Precision Oncology: Directing Genomics and Pharmacogenomics Towards Reducing Cancer Inequities

Long before the field of genomics revolutionized the health landscape, the World Health Organization exalted health equity as a global policy priority when it declared “the highest attainable standard of health as a fundamental right of every human being” (WHO, 1946). In the 75 years since, enormous advancements in genomic technologies have paralleled the growing number of countries expressing a national commitment to health equity by incorporating a right to health into their national constitutions. With most nations, including the United States, lacking formal protections for health, translating the values of health equity into the genomic era has proved challenging.

Though enduring health disparities have been documented within the U.S. healthcare system, progress on health equity lags behind other areas of health care quality reform (IOM, 2003; HHS, 2018). Cancer remains a leading cause of illness and death around the world, disproportionately affecting certain populations. Oncology has been a primary focus for genomic medicine: the implementation of genomics into clinical practice to enhance delivery of personalized health care (Cutler, 2020). Cancer genomics and pharmacogenomics research investigates the genetic differences that may impact cancer etiology and drug response respectively. Due in part to enthusiasm surrounding genomics as a new weapon to employ in the war on cancer, the public valuation of genomics has heavily weighed its successes, challenges, and failures in oncology (Shendure et al., 2019). In the midst of this genomics revolution, the racial gap in cancer mortality only slightly narrowed while socioeconomic inequalities in cancer death are widening most notably for preventable cancers like breast and colorectal cancers (Siegel et al., 2019). Leveraging genomics to eliminate such health disparities was among the many ambitious expectations for how deciphering the genetic code would transform prevention, diagnosis, and treatment of human disease. To date, catalyzing dramatic reductions in cancer disparities through genomic medicine remains beyond reach.

While a multitude of factors likely contribute to this failure, the disconcerting absence of research demonstrating the full role of genomics in reducing, exacerbating, or creating new health disparities is notable. This glaring lack of studies vital to promoting health equity perhaps seems less surprising in light of the controversy and lack of methodological consensus surrounding the study of genetic variation across diverse populations. Researchers across disciplines (e.g. population genetics, social science, public health, etc.) disagree on the utility of population labels like race, ethnicity, or genetic ancestry in genomics research, especially given the potential risk of reifying biological underpinnings of racial categorization. Race, misappropriately equated with genetic ancestry, differentiates populations and mechanistically creates a hierarchy of ascribed societal value. Such designated societal worth preferentially confers a multitude of resources and opportunities to some over others driving cancer inequities.

Genomic studies in cancer evaluating genetic ancestry are inherently political, inciting contentious debate over resource distribution across demographic populations. Risk stratification for disease or drug response, diagnostics, treatment allocation, and intensity of follow-up surveillance all represent genomic applications in cancer care that could potentially contribute to health disparities. Consequently, fervent arguments have been made against conducting comparative studies of population genetics based on race or genetic ancestry altogether. Purposefully avoiding this research would protect minority groups from the dangers of biological justifications for systemic discrimination and structural racism. Concerns about further maltreatment of underserved and underrepresented communities are warranted. However, what are the risks of permitting further advancements in precision oncology which ignore genetic ancestry?

Of the numerous potential ills to avoiding population-based studies of genetic ancestry, one is leaving little guidance on how to methodologically approach this work, creating a major obstacle to cancer health equity. Targeting efforts within genomic medicine towards increasing knowledge of ancestry-related risk factors in cancer is currently a national priority (The White House, 2016), likely to spur a wave of investigations in this overlooked area. The research methods for identifying meaningful differences in genetic makeup across populations defined based on genetic similarities vary extensively in the literature (Fujimura and Rajagopalan, 2011; Ali-Khan et al., 2011). Inconsistencies and gaps in the analytical approaches associating genetic variation with disease phenotypes make it difficult to compare results across studies. To facilitate navigating the morass of existing methodologies, the ensuing discussion presents a set of practices for evaluating genetic ancestry in genomic studies with the scientific rigor needed to progress this vital area of cancer research.

Defining “genetic ancestry” and Study Populations

Though mapping the human genome was predicted to usher in an era of personalized medicine tailored to individual genetic makeup, population differences remain the dominant focal point. Genetic ancestry is an indicator of the inheritance of DNA providing a similar genetic makeup within groups, and yet, it has been conceptualized many ways within research. No common definition exists in the literature where genetic ancestry has often been (mis)equated with race.

While genetic ancestry and membership to a racial group may be related, genetic ancestry and race are not synonymous because racial categories are highly context dependent (Ali-Khan et al., 2011). Racial categories are subject to sociopolitical forces that apportion relative power and economic influence within nations, making it a construct that is inconsistent globally. In other words, U.S. racial categories may be less relevant to a genomic dataset compiled from around the world. Classifying by race is inherently limited as it lacks the precision to function as a proxy for genetic ancestry, and alone race fails to clinically provide the sensitivity and specificity for the presence of genetic variants. Care must be taken to distinguish studies that compare groupings of genes or genetic clustering between racial groups as opposed to between ancestry-based groups based on geographic origin. In certain instances where race is used to demarcate populations, additional analyses validating genetic ancestry may be necessary.

While the research question may determine the appropriate selection of the populations evaluated, study populations need to be clearly defined. The selection of comparison groups will affect every aspect of the research study including its design, analysis, results, and interpretations/conclusions. Examining and justifying population selection within comparative studies to ensure alignment with research goals will enhance the quality and utility of the research to the field. Providing explicit clarification of terms along with the types of data and variables used for representing genetic ancestry in genomic research is essential for the development of the evidence-base and the ability to perform cross-study comparisons.

Intrapopulation Diversity and Admixture

Race-based and ancestry-based populations are far from monolithic. Intrapopulation genetic variation within these groups can influence the ability to stratify by genetic risk (Goodman and Brett, 2021). The genetic variation within geographic populations or ethnic groups can exceed that between broader racial or ancestral categories. A greater proportion of people over time are identifying as being of mixed genetic ancestry. Genetic admixture, or the presence of multiple genetic backgrounds, is more prevalent among populations of African descent and Hispanics. These very groups underrepresented in genomic science include individuals whose genetic profiles may necessitate more detailed characterization for cancer genomics and pharmacogenomics to equitably benefit these populations. Intrapopulation diversity and admixture underscores the importance of carefully selected comparison groups in population-based studies. If all comparisons involve a reference group of European ancestry, potentially significant intrapopulation genetic diversity or variation in admixture rates may be obscured. Differences in allele frequencies and population substructure produced by admixture could relate to disparities in both the incidence of and mortality from complex diseases like cancer. The feasibility of alternative, more specialized reference genomes that increase the applicability of findings should be considered.

Data Considerations and Limitations

Inadequate genomic data from diverse populations

In 2016, 19% of genome-wide association studies (GWAS) were in samples from underrepresented populations, increased from the 4% reported seven years earlier (Popejoy and Fullerton, 2016). Twenty years have elapsed since the National Institutes of Health (NIH) mandated inclusion of diverse populations in research studies, yet inequalities in representation persist across biomedical research and quite starkly within genomics.

Germline and somatic genomes

Two separate genomes must be considered for their respective independent and interactive roles in determining genetic risk for cancer and specific clinical phenotypes: 1) germline (inherited) and 2) somatic (tumor). Both patient germline and tumor DNA are highly relevant to genomic applications to clinical care. Though there is predictive value in both germline DNA variants as well as somatic DNA variants, the majority of clinical and industry efforts focus on somatic tests to identify target driver mutations and optimize therapy. Somatic, or acquired, changes account for approximately 90–95% of all cancer cases (NCI, 2017). Nevertheless, genetic variation in either or some combination of both genomes may have meaningful medical significance that is distinct across population groups. Integrating germline and somatic genetic information from ancestry-specific populations into individualized treatment may further enhance the quality of cancer care delivered to populations at risk of health disparities.

Effect size and study power

Despite rapid developments, many of the strongest GWAS associations may have already been discovered with smaller-scale studies powered to detect large effects within core gene pathways. Genetic risk for cancer is more likely due to the interaction of many variants of small effects, mandating the collection of extremely sizeable study samples to detect statistical differences between ancestry-specific populations. Acquiring such large sample sizes may be difficult given the scarcity of diverse genomic data collected via biobanking efforts thus far. In cases where challenges to sample size collection could be overcome, current approaches might be incapable of identifying unknown ancestry-specific associations, whose strength may fall below a detectable statistical threshold.

Given the significance to monitoring the impact of genomics on cancer health disparities, great value may still be derived from studies humbly acknowledging data complexity and presently unavoidable limitations.

Conclusions and Policy Recommendations

Employing the proper methodologies to study ancestry-specific genetic differences is fundamental to expanding the evidence-base, comparing across studies, evaluating progress in health equity over time, and defining clinical utility of genomics across populations of diverse ancestry. We hope that outlining this set of methodological considerations will encourage a deeper level of thinking and intentionality in the constructs for genetic ancestry that are used for cancer research across the translational cycle. Opportunities exist to improve health equity in genomic medicine if future research integrates these criteria into the study of ancestry-related variation in cancer genomics and pharmacogenomics.

