Key considerations when assessing clinical utility for biomarker testing: A checklist

Abstract

Establishing the clinical utility of biomarker tests is a critical component of benefit design. However, payers and laboratory benefit manager organizations may differ in how they evaluate clinical utility for a given test, including how they determine whether evaluated outcomes are clinically meaningful. These differences can create challenges for providers and manufacturers when navigating coverage policies across organizations. Based on expert insights from a partnership forum hosted by AMCP on June 24-25, 2025, in Alexandria, VA, on the topic of precision medicine in oncology, we developed a checklist of considerations to align payers and laboratory benefit managers around a shared, actionable approach to evaluating the clinical utility of biomarker tests and the types of evidence used to demonstrate it. The checklist begins with confirmation that analytical and clinical validity have been established before assessing clinical utility. Tests should then be categorized based on their intended use and whether they provide actionable information that informs clinical decision-making and improves patient health outcomes. Assessment of clinical utility includes consideration of the relevance, strength, and consistency of the supporting evidence base. After clinical utility is established, economic and operational considerations may also be evaluated. This checklist is intended to support a more transparent and structured approach to evaluating clinical utility in coverage decision-making.

Implications for managed care pharmacy

Evaluation of the clinical utility of biomarker tests may vary both within and across individual payer and laboratory benefit manager organizations. Forum participants emphasized that the variation may result in inconsistent benefit design and complexity in navigating coverage for patients, providers, and manufacturers. Based on forum discussions, we developed a standardized checklist that payers and laboratory benefit managers should use when evaluating the clinical utility of biomarker tests, to promote greater consistency in coverage and benefit design decisions across organizations.

Plain language summary

If a laboratory test affects the treatment decisions doctors make, it is said to have clinical utility. Different payers may evaluate clinical utility differently. Their definitions may also not match those of doctors who prescribe treatments. This can make it hard to understand why a biomarker test is or is not covered. Payers can use our proposed checklist to more consistently define how useful a test is.

Biomarker testing plays a critical role in precision oncology by identifying patients who may benefit from targeted therapies, informing treatment selection, and supporting clinical decision-making.¹ As precision medicine has evolved over the past 2 decades, the number and complexity of biomarker and genetic tests have also increased substantially.^2,3 As of November 2022, more than 129,000 genetic tests were available in the United States, with most intended for diagnostic use,³ and there are concerns that a substantial proportion of testing may be inappropriate.⁴ Estimates suggest that approximately 20% of patient testing cases involve unnecessary testing, whereas approximately 45% involve failure to perform clinically indicated testing.⁴ As a result, payers have increasingly relied on third-party laboratory benefit managers (LBMs), organizations that support management of laboratory testing utilization, coverage policy implementation, and prior authorization processes, to assist in evaluating and managing biomarker and genetic testing services.⁴ These LBMs are contracted by employer groups or insurers for the purposes of lowering medical or pharmaceutical costs and enhancing the quality of care through utilization management practices.⁴ However, the expanding complexity of biomarker testing has created ongoing challenges for payers and LBMs in evaluating whether available evidence sufficiently demonstrates clinical utility and supports coverage decision-making.¹

The term clinical utility refers to the value of information derived from a test or intervention that, by informing or prompting clinical decisions and management strategies, contributes to improvement in patient health outcomes, while considering the balance of benefits and harms.^5–11 Despite broad alignment around the general concept of clinical utility, there is currently no universally accepted framework for measuring clinical utility across biomarker tests. Variability remains in how evidence supporting clinical utility is evaluated and applied across stakeholders, including payers and LBMs.¹¹ These differences may contribute to variation in coverage policies, create operational challenges for providers navigating coverage requirements across organizations, and affect patient access to biomarker testing.¹

Panel Description

To advance equitable and effective access to precision medicine, particularly in oncology, AMCP convened a 2-day partnership forum in June 2025. The partnership forum included 39 participants representing a broad range of stakeholders, including patients, payers, pharmacy benefit managers, providers, pathologists, LBMs, and representatives from advocacy organizations, coalitions, and professional associations. All participants were recruited by AMCP based on their relevant experiences.

One forum session focused on evaluating clinical utility to support appropriate biomarker test use and treatment selection. In a moderated 2-hour panel discussion, participants discussed evidentiary and practical considerations used to evaluate clinical utility. This was followed by a breakout workshop in which participants were assigned into groups to draft parameters by which clinical utility should be measured.

The session was recorded and transcribed, and insights from group discussions and breakout activities were synthesized by the authors into a checklist of considerations for evaluating the clinical utility of biomarker testing, including 4 overarching themes: (1) prerequisites to clinical utility: analytical and clinical validity; (2) clinical utility: impact on care; (3) clinical utility: evidence and data requirements to demonstrate impact on care; and (4) after establishing clinical utility: economic and feasibility factors (Table 1).

TABLE 1.

A Checklist to Assess Clinical Utility and Related Evidence for Biomarker Testing

Category	Checklist item
Prerequisites to clinical utility: Analytical and clinical validity	□ Verify the analytical validity of the test
	□ Confirm the clinical validity of the test
	□ Examine the variant interpretation and reporting framework
Clinical utility: Impact on care	□ Distinguish between diagnostic and treatment-guiding utility
	□ Define clinically meaningful improvement of health outcomes
	□ Evaluate how the test guides clinical decisions to achieve the defined outcome
	□ Determine whether the test provides clinically useful and actionable information to guide clinical decisions
Clinical utility: Evidence and data requirements to demonstrate impact on care	□ Review the appropriateness of evidence types used
	□ Evaluate the relevance of the evidence to the intended use
	□ Appraise the strength and consistency of evidence
Economic and feasibility factors	□ Appraise the economic value and budget impact
	□ Assess the operational feasibility and turnaround time

The checklist is intended to support payers and LBMs in applying a more structured and transparent approach to evaluating clinical utility. The checklist may also help providers, laboratories, and test manufacturers better understand and navigate coverage requirements across organizations. Because payer coverage policy development remains individualized across organizations, the checklist is intended to inform, rather than determine, coverage decision-making. This checklist of considerations represents an initial effort to align evidence expectations for demonstrating the clinical utility of biomarker testing. Future clarifications are still needed to further clarify which evidence types are most appropriate across clinical settings.

These findings are intended to support informed clinical and coverage decisions and offer practical actions for payers. Although they may be used to inform future work, they should not be construed as original empirical research.

Prerequisites to Clinical Utility: Analytical and Clinical Validity

Analytical validity, clinical validity, and clinical utility represent related but distinct concepts in biomarker test evaluation.¹¹ Analytical validity refers to whether a test can accurately and reliably measure the biomarker or genetic variant it is intended to detect, whereas clinical validity refers to whether the test can accurately and reliably identify or predict the targeted disease, clinical condition, or phenotype of interest.¹² Clinical utility evaluates whether use of the test improves clinical decision-making and patient health outcomes.¹² All 3 concepts should be demonstrated for a biomarker test to support meaningful real-world clinical use.¹¹ Forum participants emphasized that analytical and clinical validity are foundational prerequisites that should be established prior to evaluating clinical utility because a test that does not reliably measure what it claims to measure will have limited or no clinical utility. In addition, demonstration of analytical and clinical validity alone does not guarantee that a test has clinical utility.¹¹

The evaluation of analytical and clinical validity generally focuses on technical performance, consistency of results, and strength of biomarker-disease association.^11,12 Forum participants noted that evidence supporting analytical and clinical validity may vary depending on the type of biomarker test, assay methodology, disease context, and intended clinical application, including regulatory approval status, published data, or internal validation studies. They also suggested that acceptable analytical and clinical performance should be evaluated in the context of the intended use of the test and whether the evidence is sufficient to support the proposed clinical application.

The following considerations are intended to guide evaluation of foundational validity requirements prior to assessment of clinical utility.

VERIFY THE ANALYTICAL VALIDITY OF THE TEST

To assess the analytical validity of a test, the technical performance must be evaluated, including how accurately and reliably the test can detect the presence or absence of a specific genetic variant.¹² Precision and reproducibility should be demonstrated across test runs and laboratories and across relevant specimen types and quality levels.¹² Precision refers to consistency of test results across repeated measurements, whereas reproducibility refers to consistency across different testing conditions such as operators, reagent lots, instruments, and temperatures.¹²

Analytical sensitivity and specificity should also be established, including limit of detection and the risk of false positives and negatives.¹² Analytical sensitivity refers to the ability of the assay to correctly detect the biomarker when present, whereas analytical specificity refers to the ability of the test to avoid detection of unintended targets or false-positive results.¹² Limit of detection describes the lowest quantity or variant allele frequency that can be reliably identified by the assay.¹³

