Genetic Ancestry Correlates with Somatic Differences in a Real-World Clinical Cancer Sequencing Cohort

Abstract

Accurate ancestry inference is critical for identifying genetic contributors of cancer disparities among populations. Although methods to infer genetic ancestry have historically relied upon genome-wide markers, the adaptation to targeted clinical sequencing panels presents an opportunity to incorporate ancestry inference into routine diagnostic workflows. We show that global ancestral contributions and admixture of continental populations can be quantitatively inferred using markers captured by the MSK-IMPACT clinical panel. In a pan-cancer cohort of 45,157 patients, we observed differences by ancestry in the frequency of somatic alterations, recapitulating known associations and revealing novel associations. Despite the comparable overall prevalence of driver alterations by ancestry group, the proportion of patients with clinically actionable alterations was lower for African (30%) compared with European (33%) ancestry. Although this result is largely explained by population-specific cancer subtype differences, it reveals an inequity in the degree to which different populations are served by existing precision oncology interventions.

Significance: We performed a comprehensive analysis of ancestral associations with somatic mutations in a real-world pan-cancer cohort, including >5,000 non-European individuals. Using an FDA-authorized tumor sequencing panel and an FDA-recognized oncology knowledge base, we detected differences in the prevalence of clinically actionable alterations, potentially contributing to health care disparities affecting underrepresented populations. This article is highlighted in the In This Issue feature, p. 2483.

Figure 1:. ADMIXTURE analysis with MSK-IMPACT markers.

(A) ADMIXTURE results on Simons Genome Diversity Project (SGDP) data using n=5,378 MSK-IMPACT SNP markers chosen from the latest version of the MSK-IMPACT panel: IMPACT-505. (B) Comparison of ancestry fractions inferred for SGDP samples using genome-wide markers and IMPACT-505-specific markers (C) Self-reported race, ethnicity, ADMIXTURE results, and assigned admixture labels in MSK-IMPACT cohort. (D) Distribution of inferred ancestral fractions (top) and assigned admixture labels (bottom) in self-reported Non-Hispanic/Latinx White, Hispanic/Latinx White, Non-Hispanic/Latinx Black, Hispanic/Latinx Black and Asian patients. ADM=admixed/other; AFR=African; EAS=East Asian; EUR=European; NAM=Native American; SAS=South Asian.

Figure 2:. Ashkenazi Jewish ancestry inference.

(A) Fraction of manually annotated Ashkenazi Jewish (n=1257) and non-Ashkenazi Jewish (n=6960) patients at different cutoffs of number of ASJ informative marker sites with het/hom-alt genotype calls. The triangles show the accuracy (secondary y-axis). Based on these results, patients with alternate alleles for at least 3 ASJ markers were labeled ASJ, and the remaining were labeled nonASJ (B) Representation of self-reported religion in patients assigned nonASJ and ASJ labels. (C) Fraction of patients with BRCA1/2 mutations in patients assigned nonASJ and ASJ labels. (D) Fraction of patients with BRCA 1/2 founder mutations and representation of self-reported religion binned by the number of het/hom-alt ASJ markers.

Figure 3:. Cancer types and somatic associations.

(A) Ancestry representation of patients in different cancer types. (B) Ancestry-gene associations in different cancer types. Associations with adjusted p-value <0.05 (after adjusting for sex, age and sample type) are highlighted. Samples with TMB score of > 20 or MSI score > 10 were excluded. (C) Gene alteration frequencies in Lung Adenocarcinoma. Two-tailed Fisher’s exact test was run to identify significant differences in gene alteration frequencies between EUR and other populations. Asterisks indicate significant p-values. ***: p-value < 0.001, **: p-value < 0.01, * p-value < 0.05

Figure 4:. Distribution of levels of actionability across ancestries and cancer types.

(A) Frequency of actionable mutations pan-cancer in the different populations. (B) Actionable mutations in different cancer types. Error bars show 95% confidence intervals.