Systematic identification of cancer driving signaling pathways based on mutual exclusivity of genomic alterations

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Abstract

We present a novel method for the identification of sets of mutually exclusive gene alterations in a given set of genomic profiles. We scan the groups of genes with a common downstream effect on the signaling network, using a mutual exclusivity criterion that ensures that each gene in the group significantly contributes to the mutual exclusivity pattern. We test the method on all available TCGA cancer genomics datasets, and detect multiple previously unreported alterations that show significant mutual exclusivity and are likely to be driver events.

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The online version of this article (doi:10.1186/s13059-015-0612-6) contains supplementary material, which is available to authorized users.

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De novo discovery of mutated driver pathways in cancer.

Fabio Vandin, Eli Upfal, Benjamin Raphael (2012)

Next-generation DNA sequencing technologies are enabling genome-wide measurements of somatic mutations in large numbers of cancer patients. A major challenge in the interpretation of these data is to distinguish functional "driver mutations" important for cancer development from random "passenger mutations." A common approach for identifying driver mutations is to find genes that are mutated at significant frequency in a large cohort of cancer genomes. This approach is confounded by the observation that driver mutations target multiple cellular signaling and regulatory pathways. Thus, each cancer patient may exhibit a different combination of mutations that are sufficient to perturb these pathways. This mutational heterogeneity presents a problem for predicting driver mutations solely from their frequency of occurrence. We introduce two combinatorial properties, coverage and exclusivity, that distinguish driver pathways, or groups of genes containing driver mutations, from groups of genes with passenger mutations. We derive two algorithms, called Dendrix, to find driver pathways de novo from somatic mutation data. We apply Dendrix to analyze somatic mutation data from 623 genes in 188 lung adenocarcinoma patients, 601 genes in 84 glioblastoma patients, and 238 known mutations in 1000 patients with various cancers. In all data sets, we find groups of genes that are mutated in large subsets of patients and whose mutations are approximately exclusive. Our Dendrix algorithms scale to whole-genome analysis of thousands of patients and thus will prove useful for larger data sets to come from The Cancer Genome Atlas (TCGA) and other large-scale cancer genome sequencing projects.

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ELM—the database of eukaryotic linear motifs

Holger Dinkel, Sushama Michael, Robert J. Weatheritt … (2011)

Linear motifs are short, evolutionarily plastic components of regulatory proteins and provide low-affinity interaction interfaces. These compact modules play central roles in mediating every aspect of the regulatory functionality of the cell. They are particularly prominent in mediating cell signaling, controlling protein turnover and directing protein localization. Given their importance, our understanding of motifs is surprisingly limited, largely as a result of the difficulty of discovery, both experimentally and computationally. The Eukaryotic Linear Motif (ELM) resource at http://elm.eu.org provides the biological community with a comprehensive database of known experimentally validated motifs, and an exploratory tool to discover putative linear motifs in user-submitted protein sequences. The current update of the ELM database comprises 1800 annotated motif instances representing 170 distinct functional classes, including approximately 500 novel instances and 24 novel classes. Several older motif class entries have been also revisited, improving annotation and adding novel instances. Furthermore, addition of full-text search capabilities, an enhanced interface and simplified batch download has improved the overall accessibility of the ELM data. The motif discovery portion of the ELM resource has added conservation, and structural attributes have been incorporated to aid users to discriminate biologically relevant motifs from stochastically occurring non-functional instances.

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Combinatorial patterns of somatic gene mutations in cancer.

Chen-Hsiang Yeang, Frank McCormick, Arnold Levine (2008)

Cancer is a complex process in which the abnormalities of many genes appear to be involved. The combinatorial patterns of gene mutations may reveal the functional relations between genes and pathways in tumorigenesis as well as identify targets for treatment. We examined the patterns of somatic mutations of cancers from Catalog of Somatic Mutations in Cancer (COSMIC), a large-scale database curated by the Wellcome Trust Sanger Institute. The frequently mutated genes are well-known oncogenes and tumor suppressors that are involved in generic processes of cell-cycle control, signal transduction, and stress responses. These "signatures" of gene mutations are heterogeneous when the cancers from different tissues are compared. Mutations in genes functioning in different pathways can occur in the same cancer (i.e., co-occur), whereas those in genes functioning in the same pathway are rarely mutated in the same sample. This observation supports the view of tumorigenesis as derived from a process like Darwinian evolution. However, certain combinatorial mutational patterns violate these simple rules and demonstrate tissue-specific variations. For instance, mutations of genes in the Ras and Wnt pathways tend to co-occur in the large intestine but are mutually exclusive in cancers of the pancreas. The relationships between mutations in different samples of a cancer can also reveal the temporal orders of mutational events. In addition, the observed mutational patterns suggest candidates of new cosequencing targets that can either reveal novel patterns or validate the predictions deduced from existing patterns. These combinatorial mutational patterns provide guiding information for the ongoing cancer genome projects.

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Author and article information

Contributors

Özgün Babur: mutex@cbio.mskcc.org

Mithat Gönen: gonenm@mskcc.org

Bülent Arman Aksoy: arman@cbio.mskcc.org

Nikolaus Schultz: schultz@cbio.mskcc.org

Giovanni Ciriello: ciriello@cbio.mskcc.org

Chris Sander: sander@cbio.mskcc.org

Emek Demir: demir@cbio.mskcc.org

Journal

Journal ID (nlm-ta): Genome Biol

Title: Genome Biology

Publisher: BioMed Central (London )

ISSN (Print): 1465-6906

ISSN (Electronic): 1465-6914

Publication date (Electronic): 26 February 2015

Publication date PMC-release: 26 February 2015

Publication date (Print): 2015

Volume: 16

Issue: 1

Electronic Location Identifier: 45

Affiliations

[ ]Computational Biology Center, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, Box 460, New York, 10065 USA

[ ]Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, 10065 USA

[ ]Tri-Institutional Training Program in Computational Biology and Medicine, 1275 York Avenue, New York, 10065 USA

Article

Publisher ID: 612

DOI: 10.1186/s13059-015-0612-6

PMC ID: 4381444

PubMed ID: 25887147

SO-VID: 7ede24b4-f5e1-409d-8930-ff52e8eea99d

License:

This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 19 November 2014

Date accepted : 10 February 2015

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ScienceOpen disciplines: Genetics

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Systematic identification of cancer driving signaling pathways based on mutual exclusivity of genomic alterations

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Genes & Diseases

Most cited references 27

De novo discovery of mutated driver pathways in cancer.

ELM—the database of eukaryotic linear motifs

Combinatorial patterns of somatic gene mutations in cancer.

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