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

被引：123

作者：

Cho, Ara ^{[1
]}

Shim, Jung Eun ^{[1
]}

Kim, Eiru ^{[1
]}

Supek, Fran ^{[2
,3
,4
]}

Lehner, Ben ^{[2
,3
]}

Lee, Insuk ^{[1
]}

机构：

[1] Yonsei Univ, Dept Biotechnol, Coll Life Sci & Biotechnol, Seoul, South Korea

[2] Ctr Genom Regulat, EMBL CRG Syst Biol Unit, Barcelona 08003, Spain

[3] Univ Pompeu Fabra, Barcelona 08003, Spain

[4] Rudjer Boskovic Inst, Div Elect, Zagreb 10000, Croatia

来源：

GENOME BIOLOGY | 2016年 / 17卷

基金：

新加坡国家研究基金会; 欧洲研究理事会;

关键词：

Cancer gene prediction; Cancer somatic mutation; Cancer genomes; Mutation frequency; Functional gene network; Pathway-centric analysis; DRIVER MUTATIONS; FUNCTIONAL IMPACT; TUMOR; BREAST; PREDICTION; PATHWAYS; EXPRESSION; LANDSCAPE; GENOMES; BLADDER;

D O I：

10.1186/s13059-016-0989-x

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

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.

引用

页数：16

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