care more broadly, and our Algos product, which is focused on developing and providing diagnostics that are algorithmic in nature.

Our two product lines and their corresponding product offerings are illustrated in the diagram below:

We believe the interrelated nature of our two product lines is unique. Our business model allows our clients to unlock value from our data, and allows us to monetize that data (in de-identified format), in different ways across our different product lines. We believe these network effects and the compounding impact on the value of each data record in our database enhance our competitive advantage. The more data we collect, the smarter our tests become, the more applications we can launch, the more physicians join our network, further growing our database, making our tests smarter for clinicians and our database more valuable for researchers.

Diagnostics

Our Diagnostics product line provides a comprehensive suite of Intelligent Diagnostics to healthcare providers, and generates a steady stream of molecular data to help fuel growth in our Data and applications product line. As we run more tests through our laboratories, and as those tests are linked to patient records and clinical outcomes, we grow our data assets and leverage them across all product lines. While we offer diagnostic tests in multiple disease areas, our primary focus is cancer, in which we offer a suite of tests for treatment purposes, which we refer to as our Oncology tests, and a suite of tests focused on hereditary tests acquired through Ambry, which we refer to as our Hereditary tests.

We operate five laboratories that provide NGS diagnostics, PCR profiling, and other anatomic and molecular pathology tests. We have broad capabilities across genomic, transcriptomic, proteomic, microbiomic, epigenetic, and methylation-based assays, and our laboratory infrastructure allows us to operate as a high-quality, low-cost NGS provider broadly serving the market. However, unlike other laboratory diagnostic testing providers, many of our tests are connected to clinical data, in some manner, which allows our suite of tests to be self-learning, becoming more accurate and precise with each new test that we run. Furthermore, rather than providing a result based on a single data modality, such as a DNA mutation, our Platform leverages data from other modalities and other patients in an effort to be more comprehensive.

We are generally paid for our Diagnostics services by billing insurance companies, or patients directly, who reimburse us for the tests we run, or by billing providers or pharmaceutical companies directly. The following diagram represents a summary of our test offerings as of December 31, 2025:

Our Oncology Tests

Our Platform’s first application was in oncology, where we have built a versatile portfolio of cancer tests spanning solid tumors and hematologic malignancies, germline and somatic variants, and tissue and liquid biopsies. Since our inception, our approach to precision oncology has been to provide comprehensive genomic profiling through NGS that enables us to both generate clinically relevant insights that may not be possible with narrower testing approaches, and contribute high-quality molecular information back to providers and to our database. We offer large-panel solid tumor and hematologic testing through multiple assays, with our core clinical assay (xT and xR) offering large panel DNA, RNA full transcriptome, and incidental germline findings through normal blood or saliva analyses. Our current offerings also include liquid biopsy (xF), minimal residual disease and treatment response monitoring (xM), whole exome (xE), and hereditary cancer risk (xG). With our acquisition of Ambry in February 2025, we have further expanded and enhanced our inherited risk screening capabilities for cancer and rare

disease patients. Our acquisition of OneOme, Inc., or OneOme, in November 2025 further enhanced our ability to provide pharmacogenetics tests, such as DPYD. Our oncology tests are differentiated not only because of their breadth, but also because in many cases they are connected to clinical data, which allows us to account for the drugs the patient took historically, how they responded, and for which clinical trials they are actually eligible. We endeavor to not recommend drugs for which a patient has been previously prescribed in a prior line of therapy and failed, and not recommend clinical trials they are not eligible to participate in, based on the inclusion or exclusion criteria of the trial.

The following table lists our current oncology test offerings:

Lab Tests Launch Year Description

Oncology tests

matched normal saliva or blood samples, when available

•Includes full TCR, BCR, and HLA typing for immuno- oncology, or IO, signatures

•Detects TMB, MSI, and fusions

•The test has an approximately 10-day quoted turnaround time

•Premarket approval (PMA) obtained from the FDA in April 2023

•Awarded Advanced Diagnostic Laboratory Test (ADLT) status

•Detects TMB, MSI, and fusions

Lab Tests Launch Year Description

•The test has an approximately 10-day quoted turnaround time

In November 2023, we entered into a Commercialization and Reference Laboratory Agreement with Personalis, Inc., or Personalis, pursuant to which we began marketing Personalis’ Personal Dx test in the United States initially in non-small cell lung cancer and breast cancer, as well as IO treatment response monitoring. Personalis will conduct additional development activities to further analytically validate the test in other indications. Personalis will perform tests ordered by patients through us and will bill such patients or payers.

We have long believed that incorporating clinical data in our diagnostic tests has widespread benefits, and have extensively studied the benefits of multiple modalities of data in cancer treatment. For example, as far back as 2019, we learned that combining clinical and molecular data resulted in improved therapy matching for patients in a study that we conducted, the results of which were published in Nature Bio. In that study, using our sequencing results and matched clinical data from 500 patient samples across a range of tumor types, we observed that 96% of patients could be matched to at least one clinical trial. Approximately 77% of patients were matched to at least one clinical trial based on a gene variant. Of the patients who were not matched to a biomarker-based clinical trial, 19.4% were matched to at least one disease-based clinical trial from clinical data alone.

The results of the Nature Bio study indicated that paired tumor-normal DNA-seq and RNA profiling of patient cancer biopsies yielded high match rates to targeted therapies and clinical trials, and also underscored the value of integrating and contextualizing clinical and molecular data to provide physicians with distilled information regarding their patients’ disease and potentially actionable characteristics. In sum, our Platform demonstrated an ability to help maximize personalized therapeutic options for a broader proportion of patients with cancer, which typically cannot be attained through smaller tumor-only DNA-seq panels.

We have long observed similar benefits from testing across modalities. In a paper we published in Nature Precision Oncology in July 2021, we highlighted the benefits of performing both solid tumor and liquid biopsy profiling. We observed that the concordance of the results of tissue sequencing and liquid testing, even when concurrently profiled, was approximately 70% at most, with both liquid testing and tissue sequencing missing a selected number of potentially actionable mutations. Yet when both are performed, as Tempus often does, the coverage of potentially actionable mutations increases.

We believe the market is recognizing the value of our products and their benefits, as they relate to sequencing both somatic and germline variants, running both solid tumor and liquid biopsies, broadly sequencing RNA in addition to DNA, making available raw files and structured clinical data, and matching the results to clinical data for the patient sequenced. As a result, our clinical oncology volume rose from approximately 31,000 samples sequenced in 2018 to approximately 367,500 samples in 2025.

Our Hereditary Tests

We have also expanded our offerings by acquiring Ambry in February 2025. Through Ambry, we offer a comprehensive menu of genetic tests focused on inherited conditions across several major clinical areas: hereditary cancer, heart conditions, neurological disorders, and rare diseases. Among other diagnostics, Ambry offers the following tests, which ordered largely by genetic counselors but increasingly by oncologists and other healthcare providers:

•

Single Gene Sequencing Tests that analyze a specific gene for known or suspected mutations (e.g., BRCA1/BRCA2 testing for breast cancer risk)

•

Multigene Panels, such as CancerNext or CardioNext, that simultaneously examine multiple genes associated with a specific set of related conditions;

•

Exome Sequencing, like ExomeNext, which analyzes approximately 20,000 protein-coding genes to identify the cause of undiagnosed rare or complex conditions; and

•

Paired DNA/RNA Testing, which is branded as +RNAinsight® and analyzes both DNA and functional RNA data to provide more accurate variant classification, potentially resolving variants of uncertain significance (VUS).

The following table lists our current hereditary test offerings:

•The test has an approximately 14-21-day quoted turnaround time

Our Neuropsychiatry Tests

We entered neuropsychiatry in 2019. We currently offer our proprietary nP assay for pharmacogenomic testing for patients with psychiatric conditions, such as depression, general anxiety disorder, bipolar disorder, and other relevant diagnoses. Despite the growing prevalence of depression and anxiety, their treatment remains largely the same as it has been for decades. Today, there are dozens of antidepressants that are often prescribed based on trial and error, where psychiatrists alter the dose and class of medications when one fails to work. The difficulties in prescribing medications leads many patients to take the wrong medications, in the wrong dose. Emerging evidence demonstrates that there are molecular mechanisms that suggest one drug, or class of drugs, may work better than another based on the genetic profile of the patient, and our assay is designed to elucidate these differences. The following table describes our nP assay.

As we continue to advance the field of psychiatric medicine, we believe our Platform is well suited to extend to additional neurological conditions beyond depression, anxiety, and bipolar disorder.

Data and applications

Our Data and applications product line consists of Insights, our data-centric product, and a suite of Applications, the primary offerings of which include Trials, Next and Algos. Our Insights product helps facilitate drug discovery and development for life sciences companies and includes, among other capabilities, a tumor-derived biological modeling (or organoid) laboratory, that enables us to provide modeling and screening services to our pharmaceutical and biotech clients. In addition, we offer a series of Applications that leverage AI to advance precision medicine across the healthcare ecosystem. For example, our Trials product identifies patients who may benefit from additional treatment or participation in clinical trials. Our Next product is an AI platform that leverages machine learning to apply an "intelligent layer" onto routinely generated data to proactively identify and minimize care gaps for oncology and cardiology patients. Finally, Algos are purely algorithmic diagnostic tests,developed and deployed based on associations and biomarkers identified through our rich, multi-modal dataset.

One way we measure our data business is based on the remaining total contract value, or the Remaining TCV, that is contractually committed to be delivered in the future. As of December 31, 2025, we have signed contracts with a Remaining TCV of more than $1.1 billion, which includes approximately $300.0 million in additional potential future contractual opt-ins. Remaining TCV is equal to the total potential value of signed contracts and assumes the exercise of all contract options, all discretionary opt-ins, and no early termination. Remaining TCV includes the total potential value of the company’s strategic collaborations with AstraZeneca AB, or AstraZeneca, and GlaxoSmithKline, or GSK, which, although listed under the Data and applications product line, could be satisfied by the purchase of any of the company’s products and services. Remaining TCV excludes any revenue recognized to date on these contracts or any future adjustments made to the contractual value as a result of amendments or terminations. Our agreements contain termination clauses, including the ability of our counterparty to terminate for convenience, and there can be no guarantee that contracts will not be terminated, that contractual options and discretionary opt-ins will be exercised, or that we will achieve the full amount of potential revenue represented by these contracts. Remaining TCV is not a calculation of revenue and should be viewed independently of revenue and deferred revenue, as Remaining TCV is not intended to be combined with or replace these items. Similarly, Remaining TCV is not a forecast of future revenue, which can be impacted by, among other things, contract start and end dates, our ability to meet performance obligations, and the exercise of contractual options or termination rights. Moreover, Remaining TCV may differ from similarly titled metrics presented by other companies and may not be comparable to such other metrics.

Insights

Historically, the primary means for pharmaceutical and biotechnology companies to build a dataset for drug discovery and development was to run a preclinical study or clinical trial, and to leverage limited datasets such as medical claims data. We believe Tempus is changing the existing paradigm. We launched our Insights product to allow researchers to access large amounts of multimodal healthcare data that historically did not exist at scale in a single consolidated database. We have amassed a large connected dataset, which we organize in near-real time across multiple modalities and multiple disease areas, allowing us to work with pharmaceutical and biotechnology companies across the drug lifecycle—from discovery, research and development, and, ultimately, commercialization.

For our Insights offering, we license libraries of linked, de-identified clinical, molecular, and imaging data, and provide a suite of analytic and cloud-and-compute tools for discovery, research, development, and other commercial purposes. Our primary customers are pharmaceutical and biotechnology companies. These customers either pay us on a per file basis or through multi-year data licensing agreements to use our de-identified patient database. We currently work with 19 of the 20 largest public pharmaceutical companies based on 2024 revenue.

We believe we offer a unique value proposition to the industry as a cost-effective source of high-quality and comprehensive data on targeted patient populations. Our data is useful across the oncology drug development value chain, and our biotechnology and pharmaceutical customers are using the data to inform decisions in a variety of discovery and development applications, selected below. One metric that illustrates the utility of our data to our customers is “Net Revenue Retention.” Net Revenue Retention compares the annual Insights product revenue generated from all customers that made an Insights purchase in one year to the annual Insights product revenue generated from the same cohort of customers in the subsequent year. Net Revenue Retention is not a calculation of revenue and should be viewed independently of revenue and deferred revenue, as Net Revenue Retention is not intended to be combined with or replace these items. Similarly, Net Revenue Retention is not a forecast of future revenue. Moreover, Net Revenue Retention may differ from similarly titled metrics presented by other companies and may not be

comparable to such other metrics. For the year ended December 31, 2025, Net Revenue Retention was approximately 126% compared to the same cohort of customers for the period ended December 31, 2024.

To illustrate an example of how our data can be applied, in December 2020, we published a peer-reviewed study in ScienceDirect in which we analyzed longitudinal real-world data, or RWD, from a large cohort of patients with breast cancer (n = 4,000) to test whether results were consistent with previous clinical studies and to demonstrate the real-world evidence validity of our database. We also evaluated whole-transcriptome sequencing as a complementary diagnostic tool (n = 400). The conclusions of the study demonstrated that our database mirrored the overall population of patients with breast cancer in the United States, and that near real-time, RWD analyses are feasible in a large, highly heterogeneous database. Furthermore, the study demonstrated that molecular data may aid deficiencies and discrepancies observed from breast cancer clinical RWD.

Because many of our data profiles regularly update with clinical outcome and response data over time, the utility of a single de-identified record may increase over time. As such, the files we generate by sequencing a patient, when connected to clinical data, are valuable to pharmaceutical and biotechnology companies, as they not only allow users to gain molecular insight into what is happening among cohorts of patients, but they also allow users to track those cohorts over time. As a result, our files behave as if they have a “lifetime value” that has the potential to increase over time, in a manner similar to a content company where you pay to create content and then monetize the content over time as people subscribe to access the content.

Tumor Derived Biological Modeling—Organoids

In addition to our efforts to collect vast amounts of phenotypic, morphologic, and molecular data, we have built a large, biological modeling lab that allows us to test various theories in vitro through our large repository of tumor-derived Organoids, and to perform drug screening for our various life sciences clients. Many of our Organoids are fully characterized and sequenced using our NGS panels, providing genomic and transcriptomic data for our models, allowing us to explore various hypotheses that enhance our data. Examples of hypotheses we are able to test in our Organoid lab include: (i) which therapeutics are most effective; (ii) differential levels of drug response by tumor type, genomic profile, or other targeted attributes; (iii) discovery of RNA signatures; (iv) attributes of responders and non-responders; and (v) response rates in therapy-resistant models. We work with numerous collaborators including biotechnology companies, pharmaceutical companies, academic institutions, and government labs. Since 2017, we have scaled our sample collection efforts and have received approximately 6,500 tumor samples to date.

These samples cover a wide range of cancer subtypes, allowing us to work on comprehensive drug screening applications across multiple epithelial based tumor types, such as breast, lung, colorectal, and pancreatic. One of the goals of this screening is to predict a series of therapeutic responses in our Organoids and then test whether or not patients are experiencing similar responses in the clinical setting.

We view biological models as another form of data. Our efforts to grow Organoids are part of our overall strategy to leverage the best of systems biology along with the best of AI to collect the requisite data needed to produce answers broadly throughout healthcare.

Applications

We offer a series of Applications, most of which leverage AI, to both advance precision medicine research and optimize patient care. We describe some of our primary Applications below.

Trials

Our Trials offering leverages the broad network of physicians we work with in oncology to provide clinical trial matching services for pharmaceutical companies trying to reach hard-to-find and underserved patient populations. Our clinical trial matching product is built on top of our near real-time data feeds and harnesses AI to accelerate the connection between patients, clinical trial sites (hospitals) and clinical trial sponsors (life sciences companies). We empower both oncologists to help their patients find clinical trials and pharmaceutical companies to enroll patients into their trials. We generate revenue from both matching the patient to the trial (through notices we send to physicians alerting them of potential trials that are a fit for their patients), and from the patient actually enrolling in the trial.

Our Trials product is a bold initiative that we do not believe has been implemented at scale in the United States by any other organization. We are endeavoring to create a just-in-time network across a wide variety of academic medical centers and community providers, that can support hundreds or even thousands of trials, in which the administrative and logistical foundation is uniform across the entire network. This network allows us to identify a patient that is a match for a targeted trial and get that patient enrolled within days, even if the trial was not previously open at the hospital (assuming consent of the trial sponsor), anywhere in the United States. Prior to Tempus, we believe it would have been virtually impossible to even attempt to build this type of just-in-time program across oncology, as the required ingredients for success are unique to our Platform, namely: (i) a large genomic sequencing business that is widely adopted and allows for the identification of patients that are molecular matches to trials; (ii) the ability to structure clinical data for those patients in near real time to filter for inclusion and exclusion criteria; (iii) direct pipelines allowing data to be transferred to and from the laboratory and provider; and (iv) an analytic engine able to stratify patients and follow each unique patient journey ensuring that patients actually enroll in the studies.

