Sensitive detection of tumor mutations from blood and its application to immunotherapy prognosis

Abstract

Liquid biopsy using cell-free DNA (cfDNA) is attractive for a wide range of clinical applications, including cancer detection, locating, and monitoring. However, developing these applications requires precise and sensitive calling of somatic single nucleotide variations (SNVs) from cfDNA sequencing data. To date, no SNV caller addresses all the special challenges of cfDNA to provide reliable results. Here we present cfSNV, a revolutionary somatic SNV caller with five innovative techniques to overcome and exploit the unique properties of cfDNA. cfSNV provides hierarchical mutation profiling, thanks to cfDNAs complete coverage of the clonal landscape, and multi-layer error suppression. In both simulated datasets and real patient data, we demonstrate that cfSNV is superior to existing tools, especially for low-frequency somatic SNVs. We also show how the five novel techniques contribute to its performance. Further, we demonstrate a clinical application using cfSNV to select non-small-cell lung cancer patients for immunotherapy treatment.