Abstract C126: Integrated whole genome and transcriptome analyses reveal ancestry-associated molecular features of breast cancer

Abstract

Women of African ancestry experience disproportionately aggressive breast cancer (BC) and poorer survival yet remain substantially underrepresented in cancer genomic studies. Consequently, the contribution of inherited genetic ancestry to breast tumor biology remains poorly understood. To address this gap, we leveraged an African ancestry-enriched whole genome and transcriptome sequencing cohort to investigate the relative influence of genetic ancestry and clinical factors on breast cancer gene expression. We analyzed matched whole genome sequencing (WGS) and RNA sequencing (RNA-seq) data from 139 women diagnosed with hormone receptor-positive (HR+, n = 51) or triple-negative breast cancer (TNBC, n = 87), recruited from the United States (n = 124), Ghana (n = 7), and Ethiopia (n = 8). Global ancestry was inferred from germline WGS, and a high-resolution local ancestry (LA) inference pipeline was implemented, creating an ancestry-aware framework for downstream analyses. Somatic characterization was performed using WGS, while RNA-seq data were analyzed using variance partitioning approaches to quantify the relative contribution of genetic ancestry and clinicopathologic variables to transcriptomic variation. Gene expression profiles were further compared with The Cancer Genome Atlas (TCGA) BC cohort to evaluate concordance and identify ancestry-associated expression patterns captured within this African ancestry-enriched dataset. The cohort was predominantly of African ancestry (mean African ancestry = 70.9%). Consistent with previous observations, African American women with TNBC demonstrated higher African ancestry than those with HR+ disease. Cohort-wide LA inference was successfully completed, providing a foundational genomic resource for future ancestry-aware analyses, including LA-expression quantitative trait locus discovery. Variance partitioning revealed that transcriptomic variation is influenced by both inherited ancestry and established clinicopathologic factors, with the relative contribution varying across genes and biological pathways. Comparison with TCGA identified shared transcriptional pathways while also highlighting expression patterns uniquely represented within this African ancestry-enriched cohort, emphasizing the importance of studying diverse populations to fully characterize BC biology. This work represents a progression from ancestry characterization to ancestry-informed functional genomics. By integrating global and local ancestry estimates, and transcriptomic analyses, we establish a framework for identifying ancestry-associated regulatory mechanisms underlying BC disparities. Ongoing analyses will leverage these data to map local ancestry-associated regulatory variation and integrate genomic findings with social and environmental determinants through the SAMBAI (Social, Ancestry, Molecular and Biological Analyses of Inequities) initiative, advancing more equitable precision oncology for historically underrepresented populations. Rachel Martini, Will Hooper, Timothy Chu, Ali Oku, Heather Geiger, Jason White, Kenya Bynes, Alexandria Awai, Nyasha Chambwe, Kyriaki Founta, Lara Winterkorn, Zoe Goldstein, Bertha Reuda-Zarazua, Isidro Cortes Ciriano, Laura Crandon, Brian Stonaker, Lisa Newman, John Carpten, Olivier Elemento, Melissa Davis, Nicolas Robine. Integrated whole genome and transcriptome analyses reveal ancestry-associated molecular features of breast cancer [abstract]. In: Proceedings of the 19th AACR Conference on the Science of Cancer Health Disparities; 2026 Oct 18-21; Atlanta, GA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2026;35(10 Suppl):Abstract nr C126.