Predicting high confidence ctDNA somatic variants with ensemble machine learning models.

ABSTRACT: Circulating tumour DNA (ctDNA) is a minimally invasive cancer biomarker that can be used to inform treatment of cancer patients. The utility of ctDNA as a cancer biomarker depends on the ability to accurately detect somatic variants associated with cancer. Accurate somatic variant detection in circulating cell free DNA (cfDNA) NGS data requires filtering strategies to remove germline variants, and NGS artifacts. Rule-based variant filtering methods either remove a substantial number of true positive ctDNA variants along with false variant calls or retain an implausibly large number of total variants. Machine Learning (ML) enables identification of complex patterns which may improve ability to distinguish between real somatic ctDNA variants and false positive calls. We built two Random Fore

SUBMITTER: Maruzani R 

PROVIDER: S-EPMC12106698 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature