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

Ara Cho; Jung Eun Shim; Eiru Kim; Fran Supek; Ben Lehner; Insuk Lee

doi:10.1186/s13059-016-0989-x

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

Ara Cho, Jung Eun Shim, Eiru Kim, Fran Supek, Ben Lehner, Insuk Lee

Research output: Contribution to journal › Article › peer-review

73 Citations (Scopus)

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.

Original language	English
Article number	129
Journal	Genome biology
Volume	17
Issue number	1
DOIs	https://doi.org/10.1186/s13059-016-0989-x
Publication status	Published - 2016 Jun 23

Bibliographical note

Funding Information:
This research was partly supported by grants from the National Research Foundation of Korea (2012M3A9B4028641, 2012M3A9C7050151, 2015R1A2A1A15055859), Brain Korea 21 (BK21) PLUS program to I.L., Global PH.D Fellowship Program through the National Research Foundation of Korea (2011-0008548) to A.C., the European Research Council (Consolidator grant IR-DC, 616434), the Spanish Ministry of Economy and Competitiveness (BFU2011-26206 and SEV-2012-0208), the AXA Research Fund, and AGAUR to B.L., the FP7 FET grant MAESTRA (ICT-2013-612944) and Marie Curie Actions to F.S.

Publisher Copyright:
© 2016 The Author(s).

All Science Journal Classification (ASJC) codes

Ecology, Evolution, Behavior and Systematics
Genetics
Cell Biology

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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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AU - Lehner, Ben

AU - Lee, Insuk

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

Abstract

Bibliographical note

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