Our clinical trial matching offering is called the TIME Trial® program, which we launched in June of 2019. Since its introduction, this program continues to gain significant traction with more than 1,400 clinical trials signed into the network. More than 40,000 patients were identified for potential enrollment into clinical trials in our network as of December 31, 2025. We believe the breadth of our network, the data to which we have near real-time access, and our relationships with oncologists enable

us to offer a clinical trial matching service that has the potential to materially expand patient access to and accelerate enrollment in clinical trials in the United States.

One of the primary benefits of our Trials product is our ability to facilitate the initiation of a clinical trial in a new location in a short amount of time. Third-party research suggests that it takes 6-12 months, on average, to initiate a new trial site for an ongoing clinical trial in the United States. We have been able to substantially streamline this process by leveraging technology and introducing a standard methodology, with activation of new sites through our Trials product taking approximately 10 days on average in 2025. A comparison of our average time from site initiation to patient consent with the industry average is below:

In addition to TIME, we provide other clinical trial services and conduct our own studies as part of our Trials program, all with a goal of identifying new therapies and bringing them to market more efficiently. In January 2022, we acquired Highline Consulting, LLC, a contract research organization, or CRO, which we subsequently renamed Tempus Compass, LLC, or Tempus Compass. Tempus Compass manages and executes early and late-stage clinical trials, primarily in oncology. We also partner with life sciences companies to sponsor studies of drugs, devices, and diagnostics, integrating our life science solutions to help bring new drugs to market faster. Each of the products and services within our Trials program complement each other to create a suite of integrated solutions for life sciences companies from early discovery to commercialization.

Our Next application is an AI platform that leverages machine learning to apply an “intelligent layer” on top of routinely generated data to proactively identify and minimize care gaps for oncology and cardiology patients. As this product gains adoption, we intend to leverage large language models, generative AI algorithms, and our vast database of de-identified data to develop algorithmic diagnostics designed to identify these patients earlier in their disease progression, when treatments are most effective. For example, the Next application can monitor clinical data in real-time to identify patients suffering from non-small cell lung cancer (NSCLC) who have not received testing to identify potential EGFR mutations, a biomarker that could help determine an appropriate course of treatment. In the cardiology space, Next can help identify patients who might be at higher risk of atrial fibrillation through the use of an FDA-cleared algorithmic diagnostic. We discuss further below some of our other algorithmic diagnostic tests in cardiology. The ultimate goal of Next is to leverage AI to identify and close care gaps wherever they might exist to ensure each patient is receiving the appropriate care at the appropriate time.

Algos

The vastness of our dataset, along with our connected platform, creates an opportunity to use data to algorithmically diagnose and treat patients. For example, we are focused on developing and providing diagnostics that are wholly algorithmic in nature, as well as implementing new software as a medical device, and building and deploying clinical decision support tools.

Algorithmic diagnostics that integrate multimodal data can be used to create a more accurate risk profile for patients, leading to improved outcomes and reduced cost. Our repository of multimodal data allows us to find associations and patterns that are largely invisible through a single data modality, but readily apparent when combined using sophisticated analytical tools. In addition, we find the strength of our analytic models, and our ability to deploy them clinically, improves as we add additional datasets. While we plan to continue developing our own proprietary software and algorithms, from time to time, we also utilize open source technologies or in-license technologies from third parties.

Algorithm-based diagnostics are already being used in healthcare, but are not widespread. For example, algorithms exist today that leverage EHR data and lab results to predict early onset of hospital-borne infections, but these tools are still in the very early stages of adoption and validation. While Algos today represent only a small proportion of the diagnostics market, we expect their adoption to grow substantially in the future. We believe Algos represent a significant long-term opportunity that may be substantially larger than our other existing product lines. We believe our ability to launch Algos at scale is a key differentiator of our Platform.

Our Oncology Algos

We believe our robust, multimodal dataset creates an opportunity for Algos that otherwise would not be possible and allows us to build AI models at scale, clinically validate them, and deploy the resulting Algos into clinical practice. We currently offer a suite of Algos in oncology, and have more in various stages of development. As of December 31, 2025, more than 123,000 molecular oncology Algos have been ordered with our various genomic assays. Most of the Algos we currently offer are part of our xR assay, and we do not bill separately for them. Some Algos will likely yield little to no reimbursement until their clinical utility is established or will be ordered separately with our existing NGS assays or diagnostics to enhance the actionable information for physicians, and some may obtain reimbursement at prevailing rates for comparable tests.

Algo Launch Year Description

Oncology

Algo Launch Year Description

Our Cardiology Algos

Heart disease is the leading cause of death in the United States. About 700,000 Americans die from heart disease annually, with more than 11% of American adults diagnosed with heart disease and millions of patients suffering from undiagnosed, life-threatening, yet highly treatable conditions such as AFib, cardiomyopathy, and valvular heart disease, to name a few. Tempus is working on solutions to find, diagnose, and help treat these patients earlier in order to improve patient outcomes, using routinely generated clinical data, such as data from a 12-lead ECG, a widely used and easily acquired medical test that measures the electrical activity of the heart, to screen patients who might be at high risk and help navigate them to the appropriate interventional therapy.

In cardiology, we ingest multimodal data and use algorithms to identify potential care gaps across 15 disease areas and continuously monitor patient data to find at-risk patients who may be falling through a care gap unbeknownst to their physician, and automatically notify care teams of any needed follow-up or disease progression. Around 150 hospitals nationwide are currently powered by Tempus Next and more than 60,000 patients are screened per month.

We are also developing algorithmic models that aid clinicians in identifying patients at increased risk of developing atrial fibrillation, or AFib, along with a variety of other cardiac conditions. These Algos are trained using a de-identified subset from approximately 3.5 million ECGs, across more than 800,000 patients, with decades of longitudinal clinical data, including outcome and response data. The FDA granted Tempus breakthrough status for our first ECG software device, which employs a diagnostic algorithm designed to identify patients at high risk of developing AFib in certain populations (patients 40 years of age and older, without pre-existing or concurrent AFib or atrial flutter, and who are at elevated risk of stroke based on a commonly used clinical stroke risk assessment tool (i.e., CHA2DS2-VASc score of ≥4)).

Algo LaunchYear Description

Cardiology

We are also advancing Algos that are designed to predict aortic stenosis, and we are working on other disease areas within cardiology, such as low ejection fraction and familial hypercholesterolemia. If broadly deployed, we believe these Algos could have widespread clinical applicability, increase life expectancy, and reduce the total cost of care.

In addition to algorithms based on NGS testing or in the cardiology space, we currently offer more than 50 algorithms and are continuing to develop additional algorithms derived from radiologic images and digital pathology slides. In October 2022, we acquired Arterys, Inc., a company that provides a platform to derive insights from radiologic medical images to improve diagnostic decision-making, efficiency, and productivity across multiple disease areas. We have also developed algorithms based on Immunohistochemistry, or IHC, and H&E staining, which can be used, among other things, to help identify patients who may be eligible for additional treatments or clinical trials. In August 2025, we acquired Paige, Inc., or Paige, a company specializing in digital pathology. Founded in 2017, Paige has developed and deployed several applications, including the first FDA-cleared AI application in pathology, allowing researchers and pathologists to better detect cancer, which enables care teams to make more precise and informed treatment decisions. Paige has developed and refined its products through a dataset that includes almost 7 million digitized pathology slide images and associated clinical and molecular data, stripped of patient identifiers to protect privacy. Leveraging a dataset of de-identified data and images that spans numerous countries and diverse genders, races, ethnicities, and regions, Paige has also developed the first million-slide foundation model for cancer, empowering researchers and life sciences companies to better understand pathology data, and enabling the advancement of drug discovery and development.

Commercialization

Our commercial efforts are generally focused on driving increased adoption of our various products and services, both by increasing the utilization of existing customers and securing new customers. We employ targeted sales and business development organizations, whose team members are engaged in direct sales and marketing efforts. Our commercial teams typically target healthcare providers and life sciences companies, which are the main purchasers of our products and services. We describe below our overall commercial strategy for our two product lines.

Diagnostics

Our primary customers in our Diagnostics product line, which is largely made up of molecular testing in both Oncology and Hereditary, are healthcare providers, such as physicians and genetic counselors, who order our tests, and bio-pharma companies who use them to conduct research. When we sell our tests to healthcare providers we are typically providing them as part of routine clinical care and we are often billing insurance and seeking reimbursement on behalf of the patients for whom the test was ordered. When we sell our test to bio-pharma, we are typically being paid as a contract sequencing provider, either for the trials they are running or as a companion diagnostic to their drug. On the provider side, we commercialize our Diagnostics products in the United States to clinicians and healthcare providers largely through our dedicated clinical sales organization, that calls on individual doctors, genetic counselors, medical practices, or other healthcare institutions. As of December 31, 2025, our clinical sales organization in the United States included approximately 205 sales representatives who are primarily contacting oncologists, psychiatrists, and other healthcare providers. Our sales representatives typically have backgrounds either in a particular disease area (such as oncology or neuropsychiatry) or in laboratory testing and therapeutics more generally. We supplement our commercial team with clinical specialists with extensive medical affairs experience who provide molecular support in the field.