Forum participants suggested that analytical validity may be demonstrated through regulatory approval status or internal validation studies. For example, approval status of a companion diagnostic (CDx) demonstrates that the test has met US Food and Drug Administration (FDA) standards for analytical validity for a specific intended use.¹⁴ CDx refers to an in vitro diagnostic test that is essential for the safe and effective use of a specific therapeutic product, as defined in the drug’s labeling and approved by the FDA.¹⁵ Conversely, a laboratory-developed test (LDT) is an in vitro diagnostic assay that is designed, validated, and performed within a single clinical laboratory and is neither FDA approved nor required by a drug’s labeling.¹⁵ For LDTs, analytical validity is typically established through internal validation studies performed within laboratories regulated under the federal Clinical Laboratory Improvement Amendments, which establish quality standards for laboratory testing in the United States.¹⁶

CONFIRM THE CLINICAL VALIDITY OF THE TEST

Clinical validity should be assessed by establishing how strongly the genetic variant being analyzed is with the targeted disease and how consistently the association can be demonstrated across studies and populations.¹⁷ For example, clinical validity may be demonstrated through evidence showing consistent association between a biomarker and treatment response, prognosis, disease subtype, or resistance mechanisms.¹⁷

In terms of evidence supporting the clinical validity of a test, forum participants noted that clinical validity of a CDx may be demonstrated through FDA approval tied to a specific therapeutic product and supported by clinical trial or bridging evidence.¹⁴ For LDTs, evidence supporting clinical validity may include published peer-reviewed literature, retrospective or prospective clinical studies, clinical guideline references, or real-world evidence relevant to the intended use of the test. These sources may help demonstrate the strength and clinical relevance of the biomarker-disease association for the intended use.¹⁸

EXAMINE THE VARIANT INTERPRETATION AND REPORTING FRAMEWORK

A third consideration emphasized by forum participants when determining validity is ensuring that test results can be interpreted and used appropriately in clinical practice. They suggested that although interpretation approaches may vary across laboratories, the use of transparent and well-defined variant interpretation frameworks is important to support consistency. Forum participants expressed that reporting of test results should be clear and clinically interpretable to support downstream decision-making and identified clinically interpretable results as those that can be meaningfully applied to patient management and clinical decision-making, such as informing therapy selection, prognosis, or eligibility for targeted treatment.

Clinical Utility: Impact on Care and Evidence Requirements

According to forum discussion, after establishing analytical and clinical validity, evaluation of clinical utility should focus on whether use of the biomarker test meaningfully impacts patient care and clinical decision-making. The following sections define core evidentiary expectations payers and LBMs should consider when evaluating whether a test meets criteria for clinical utility.

CLINICAL UTILITY: IMPACT ON CARE

A test demonstrates clinical utility only when valid results are translated into real-world clinical decisions that meaningfully alter patient management and improve patient outcomes.^11,17 However, forum participants noted that a key challenge in assessing clinical utility is defining what constitutes a clinically meaningful improvement in health outcomes, as this may vary by disease and stakeholder perspective. They proposed that the assessment of clinical utility should therefore proceed backward: the desired patient outcomes should be defined first, followed by a demonstration of how the test directly guides decisions to achieve them. Participants identified 4 considerations: (1) distinguish between diagnostic and treatment-guiding utility; (2) define clinically meaningful improvement of health outcomes; (3) evaluate how the test guides clinical decisions to achieve the defined outcome; and (4) assess whether the test provides clinically useful and actionable information to guide clinical decisions.

Distinguish Between Diagnostic and Treatment-Guiding Utility

Forum participants described that decision-makers must first differentiate diagnostic utility and treatment-guiding utility based on the intended use of the test. They recognized diagnostic utility as the use of a test to define or classify a disease subtype, whereas treatment-guiding utility refers to how test results contribute to improved therapy selection, sequencing, monitoring, or outcomes. Based on the forum discussion, this manuscript will focus on the evaluation of treatment-guiding utility, which may require additional consideration of downstream clinical decision-making, patient outcomes, and operational factors relevant to coverage and benefit design.

