Overcoming Barriers to Discovery and Implementation of Equitable Pharmacogenomic Testing in Oncology
Precision medicine is an innovative treatment and prevention approach that considers individual variability in lifestyle, environment, and biological makeup. Internal factors that may lead to different responses to a given drug among individuals include variations in genes that encode proteins involved in the metabolism or transport of drugs. Pharmacogenomics (PGx) is the study of how inherited genomic variation is associated with the clinical effectiveness or safety of drugs; oncology applications of PGx include drugs used for treatment of cancer as well as for patient comorbidities and supportive care. Although the prevalence of individual gene variations may be low, approximately 60% of patients with advanced cancer have been reported to be taking medications with PGx interactions. , Individual response to treatment can be affected by gene-drug interactions as well as drug-drug and drug-lifestyle interactions, all of which can be monitored and modified through polypharmacy assessment. , A well-characterized example of an oncology-related drug with significant PGx effects is fluorouracil (5-FU), a drug commonly used to treat solid tumors. Estimates indicate that 1,300 Americans die annually from drug toxicity after administration of 5-FU. 5-FU is metabolized in the liver by the enzyme dihydropyrimidine dehydrogenase (DPD), which breaks down 5-FU before excretion. Hundreds of variants have been identified in the DPYD gene, which encodes the DPD enzyme, with a subset known to cause pathogenic DPD deficiency. Variations in the DPYD gene leading to partial or full DPD deficiency result in toxic buildup of 5-FU after administration at standard doses, which in some cases can be fatal. Although testing for deleterious germline variants of the DPYD gene before 5-FU treatment is standard of care in Europe, similar recommendations for preemptive testing have not been implemented in the United States. - The Clinical Pharmacogenetics Implementation Consortium (CPIC), funded by the US National Institutes of Health (NIH), develops evidence-based guidelines for the interpretation of pharmacogenomic data. For DPYD , CPIC assigns an activity score to each variant, which reflects the functionality of the encoded protein, with 0 representing no function, 0.5 decreased function, and 1.0 representing a fully functioning protein. The total activity score for a patient is the sum of the two copies of the gene present in an individual, with a score of 2.0 representing two fully functioning copies and 0.0 representing two nonfunctional variants. CPIC recommends 5-FU dose reductions for total DPD activity scores of 1.5 or below; 5-FU should be avoided altogether for patients with scores of 0.5 or 0. For oncology patients, pretreatment testing for DPYD variants reduced the frequency of fluoropyrimidine-related hospitalization from 19% to 0%, and the number of toxicity-related deaths from 4.8% to 0%. Another recent study showed that DPYD -guided fluoropyrimidine dosing did not negatively affect progression-free survival and overall survival. 

Populations that share ancestry tend to share genetic variants, contributing to differences in disease incidence and drug metabolism in these subpopulations. In research and clinical practice, however, genetic ancestry is typically replaced by the demographic categories of race, region, and ethnicity, which are usually self-reported values that do not directly correspond to genetic ancestry. Using race or ethnicity as a proxy for genetic ancestry may support erroneous assumptions about genetic homogeneity and lead to genetic misdiagnosis and adverse drug reactions. , This challenge is illustrated by a recent analysis of a gene encoding a protein critical for immune function within the major histocompatibility complex, HLA-B*58:01 , which found the magnitude of allele frequency differences between residents of different Swiss cities to be comparable with allele frequency differences between US Black and White populations. The CPIC prevalence estimates of DPYD variants leading to DPD deficiency vary between populations from 0.02% to 8%, with up to tenfold differences in individual variant frequency observed between populations. - However, as with most human genetic studies, most data on DPYD alleles come from studies of relatively homogeneous populations of Western European ancestry. Recent work has identified new DPYD variants prevalent in those with African genetic ancestry (Table ), highlighting that past discoveries on the basis of limited and homogeneous study populations have been inequitable. Further research is needed to identify and characterize particular DPYD variants associated with currently underrepresented ancestry groups, and increased ubiquity of PGx screening of individuals to identify pathogenic alleles would aid this work and contribute to better outcomes. The