In oncology, which currently is our largest market, we are focused on driving adoption by targeting individual treating physicians, academic medical centers, community oncology practices, leading physician networks, and industry associations. We also are exploring relationships with third-party payers and governmental institutions. We have a land and expand strategy, by account, whereby we attempt to sign new accounts and increase adoption of our platform within these accounts over time. As such, we often begin a relationship that is transactional in nature, but seek over time, to work on a more comprehensive basis with healthcare providers, serving an ever increasing percentage of our molecular diagnostic needs over time. We find that once a physician starts using Tempus, if they order more than 5 oncology NGS tests from us, their 12-month retention rate is 87%.

In addition, we believe that interactions among treating physicians help drive adoption of our products. We are focused on key opinion leaders in the industry through direct outreach and indirect marketing efforts. As of December 31, 2025, we have either published or been acknowledged in more than 2,000 publications, including the following:

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>800 total peer-reviewed articles published in major journals, including publications such as Nature Biotechnology, Clinical Breast Cancer, Nature Medicine, and Cell.

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>1,000 total abstracts based on clinical and research data that have been accepted and presented at major scientific conferences.

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>200 oral presentations at scientific meetings such as the ASCO, ASCO Gastrointestinal and Genitourinary Cancer Symposiums, San Antonio Breast Cancer Symposium, and the American Heart Association Scientific Sessions.

We have a similar strategy in neuropsychiatry, in which we aim to increase the commercial adoption of our nP test for depression as part of the rapidly growing market for pharmacogenomic testing, with a goal to better understand, diagnose and treat neuropsychiatric disorders.

Our commercial strategy for other disease areas is expected to follow our strategy in oncology and hereditary, which is to focus on offering a broad range of molecular diagnostics to the market, that are connected to clinical data, so we can track how molecular results correlate with outcomes and responses, thereby making our tests smarter and more personalized overtime.

Research Testing

Another component of our genomic testing involves testing performed in a research capacity, either by healthcare providers, such as academic medical centers or bio-pharma companies. This type of testing is typically done under an agreed upon contracted arrangement for specific tests at specific prices and volumes. Typical customers in these arrangements are pharmaceutical companies engaged in testing for clinical trials, researchers who need genomic testing to further research activities, or a company marketing products or services of their own who elects to use us as a reference laboratory. In this type of research testing, the agreed upon rate for testing may vary significantly, and in some cases may even be offered as an in-kind service in exchange for other rights we obtain in the contracted relationship.

As it relates to selling our Diagnostic products to bio-pharma, we have a dedicated team of sales executives focused on calling on biotech and pharmaceutical companies who use genomic sequencing services predominantly for the research they are conducting, the clinical trials they are running, or as a companion diagnostic to the extent their therapeutic relies on a bio-marker. To this group, we are typically selling retrospective and prospective sample testing services, as well as companion diagnostic development to support the approval and commercialization of therapeutics.

Data and applications

In addition to our field sales force, our Data and applications products rely on a dedicated business development team focused on enterprise sales to pharmaceutical and biotech companies in the United States and abroad. Our strategy with each customer is to demonstrate the value proposition of our Platform, de-identified datasets, and broader product portfolio, and then to expand the utilization of our Data and applications products across the organization from early-stage research through clinical development to commercialization. Given the broad and differentiated utility of our Platform, we believe we can support our pharmaceutical and biopharmaceutical customers across many applications, including:

• early stage research and development;

• discovery of new targets and mechanisms of acquired resistance;

• clinical trial patient identification and enrollment; and

• Analytic services, including cloud and compute.

We also expect to be able to capture other commercial opportunities from our genomic data, which can be used in combination with clinical outcomes or claims data for multiple applications, including novel target identification, label expansion, and other commercial applications.

As of December 31, 2025, we had approximately 30 sales executives in our Data and applications product line development organization. We divide these individuals by both geography and strategic account to ensure consistency and coordination across our sales efforts.

The business development personnel contacting life sciences companies is also responsible for commercializing our Applications. Our primary Application product is currently “Next,” an AI platform that leverages machine learning to apply an "intelligent layer" onto routinely generated data to proactively identify and minimize care gaps for oncology and cardiology patients. As this product gains adoption, we intend to leverage large language models, generative AI algorithms, and our vast database of de-identified data to develop algorithmic diagnostics designed to identify these patients earlier in their disease progression, when treatments are most effective.

In addition, we continuously seek to bring new clinical diagnostic algorithms to market. We develop Algos in three ways: (i) we may develop them internally based on our robust de-identified dataset; (ii) we may collaborate with a third party to develop Algos together (again leveraging our de-identified dataset); and (iii) we may license an existing Algo from a third party. Once we clinically validate an Algo, we typically bring it to market through our existing provider network by leveraging our Diagnostics sales force. For example, our HRD and TO Algos in oncology have been added to our standard requisition forms, online portal, and EHR integrations. Treating clinicians can order these Algos at the same time they place their standard clinical testing orders for our other Diagnostics products. We believe clinicians find significant value in being able to receive multiple answers from Tempus while only needing to provide one set of biospecimens, thereby reducing the burden on their patients and their staff. At present, we expect our Algos in other disease areas to go to market through our network of EHR integrations and clinical collaborations.

The commercialization of future Algos will depend the nature of each and whether we are able to bill insurance separately. When we do so, we expect reimbursement will be limited for most Algos at launch and may grow over time as we build additional evidence to support the clinical utility and benefit of each Algo.

Competition

The increasing value of using data to inform clinical care and drug development decisions is leading more companies to attempt to develop offerings that are marketed in a manner that makes them appear comparable to ours. As a result, each of our products faces increasing competition from a number of other companies.

Our Diagnostics product line primarily faces competition from diagnostics companies that profile genes in cancers and other disease areas, based on either single-marker or comprehensive genomic profile testing, using NGS to evaluate either blood or tissue. Our primary competitors for our currently marketed precision oncology tests include Foundation Medicine, Inc., which was acquired by Roche Holdings, Inc., Caris Life Sciences, Guardant Health, Inc., Natera, Neogenomics, ResolutionBio, which was acquired by Agilent, and others. As we expand into other applications such as recurrence monitoring or minimal residual disease, as well as potentially testing for early detection in the future, we anticipate facing competition from a broader universe of companies. Legacy diagnostic laboratories, such as Quest and LabCorp may also pose competitive threats within the market. Competitors for our pharmacogenetic test in neuropsychiatry include Myriad Genetics, Inc. and Genomind, Inc. Our primary competitors for our hereditary tests include GeneDx, Variantyx, and Baylor Genetics.

Our Data and applications product line primarily faces competition from companies that help pharmaceutical and biotechnology companies acquire data to inform drug discovery and development. Our main competitors in this area are Flatiron Health, Inc., IQVIA Holdings Inc., ConcertAI, and others. Our Data and applications products also face competition from CROs, such as Fortrea, ICON, Syneos, PPD, and others, who provide data and clinical trial matching services to pharmaceutical and biotechnology companies.

Our Applications products face competition from providers that are focused on providing laboratory testing or algorithm-based diagnostics for the disease and application areas in which our Algos are focused. With respect to Trials, our primary competitors include IQVIA, PPD, ICON and Syneos Health. With respect to Algos, our TO test competes with liquid or tissue-based diagnostic tests from Roche Holdings, Inc., Caris Life Sciences, Guardant Health, Inc. Illumina, Inc, and others. Our HRD test competes with tests from Myriad Genetics, Inc., Caris Life Sciences, and others. We may also compete with companies developing or commercializing algorithm-based diagnostics using a variety of different data modalities, including digital pathology companies such as PathAI, Inc. In cardiology we may compete with companies such as HeartFlow Inc. and Eko Devices, Inc. We expect other competitors to enter this market, including academic medical centers who develop their own Algos and are looking for new ways to commercialize them. We believe we are positioned well against this competition given our broad provider network and our ability to deploy AI solutions at scale through our Platform.

Many of our competitors may have substantially greater financial and other resources than us, including larger research and development staff, or more established marketing and sales forces. Other competitors are in the process of developing novel technologies for the diagnostics and healthcare data markets that may lead to products that rival or replace our products. While we cannot be certain as to how the market will evolve, today we believe we are substantially differentiated from our competitors for many reasons, including the network effects of our products, proprietary technologies, rigorous product development processes and scalable infrastructure, customer experience, and multidisciplinary teams.