Define Clinically Meaningful Improvement of Health Outcomes

Forum participants emphasized that for a test to demonstrate treatment-guiding utility, clinically meaningful improvement of health outcomes must first be defined. Participants acknowledged that different stakeholders (eg, payers, clinicians, laboratories, patients) may value different outcomes as clinically meaningful.¹¹ For example, in oncology, clinicians and patients may consider earlier identification of actionable biomarkers and faster turnaround time clinically meaningful because they may help avoid ineffective therapies and unnecessary toxicities, whereas payers may place greater emphasis on clinical effectiveness.¹⁹

What a clinically meaningful improvement of health outcomes is perceived to be may also differ depending on disease setting and feasibility. For example, in rare diseases, the availability of any effective therapy may be clinically meaningful, whereas overall survival remains the gold standard in oncology and intermediate endpoints (eg, progression-free survival) are employed when overall survival data are unavailable.²⁰

Therefore, forum participants emphasized that clinically meaningful health outcomes should be evaluated within the context of the disease setting and feasibility. Participants identified several outcomes that may be considered clinically meaningful depending on the clinical context, including improvement in quality of life and overall survival when feasible, progression-free survival or response rates when biologically relevant, avoidance of ineffective or harmful therapies, prediction of recurrence or guidance of surveillance, reduction in diagnostic delay or patient burden, and determination of clinical trial eligibility. Surrogate endpoints, which refer to substitute measures expected to predict meaningful clinical outcomes, such as progression-free survival or tumor response rate in a certain oncology setting,^21,22 should be considered only if data on clinical endpoints are unavailable and if they correlate with meaningful downstream outcomes.²⁰

Evaluate How the Test Guides Clinical Decisions to Achieve the Defined Outcome

Forum participants noted that with a clinically meaningful outcome defined, evaluation should next assess how use of the test informs clinical decisions to achieve that outcome. This includes identifying the specific clinical decisions influenced by the tests, demonstrating meaningful changes in clinical management, and specifying how test use fits into the clinical workflow and timing of decisions. Participants gave examples that if improved overall survival is the defined clinically meaningful outcome, evidence may demonstrate that use of the test leads to different treatment selection compared with if the test had not been used, thus resulting in improved overall survival. Similarly, if reduction in diagnostic delay is the defined outcome, evidence may evaluate differences in turnaround time, time to treatment, or how test results influence subsequent clinical management.

Forum participants suggested that evidence supporting these considerations may include clinical studies, real-world evidence demonstrating changes in treatment selection following testing, concordance between test results and treatment decisions, or avoidance of ineffective or potentially harmful therapies, depending on the defined clinically meaningful outcomes. These evidence considerations are discussed in greater detail in the Clinical Utility: Evidence and Data Requirements to Demonstrate Impact on Care section below.

Assess Whether the Test Provides Clinically Useful and Actionable Information to Guide Clinical Decisions

The clinical utility of a test is also influenced by the clinical usefulness of the information derived from the test and whether the findings provide actionable insights to drive clinical decisions.¹¹ Evidence supporting clinical utility should demonstrate a clear and evidence-based relationship between the biomarker or genetic variant identified and a corresponding clinical implication, management strategy, or therapeutic action.¹¹

For example, in breast cancer, identification of a germline BRCA1/2 mutation may guide selection of PARP inhibitor therapy. In contrast, identification of a variant of uncertain significance or a genetic alteration without established therapeutic, prognostic, or management implications may not directly alter patient management at the time of testing.²³

Forum participants recognized that actionable and non-actionable findings may be identified within the same test, noting that actionable findings may include information that informs therapy selection, treatment sequencing, monitoring strategies, prognosis, surveillance, or clinical trial eligibility depending on the intended clinical application of the test. They emphasized that the clinical utility of a test should be evaluated based on the overall clinical value of the information generated by the test.

CLINICAL UTILITY: Evidence and Data Requirements to Demonstrate Impact on Care

After defining how biomarker testing may meaningfully influence clinical decision-making and patient outcomes, forum participants noted that evaluation of clinical utility should next consider the evidence required to demonstrate that impact on care. Evidence requirements for establishing clinical utility vary by disease context, test intent, and feasibility of study design.^11,24,25 Participants suggested that evidence should be evaluated based on its relevance, strength, and ability to demonstrate the effect of the test result on treatment decisions and subsequent improvement in defined clinical outcomes.

The following considerations are intended to guide consistent evaluation of the evidence base supporting clinical utility.