PGx relationships of the thiopurine drugs, commonly used in treating cancer, autoimmune disorders, and in organ transplantation, provide an important example of health disparities in drug response on the basis of genetic ancestry. Population-specific variation affects the roles of the enzymes thiopurine S-methyltransferase (TPMT) and nucleoside diphosphate-linked moiety X-type motif 15 (NUDT15) in the metabolism of thiopurine drugs. Ancestry-associated allele variation of the TPMT and NUDT15 genes leads to significant differences in the safety and effectiveness of azathioprine, mercaptopurine, and thioguanine. It has long been known that patients with a nonfunctional variant allele of TPMT have lower TPMT enzyme activity and frequently experience toxicity because of elevated levels of thiopurine metabolites after treatment. However, the known variants of TPMT are insufficient to explain the observed individual- and population-level variations in drug response, especially the higher incidence of thiopurine toxicity in East Asian patients, who have a lower frequency of TPMT alterations than Europeans. , In 2015, nearly 20 years after loss-of-function TPMT alterations had been identified, NUDT15 was identified as a moderator of thiopurine tolerance. , Deleterious NUDT15 alleles occur more frequently among East Asian populations than European and African populations and are the predominant drivers of thiopurine toxicity in this group (Fig ). The fact that NUDT15 was identified more than 20 years after the characterization of PGx effects of TPMT alleles highlights the delays in discovery for underrepresented populations. These examples underscore the importance of studying PGx in a large and diverse population; accurately characterizing participants' genetic ancestry to detect low-frequency genetic variants; and characterizing how these variants affect the safety and effectiveness of drugs. DPYD , TPMT , and NUDT15 represent three genes for which genomic variants have been designated clinically actionable by CPIC, and dosing guidelines made available, on the basis of the review of evidence from the literature. Other gene-drug pairs for which oncology pharmacogenomic dosing recommendations have been published to improve safety include UDP-glucuronosyltransferase (UGT) UGT1A1 -irinotecan and cytochrome P450 (CYP) enzyme 2D6 CYP2D6 -tamoxifen. For a number of other gene-drug pairs, such as ABCB1 -imatinib, preliminary data suggest pharmacogenomic interactions but the evidence has not yet reached the threshold for review by CPIC. 

Although in the past 30 years many genes that encode proteins involved in the pharmacokinetics (PK) of drugs have been identified, new genes and new variants of known genes that affect drug safety and effectiveness continue to be discovered. Furthermore, PGx interactions influence the development of new drugs, on the basis of known genes that lead to large differences between patients in PK (eg, CYP2D6 ). In hopes of avoiding subsets of patients who do not respond or have excess toxicity, drug developers often design drugs to be insusceptible to CYP2D6 metabolism to avoid large differences in drug levels between patients. Thus, drugs are more likely to be developed if they are characterized by more desirable CYP metabolic and polymorphic profiles. , In cases where known adverse PGx drug-gene interactions cannot be avoided or have not been identified, discovery of new interactions is often retrospective, with safety events or unexpected differences in effectiveness driving further exploration that can lead to the discovery of new PGx drug-gene pairs. Identifying new PGx relationships generally requires drug administration at relevant doses, observable clinical response (phenotype), genetic information to identify gene variants (genotype), and sufficient numbers of patients to evaluate potentially rare variants. Measured systemic exposure (eg, plasma AUC) may be used to link dose with clinical outcome. Premarket research environments where drugs are initially examined (ie, registrational trials) generally collect dose, exposure, and phenotype, and may include genotype. The US Food and Drug Administration (FDA) has called in its Guidance for Industry to increase diversity when designing and enrolling patients for clinical trials to understand interpatient variability in the effectiveness and toxicity of a drug. However, the lack of enrollment of diverse participants makes discovery of new PGx relationships, especially in underrepresented populations, unlikely. This point is highlighted by a review of FDA drug approvals from 1997 to 2014, indicating the number of countries represented in clinical trials increased from 32 (in 1997) to 57 (in 2014), but with the fraction of participants classified as White remaining above 86%. In addition to the diversity of trial enrollees, the number of patients enrolled in trials can affect the ability to reliably detect actionable PGx