For further discussion of the risks we face relating to competition, see the section titled “Risk factors— Risks Related to Our Business and Strategy.”

Payer coverage and reimbursement

Clinical Testing

A majority of the genomic testing we perform is clinical in nature. We typically receive reimbursement for these tests from commercial payers and from government health benefits programs, such as Medicare and Medicaid. In almost all of our arrangements for clinical testing, we take on the obligation (and risk) to bill the patient’s insurance for the testing being provided, subject to other laws that may require us to directly bill the healthcare provider in limited circumstances. We also have a small number of “direct pay” arrangements where the provider may agree to pay us a specific amount and take on the billing obligation (and associated risk of payment) for the testing performed for that customer’s patients, or where a third-party advocacy group or government agency has arranged for and agreed to pay for testing.

Laboratory tests such as our genomic tests, as with most other healthcare services, are classified for reimbursement purposes under a coding system maintained by the American Medical Association known as current procedure terminology, or CPT, which we use to bill and receive reimbursement for our tests. CPT codes are associated with the particular test that we have provided to the patient, but do not always precisely describe the testing offered.

Once the American Medical Association establishes a CPT code, the Centers for Medicare & Medicaid Services, or CMS, establish payment levels and coverage rules under Medicare (sometimes through national coverage determinations, or NCDs), although it delegates some of that authority to local Medicare administrative contractors, or MACs, who may have local coverage determinations, or LCDs, in place. Private payers establish their rates and coverage rules independently.

As of December 31, 2025, we had received payment on approximately 55% of our clinical oncology NGS tests and 50% of our hereditary tests across all payers performed from January 1, 2023 through December 31, 2024. We calculated this metric on a trailing basis based on payer adjudication timing. However, we continued to perform our NGS tests through December 31, 2025. For the years ended December 31, 2025, 2024 and 2023, our average reimbursement for NGS tests in oncology (i.e., excluding hereditary testing) was approximately $1,600, $1,510 and $1,450, respectively. For the year ended December 31, 2025 and 2024, our average reimbursement for NGS tests in hereditary testing was approximately $770 and $760, on a pro forma basis, for which pro forma amounts have been calculated after applying the Company's accounting policies. Our strategy to improve reimbursement is as follows:

• Continue to work with NGS, our local MAC in Chicago, to maintain coverage of current assays, obtain coverage of new assays through engagement and reconsideration requests, and to continue various appeals when coverage is denied.

• Continue to work with our new MAC, Palmetto, which covers our tests when performed out of our newest lab in Raleigh, North Carolina, to get the technical assessment of our assays approved and coverage policy in place for reimbursement.

• Continue to seek FDA approval of additional assays.

• Continue to work with commercial payers to both get in network and get our assay approved and reimbursement at a higher rate than it currently is.

At present, we have a team that is dedicated to the above, and if we are successful we would expect our reimbursement per assay to be more in line with other NGS providers who have adopted similar strategies, such as FMI and Guardant.

Algos

Because we expect the Algos we bring to market to provide value to a wide variety of stakeholders in the healthcare ecosystem, we anticipate that the payment we may be able to obtain will vary substantially. Value obtained is likely to depend on the nature of the underlying product or service developed, as well as the disease area and manner in which the product or service is made available. For example, while the current HRD and TO offerings are point-of-care ordered, and are reimbursed through our xR assay, we do not expect to be limited only to payment and reimbursement through the typical fee-for-service reimbursement model based solely on point-of-care clinical testing. We may also develop Algos in combination with life sciences companies in which we are paid directly or through alternative payment structures.

In sum, we expect that reimbursement for our Diagnostics products and Algos may provide value to, and potentially be paid for by, pharmaceutical companies, health maintenance organizations, managed care organizations, pharmacy benefit managers, large employers, and integrated delivery network health systems, in addition to being reimbursed by government healthcare programs, private insurers and other third-party payers. Those arrangements may take many forms. Pharmaceutical companies have expressed interest in using some of our Algos to better identify, screen, stratify, and enroll patients in clinical trials, payers have expressed interest in Algos that could assist them in value-based care initiatives that reduce spending waste in the healthcare system, and large health systems have expressed interest in certain population health screening Algos that could assist them in providing higher quality care, better outcomes for patients, and/or in reducing costs.

Operations

We currently perform our laboratory tests in our clinical laboratories in Chicago, Atlanta, Raleigh, Aliso Viejo, and, effective December 2025, Minneapolis, Minnesota through our acquisition of OneOme. Our Chicago, Atlanta, Raleigh, Aliso Viejo and Minneapolis laboratories are CAP-accredited and CLIA-certified, and licensed in other states including, among others, New York, California, Maryland, Pennsylvania, and Rhode Island.

The scale our laboratories have been able to achieve in the approximately 10 year period since we ran our first clinical test is a direct result of the quality and experience of our laboratory staff, our investment in technologies in the laboratory that assist with automation and workflow improvements, and the ability of our engineering staff to build fit for purpose applications in a rapid development environment to support the laboratory’s evolving needs. Our leadership staff in laboratory operations has decades of experience in running high-quality, high-throughput assays and have been instrumental in putting in place the necessary standard operating procedures to perform the volume of testing we do in a repeatable, reliable manner while constantly looking for opportunities to improve and refine our processes. The workflows in our laboratory are designed for high-throughput testing and numerous steps in the process are fully automated or semi-automated using robotics and other advanced workflow technologies. At present, for our xT and xF tests, our laboratory workflows enable us to successfully deliver results over 98% of the time, assuming tissue is received that meets the minimum requirements we have outlined for our assays.

Our investments have allowed us to continuously drive turnaround time downward, to provide results to doctors and their patients in a timeframe that we believe now meets or exceeds many of our competitors who have been operating in the NGS space for longer. As of December 31, 2025, our average turnaround time for our xT assays was approximately nine days, and our average turnaround time for xF was approximately eight days.

We believe that the strong foundational infrastructure in our laboratory operations, along with the technology used in our lab and the engineering expertise we have on hand is further differentiated when coupled with the connections we can rapidly deploy with our customers, and the experienced research scientists and doctors we employ, who are able to design and refine our highest volume assays in-house. We believe this unique combination will continue to allow us to rapidly respond to the changing needs of our customers and evolving market conditions.

Our Strategic Collaborations

AstraZeneca and Pathos

In April 2025, we entered into a series of agreements with AstraZeneca AB, or AstraZeneca, and Pathos regarding both the development of a foundation large multimodal model in the field of oncology, or the Foundation Model, and the licensing of certain de-identified multi-modal data to assist in the development of the Foundation Model.

Specifically, we entered into a Statement of Work with AstraZeneca under the previously disclosed Master Services Agreement, dated November 17, 2021, as amended in October 2022, February 2023 and December 2023 (and as further amended from time to time, together with the Statement of Work, collectively referred to herein as the MSA). Pursuant to the MSA, (i) we will ensure that Pathos develops, and we provide AstraZeneca with, a Foundation Model which has been developed, validated, and maintained using de-identified datasets contributed by us, (ii) the Foundation Model will be developed, validated, and maintained by Pathos, (iii) AstraZeneca will pay us a fee of $35 million, and (iv) a syndicate of investors including AstraZeneca will contemporaneously execute a Stock Purchase Agreement with Pathos, or the SPA, as part of a preferred stock financing round of sufficient size given the obligations described herein.

We also entered into an Order Form with Pathos under the previously disclosed Amended and Restated Master Agreement, restated effective February 12, 2024, (the Amended and Restated Master Agreement and the Order Form collectively referred to herein as the “Pathos Master Agreement”). Pursuant to the Pathos Master Agreement, (i) Pathos will be responsible for Foundation Model development activities under the MSA, (ii) we will license Pathos a comprehensive de-identified multi-modal dataset for the sole purpose of assisting in the development and training of the Foundation Model under the MSA, (iii) Pathos will pay us data license fees of $200 million over a three-year period, including an upfront payment of $50 million that has been paid

as of April 2025 (iv) we will receive a license to use the Foundation Model upon its completion (with certain field restrictions and the right of sublicense to AstraZeneca), and (v) in consideration of Pathos’ commitments under the Pathos Master Agreement, we will pay Pathos $35 million, of which $25 million has been paid to date. Pathos, in its sole discretion, may pay up to 50% of the data license fees owed to us in shares of Pathos’ Series D Preferred Stock.

AstraZeneca

In November 2021, we entered into the MSA with AstraZeneca. Under the MSA, we agreed, on a non-exclusive basis, to provide AstraZeneca with certain of our products and services, including licensed data, sequencing, clinical trial matching, organoid modeling services, algorithm development, and others. In exchange for certain discounted prices, AstraZeneca has committed to spend a minimum of $220 million on such products and services during the term of the MSA. The term of the MSA will continue through December 31, 2026, unless terminated sooner. The minimum commitment may increase from $220 million to $320 million through December 2028 at AstraZeneca's election.

