Detecting somatic single nucleotide variants from cell-free nucleic acid with application to minimal residual disease monitoring

Abstract

The present disclosure provides a probabilistic model for accurate and sensitive somatic single nucleotide variant (SNV) detection in cell-free nucleic acid samples comprising a set of sequence data. A joint genotype may be determined for each locus in the set of sequence data, and germline mutations may be intrinsically removed. A set of filtrations can be applied to eliminate low quality somatic variant calls. Further, a global tumor cell-free deoxyribonucleic acid (cfDNA) fraction and overlapping read mates can be considered, thereby enabling accurate SNV detection and variant allele frequency estimation from samples with low tumor cfDNA fraction. A sensitive early detection of minimal residual disease (MRD) is designed by using the probabilistic model and the machine learning model for distinguishing true variants from sequencing errors.