interactions. Table shows a calculated sample size needed to detect a two-fold increase in the PK (AUC) for different variant frequencies in a population, assuming higher or lower AUC variability (ie, coefficient of variation [CV] of 66% or 31%, respectively). These calculations indicate that for low variant frequencies (eg, 1% in the United States) with moderately high variability (CV% = 66), 13 variant-positive patients must be in a clinical trial to confidently conclude an association. Although as few as four variant-positive patients would meet the statistical assumptions to detect an effect for a drug with low PK variability (eg, CV% = 31), typically up to 12 variant-positive patients may still be required to be confident in the conclusions, leading to similar sample sizes. Therefore, for a 1% variant frequency in the population, the clinical trial would need to enroll approximately 1,300 total patients to obtain 13 variant-positive patients, which is higher than the number of patients enrolled into many registrational oncology trials and may not be feasible. For context, the NUDT15*2 allele is found in East Asian and Latino populations at 3.5% and 3.7%, respectively, but is not detectable in European populations (Table ), meaning that the PGx effect would not be found regardless of the trial size if only participants of European descent were included. Importantly, this calculation also assumes a single hypothesis for a single genetic driver. If multiple hypotheses will be tested, or an unsupervised genome-wide search will be conducted, much greater sample sizes are required because of the increased potential for false positives deriving from multiplicity (see the study by Shrestha et al and references therein). These calculations show that typical premarket registrational studies are unlikely to identify pharmacogenomic associations for PGx drivers with low variant frequency, even if a diverse population is enrolled, given the relatively small trial sizes in oncology. Thus, evaluation of larger-scale postmarketing data (also known as real-world data [RWD]) may be needed to detect signals indicating potential PGx liabilities. Other nonclinical approaches, such as in vitro assays and in silico algorithms, may provide additional complementary sources of discovery. - RWD derived from electronic health records (EHRs) is increasingly used to learn more about drugs post approval. These data may include millions of patients and often better reflect the diversity of the broader population. - RWD from the clinical practice environment typically have drug exposure and phenotype information, but associated germline data are less likely to be found in EHRs than somatic data. Furthermore, these data may be limited to only alleles already known to have PGx impacts, limiting the ability to make new associations with novel variants. However, RWD may identify potential safety or effectiveness signals that can be further explored in targeted studies searching for PGx causes of phenotype differences. Best practices for using RWD are still emerging, , but better aggregation of data from diverse populations, from multiple data sources, is a good first step. These data are collected and reviewed by CPIC experts and incorporated into guidelines when the evidence is clear and actionable. Increasingly, genomic databases have been developed to enable genome-wide association studies. Rich genomic data repositories can be obtained from online public databases such as dbGAP, 1000 Genomes, International HapMap Consortium, Gene Expression Omnibus, and Sequence Read Archive. Although such databases typically have extensive germline data on hundreds of thousands or even millions of individuals, historically, they have been primarily composed of individuals of European ancestry. These databases are becoming increasingly diverse and offer the ability to better understand allele frequencies. However, even with large numbers of diverse individuals with full genetic information, these databases typically do not include clinical information indicating dosing information for drugs of interest or the associated clinical outcomes. Recently, integrated registries have been developed to enable more comprehensive individual data on genetics, health care utilization, and outcomes. For example, the UK Biobank, with information on 500,000 individuals, includes more than 52,000 incident cancer cases in the cohort. - In the United States, analysis of data representing 203,813 participants enrolled in the All of Us research program for whom EHR data were available identified 23,520 cancer cases. , These programs combine all the needed elements for discovery; however, they are typically composed of mostly healthy volunteers and it is unclear, despite the relatively large cohort size, if the number of individuals comprising ancestral subgroups with a relevant disease and drug exposure would be sufficient to identify novel PGx associations. 

