VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing.

By Daniel C. Koboldt, Qunyuan Zhang, David E. Larson, D. Shen, M. McLellan, Ling Lin, Christopher A. Miller, Elaine R. Mardis, Li Ding, Richard K. Wilson. Genome Research, 2012

4,654 citations recorded by the source corpus.

Cancer is a disease driven by genetic variation and mutation. Exome sequencing can be utilized for discovering these variants and mutations across hundreds of tumors. Here we present an analysis tool, VarScan 2, for the detection of somatic mutations and copy number alterations (CNAs) in exome data from tumor-normal pairs. Unlike most current approaches, our algorithm reads data from both samples simultaneously; a heuristic and statistical algorithm detects sequence variants and classifies them by somatic status (germline, somatic, or LOH); while a comparison of normalized read depth delineates relative copy number changes. We apply these methods to the analysis of exome sequence data from 151 high-grade ovarian tumors characterized as part of the Cancer Genome Atlas (TCGA). We validated some 7790 somatic coding mutations, achieving 93% sensitivity and 85% precision for single nucleotide variant (SNV) detection. Exome-based CNA analysis identified 29 large-scale alterations and 619 focal events per tumor on average. As in our previous analysis of these data, we observed frequent amplification of oncogenes (e.g., CCNE1, MYC) and deletion of tumor suppressors (NF1, PTEN, and CDKN2A). We searched for additional recurrent focal CNAs using the correlation matrix diagonal segmentation (CMDS) algorithm, which identified 424 significant events affecting 582 genes. Taken together, our results demonstrate the robust performance of VarScan 2 for somatic mutation and CNA detection and shed new light on the landscape of genetic alterations in ovarian cancer.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3937df87f1990cdaa9999db8302ba05f871cab01

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Citations in the corpus

pVAC-Seq: A genome-guided in silico approach to identifying tumor neoantigens (2016, methodology): “(2) VarScan Somatic version 2.2.6 [33, 34] was run with default parameters and the variant calls were filtered by GMS filter ‘ varscan-high-confidence filter version v1 ’ .”
CNVkit: Genome-Wide Copy Number Detection and Visualization from Targeted DNA Sequencing (2016, methodology): “…copy number analysis of these datasets, as well, including CNVer [6], ExomeCNV [7], exomeCopy [8], CONTRA [9], CoNIFER [10], ExomeDepth [11], VarScan 2 [12], XHMM [13], ngCGH [14], EXCAVATOR [15], CANOES [16], PatternCNV [17], CODEX [18], and recent versions of Control-FREEC [19] and cn.MOPS [20].”
SciClone: Inferring Clonal Architecture and Tracking the Spatial and Temporal Patterns of Tumor Evolution (2014, methodology): “Copynumber neutral LOH was detected using VarScan 2 [38] and filtered to retain regions with 95% LOH and at least 10 sites.”
Pathogenic germline variants in 10,389 adult cancers (2018, methodology): “…Mashl et al., 2017 https://github.com/ding-lab/GenomeVIP GATK McKenna et al., 2010 https://software.broadinstitute.org/gatk/ VarScan2 Koboldt et al., 2012 http://varscan.sourceforge.net/ Pindel Ye et al., 2009 https://github.com/genome/pindel XHMM Fromer and Purcell, 2014…”
Primary resistance to PD-1 blockade mediated by JAK1/2 mutations (2016, methodology): “6) (41), and the GATK-HC) for SNVs, and those called by both Varscan2 and the GATK-HC for insertions/deletions.”
The Cancer Genome Atlas Comprehensive Molecular Characterization of Renal Cell Carcinoma (2018, methodology): “…the capstone project using all available of cases within TCGA using six different algorithms (MuTect, MuSE, Pindel, Somatic Sniper, VarScan2 and Radia) from four centers (Cibulskis et al., 2013; Fan et al., 2016; Koboldt et al., 2012; Larson et al., 2012; Radenbaugh et al., 2014; Ye et al., 2009).”
SpeedSeq: Ultra-fast personal genome analysis and interpretation (2014, methodology): “Using this evaluation paradigm, we compared SpeedSeq’s performance to three other leading somatic variant calling tools: MuTect[22], SomaticSniper[23], and VarScan 2[24].”