Bold actions to accelerate the generation of critically needed knowledge in this under-resourced research area must accompany these shifts in our approach to genomic research involving genetic ancestry:

Comprehensive investments must be made in building the genomics evidence-base for underserved and underrepresented populations.

In particular, the field must swiftly initiate thorough evaluations of ongoing genomic implementation studies involving underserved and underrepresented populations where health care is actively being delivered with limited evidence of clinical utility. Without these implementation studies creating learning networks from real-world data to inform interventions cannot be formed nor can new knowledge produced from scientific discoveries or on the facilitators/barriers to genomics’ implementation be shared in a timely manner to avoid harm or enhance clinical care.

Efforts to expand the literature must address the inclusion of underserved and underrepresented groups as research participants, but they also must be directed to diversifying the workforce pursuing studies involving these populations. The success of such efforts will be linked to disrupting inefficient cycles counterproductive to progress in equity. One such cycle is perpetuated by peer review which traditionally screens out this research citing unavoidable data limitations and/or insufficient prior evidence as rationale for exclusion from publication and funding opportunities. Long-term, dedicated financial commitments as opposed to the sporadic investments that have been the status quo is one first step in making amends for the past harms medicine inflicted on communities of color. Sustained support will be required to uncover how to tailor innovations that realize the potential of personalized medicine for all populations that the field continues to promise. This has to include expanding current investments as well as targeting new resources to accelerate diversity and inclusion in biomedical research.

Initiatives like NIH UNITE must be recognized as a powerful lever to enact overdue reforms that can close health equity gaps in biomedical research (NIH, 2021).

The NIH is uniquely situated to influence the federal research agenda and set standard requirements for research involving the study of genetic ancestry. For example, cost-benefit analyses for genetic testing that incorporate patient values across ancestry-based populations and not simply health outcomes achieved within a specific price range could be designated as a high priority research area through funding opportunities. The demand for studies illustrating the value of genomic medicine is high. Cost-benefit analyses that integrate the patient perspective may be an innovative and more culturally competent approach that places health equity at the center of the research.

The NIH can foster coordination with other influential agencies like the Centers for Medicare and Medicaid Services (CMS) and the U.S. Food and Drug Administration (FDA). With CMS, the NIH might encourage policies like insurance coverage with evidence development (CED) for genetic testing during clinical trial and research studies. CED provides remuneration for new technologies delivered in research studies that are yet to have sufficient evidence of efficacy. Insurance access and regulation plays an important role in ensuring genomic information collected across populations is used to the broad benefit of society not simply as a means for continued disenfranchisement. Buy-in from national insurers like Medicare is a critical step in motivating third-party payors to cover genomic testing for risk assessment within a diverse U.S. population.

The NIH and FDA collaboration instituted to ensure regulatory considerations form an integral component of biomedical research must devote attention to genomics with increased vigilance. Together, these influential organizations should strengthen transparency within the drug approval process and necessitate warning labels be placed with companion diagnostics or biomarkers. NIH-FDA coordination is an essential component to establishing the proper protections as genomic technologies are delivered to underserved and underrepresented populations.

Organized, interdisciplinary collaboration across all sectors of the biomedical enterprise will determine the success of systemic health equity reform.

President Joe Biden’s Executive Order on Advancing Racial Equity and Support for Underserved Communities (The White House, 2021) signals commitment from the federal government which should be complemented at the local and state levels. This multi-level work within the U.S. must be synergized with global efforts towards the common goal of genomic justice and equity. Incorporating genetic ancestry into cancer research presents an incomparable utilitarian societal benefit. Populations of African ancestry are the most diverse, and the majority of populations derive from African ancestral origin (Oni-Orisan et al., 2021). Studies in populations of African ancestry with less linkage disequilibrium can facilitate identifying associations in other population groups. Thus, advancing health equity will not only produce positive societal benefits, it will also likely improve the quality of genomic medicine for all populations.

Emerging evidence continues to demonstrate that genomic applications studied primarily in European populations are not universally generalizable to underrepresented patients, exposing them to greater risk for adverse health outcomes. Ignoring ancestry-specific genomic variation endangers the translational significance of genomic research findings. What is at stake without urgent corrective action is the compounding of persistent barriers to health equity in cancer genomics and pharmacogenomics. These barriers include the unequal access for populations of diverse genetic backgrounds to truly personalized care and tacit acceptance of a precedent whereby health systems, equipped with inadequate research from diverse populations, can only react, rather than avoid, suboptimal cancer outcomes for underrepresented patients.