GlaxoSmithKline

In August 2022, we entered into a Strategic Collaboration Agreement, or, as amended in May 2024, the GSK Agreement, with GSK. Under the GSK Agreement, we agreed, on a non-exclusive basis, to provide GSK with certain of our products and services, including licensed data, sequencing, clinical trial matching, organoid modeling services, algorithm development, and others. In exchange for certain discounted prices, GSK has committed to spend a minimum of $180 million on such products and services during the term of the GSK Agreement, of which $70 million was paid upon execution. The term of the GSK Agreement will continue through December 31, 2027, unless terminated sooner. An additional commitment of up to $120 million may be triggered at GSK’s election for the years 2028, 2029 and 2030.

Recursion Master Agreement

In November 2023, we entered into a Master Agreement, or the Recursion Agreement, with Recursion Pharmaceuticals, Inc., or Recursion. Under the Recursion Agreement, we agreed to provide certain of our services and to license certain data to Recursion, including a limited right to access our proprietary database of de-identified clinical and molecular data for certain therapeutic product development purposes. In exchange for these rights, Recursion will pay an initial license fee of $22 million and an annual license fee throughout the term of the agreement, which, together with the initial license fee, totals up to $160 million. The term of the Recursion Agreement will continue through November 3, 2028, unless terminated sooner. In addition to mutual rights to terminate for an uncured breach of the Recursion Agreement, Recursion may terminate the agreement for convenience after three years upon 90 days prior notice, subject to payment by Recursion of an early termination fee.

The initial license fee and each annual license fee are payable at Recursion’s option either in the form of (x) cash, (y) shares of Recursion’s Class A common stock, or (z) a combination of cash and shares of Recursion’s Class A common stock in such proportion as is determined by Recursion in its sole discretion; provided that the aggregate number of shares of Recursion’s Class A common stock to be issued to us under the Recursion Agreement shall not exceed 19.9% of the aggregate total of shares of Recursion Class A common stock and Class B common stock outstanding on November 3, 2023, or the date immediately preceding the date any shares of Class A common stock are issued pursuant to the Recursion Agreement, whichever is less. We have customary registration rights with respect to any shares of Recursion’s Class A common stock issued pursuant to the Recursion Agreement.

Quality Assurance

We are committed to providing reliable and accurate molecular information to our customers. We have established sophisticated laboratory workflows and automated procedures to ensure accurate specimen identification, timely communication of results, and prompt discovery and correction of errors. We monitor our quality through a variety of methods, including objectively measured performance improvement indicators. Any quality concerns and incidents are subject to risk assessment, root cause analysis, and corrective action plans. Safeguarding protected health information, or PHI, is of primary importance.

We have established a comprehensive quality assurance program for our laboratory. Our quality assurance program includes policies and procedures covering personnel qualifications and training requirements, process and test validation, quality control of reagents and test processes, proficiency testing, routine monitoring, and internal audit. We have implemented policies and procedures to adhere to applicable requirements necessary for federal and state licenses and accreditation for clinical diagnostic laboratories, including policies and procedures related to patient and employee safety, hazardous waste disposal, and general laboratory management.

Supply Chain

We have a highly automatic system in place to manage our workflow called LIMS, which also connects to our various supply chain systems through which we ensure materials our ordered in a timely manner, and the logistics of each order are overseen to ensure we are delivering orders, in the shortest time possible, with the highest quality possible.

We maintain significant inventory on hand of both laboratory consumables and other materials to avoid work stoppages and/or material delays. Our systems, processes, and procedures are designed to scale, as evidenced by the fact that we have become one of the largest sequences of cancer patients in the United States in just a few years.

We rely on a limited number of suppliers, or, in some cases, sole suppliers to provide our products and services. Illumina, Inc., is our primary supplier of sequencers and laboratory reagents; however, we purchase laboratory supplies from other companies as well, such as Roche Holdings, Inc., Integrated DNA Technologies, and Tecan US, Inc. We rely on standard commercial carriers for the delivery of samples to our laboratories.

In June 2021, we entered into a supply agreement with Illumina to provide products and services that can be used for certain research and clinical activities, including certain sequencers, reagents, and other consumables for use with the Illumina sequencers, as well as service contracts for the maintenance and repair of the sequencers. The supply agreement does not require us to order minimum amounts of hardware, or to use exclusively the Illumina platform for conducting our sequencing. The term of the supply agreement continues for a period of 12 years, unless either we or Illumina terminate the supply agreement for the other’s uncured material breach, bankruptcy or insolvency-related events, or in the event a regulatory authority notifies such party that continued performance under the supply agreement would violate applicable laws or regulations. Illumina may terminate the agreement in the event we consummate a change of control transaction with a sequencing products company, and we may terminate the supply agreement for convenience upon 90 days’ prior written notice.

In addition to suppliers who provide products supporting our provision of laboratory tests, we have cloud agreements with both AWS and Google. In June 2020, we signed a multi-year strategic partnership with Google that included an agreement through which Tempus procures extensive cloud services from Google. The cloud agreement includes a convertible note that is reduced as we procure services from Google and also contemplates co-innovation projects that we may work on with Google from time to time. We have updated and amended the Google Cloud Agreement from time to time and signed an extension in February 2025.

Laboratory Workflow Applications

With respect to the provision of laboratory services, in addition to Hub, our consumer- facing application, we have developed multiple software tools that facilitate back-end processing, workflow, and report generation. Our back-office software

stack was custom developed around our workflow, allowing us to automate material components of our laboratory and order generation process. The following diagram represents the software applications supporting our laboratory workflow.

We have also developed a series of tools that allow us to access our connected dataset and our internal workflow tools, as we seek to query our own data and make it available both internally and externally. In an effort to facilitate a connection between our providers and our data, we built an application called Tempus One, which is an application available in Hub or via mobile applications and which has AI assistant capabilities and relays information contained in our oncology reports and supporting database to physicians through interactions in real time. We believe Tempus One has the potential to create a more efficient workflow for healthcare professionals, reducing the time needed to review and process information, providing more time for them to focus on patient care. Over time, we intend to embed more insights into Tempus One, and other similar applications we develop, thereby enhancing the amount of information readily available to our ordering physicians.

Data Structuring Applications

After we generate a clinical report through the provision of laboratory services, or once we obtain data through one of our dedicated connections to providers, we utilize a different suite of proprietary software applications to abstract, structure, and de-identify the resulting data to help augment our existing multimodal dataset and provide additional healthcare services to our customers. Our tools have become highly efficient over time allowing us to abstract data, often between 50-100 discrete data elements per patient case, in approximately an hour (or the cost equivalent), which we perform both onshore and offshore through dedicated teams we have established to perform the data curation and abstraction. In addition, we have the capability to perform enhanced abstraction, which can take several hours per patient case, allowing us to define a custom set of features over a defined period of time that we want abstracted. We have also developed data abstraction tools that leverage artificial intelligence in a manner that significantly reduces the human cost of our abstraction efforts. Each of our proprietary tools is designed to enhance our customers’ experience, either by creating useful information that assists in the treatment of patients, or by creating an efficient back-end infrastructure that allows us to deliver our services more quickly and efficiently.

Information Security

We endeavor to maintain a robust information security program in an effort to protect all of the sensitive data we maintain, including PHI and PII and we take all threats to the availability, integrity and confidentiality of that data with the utmost seriousness. Our security program consists of a layered defense approach starting with appropriate data and system design through architectural principles that include security as a core component at every step of the process. This security by design approach is enhanced with physical security, host and endpoint device management, application security, and infrastructure and cloud security. In each of those areas, we utilize industry-standard third-party tools that are designed to assist our team of security professionals in their various tasks and we work closely with our vendors, including those who provide cloud computing services that make up substantial parts of our infrastructure (e.g., Google and Amazon).

Our security program is operationalized through documented policies, procedures and required training for all staff in the entire company, with special emphasis on key teams in engineering and IT operations who develop, monitor and maintain the applications and systems used in our business. In an effort to ensure that these policies are adhered to and that no new vulnerabilities arise, we conduct regular auditing of a wide swath of our security related measures, including a mix of self-audits, external penetration testing, external application security audits and audits performed by our customers and partners. Our security team is also instrumental in maintaining our ISO 27001 certification and assisting the compliance and legal teams with other legally required audits and provides detailed reports regularly to upper management and the Board on security related matters.

Intellectual Property

Our success depends in part on our ability to obtain and maintain intellectual property and proprietary protection for our products and technology, defend and enforce our intellectual property rights, preserve the confidentiality of our trade secrets, and operate without infringing, misappropriating or otherwise violating valid and enforceable intellectual property and proprietary rights of others. We are actively involved in research and development and therefore seek to protect the investments we have made into the development of our products and technology by relying on a combination of patents, trademarks, trade secrets, know-how, and license agreements. We also seek to protect our proprietary technology, in part, by requiring our employees, consultants, contractors and other third parties to execute confidentiality agreements and invention assignment agreements and by implementing technological protections for our intellectual property.

As of December 31, 2025, our patent portfolio and patent applications included 230 issued U.S. patents and allowed applications, 210 pending U.S. non-provisional patent applications, 5 pending U.S. provisional patent applications, 18 pending Patent Cooperation Treaty (international) patent applications, 138 issued foreign patents, 341 pending foreign patent applications, 31 licensed issued U.S. patents, 22 licensed pending U.S. patent application, 17 licensed issued foreign patents and 24 licensed pending foreign patent applications. Our issued patents are expected to begin expiring in 2033, assuming payment of all appropriate maintenance, renewal, annuity or other governmental fees. These patents and applications generally fall into four broad categories:

• applications and patents relating to our Platform, including claims directed to product ordering processes; data processing and multimodal data analytics;

• applications and patents relating to our Diagnostics business, including claims directed to detecting and monitoring cancer and other diseases by determining genetic variations and other biomarkers in biological samples;

• applications and patents relating to our Data business, including claims directed to analysis of healthcare records and patient outcomes; and

• applications and patents related to our Algos business, including claims directed to machine learning diagnostics and predictions in cancer and cardiology.

The term of individual patents depends upon the legal term of the patents in the countries in which they are obtained. In most countries in which we file or intend to file, including the United States, the patent term is 20 years from the earliest date of filing a non-provisional patent application. Additionally, a U.S. provisional patent application expires twelve months from its filing date, and its subject matter can only be claimed in an issued patent if, among other things, we timely file a non-provisional patent application making a valid priority claim to that provisional patent application before it expires. In the United States, a patent’s term may be lengthened by patent term adjustment, which compensates a patentee for administrative delays by the USPTO in examining and granting a patent, or may be shortened if a patent is terminally disclaimed over an earlier filed patent. We cannot be sure that patents will be granted with respect to any current pending patent application or with respect to any patent applications filed by us in the future, nor can we be sure that any current or future patents will be commercially useful in

protecting our platform, products, services, technologies and processes. In addition, any patents that we may hold, whether owned or licensed, may be challenged, circumvented or invalidated by third parties.

The success of our business strategy also depends in part on our continued ability to protect our branded services, and we own registered trademarks on “TEMPUS” and product related brand names in the United States and worldwide.

We also rely on trade secrets, including know-how, unpatented technology and other proprietary information, to strengthen our competitive position. We seek to protect trade secrets and confidential and unpatented know-how, in part, by entering into non-disclosure and confidentiality agreements with parties who have access to such knowledge, such as our employees, collaborators, manufacturers, consultants, advisors and other third parties. We also seek to enter into confidentiality and invention or patent assignment agreements with our employees and consultants that obligate them to maintain confidentiality and assign their inventions to us.

Our ability to stop third parties from making, using, selling, offering to sell or importing our Platform, services and products depends on the extent to which we have rights under valid and enforceable patents, trade secrets or other intellectual property and proprietary rights that cover these activities. We pursue intellectual property protection to the extent we believe it would advance our business objectives. Notwithstanding these efforts, there can be no assurance that we will adequately protect our intellectual property or provide any competitive advantage. For more information regarding risks relating to intellectual property, see “Risk Factors—Risks Related to Our Intellectual Property.”

Government Regulation

Regulation of Medical Devices in the United States

Some of our diagnostic products and services are subject to regulation by the FDA under the Federal Food, Drug, and Cosmetic Act of 1938 and its implementing regulations, collectively referred to as the FDCA, as well as other federal and state regulatory bodies in the United States. The laws and regulations govern, among other things, product design and development, pre-clinical and clinical testing, manufacturing, packaging, labeling, storage, record keeping and reporting, clearance or approval, marketing, distribution, promotion, import and export and post-marketing surveillance. Failure to comply with applicable requirements may subject a device and/or its manufacturer to a variety of administrative sanctions, such as FDA refusal to approve pending premarket applications, issuance of warning letters, mandatory product recalls, import detentions, civil monetary penalties, and/or judicial sanctions, such as product seizures, injunctions and criminal prosecution.

FDA Premarket Clearance and Approval Requirements

Unless an exemption applies, each medical device commercially distributed in the United States requires either FDA clearance of a 510(k) premarket notification, approval of a petition for premarket approval, or PMA, or grant of a de novo request for classification. During public emergencies, the FDA also may grant emergency use authorizations, or EUA, to allow commercial distribution of devices intended to address the public health emergency. Under the FDCA, medical devices are classified into one of three classes—Class I, Class II or Class III—depending on the degree of risk associated with each medical device and the extent of manufacturer and regulatory control needed to provide reasonable assurance of its safety and effectiveness. Classification of a device is important because the class to which a device is assigned determines, among other things, the necessity and type of FDA review required prior to marketing the device.

Class I devices include those with the lowest risk to the patient and are those for which safety and effectiveness can be reasonably assured by adherence to the FDA’s ‘‘general controls’’ for medical devices, which include compliance with the applicable portions of the FDA’s Quality System Regulation, or QSR, facility registration and product listing, reporting of adverse medical events and malfunctions through the submission of Medical Device Reports, or MDRs, and appropriate, truthful and non-misleading labeling, advertising, and promotional materials. Some Class I devices also require 510(k) premarket notification clearance as described below.

Class II devices are moderate risk devices subject to the FDA’s general controls, and any other ‘‘special controls’’ deemed necessary by the FDA to ensure the safety and effectiveness of the device, such as performance standards, product-specific guidance documents, special labeling requirements, patient registries or post-market surveillance. Premarket review and clearance by the FDA for Class II devices is accomplished through the 510(k) process. The 510(k) submission must demonstrate that the device is ‘‘substantially equivalent’’ to a legally marketed predicate device, which in some cases may require submission of clinical data.

Class III devices include devices deemed by the FDA to pose the greatest risk, such as life-sustaining, life-supporting or implantable devices and devices deemed not substantially equivalent to a predicate device following a 510(k) submission. The safety and effectiveness of Class III devices cannot be reasonably assured solely by general or special controls. Submission and FDA approval of a PMA application is required before marketing of a Class III device can proceed. A PMA application is intended to demonstrate that the device is reasonably safe and effective for its intended use and must be supported by extensive data, typically including data from pre-clinical studies and clinical trials.

Emergency Use Authorization

In emergency situations, such as a pandemic, the FDA has the authority to allow unapproved medical products or unapproved uses of cleared or approved medical products to be used in an emergency to diagnose, treat or prevent serious or life-threatening diseases or conditions when there are no adequate, approved, and available alternatives.

Under this authority, the FDA may issue an EUA for an unapproved device if the following four statutory criteria have been met: (1) a serious or life-threatening condition exists; (2) evidence of effectiveness of the device exists; (3) a risk-benefit analysis shows that the benefits of the product outweigh the risks; and (4) no other alternatives exist for diagnosing, preventing or treating the disease or condition. Evidence of effectiveness includes medical devices that ‘‘may be effective’’ to prevent, diagnose, or treat the disease or condition identified in a declaration of emergency issued by the Secretary of U.S. Department of Health and Human Services, or HHS. The ‘‘may be effective’’ standard for EUAs requires a lower level of evidence than the ‘‘effectiveness’’ standard that the FDA uses for product clearances or approvals in non-emergency situations. Once granted, an EUA will remain in effect and generally terminate on the earlier of (1) the determination by the Secretary of U.S. HHS that the public health emergency has ceased or (2) a change in the approval status of the product such that the authorized use(s) of the product are no longer unapproved. After the EUA is no longer valid, the product is no longer considered to be legally marketed and one of the FDA’s non-emergency premarket pathways would be necessary to resume or continue distribution of the subject product.

The FDA also may revise or revoke an EUA if the circumstances justifying its issuance no longer exist, the criteria for its issuance are no longer met, or other circumstances make a revision or revocation appropriate to protect the public health or safety.

