MuSiC: Identifying mutational significance in cancer genomes

By N. Dees, Qunyuan Zhang, Cyriac Kandoth, Michael C. Wendl, W. Schierding, Daniel C. Koboldt, Thomas B Mooney, Matthew B. Callaway, D. Dooling, Elaine R. Mardis and 2 more. Genome Research, 2012

667 citations recorded by the source corpus.

Massively parallel sequencing technology and the associated rapidly decreasing sequencing costs have enabled systemic analyses of somatic mutations in large cohorts of cancer cases. Here we introduce a comprehensive mutational analysis pipeline that uses standardized sequence-based inputs along with multiple types of clinical data to establish correlations among mutation sites, affected genes and pathways, and to ultimately separate the commonly abundant passenger mutations from the truly significant events. In other words, we aim to determine the Mutational Significance in Cancer (MuSiC) for these large data sets. The integration of analytical operations in the MuSiC framework is widely applicable to a broad set of tumor types and offers the benefits of automation as well as standardization. Herein, we describe the computational structure and statistical underpinnings of the MuSiC pipeline and demonstrate its performance using 316 ovarian cancer samples from the TCGA ovarian cancer project. MuSiC correctly confirms many expected results, and identifies several potentially novel avenues for discovery.

Subfields: Cancer Research, Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/7216908d7e473487cbbd4503fb21f1186139bd06

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Citations in the corpus

Comprehensive and Integrated Genomic Characterization of Adult Soft Tissue Sarcomas. (2017, neutral): “1.1 Shabalin, 2012 https://cran.r-project.org/web/packages/MatrixEQTL/ MClust Yeung et al., 2001 https://cran.r-project.org/package=mclust MicroVigene VigeneTech RRID:SCR_002820; http://www.vigenetech.com/ microvigeneV3.0.htm MuSiC2 Dees et al., 2012 https://github.com/ding-lab/MuSic2 MuTect v1.”
Comprehensive Characterization of Cancer Driver Genes and Mutations (2018, methodology): “For example, MuSiC2 andMutSig2CV are highly correlated, as are CompositeDriver and OncodriveFML (Figure S2G).”
Genomic and Epigenomic Landscapes of Adult De Novo Acute Myeloid Leukemia (2013, neutral): “Acute myelomonocytic leukemia: M4 41 (20)”
Comprehensive molecular portraits of human breast tumors (2012, neutral): “The MuSiC package 16 , which determines the significance of the observed mutation rate of each gene based on the background mutation rate, identified 35 significantly mutated genes (excluding LOC or Ensembl gene IDs) by at least two tests (convolution and likelihood ratio tests) with false…”
The landscape of genomic alterations across childhood cancers (2018, neutral): “MuSiC identified 77 significantly mutated genes (SMGs), which were ranked according to their pan-cancer mutation frequency 24 (Fig.”
Clinical sequencing defines the genomic landscape of metastatic colorectal cancer (2018, result): “POLEmutant cases were predominantly early-stage male patients, consistent with the known clinical features of this group (Domingo et al., 2016).”
FunSeq2: a framework for prioritizing noncoding regulatory variants in cancer (2014, background): “Systematic studies of human cancer genomes have discovered a wide range of driver genes [2-6].”