Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples

By Kristian Cibulskis, Mike S. Lawrence, Scott L. Carter, A. Sivachenko, David B. Jaffe, C. Sougnez, S. Gabriel, Matthew Meyerson, Eric S. Lander, Gad Getz. Nature Biotechnology, 2013

3,465 citations recorded by the source corpus.

Detection of somatic point substitutions is a key step in characterizing the cancer genome. However, existing methods typically miss low-allelic-fraction mutations that occur in only a subset of the sequenced cells owing to either tumor heterogeneity or contamination by normal cells. Here we present MuTect, a method that applies a Bayesian classifier to detect somatic mutations with very low allele fractions, requiring only a few supporting reads, followed by carefully tuned filters that ensure high specificity. We also describe benchmarking approaches that use real, rather than simulated, sequencing data to evaluate the sensitivity and specificity as a function of sequencing depth, base quality and allelic fraction. Compared with other methods, MuTect has higher sensitivity with similar specificity, especially for mutations with allelic fractions as low as 0.1 and below, making MuTect particularly useful for studying cancer subclones and their evolution in standard exome and genome sequencing data.

Subfields: Cancer Research, Genetics, Pathology and Forensic Medicine

View Paper: https://www.semanticscholar.org/paper/e9e4778b3916dfee491a76fdd2bdb19c10756134

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Citations in the corpus

Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection (2015, neutral): “As a supplement to local computing resources, participants were provided cost-free access to the Google Cloud Platform, where Google Cloud Storage hosted the data and the Google Compute Engine enabled scalable computing.”
The Cancer Genome Atlas Comprehensive Molecular Characterization of Renal Cell Carcinoma (2018, positive): “…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).”
Tumor and Microenvironment Evolution during Immunotherapy with Nivolumab (2017, neutral): “…and Seshan, 2016; PMID: 27270079 https://github.com/mskcc/facets MAC Wei et al., 2015; PMID: 26231518 https://github.com/hubentu/MAC MuTect 1.1.4 Cibulskis et al., 2013; PMID: 23396013 http://archive.broadinstitute.org/cancer/cga/mutect NetMHC 4.0 Nielsen et al., 2003; PMID: 12717023…”
Calling Somatic SNVs and Indels with Mutect2 (2019, positive): “The callers against which we compared Mutect2 were those in the MC3 dataset: VarScan 2 [5], SomaticSniper [6], Mutect [7], RADIA [8], and MuSE [9] for SNVs and Pindel [10], VarScan 2 [5], and Indelocator [11] for indels.”
Sequenza: allele-specific copy number and mutation profiles from tumor sequencing data (2014, neutral): “Several methods have been described that estimate, and then correct for, tumor cellularity in SNP array data in order to improve copy number pro ﬁ les [2 – 5] or in DNA sequencing data for mutation calling [6].”
Pan-tumor genomic biomarkers for PD-1 checkpoint blockade–based immunotherapy (2018, positive): “MuTect was used to generate somatic SNV calls using default parameters by comparing BAM files from tumor and matchednormal samples (45).”
SvABA: genome-wide detection of structural variants and indels by local assembly (2018, positive): “Todetermineifanindelissomatic,wefollowasimilarap-proach to the calculations performed by MuTect (Cibulskis et al. 2013).”
Comprehensive Molecular Characterization of Urothelial Bladder Carcinoma (2014, neutral): “Cherniack(9), Samuel S Freeman(9), Chandra Sekhar Pedamallu(9,10), Michael S.”