Clinical Trials

Clinical trials are typically required to support a PMA and are sometimes required to support a 510(k) submission. All clinical investigations of devices to determine safety and effectiveness must be conducted in accordance with the FDA’s investigational device exemption, or IDE, regulations which govern investigational device labeling, prohibit promotion of the investigational device, and specify an array of recordkeeping, reporting and monitoring responsibilities of study sponsors and study investigators. If the device presents a ‘‘significant risk’’ to human health, the FDA requires the device sponsor to submit an IDE application to the FDA, which must be approved prior to commencing clinical trials. A significant risk device is one that presents a potential for serious risk to the health, safety or welfare of a patient and either is implanted, purported or represented to be used in supporting or sustaining human life, is for a use that is substantially important in diagnosing, curing, mitigating or treating disease or otherwise preventing impairment of human health, or otherwise presents a potential for serious risk to a subject.

An IDE supplement must be submitted to, and approved by, the FDA before a sponsor or investigator may make a change to the investigational plan that may affect its scientific soundness, study plan or the rights, safety or welfare of human subjects. In addition, the clinical trials must be approved by, and conducted under the oversight of, an Institutional Review Board, or IRB, for each clinical site. The IRB is responsible for the initial and continuing review of the IDE and may pose additional requirements for the conduct of the study. If an IDE application is approved by the FDA and one or more IRBs, clinical trials may begin at a specific number of investigational sites with a specific number of patients, as approved by the FDA. If the device is considered a ‘‘non-significant risk,’’ IDE submission to FDA is not required. Instead, only approval from the IRB overseeing the investigation at each clinical trial site is required.

Post-market Regulation

After a device is cleared or approved for marketing, numerous and pervasive regulatory requirements continue to apply. These include:

• establishment of registration and device listing with the FDA;

• QSR requirements, which require manufacturers and contract manufacturers, including any third-party manufacturers, to follow stringent design, testing, control, documentation and other quality assurance procedures during all aspects of the design and manufacturing process;

• labeling regulations and FDA prohibitions against the promotion of investigational products, or “off-label” uses of cleared or approved products;

• requirements related to promotional activities;

• clearance or approval of product modifications to 510(k)-cleared devices that could significantly affect safety or effectiveness or that would constitute a major change in intended use of a cleared device;

• medical device reporting regulations, which require that a manufacturer report to the FDA if a device it markets may have caused or contributed to a death or serious injury, or has malfunctioned and the device or a similar device that it markets would be likely to cause or contribute to a death or serious injury, if the malfunction were to recur;

• correction, removal and recall reporting regulations, which require that manufacturers report to the FDA field corrections, product removals or recalls if undertaken to reduce a risk to health posed by the device or to remedy a violation of the FDCA that may present a risk to health;

• the FDA’s recall authority, whereby the agency can order device manufacturers to recall from the market a product that is in violation of governing laws and regulations; and

• post-market surveillance activities and regulations, which apply when deemed by the FDA to be necessary to protect the public health or to provide additional safety and effectiveness data for the device.

The FDA has broad regulatory compliance and enforcement powers. If the FDA determines that we failed to comply with applicable regulatory requirements, it can take a variety of compliance or enforcement actions, which may result in any of the following sanctions:

• untitled letters, warning letters, fines, injunctions, consent decrees and civil penalties;

• unanticipated expenditures to address or defend such actions;

• customer notifications for repair, replacement, refunds;

• recall, withdrawal, administrative detention or seizure;

• operating restrictions or partial suspension or total shutdown of production;

• refusal of or delay in granting our requests for 510(k) clearance or PMA approval of new tests or modified tests;

• operating restrictions, partial suspension or total shutdown of production;

• withdrawing 510(k) clearance or PMA approvals that are already granted;

• refusal to grant export approval; or

• criminal prosecution.

Laboratory-Developed Tests (LDTs)

LDTs have generally been considered to be tests that are designed, developed, validated and used within a single laboratory. The FDA previously took takes the position that it had the authority to regulate such tests as medical devices under the FDCA. The FDA also historically exercised enforcement discretion and did not required clearance or approval of LDTs prior to marketing. On May 6, 2024, the FDA published final regulations that purported to phase-out enforcement discretion over a period of four years and required compliance with device registration and listing requirements, medical device reporting requirements, 510(k) clearance, denovo authorization or Premarket Approval and the requirements of the FDA’s Quality System Regulation. In March 2025, however, a federal district court in the Northern District of Texas invalidated the proposed rule and held the FDA

did not have the authority to regulate laboratory tests under the FDCA. In addition, the New York Clinical Laboratory Evaluation Program separately approves certain LDTs offered to New York State patients.

CLIA and State Laboratory Licensing

Under the Clinical Laboratory Improvement Amendments, or CLIA, a laboratory is any facility that performs laboratory testing on specimens derived from humans for the purpose of providing information for the diagnosis, prevention or treatment of disease, or the impairment of or assessment of health. CLIA requires that a laboratory hold a certificate applicable to the type of laboratory examinations it performs and that it complies with, among other things, standards covering operations, personnel, facilities administration, quality systems and proficiency testing, which are intended to ensure, among other things, that clinical laboratory testing services are accurate, reliable and timely. We have a current CLIA certificate to perform our tests at our laboratories in Chicago, Illinois, Atlanta, Georgia, Raleigh, North Carolina, Aliso Viejo, California and Minneapolis, Minnesota. To renew our CLIA certificate, we are subject to survey and inspection every two years to assess compliance with program standards.

Laboratories performing high complexity testing are required to meet more stringent requirements than laboratories performing less complex tests. In addition, a laboratory that is certified as “high complexity” under CLIA may develop, manufacture, validate and use LDTs. CLIA requires analytical validation including accuracy, precision, specificity, sensitivity and establishment of a reference range for any LDT used in clinical testing. The regulatory and compliance standards applicable to the testing we perform may change over time and any such changes could have a material effect on our business.

CLIA provides that a state may adopt laboratory regulations that are more stringent than those under federal law, and a number of states have implemented their own more stringent laboratory regulatory requirements. State laws may require that nonresident laboratories, or out-of-state laboratories, maintain an in-state laboratory license to perform tests on samples from patients who reside in that state. As a condition of state licensure, these state laws may require that laboratory personnel meet certain qualifications, specify certain quality control procedures or facility requirements or prescribe record maintenance requirements.

Failure to comply with CLIA certification and state clinical laboratory licensure requirements may result in a range of enforcement actions, including certificate or license suspension, limitation, or revocation, directed plan of action, onsite monitoring, civil monetary penalties, criminal sanctions, and revocation of the laboratory’s approval to receive Medicare and Medicaid payment for its services, as well as significant adverse publicity.

The College of American Pathologists, or CAP, maintains a clinical laboratory accreditation program. While not required to operate a CLIA-certified laboratory, many private insurers require CAP accreditation as a condition to contracting with clinical laboratories to cover their tests. In addition, some countries outside the United States require CAP accreditation as a condition to permitting clinical laboratories to test samples taken from their citizens. We have obtained CAP accreditation for our Chicago, Illinois, Atlanta, Georgia, Raleigh, North Carolina, Aliso Viejo, California and Minneapolis, Minnesota laboratories. In order to maintain CAP accreditation, we are subject to survey for compliance with CAP standards every two years. Failure to maintain CAP accreditation could have a material adverse effect on the sales of our tests and the results of our operations.

Federal and State Health Care Laws

Federal Physician Self-Referral Prohibition

We are also subject to the federal physician self-referral prohibition, commonly known as the Stark Law, and to comparable state laws. Together these restrictions generally prohibit us from billing a patient or governmental or private payer for certain designated health services, including clinical laboratory services, when the physician ordering the service, or a member of such physician’s immediate family, has a financial relationship, such as an ownership or investment interest in or compensation arrangement, with us, unless the relationship meets an applicable exception to the prohibition. Several Stark Law exceptions are relevant to many common financial relationships involving clinical laboratories and referring physicians, including: (1) fair market value compensation for the provision of items or services; (2) payments by physicians to a laboratory for clinical laboratory services; (3) space and equipment rental arrangements that satisfy certain requirements and (4) personal services arrangements that satisfy certain requirements. The laboratory cannot submit claims to the Medicare Part B program for services furnished in violation of the Stark Law, and Medicaid reimbursements may be at risk as well. These prohibitions apply regardless of any intent by the parties to induce or reward referrals or the reasons for the financial relationship and the referral. Penalties for

violating the Stark Law include significant civil, criminal and administrative penalties, such as the return of funds received for all prohibited referrals, fines, civil monetary penalties, exclusion from the federal healthcare programs, integrity oversight and reporting obligations, and imprisonment. In addition, knowing violations of the Stark Law may also serve as the basis for liability under the federal False Claims Act, or FCA, which can result in additional civil and criminal penalties.

Federal Anti-Kickback Law

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TEM · 10-K · period ended 2025-12-31

Item 1. Business.

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TEM · 10-K · period ended 2025-12-31

Item 1. Business.

TEM US Equity