Abstract
Breast cancer includes several subtypes with distinct characteristic biological, pathologic, and clinical features. Elucidating subtype-specific genetic etiology could provide insights into the heterogeneity of breast cancer to facilitate the development of improved prevention and treatment approaches. In this study, we conducted pairwise case-case comparisons among five breast cancer subtypes by applying a case-case genome-wide association study (CC-GWAS) approach to summary statistics data of the Breast Cancer Association Consortium. The approach identified 13 statistically significant loci and eight suggestive loci, the majority of which were identified from comparisons between triple-negative breast cancer (TNBC) and luminal A breast cancer. Associations of lead variants in 12 loci remained statistically significant after accounting for previously reported breast cancer susceptibility variants, among which, two were genome-wide significant. Fine mapping implicated putative functional/causal variants and risk genes at several loci, e.g., 3q26.31/TNFSF10, 8q22.3/NACAP1/GRHL2, and 8q23.3/LINC00536/TRPS1, for TNBC as compared with luminal cancer. Functional investigation further identified rs16867605 at 8q22.3 as a SNP that modulates the enhancer activity of GRHL2. Subtype-informative polygenic risk scores (PRS) were derived, and patients with a high subtype-informative PRS had an up to two-fold increased risk of being diagnosed with TNBC instead of luminal cancers. The CC-GWAS PRS remained statistically significant after adjusting for TNBC PRS derived from traditional case-control GWAS in The Cancer Genome Atlas and the African Ancestry Breast Cancer Genetic Consortium. The CC-GWAS PRS was also associated with overall survival and disease-specific survival among patients with breast cancer. Overall, these findings have advanced our understanding of the genetic etiology of breast cancer subtypes, particularly for TNBC. Significance: The discovery of subtype-informative genetic risk variants for breast cancer advances our understanding of the etiologic heterogeneity of breast cancer, which could accelerate the identification of targets and personalized strategies for prevention and treatment.