Implementing PGx knowledge into routine clinical practice is merited once robust evidence has been generated supporting the impact of specific alleles on drug function and corresponding clinical dosing modifications can be made to address the altered function. For example, a 2021 study identified 154 drug-gene pairs in the Pharmacogenomics Knowledgebase (PharmGKB) database annotated with actionable gene/drug interactions, of which 92 represented oncology drugs. The need for PGx testing implementation in oncology is highlighted by a recent study of patients with advanced cancer, which found that over half (approximately 60%) received at least one drug with relevant CPIC recommendations and one in seven patients had an actionable genotype for the drug they were prescribed. Examples of systemic implementation of PGx testing and follow-up have shown important improvements in clinical outcomes and cost reduction. A study in several care settings across Europe, including cancer care, demonstrated a 30% reduction in clinically significant adverse drug events in just 12 weeks of incorporating a PGx panel. In another case, implementation of PGx testing by the Kentucky Retired Teachers Association yielded $7,000 in US dollars per patient savings in reduced health care costs over 32 months. A systematic review and meta-analysis of PGx testing studies confirmed consistent changes in prescribing and a statistically significant 43% reduction in hospitalizations. Despite the prevalence of actionable findings from PGx testing, the reduction in avoidable toxicity and death, and the return on investment from testing, the integration of PGx into oncology care is still lacking. Better adoption of PGx testing in routine clinical care depends on improvements in drug labeling clarity and consistency; better evidence synthesis, communication, and education; improvements in infrastructure such as clinical decision support (CDS) systems; and greater insurance coverage of PGx testing and follow-up. These improvement areas are discussed in more detail below. Care must be taken to ensure that improved testing and infrastructure apply to all populations and are equitable in their implementation. 

Synthesized evidence and associated treatment recommendations related to PGx can be found in some drug labels, which in the United States are reviewed and approved by FDA. These labels serve as a resource for clinical practice and a summary source of information known about a drug. FDA maintains a Table of Pharmacogenetic Associations (the FDA Table), which includes three categories of drug-gene associations: those for which data support therapeutic management; those for which data indicate a potential impact on safety or response; and those for which data demonstrate a potential impact on pharmacokinetic properties only. According to FDA, the location of PGx information within the different sections of an FDA-approved drug label differs depending on the actions that can be taken. Additionally, the FDA guidance document Clinical Pharmacogenomics: Premarket Evaluation in Early-Phase Clinical Studies and Recommendations for Labeling contains nonbinding recommendations for the pharmaceutical industry and other investigators engaged in new drug development. The guidance recommends that PGx information be included in the most appropriate labeling section such as Indications and Use , Dosage and Administration , Boxed Warning , Contraindications , Warnings and Precautions , Adverse Reactions , Use in Specific Populations , Drug Interactions , Clinical Pharmacology , or Clinical Studies . In practice, different drugs that implicate the same gene may list PGx information in different sections throughout a drug label and, in some cases, the same drug in different formulations may offer different PGx considerations; see examples in Table . For example, topical fluorouracil lists PGx information for DPYD in the Contraindications section of its label, whereas there is no such listing for intravenous 5-FU (Table ). Some FDA-approved drug labels provide information on carrier frequency across various populations (eg, race, ethnicity, and ancestry) for a given gene variant with PGx implications. However, drugs with the same drug-gene interaction can differ on whether this information is provided. For example, intravenous fluorouracil and Xeloda (capecitabine) are both affected by DPYD ; however, only capecitabine comments on variant frequency among different populations (Table ). 

