Introduction: Triple-negative breast cancer (TNBC) is a highly aggressive subtype of breast cancer, with a significant prevalence in India, potentially due to distinct etiological factors. Genetic mutations are closely associated with an increased risk of TNBC. While chemotherapy remains the primary treatment, it is associated with a high risk of adverse drug reactions (ADRs). This study aims to investigate genetic mutations and their association with TNBC in the Mizo geo-ethnic population, which has a high incidence of TNBC. Methods: A cross-sectional study including 27 participants, comprising TNBC patients and healthy controls, was conducted, and ADR events were monitored in patients. Wholeexome sequencing was performed using blood genomic DNA. The sequence data were evaluated, and the pathogenicity of variants was predicted using in-silico tools. Associations and correlations of the variants with ADRs were analyzed using statistical methods. Results: Genetic variants were observed in BRCA1, BRCA2, ABCB1, ALKBH3, CYP4F2, DPYD, MTHFR, and SLC22A10. Pathogenic variants in pharmacogenes, including DPYD (rs1801265), CYP2C9 (T620C), SLC22A16 (rs201574154), SLCO1B1 (rs201722521), RYR1 (rs777680485), AHR (C1282A), and NUDT15 (rs116855232), were identified in association with ADRs. Patients carrying variants in UGT1A1 (rs4148323) and CYP2B6 (rs8192709) experienced ADRs following chemotherapy treatment regimens. Discussion: In addition to known BRCA1 mutations, novel gene associations were identified, including CYP4F2, DPYD, and ABCB1. Some variants were associated with side effects such as hair loss, fatigue, and cardiac complications. G6PD variants may also contribute to drug resistance. Conclusion: This study identified certain gene variants linked to a higher risk of TNBC and ADRs during chemotherapy. Alongside established BRCA1 mutations, associations were observed in CYP4F2, DPYD, and ABCB1. Some variants were linked to side effects including hair loss, fatigue, and heart-related issues. As a pilot study with a small sample size, it was underpowered to detect small or medium effects, and its primary purpose is to estimate variability and effect sizes to inform the design of a larger study.
Namdev et al. (Wed,) studied this question.