Abstract 5231: Whole genome sequencing reveals significant genetic admixture in multiple myeloma patients, impacting assessment of etiology

Abstract

Abstract Background: In the U.S. population, having African ancestry (AA) conveys a higher risk of multiple myeloma (MM) than white ancestry (WA), however, published whole genome sequencing (WGS) data is predominantly from WA populations. The Polyethnic-1000 (P1000) is a multi-institutional WGS initiative together with the New York Genome Center, investigating cancers having a higher prevalence in AA populations. We are extending on the P1000 in MM precursor disease; prospectively defining genomic, immune and microbiome factors driving progression, focused on the impact of racial origin. Hypothesis: Self-described race, according to proscribed categories, is insufficient to delineate biological contribution to MM development. We propose that unique biological insights first require a comprehensive definition of genetic origin, considering heterogeneity within historical groups. Methods: For collaborative large-scale analysis, we’ve developed a comprehensive cross-institutional bioinformatic pipeline (https://github.com/pblaney/mgp1000). We’ve included admixture; a composition profile based on SNPs with genotypes corresponding to reference samples from 23 geographic populations. AA is divided into 5 populations (Esan, Luhya, Mende, Gambian and Yoruba), and 3 American populations are defined (Peruvian, Columbian and Puerto Rican). WGS was performed on blood mononuclear cells from 44 patients with MM or precursor disease, 29 self-identified as AA and 18 as Hispanic. Results: Estimating directly from WGS data, complexity hidden by self-reported race was revealed by genetically determined admixture. Filtering to ≥1%, 43/44 patients had contribution from >1 of the 23 reference populations. Patients varied widely in their genetic diversity; 3 had ≥90% from a single population, 21 had ≥25% from at least 2 populations (consistent with grandparents) and 37 had ≥12.5% from different populations (consistent with great-grandparents). Within those 37, 18 patients had contributing populations within the same superfamily (i.e., all African), while 19 were across superfamily’s (i.e., African + East Asian). Hierarchical clustering produced 5 main clusters, with samples in 2 clusters having predominantly Esan and Mende contribution, while differing in contribution from other AA/non-AA populations. All samples in one cluster contained American contribution >60%, another had predominantly European and American contribution, and the final cluster had AA <10%, and heterogenous population contributions. Conclusion: Self-reported race does not consider the significant variability of genetic admixture demonstrated by this analysis, and likely has insufficient granularity regarding inherited risk. Ongoing studies will incorporate genetic admixture alongside somatic genomic assessment to accurately investigate progression risk from precursor disease to MM. Citation Format: Kylee H. Maclachlan, Patrick Blaney, Dylan Gagler, Eileen M. Boyle, Benjamin Diamond, Urvi A. Shah, Neha Korde, Sham Mailankody, Malin Hultcrantz, Hani Hassoun, Carlyn Tan, Faith E. Davies, Alexander M. Lesokhin, C. Ola Landgren, Saad Z. Usmani, Francesco Maura, Gareth J. Morgan. Whole genome sequencing reveals significant genetic admixture in multiple myeloma patients, impacting assessment of etiology. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5231.