MuSiC: Identifying mutational significance in cancer genomes
- Nathan D. Dees1,4,
- Qunyuan Zhang1,4,
- Cyriac Kandoth1,
- Michael C. Wendl1,2,
- William Schierding1,
- Daniel C. Koboldt1,
- Thomas B. Mooney1,
- Matthew B. Callaway1,
- David Dooling1,
- Elaine R. Mardis1,2,3,
- Richard K. Wilson1,2,3 and
- Li Ding1,2,5
- 1The Genome Institute, Washington University, St. Louis, Missouri 63108, USA;
- 2Department of Genetics, Washington University, St. Louis, Missouri 63110, USA;
- 3Siteman Cancer Center, Washington University, St. Louis, Missouri 63110, USA
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↵4 These authors contributed equally to this work.
Abstract
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.
Footnotes
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↵5 Corresponding author
E-mail lding{at}genome.wustl.edu
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.134635.111.
- Received November 9, 2011.
- Accepted May 16, 2012.
This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see http://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 3.0 Unported License), as described at http://creativecommons.org/licenses/by-nc/3.0/.