Review the Appropriateness of Evidence Types Used

According to the forum discussion, the appropriateness of evidence types used to support clinical utility should be evaluated based on the clinical context and intended use of the test. Forum participants suggested that a range of study types and peer-reviewed publications linking the test to improved outcomes may exist and can be considered. This includes systematic reviews and meta-analyses to offer complete insight; randomized controlled trials (RCTs) addressing whether the test can work; prospective or retrospective observational studies addressing whether the test does work; patient- and provider-perspective studies; and small-cohort, single-arm, or genome-wide studies, particularly in rare diseases or settings where large, randomized, or comparative trials are not feasible (Table 2). Participants noted that guideline alignment should also be considered as supportive evidence; however, the absence of guideline inclusion does not necessarily preclude clinical utility.

TABLE 2.

Evidence Types and Their Relevance to Clinical Utility Assessment

Evidence type	Definition
Systematic reviews and meta-analyses	Structured evidence syntheses that systematically identify, evaluate, and combine results across multiple studies
Randomized controlled trials	Experimental studies in which participants are randomly assigned to intervention and comparator groups to evaluate treatment effect
Prospective observational studies	Studies that follow patients forward in time without randomized treatment assignment
Retrospective observational studies	Studies using previously collected clinical or laboratory data to evaluate associations and outcomes
Small-cohort studies	Studies involving limited patient populations, often in rare disease or low-biomarker-prevalence settings
Single-arm studies	Studies evaluating outcomes in a single treatment group without a parallel control arm
Case series and reports	Information on individual or grouped clinical cases that often highlight unique diseases, therapies, procedures, or treatment responses
Genome-wide association studies	A research approach used to identify genomic variants that are statistically associated with a risk for a disease or a particular trait

Definitions adapted from Vatkar et al,²⁸ Wang et al,³⁴ and Uffelmann et al.³⁵

Additional considerations discussed during the forum include that not all study types are required in every situation. For example, some payers may not require RCTs for all tests in all scenarios.¹¹ Although evidence hierarchies are commonly used to guide assessment of study quality and applicability, with systematic reviews/meta-analyses and RCTs generally considered the highest levels of evidence, stakeholders may value different levels and types of evidence depending on the clinical context.¹¹ Forum participants emphasized that the appropriateness and feasibility of different evidence types should be considered depending on disease context, biomarker prevalence, and practical limitations of study design. Participants acknowledged ongoing challenges regarding acceptance of genome-wide analyses in small cohorts or rare disease settings and the use of surrogate endpoints with demonstrated biological relevance.²⁵ However, participants also noted that these evidence types should still be considered, particularly in situations in which large comparative trials are infeasible because of small patient populations, low biomarker prevalence, or practical limitations in evidence generation.

Evaluate the Relevance of the Evidence to the Intended Use

Forum participants identified several considerations relevant to evaluating whether available evidence is applicable to the intended clinical use of the test. Examples of factors identified through forum discussion that may be considered include the extent to which the study population reflects the proposed covered population, alignment of disease type, disease stage, line of therapy, and care setting with the intended use of the test, and whether study findings are generalizable to real-world managed care populations. For example, evidence generated in a population with advanced-stage cancer may not be relevant to patients with early-stage cancer; and evidence generated in narrowly selected academic-center populations may have limited applicability to broader community oncology settings. Participants also noted that emerging or proprietary tests may require dedicated clinical utility evidence when testing methodology, biomarker composition, or intended use differ from previously validated assays.

Appraise the Strength and Consistency of Evidence

Forum participants also identified several considerations relevant to appraising the overall strength and consistency of evidence supporting clinical utility. Examples of factors identified during the forum that may be considered include whether findings are directionally consistent across studies and data sources, the magnitude and precision of observed effects, and the degree of uncertainty associated with the available evidence. For example, studies evaluating plasma-based liquid biopsy in non–small cell lung cancer have reported variable sensitivity and concordance with tissue-based testing across different assays and study populations, highlighting the importance of evaluating consistency, methodology, and applicability across evidence sources.^26,27 Conversely, evidence demonstrating consistent changes in treatment selection across multiple studies may strengthen confidence that use of the test meaningfully influences clinical management.²⁸

Participants also emphasized the importance of evaluating whether observed effects are clinically meaningful within the intended disease context rather than achieving statistical significance alone. For example, a biomarker-guided therapy strategy may demonstrate a statistically significant improvement in overall survival compared with standard care. However, analyses of non–small cell lung cancer clinical trials suggest that the magnitude of survival benefit considered clinically meaningful may exceed the magnitude observed in some statistically significant studies.¹ Therefore, statistical significance alone may not be sufficient to demonstrate clinically meaningful benefit, and the magnitude and practical relevance of the observed effect should also be considered.²⁹

After Establishing Clinical Utility: Economic and Feasibility Factors

According to forum discussion, economic and operational feasibility considerations do not determine whether a biomarker test demonstrates clinical utility and should not replace assessment of clinical utility in coverage evaluation. However, forum participants acknowledged that these considerations are often relevant within managed care settings after clinical utility has been established. Participants noted that assessment of economic value, anticipated budget impact, turnaround time, and feasibility of testing workflows may help inform how coverage is implemented and how biomarker testing is integrated into routine clinical practice to support timely and appropriate patient access.

