Abstract 3280: Utah somatic variant calling pipeline featuring multi-sample joint calling, variant-graph based accurate allele frequency estimation and subclone analysis

Current tumor variant detection software tools are focused on the identification of somatic mutations in a single tumor sample (e.g. the primary tumor) compared to normal control tissue, and are not adequate for studies where multiple samples from the same patient, either at consecutive time points or at different metastatic sites are collected. Here we present a software framework that facilitates simultaneous analysis of such multi-sample datasets. Our framework utilizes our FreeBayes variant caller for jointly calling all tumor samples and normal controls from the patient. This ensures that even very low frequency mutations are called because read evidence is aggregated across all samples; and that allele frequency information for a called somatic variant is provided uniformly for all samples. Short and medium-length INDELs, and structural variants are identified by our RUFUS software, a new reference-free mutation calling algorithm for both germline and somatic mutation detection, with extremely high sensitivity and specificity. We use our Graphite tool, a graph-genome based read realignment algorithm, to eliminate false positive somatic calls i.e. inherited variants masquerading as somatic mutations, and to substantially increase accuracy of allele frequency estimation of the true somatic calls, a feature critical for downstream subclone analysis. In addition, we use the FACETS package for CNV and LOH analysis, also critical for downstream subclone analysis. Finally, we utilize SubcloneSeeker, our algorithm to reconstruct the evolution of the cancer at the subclonal level from the somatic variant allele frequencies, to understand how the tumor evolved over time, in response to multiple courses of treatment, or over space, across multiple metastatic sites. We demonstrate the use of this pipeline in published and ongoing studies involving longitudinal and multi-site tumor sample datasets. Citation Format: Yi Qiao, Xiaomeng Huang, Dillon Lee, Andrew Farrell, Thomas Nicholas, Brent Pederson, Aaron Quinlan, Gabor Marth. Utah somatic variant calling pipeline featuring multi-sample joint calling, variant-graph based accurate allele frequency estimation and subclone analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3280.