Mutations That Matter: Domain-Specific Mutational Hotspots in BRCA1 and Its Key Interactors Differ Between TCGA and BCGA Breast Cancer Cohort—An In Silico Analysis

Background: Patients with breast cancer (BC), and mostly triple-negative BC (TNBC), often exhibit poor prognosis due to variability in symptom manifestation and differential responses to treatment. TNBC among Indian women is the highest globally, contributing to increased mortality. A deeper understanding of the molecular factors influencing BC severity is essential for improving the therapeutic strategies. Objective: This study investigates differences in mutation patterns and positions in BRCA1 and its interacting partners, which contribute to variability in cancer manifestation and pathogenic burden. Methods: Mutation analysis is performed using publicly available cancer genomics datasets. Independent cross-cohort comparisons were performed using the non-overlapping datasets to ensure multi-layered independent validation of the analysis. Additionally, multi-database validation was performed to confirm the consistency of pathogenic and likely pathogenic variants. The FDR-adjusted p-value was used to assess the statistical significance of the difference in pathogenicity between the two cohorts. To minimize domain-length bias and avoid false enrichment signals, domain-length normalization was implemented alongside the chi-square test, enabling accurate assessment of mutation enrichment. Results: Functional domains differ in their impact on the disease manifestation. Pathogenic mutations cluster in specific functional domains of BRCA1 and its interactors, with distinct patterns between BCGA and TCGA cohorts. These differences support the view that both mutation type and positional context shape pathogenic outcomes and highlight demographic variability in disease profiles. Conclusion: This study emphasizes that domain-specific mutation clustering is associated with increased pathogenic burden. Hence, consideration of protein-domain context may provide additional information beyond gene-level mutation to optimize stratification and treatment outcomes. The inclusion of the BCGA dataset provides novel population-specific insights, representing one of the first domain-level mutational analyses in an Indian breast cancer cohort.