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.
- 1Cell Biology and Molecular Genetics Division (CBMG), Yenepoya Research Centre (YRC), Yenepoya (Deemed to be University), Deralakatte, Mangalore 575018, India.
- 2Department of Psychiatry and Behavioral Science, UT Health, Houston, TX 77054, USA.
- 3Data Analytics, Bioinformatics and Structural Biology (DABS), Yenepoya Research Centre (YRC), Yenepoya (Deemed to be University), Deralakatte, Mangalore 575018, India.
Abstract
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.
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BibTeX
@article{kulkarni2026mutations,
title = {{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.}},
author = {Mithila Kulkarni and Katheeja Muhseena Neeraje and Ranajit Das and Suparna Laha},
journal = {Cancers},
year = {2026},
month = sep,
doi = {10.3390/cancers18193083},
url = {https://europepmc.org/article/MED/42882778},
note = {CC BY},
}
RIS
TY - JOUR
TI - 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.
AU - Mithila Kulkarni
AU - Katheeja Muhseena Neeraje
AU - Ranajit Das
AU - Suparna Laha
PY - 2026
DA - 2026/09/23
JO - Cancers
DO - 10.3390/cancers18193083
UR - https://europepmc.org/article/MED/42882778
AB - 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.
SN - 2072-6694
ER -