ABSTRACT
Precision oncology is predicated on optimal molecular profiling that is "fit for purpose" to identify therapeutic vulnerabilities. Liquid biopsies may compensate for inadequate genotyping, but remain less sensitive and specific compared with tissue biopsies. The liquid biopsy toolbox is poised to expand through novel assays and insights from longitudinal profiling. See related article by Sugimoto et al., p. 1506.
Subject(s)
Carcinoma, Non-Small-Cell Lung , Lung Neoplasms , Humans , Prospective Studies , Genotype , Biomarkers, Tumor/genetics , Precision Medicine , Liquid BiopsyABSTRACT
SUMMARY: Somatic Mutation calling method using a Random Forest (SMuRF) integrates predictions and auxiliary features from multiple somatic mutation callers using a supervised machine learning approach. SMuRF is trained on community-curated matched tumor and normal whole genome sequencing data. SMuRF predicts both SNVs and indels with high accuracy in genome or exome-level sequencing data. Furthermore, the method is robust across multiple tested cancer types and predicts low allele frequency variants with high accuracy. In contrast to existing ensemble-based somatic mutation calling approaches, SMuRF works out-of-the-box and is orders of magnitudes faster. AVAILABILITY AND IMPLEMENTATION: The method is implemented in R and available at https://github.com/skandlab/SMuRF. SMuRF operates as an add-on to the community-developed bcbio-nextgen somatic variant calling pipeline. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
