BRACNAC: A BRCA1 and BRCA2 Copy Number Alteration Caller from Next-Generation Sequencing Data

Area of Science:

• Genomics
• Bioinformatics
• Cancer Genetics

Background:

• Copy number variations (CNVs) and alterations (CNAs) in BRCA1 and BRCA2 genes are critical for targeted therapy selection.
• Existing bioinformatics tools lack specificity for BRCA1/2 CNV/CNA detection and are often limited by data type and sample size.

Purpose of the Study:

• To develop and validate BRACNAC, a novel bioinformatics tool for accurate detection of CNVs and CNAs specifically in the BRCA1 and BRCA2 genes.
• To assess the performance of BRACNAC across diverse next-generation sequencing (NGS) data types and sample origins.

Main Methods:

• BRACNAC employs advanced coverage normalization techniques and CNV probability evaluation.
• Performance was evaluated using NGS data from 213 ovarian and prostate cancer samples, validated against MLPA.
• The tool's applicability to whole-genome (WES) and targeted NGS data was confirmed using public datasets.

Main Results:

• BRACNAC achieved 100% sensitivity and 94% specificity, with an AUC of 94% on tested samples.
• Demonstrated high performance (AUC up to 99.9%) on external WES and targeted NGS datasets.
• Identified optimal usage parameters: minimum 20 samples per run and at least 80% CNV-negative samples.

Conclusions:

• BRACNAC offers a robust and versatile solution for detecting BRCA1/2 CNVs/CNAs across various NGS data.
• The tool is expected to significantly improve the accuracy and efficiency of BRCA1/2 diagnostics for targeted cancer therapies.
• BRACNAC is freely available, promoting wider adoption in clinical and research settings.