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 somaticvariant 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 fordistinguishing true variants from sequencing errors.

Description

[0001] Cross References to Related Applications [0002] This application claims the benefit of U.S. Provisional Patent Application No. 62 / 559,366, filed September 15, 2017, which is expressly incorporated herein by reference in its entirety. [0003] Statement of Government Interests [0004] This invention was made with government support under HL108634 awarded by the National Institutes of Health. The government has certain rights in this invention. Background technique [0005] Somatic mutations can accumulate in cells throughout a subject's lifetime. While most such mutations may have little or no apparent effect, some may alter genes and / or key cellular functions and thus produce phenotypic changes. The products of somatic mutations can be cancers due to clonal expansion of cells and escape from both the in-built programs of normal somatic cell behavior and exogenous constraints on cell proliferation. Somatic mutations that trigger cancer progression may be referred ...

AI Technical Summary

Problems solved by technology

This probabilistic model may be too simplistic and may fail to account for the impure nature of cfDNA, resulting in a large number of false positives

In addition, these methods may fail to account for overlapping read mates in cfDNA sequencing data that can provide valuable information for detecting mutations in cfDNA

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