MUFFINN: cancer gene discovery via network analysis of somatic mutation data

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Abstract

A major challenge for distinguishing cancer-causing driver mutations from inconsequential passenger mutations is the long-tail of infrequently mutated genes in cancer genomes. Here, we present and evaluate a method for prioritizing cancer genes accounting not only for mutations in individual genes but also in their neighbors in functional networks, MUFFINN (MUtations For Functional Impact on Network Neighbors). This pathway-centric method shows high sensitivity compared with gene-centric analyses of mutation data. Notably, only a marginal decrease in performance is observed when using 10 % of TCGA patient samples, suggesting the method may potentiate cancer genome projects with small patient populations.

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Most cited references 44

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The genomic landscapes of human breast and colorectal cancers.

Laura Wood, D. Parsons, Siân Jones … (2007)

Human cancer is caused by the accumulation of mutations in oncogenes and tumor suppressor genes. To catalog the genetic changes that occur during tumorigenesis, we isolated DNA from 11 breast and 11 colorectal tumors and determined the sequences of the genes in the Reference Sequence database in these samples. Based on analysis of exons representing 20,857 transcripts from 18,191 genes, we conclude that the genomic landscapes of breast and colorectal cancers are composed of a handful of commonly mutated gene "mountains" and a much larger number of gene "hills" that are mutated at low frequency. We describe statistical and bioinformatic tools that may help identify mutations with a role in tumorigenesis. These results have implications for understanding the nature and heterogeneity of human cancers and for using personal genomics for tumor diagnosis and therapy.

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Pan-Cancer Network Analysis Identifies Combinations of Rare Somatic Mutations across Pathways and Protein Complexes

Mark Leiserson, Fabio Vandin, Hsin-Ta Wu … (2014)

Cancers exhibit extensive mutational heterogeneity and the resulting long tail phenomenon complicates the discovery of the genes and pathways that are significantly mutated in cancer. We perform a Pan-Cancer analysis of mutated networks in 3281 samples from 12 cancer types from The Cancer Genome Atlas (TCGA) using HotNet2, a novel algorithm to find mutated subnetworks that overcomes limitations of existing single gene and pathway/network approaches.. We identify 14 significantly mutated subnetworks that include well-known cancer signaling pathways as well as subnetworks with less characterized roles in cancer including cohesin, condensin, and others. Many of these subnetworks exhibit co-occurring mutations across samples. These subnetworks contain dozens of genes with rare somatic mutations across multiple cancers; many of these genes have additional evidence supporting a role in cancer. By illuminating these rare combinations of mutations, Pan-Cancer network analyses provide a roadmap to investigate new diagnostic and therapeutic opportunities across cancer types.

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Prioritizing candidate disease genes by network-based boosting of genome-wide association data.

Insuk Lee, U Martin Blom, Peggy Wang … (2011)

Network "guilt by association" (GBA) is a proven approach for identifying novel disease genes based on the observation that similar mutational phenotypes arise from functionally related genes. In principle, this approach could account even for nonadditive genetic interactions, which underlie the synergistic combinations of mutations often linked to complex diseases. Here, we analyze a large-scale, human gene functional interaction network (dubbed HumanNet). We show that candidate disease genes can be effectively identified by GBA in cross-validated tests using label propagation algorithms related to Google's PageRank. However, GBA has been shown to work poorly in genome-wide association studies (GWAS), where many genes are somewhat implicated, but few are known with very high certainty. Here, we resolve this by explicitly modeling the uncertainty of the associations and incorporating the uncertainty for the seed set into the GBA framework. We observe a significant boost in the power to detect validated candidate genes for Crohn's disease and type 2 diabetes by comparing our predictions to results from follow-up meta-analyses, with incorporation of the network serving to highlight the JAK-STAT pathway and associated adaptors GRB2/SHC1 in Crohn's disease and BACH2 in type 2 diabetes. Consideration of the network during GWAS thus conveys some of the benefits of enrolling more participants in the GWAS study. More generally, we demonstrate that a functional network of human genes provides a valuable statistical framework for prioritizing candidate disease genes, both for candidate gene-based and GWAS-based studies.

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Author and article information

Contributors

Ben Lehner: ben.lehner@crg.eu

Insuk Lee: insuklee@yonsei.ac.kr

Journal

Journal ID (nlm-ta): Genome Biol

Journal ID (iso-abbrev): Genome Biol

Title: Genome Biology

Publisher: BioMed Central (London )

ISSN (Print): 1474-7596

ISSN (Electronic): 1474-760X

Publication date (Electronic): 23 June 2016

Publication date PMC-release: 23 June 2016

Publication date Collection: 2016

Volume: 17

Electronic Location Identifier: 129

Affiliations

[ ]Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, Korea

[ ]EMBL-CRG Systems Biology Unit, Centre for Genomic Regulation (CRG), 08003 Barcelona, Spain

[ ]Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain

[ ]Division of Electronics, Rudjer Boskovic Institute, 10000 Zagreb, Croatia

Article

Publisher ID: 989

DOI: 10.1186/s13059-016-0989-x

PMC ID: 4918128

PubMed ID: 27333808

SO-VID: 49f78309-da87-40f0-9116-f3069a255f7a

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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 : 17 November 2015

Date accepted : 24 May 2016

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100003725, National Research Foundation of Korea;

Award ID: 2012M3A9B4028641

Award ID: 2012M3A9C7050151

Award ID: 2011-0008548

Award ID: 2015R1A2A1A15055859

Award Recipient : Ara Cho Insuk Lee

Funded by: FundRef http://dx.doi.org/10.13039/501100000781, European Research Council;

Award ID: Consolidator grant IR-DC, 616434

Award Recipient : Ben Lehner

Funded by: Spanish Ministry of Economy and Competitiveness

Award ID: BFU2011-26206

Award ID: SEV-2012-0208

Award Recipient : Ben Lehner

Funded by: FundRef http://dx.doi.org/10.13039/501100001961, AXA Research Fund;

Funded by: AGAUR

Funded by: FP7 FET grant MAESTRA

Award ID: ICT-2013-612944

Award Recipient : Fran Supek

Funded by: Marie Curie Actions

Funded by: Brain Korea 21 PLUS

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ScienceOpen disciplines: Genetics

Keywords: cancer gene prediction,cancer somatic mutation,cancer genomes,mutation frequency,functional gene network,pathway-centric analysis

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ScienceOpen disciplines: Genetics

Keywords: cancer gene prediction, cancer somatic mutation, cancer genomes, mutation frequency, functional gene network, pathway-centric analysis

MUFFINN: cancer gene discovery via network analysis of somatic mutation data

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Abstract

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Genes & Diseases

Most cited references 44

The genomic landscapes of human breast and colorectal cancers.

Pan-Cancer Network Analysis Identifies Combinations of Rare Somatic Mutations across Pathways and Protein Complexes

Prioritizing candidate disease genes by network-based boosting of genome-wide association data.

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