Citations

Abstract Introduction: Breast cancer genome-wide association studies (GWAS) have identified over 200 susceptibility loci, many replicated in diverse populations. However, cross-ancestry evaluation of breast cancer genetic architecture remains limited. We examined breast cancer genetic architecture using GWAS summary results from European (EUR; cases (ca) = 188,474, controls (co) = 96,201), East Asian (EAS; ca = 20,393, co = 86,329), African American (AA; ca = 9,235, co = 10,184), and US Hispanic/Latina and Latin American (H/L; ca = 2,396, co =7,468) studies. Methods: GWAS results were derived from the Breast Cancer Association Consortium, the African Ancestry Breast Cancer Genetics Consortium (AABCG), Biobank Japan, and a meta-analysis of five studies of H/L women with breast cancer. Linkage disequilibrium (LD) scores were generated for EUR, EAS, and AMR using the 1000 Genomes Project, while AA LD scores were derived from a subset of AABCG controls. Heritability was estimated for each ancestry group using LD score regression. Genetic correlations between populations were assessed via Popcorn. Polygenicity was analyzed using GENESIS; however, due to the limited sample size in the H/L studies, these estimates were restricted to AA, EAS, and EUR. Enrichment of heritability across regulatory elements was evaluated using stratified LD score regression across all populations. Results: The logit-scale heritability and corresponding standard error (SE) were 0.466 (0.066) for EAS, 0.501 (0.050) for EUR, 0.588 (0.360) for H/L, and 0.614 (0.095) for AA. The estimated number of independent susceptibility loci was 4,446 for EAS, 5,235 for EUR, and 8,308 for AA. Using a clumping and thresholding approach, an optimal set of common variants were projected to explain 38.6% (EUR), 39.4% (EAS), and 26.2% (AA) of genetic variance for samples of 100,000 cases and 100,000 controls with AUCs from corresponding estimated polygenic risk scores (PRSs) of 0.621 (EUR), 0.634 (EAS), and 0.611 (AA). Genetic correlations were strongest between EUR and EAS (ρ = 0.79, SE = 0.08) and EUR and H/L (ρ = 0.68, SE = 0.21), and weakest between AA and EAS (ρ = 0.42, SE = 0.14) and AA and H/L (ρ = 0.26, SE = 0.24). Among 73 genomic features, we found significant (P < 0.05/73) enrichment in heritability in EUR for ‘ancient promoter’ regions, transcription factor binding sites, H3K4me3, ‘super-enhancers’, H3K4me1, H3K27ac; in EAS for ‘super-enhancers’; and in AA for H3K27ac. No genomic features were significantly enriched in H/L, likely due to limited power. Conclusion: These findings suggest a shared breast cancer genetic architecture across diverse populations, as well as the potential for a similar level of breast cancer risk stratification by PRS in these populations. Expanding GWAS in underrepresented populations is essential to improve genetic risk predictions and foster equitable cancer prevention. Citation Format: James L. Li, Maria Zanti, Jacob Williams, Om Jahagirdar, Guochong Jia, Qiang Hu, Jean-Tristan Brandenburg, Li Yan, Weang-Kee Ho, Jingmei Li, José P. Miranda, Devika Godbole, Julie-Alexia Dias, Leila Dorling, Wenlong C. Chen, Nicholas Boddicker, Ying Wang, Alicia Martin, Martin J. Zhang, Yan Zhang, Joe Dennis, Esther M. John, Gabriela Torres-Mejia, Larry Kushi, Jeffrey Weitzel, Susan L. Neuhausen, Luis Carvajal-Carmona, Christopher Haiman, Elad Ziv, Laura Fejerman, Wei Zheng, Dezheng Huo, Douglas Easton, Nilanjan Chatterjee, Peter Kraft, Montserrat Garcia-Closas, Wendy Wong, Kyriaki Michailidou, Qianqian Zhu, Diptavo Dutta, Thomas U. Ahearn, Haoyu Zhang. Genetic architecture of breast cancer across diverse populations: Assessing heritability, genetic correlation, and polygenicity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2282.

Abstract In 1997, the American Institute for Cancer Research (AICR) published 14 recommendations related to diet for individuals to reduce cancer incidence on a global basis; smoking was also discouraged. We operationalized these into nine recommendations that are particularly relevant to western populations in a cohort of 29,564 women ages 55 to 69 years at baseline in 1986 who had no history of cancer or heart disease. The cohort was followed through 1998 for cancer incidence (n = 4,379), cancer mortality (n = 1,434), cardiovascular disease (CVD) mortality (n = 1,124), and total mortality (n = 3,398). The median number (range) of recommendations followed was 4 (0-8), and 33% of the cohort had ever smoked. Women who followed no or one recommendation compared with six to nine recommendations were at an increased risk of cancer incidence [relative risk (RR) 1.35, 95% confidence interval (CI) 1.15-1.58] and cancer mortality (RR 1.43, 95% CI 1.11-1.85), but there was no association with CVD mortality (RR 1.06, 95% CI 0.78-1.43). We calculated the population attributable risk (PAR) to estimate the proportion of cancer incidence, cancer mortality, and CVD mortality that theoretically would have been avoidable if the entire cohort had never smoked, had followed six to nine recommendations, or had done both. The PARs for smoking were 11% (95% CI 10-13) for cancer incidence, 21% (95% CI 17-24) for cancer mortality, and 20% (95% CI 16-23) for CVD mortality. The PARs for not following six to nine recommendations were 22% (95% CI 12-30) for cancer incidence, 11% (95% CI −5 to 24) for cancer mortality, and 4% (95% CI −20 to 19) for CVD mortality. When smoking and the operationalized AICR recommendations were combined together, the PARs were 31% (95% CI 19-37) for cancer incidence, 30% (95% CI 15-40) for cancer mortality, and 22% (95% CI 4-36) for CVD mortality. These data suggest that the adherence to the AICR recommendations, independently and in conjunction with not smoking, is likely to have a substantial public health impact on reducing cancer incidence and, to a lesser degree, cancer mortality at the population level.