Although drug labels serve as an important source of PGx-related information, they may not represent the totality of what is known about a drug's PGx interactions because of additional research conducted after a drug's approval. Additionally, some older drugs, such as 5-FU, no longer have a nongeneric version on the market, effectively freezing the label contents in perpetuity. Although FDA's Oncology Center of Excellence has initiated Project Renewal to try to update some older drug labels, for many drugs, the most current information about general usage, and specifically PGx issues, may come from sources other than the label. For example, CPIC guidelines note clinically actionable gene/drug pairs to guide providers through translating genetic testing into treatment decision making. Another example is PharmGKB, which curates annotated PGx-based guidelines published by groups such as CPIC, the Royal Dutch Association for the Advancement of Pharmacy—Pharmacogenomics Working Group (DPWG), the Canadian Pharmacogenomic Network for Drug Safety, and the French National Network of Pharmacogenomics (RNPGx), and assigns levels of evidence on gene/drug pairings (PharmGKB). Previous research has highlighted discrepancies and levels of concordance among the various consortia, regulatory agencies, and drug labels. , For example, an evaluation of 54 drugs known to have an actionable gene/drug interaction by CPIC guidelines found that only half had actionable information within the drug label. Furthermore, only 18% of the drugs had agreement across regulatory agencies, including FDA and European Medicines Agencies. Manchester University maintains a cross-reference of PGx drug-gene association listings in the FDA's Table and CPIC Clinical Guidelines. As of its most recent quarterly update (January 2023), 207 drugs were listed with PGx information in the FDA Table, the CPIC guidelines, or both. A total of 48 drugs are included in both sources, indicating that fewer than one in four drugs have concordant recommendations. There is clearly a need for alignment on a central source of evidence (such as the CPIC guidelines) in order for PGx implementation to be effective. Although PGx-specific guidelines are clear on the clinical relevance of specific variants, oncology practice guidelines such as those promulgated by the National Comprehensive Cancer Network or ASCO have been slow to adopt testing recommendations. Concerns have been raised as to the level of evidence for dose reduction strategies to be included into guidelines; however, others have argued that the evidence is more than sufficient to include specific recommendations within guidelines. Nonetheless, rigorous prospective studies are underway to augment clinical utility evidence that may be more convincing to guideline authors. , Treatment guidelines in other countries already include regular recommendations for PGx testing. - 

PGx tests can be designed to test single genes or a panel of multiple genes simultaneously. Such tests can use next-generation sequencing to probe for any variants in a pharmacogene or they can be designed to only probe for specific variants. When the number of variants on a test is limited, tests may erroneously report an individual as having normal gene function, although clinically relevant variants that were not interrogated may be present. This is especially problematic when omitted variants are preferentially present in population(s) with specific ancestral backgrounds. For example, of five major US laboratories accessed, only three test for DPYD variant c.557A>G (rs115232898) (Tyr186Cys), a clinically actionable variant associated with decreased function and most commonly found in patients of African ancestry. Before 2023, two of these laboratories only tested a single variant rather than the four clinically actionable variants recommended by DPWG. , , - The omission of variants found predominantly in non-European populations risks the creation of disparities as testing becomes more widespread. Until recently, there has been little effort to standardize the specific variants that should be included in clinical PGx tests; however, the Association for Molecular Pathology Pharmacogenomics Working Group is developing recommendations for pharmacoalleles to be included in tests to improve pan-genetic ancestry. - PGx test usability and turnaround time for results can also be a barrier to implementation. In a qualitative study, providers found PGx tests confusing and difficult to order, and that the waiting time for test results was unacceptable for the urgent need to treat oncology patients. A recent implementation study showed how these barriers were overcome in implementing PGx testing for GI cancer. 

Payor coverage plays a large role in access to PGx testing. The US government pays for approximately half of the country's health care through agencies such as the Centers for Medicare & Medicaid Services (CMS). CMS uses Medicare Administrative Contractors (MACs), who are private entities, to administer the Medicare program through geographic jurisdictions. The MACs use a process called Local Coverage Determinations (LCDs) to determine whether they will cover a particular service on a MAC-wide basis. Most MACs have issued LCDs that cover PGx testing for medications under consideration or prescribed if the provider will alter treatment on the basis of results of testing gene/drug interactions that are clinically actionable as defined by FDA (PGx information required for safe drug administration) or CPIC guidelines (category A and B). CMS also issues National Coverage Decisions (NCDs) for the entire country. To date, to our knowledge, there is only one Medicare NCD related to PGx testing and it is outside of oncology, covering the testing of CYP2C9 or VKORC1 alleles to predict a patient's response to warfarin. Some private insurers have explicit coverage policies related to PGx testing, with policies varying by company and gene/drug pairs. Insurance may or may not cover PGx testing when a drug's label contains gene-drug information noted in the FDA Table. FDA cautions that inclusion of a gene/drug interaction in its table “…does not necessarily mean the FDA advocates using a pharmacogenetic test before prescribing the corresponding medication, unless the test is a companion diagnostic.” No PGx tests are considered to be a companion diagnostic; however, for a couple of gene/drug pairs, such as HLA-B*15:02 -carbamazepine, FDA makes an explicit requirement for genotype testing before prescribing within the label. In cases when testing is required , coverage is more favorable compared with coverage for when labeling only mentions genotype testing or dose reductions on the basis of genotype testing as a consideration for prescribers, and FDA's lack of explicit testing requirements have been cited by payors in decisions not to cover PGx testing. In recent years, a number of states have enacted requirements for state-regulated private insurers, and state Medicaid plans cover evidence-based biomarker testing, including PGx testing. 

