Machine learning-based somatic variant calling in cell-free DNA of metastatic breast cancer patients using large NGS panels.

ABSTRACT: Next generation sequencing of cell-free DNA (cfDNA) is a promising method for treatment monitoring and therapy selection in metastatic breast cancer (MBC). However, distinguishing tumor-specific variants from sequencing artefacts and germline variation with low false discovery rate is challenging when using large targeted sequencing panels covering many tumor suppressor genes. To address this, we built a machine learning model to remove false positive variant calls and augmented it with additional filters to ensure selection of tumor-derived variants. We used cfDNA of 70 MBC patients profiled with both the small targeted Oncomine breast panel (Thermofisher) and the much larger Qiaseq Human Breast Cancer Panel (Qiagen). The model was trained on the panels' common regions using Oncomine hots

SUBMITTER: Jongbloed EM 

PROVIDER: S-EPMC10300101 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature