Abstract A037: Retrospective analysis of ancestry-driven disparities in tumor biology: Insights from combined genomic breast cancer datasets

Abstract Breast cancer (BC) remains a leading cause of cancer-related morbidity and mortality among women worldwide. However, profound disparities persist in incidence, tumor biology, and outcomes for women of African ancestry. Despite lower overall incidence rates, these women are more likely to be diagnosed at younger ages, with aggressive subtypes such as triple-negative breast cancer, and experience disproportionately higher mortality, underscoring an urgent need to understand the biological, social, and systemic drivers of these inequities. To better understand ancestry-associated molecular features contributing to these disparities, we conducted whole genome sequencing (WGS) and RNA sequencing (RNAseq) on tumor and normal (blood) samples from 144 women of African ancestry diagnosed with either hormone receptor positive (HR+, n = 53) or triple negative BC (TNBC, n = 90). Samples were collected from patients in the United States (n = 129), Ghana (n = 7) and Ethiopia (n = 8). Global and local genetic ancestry was inferred from the normal WGS samples, revealing the cohort was predominantly African ancestry (mean AFR = 70.93%). Among AA BC cases alone, we observed higher AFR ancestry among TNBC cases compared to HR+ cases, consistent with our previous findings linking increased west African ancestry with TNBC subtype. To characterize the somatic landscapes of tumors in this AFR ancestry-enriched cohort, we identified single nucleotide variants (SNV), small insertion and deletions (indels), copy-number alterations and structural variants. We further assessed tumor mutational burden, mutational signatures, and homologous recombination deficiencies to uncover subtype-specific and ancestry-informed genomic patterns. RNAseq data will inform our analyses to identify functional consequences of ancestry-specific variation that will link ancestry-associated gene expression profiles within the tumor microenvironment to germline variants. Specifically, we will characterize local ancestry to identify the expression quantitative trait loci that regulate ancestry-associated gene expression. To further contextualize factors that modify these molecular profiles, we have integrated area-level indicators of neighborhood deprivation and environmental exposures derived from geocoded residential data for our US-based study participants. This multi-dimensional approach reflects the core mission of our SAMBAI program (Social, Ancestry, Molecular and Biological Analyses of Inequities), funded by the Cancer Grand Challenges research initiative. Through integrated analysis of genomic, social and environmental data, we aim to uncover mechanisms driving cancer disparities in breast, prostate and pancreatic cancer outcomes. Our findings will advance equitable representation in cancer genomics and lay the groundwork for precision oncology approaches tailored to African ancestry populations. Citation Format: Rachel Martini, Jason White, Kenya Bynes, Nyasha Chambwe, Kyriaki Founta, Lara Winterkorn, Zalman Vaksman, Will Hooper, Zoe Goldstein, Timothy Chu, Laura Crandon, Brian Stonaker, Lisa Newman, John Carpten, Olivier Elemento, Melissa Davis, Nicolas Robine. Retrospective analysis of ancestry-driven disparities in tumor biology: Insights from combined genomic breast cancer datasets [abstract]. In: Proceedings of the 18th AACR Conference on the Science of Cancer Health Disparities; 2025 Sep 18-21; Baltimore, MD. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2025;34(9 Suppl):Abstract nr A037.