Equitable clinical implementation of PGx testing and knowledge depends on both providers and patients having sufficient understanding of genetics and PGx; the awareness of and ability to understand and make decisions about PGx testing; and the availability of educational resources. A review of 33 studies on providers' experiences with using PGx information revealed that although providers find PGx useful, they lack the knowledge to effectively implement it. One way to augment provider knowledge is through the use of CDS systems to provide information, prompts, or preset orders directly in the course of care, often through an EHR or a clinical prescribing system. Several projects have been described to integrate CDS with PGx testing such as the Pharmacogenomic Resource for Enhanced Decisions in Care and Treatment (PREDICT) initiative at Vanderbilt and the eMERGE-PGx project, a collaboration between the Electronic Medical Records and Genomics Network and the Pharmacogenomics Research Network (PGRN). - The feasibility of a preemptive standardized PGx-CDS system has been demonstrated in a multinational, multilanguage, and multi-health care center setting. A PGx practice model using a CDS tool integrated into the EHR and implemented as a collaborative effort between the pharmacist, primary care provider, specialists, and patient proved effective. However, barriers remain to harmonizing PGx guidelines and drug labels within the system, adopting PGx-CDSs within institutions and providers, and compliance with regulatory and legal frameworks. Most PGx implementation has occurred in academic medical centers. Integration of CDS tools with the EHR is necessary for effective implementation. CPIC has worked to make implementation easy by providing CDS guidance, including implementation workflow, alerts, and flowcharts along with guidelines. These approaches still require significant internal information technology and clinical resources, so several CDS tool providers offer scalable solutions that can be easily integrated into the EHR including YouScript, TreatGx, ActX, or 2bprecise. This enables community-based settings with limited genomic expertise to implement. However, a recent survey of barriers to CDS implementation identified the greatest barrier to using CDS systems was an increased workload. Approximately half of the health systems cited lack of resources to build or implement as a barrier to CDS and one third cited lack of staff or provider training. Implementation or updating of CDS tools is also challenging in an already resource-strained community health system without an associated reimbursement mechanism such as payments for PGx consultations, an associated quality measure, or a requirement similar to the EHR certification requirement for drug-drug interaction alerting extending to drug-gene interactions. With over half of the hospitals facing budget shortfalls, investment that does not align with revenue or requirements is unlikely to be prioritized. In conclusion, PGx is a critical element of precision medicine, ensuring that patients with cancer receive the right medicines at the right doses on the basis of their individual genetic makeup. A lack of PGx testing and corresponding modification of drug dosing in oncology is implicated in over 1,300 drug toxicity fatalities per year. Implementation of PGx programs in oncology has led to reduced adverse events and improvement in outcomes. , , Comprehensive medication management programs across diseases with PGx as a key component have shown improved outcomes and reductions in cost. , , However, despite the evidence pointing to the clinical utility of PGx programs and successful implementation in other countries, , uptake in the United States remains dismally low. Furthermore, as described above, available data on PGx allele frequency are not representative of diversity in genetic ancestry; current methods of discovery rely on genetic databases or clinical trial populations that are similarly unrepresentative; and implementation challenges (testing availability and payor coverage) are also likely to lead to inequities. The barriers to greater adoption have been outlined here, and there have been widespread calls within the oncology community to advance PGx adoption. Recently, a group of stakeholders created a list of nine targeted recommendations, endorsed by 12 oncology organizations, to overcome barriers to PGx implementation and address disparities in discovery and application (Table ). As progress is made, it will be critical to avoid repeating the historical inequities that have been part of the PGx field to date. 

