Joint Somatic Mutation And Germline Variant Identification And Scoring From Tumor Molecular Profiling And Ct-Dna Monitoring Of Cancer Patients By High-Throughput Sequencing

Cancer tumor profiling by targeted resequencing of actionable cancer genes is rapidly becoming the standard approach for selecting targeted therapies and clinical trials in refractory cancer patients. In this clinical scenario, a tumor sample is obtained from an FFPE block and sequenced by targeted next-generation sequencing (NGS) to uncover actionable somatic mutations in relevant cancer genes. Some of the challenges that arise in analyzing tumor-derived NGS data include distinguishing between somatic and germline variants in the absence of normal tissue data, recognizing pathogenic germline variants, and identifying sequencing errors (which occur at about 0.5% rate). Additional challenges arise when considering other clinical applications of NGS such as sequencing cell-free tumor DNA (cf-DNA) from plasma samples to monitor disease response or disease recurrence. Here we present a principled approach to identify both single-nucleotide and small insertion/deletion somatic mutations and germline variants from NGS data of tumor tissue that leverages the allelic fraction patterns in tumors and prior information from external databases through the use of a Bayesian Network algorithm. Our approach allows us to score each putative mutation or variant with respect to its probability of belonging to each variant class, versus classification as a sequencing error. The method enables the joint calling of related samples form the same patient, such as cases where a cf-DNA sample and primary tumor sample are both profiled improving sensitivity and specificity. We validated our method by analyzing data obtained with the TOMA OS-Seq targeted sequencing RUO assay for 98 cancer genes from a mixture of well-known genomes, patient case triads (where normal, tumor and cf-DNA are available), and a retrospective analysis of tumor patient data that underwent clinical tumor profiling for therapy selection. Citation Format: Francisco M. De La Vega, Ryan T. Koehler, Yannick Pouliot, Yosr Bouhlal, Austin So, Federico Goodsaid, Sean Irvine, Len Trigg, Lincoln Nadauld. Joint somatic mutation and germline variant identification and scoring from tumor molecular profiling and ct-DNA monitoring of cancer patients by high-throughput sequencing. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2712.