Decoding mutational signatures in breast cancer: Insights from a multi-cohort study

Area of Science:

• Oncology
• Genomics
• Bioinformatics

Background:

• Genomic mutations guide hormonal breast cancer (BC) diagnosis and treatment.
• Understanding the genomic landscape is crucial for personalized medicine.

Purpose of the Study:

• Compare genomic data and mutational signatures from the CICLADES study to existing BC cohorts.
• Validate the accuracy of smaller cohorts in genomic analysis.
• Provide new, relevant genomic data for hormonal BC.

Main Methods:

• DNA extraction and sequencing from the CICLADES cohort.
• Analysis of genomic data from 6 publicly available BC cohorts (2303 samples total).
• Extraction and matching of mutational signatures to COSMIC database; estimation of Tumor Mutation Burden (TMB).

Main Results:

• PIK3CA and TP53 were the most frequently mutated genes across all cohorts.
• TMB was similar between CICLADES and CBSM, but higher in MSKCC.
• Nine mutational signatures were identified, including recurrent SBS signatures and APOBEC-enrichment concordant signatures.

Conclusions:

• Comprehensive genomic profiling accurately evaluates TMB and extracts mutational signatures.
• Genomic analysis of the CICLADES cohort provides valuable data comparable to larger cohorts.
• This supports the utility of smaller cohorts for robust genomic research in hormonal BC.