Abstract
PURPOSE: Understanding the differences in biomarker prevalence that may exist among diverse populations is invaluable to accurately forecast biomarker-driven clinical trial enrollment metrics and to advance inclusive research and health equity. This study evaluated the frequency and types of PIK3CA mutations (PIK3CAmut) detected in predicted genetic ancestry subgroups across breast cancer (BC) subtypes. METHODS: Analyses were conducted using real-world genomic data from adult patients with BC treated in an academic or community setting in the United States and whose tumor tissue was submitted for comprehensive genomic profiling. RESULTS: Of 36,151 patients with BC (median age, 58 years; 99% female), the breakdown by predicted genetic ancestry was 75% European, 14% African, 6% Central/South American, 3% East Asian, and 1% South Asian. We demonstrated that patients of African ancestry are less likely to have tumors that harbor PIK3CAmut compared with patients of European ancestry with estrogen receptor-positive/human epidermal growth factor receptor 2-negative (ER+/HER2-) BC (37% [949/2,593] v 44% [7,706/17,637]; q = 4.39E-11) and triple-negative breast cancer (8% [179/2,199] v 14% [991/7,072]; q = 6.07E-13). Moreover, we found that PIK3CAmut were predominantly composed of hotspot mutations, of which mutations at H1047 were the most prevalent across BC subtypes (35%-41% ER+/HER2- BC; 43%-61% HER2+ BC; 40%-59% triple-negative breast cancer). CONCLUSION: This analysis established that tumor PIK3CAmut prevalence can differ among predicted genetic ancestries across BC subtypes on the basis of the largest comprehensive genomic profiling data set of patients with cancer treated in the United States. This study highlights the need for equitable representation in research studies, which is imperative to ensuring better health outcomes for all.