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
TL;DR
Differences in mutation patterns and positions in BRCA1 and its interacting partners contribute to variability in cancer manifestation and pathogenic burden, supporting the view that both mutation type and positional context shape pathogenic outcomes and highlight demographic variability in disease profiles.
Abstract
Simple Summary Breast cancer remains one of the leading causes of cancer-related mortality worldwide, with particularly aggressive forms, such as triple-negative breast cancer, disproportionately affecting younger women in India. A key driver of breast cancer risk is a mutation in the BRCA1 gene and in several proteins that work alongside it, including PALB2, RAD50, BARD1, and RB1. These proteins form a molecular network that normally detects and repairs DNA damage; when mutated, this network of interacting complexes breaks down, and cells can become cancerous. In this study, we investigated the significance of the position of mutations, whether the specific location of a mutation within the functional domains of these proteins influences pathogenic outcomes, and finally, whether such domain-level mutational patterns vary demographically. Using publicly available genomic databases and computational analyses, we found that pathogenic mutations tend to concentrate within specific functional domains of each protein, and that the identity and location of these mutations differ substantially between the TCGA cohort (a large, US-based multi-ancestry cancer genomics dataset) and the BCGA cohort (an Indian breast cancer genomics resource). Notably, mutations at identical positions in the same gene were sometimes associated with different levels of disease pathogenicity, depending on the type of amino acid change and the cohort. These findings highlight that the precise position of a mutation within a protein’s functional architecture, rather than gene-level mutation status alone, can be a critical determinant of pathogenic outcome. The cohort-specific mutational landscapes may warrant distinct diagnostic considerations. This exploratory, in silico analysis provides a foundation for future functional and clinical validation studies aimed at improving precision medicine for breast cancer.