The following criteria are intended to guide evaluation of economic and feasibility considerations after clinical utility has been established.

APPRAISE THE ECONOMIC VALUE AND BUDGET IMPACT

Forum participants noted that economic value refers to the extent to which use of the test may improve efficiency of care delivery, reduce unnecessary health care utilization, or help avoid downstream costs associated with ineffective care. They identified that when assessing the economic value of a test, anticipated budget impact should consider expected testing volume; savings from less health care resource utilization, such as avoidance of ineffective or unnecessary therapies; reduction in procedures or repeat testing; and fewer adverse events or associated complications. For example, a test may lead to a minimum increase in per-member-per-month premiums,³⁰ but identification of actionable biomarkers early in the treatment pathway may help avoid use of ineffective systemic therapies and associated toxicities, potentially reducing unnecessary treatment-related utilization and downstream health care burden.^31,32

ASSESS THE OPERATIONAL FEASIBILITY AND TURNAROUND TIME

Forum participants also emphasized that operational feasibility may influence whether clinically useful testing can be effectively integrated into real-world clinical practice and managed care workflows. Operational considerations identified at the forum include turnaround time, ordering workflows, specimen acquisition requirements, and feasibility of incorporating testing into routine clinical practice. Participants highlighted that operational feasibility is not solely a provider-side consideration, as payer policies and utilization management approaches may either facilitate or impede efficient biomarker testing workflows.

One example is reflex testing or pathologist-initiated testing. In these workflows, pathologists may automatically initiate biomarker testing at the time of diagnosis without requiring separate downstream ordering, which may help reduce delays in testing and treatment decision-making. Forum participants emphasized that payer coverage policies and utilization management approaches may influence the ability to operationalize these workflows in real-world practice settings and, consequently, may affect timely patient access to biomarker-informed care.³³

Similarly, a test may have clinical validity, analytical validity, and clinical utility, but if implementation challenges, such as ineffective ordering workflows, do not allow results to be returned within a time frame that supports clinically actionable decision-making, the value of that test will be reduced. In oncology, delayed turnaround time may contribute to initiation of empirical therapy before biomarker results become available, limiting the ability of testing to guide biomarker-informed treatment selection.³¹ Forum participants noted that payers should consider operational factors when evaluating coverage and utilization management approaches for biomarker testing.

Conclusions

Variation in evidentiary expectations and what constitutes a clinically meaningful improvement in health outcomes when evaluating clinical utility continues to create challenges for providers and patients navigating coverage decisions. Although payers and LBMs may differ in how outcomes and evidence types are prioritized, forum participants identified opportunities for better alignment around the evidentiary standards required to support clinical utility for biomarker testing. This checklist of considerations represents an initial effort to align evidence expectations for demonstrating the clinical utility of biomarker testing. Future clarification is still needed regarding which evidence types, such as RCTs, single-arm studies, observational cohorts, real-world evidence, or small-sample genomic studies, are most appropriate across clinical settings.

Disclosures

Ms Wang, Ms Tsai, and Mrs McCormick report no disclosures. Dr Kheloussi discloses consulting fees, honoraria, and travel/meeting support from the Academy of Managed Care Pharmacy. Dr Pawloski discloses research support from Lilly, LUNGevity, and American Cancer Society Cancer Action Network.

The idea for this supplement originated from AMCP Precision Medicine Initiative, which is sponsored by, in alphabetical order, AbbVie, AstraZeneca, Genentech, Illumina, Johnson and Johnson, KlearTrust, Lilly, Pfizer, Merck, Sanofi, and Omnicom Health Market Access (previously Valuate Health Consultancy). While sponsors attending the partnership forum participated in discussions that informed the development of the clinical utility checklist presented in this manuscript, these recommendations are not product-specific and were developed independently of any individual commercial product. AMCP provided funding for the publication of this supplement.