Deciphering oncogenic drivers: from single genes to integrated pathways

被引：22

作者：

Chen, Jiajia ^{[1
,2
]}

Sun, Maomin ^{[1
]}

Shen, Bairong ^{[1
,3
]}

机构：

[1] Soochow Univ, Ctr Syst Biol, Suzhou 215006, Jiangsu, Peoples R China

[2] Suzhou Univ Sci & Technol, Suzhou, Peoples R China

[3] Fudan Univ, Shanghai, Peoples R China

来源：

BRIEFINGS IN BIOINFORMATICS | 2015年 / 16卷 / 03期

基金：

中国国家自然科学基金; 国家高技术研究发展计划(863计划); 高等学校博士学科点专项科研基金;

关键词：

cancer driver; pathway; mutation; network; HIGH-THROUGHPUT ANNOTATION; COPY-NUMBER ALTERATION; SOMATIC MUTATIONS; BREAST-CANCER; FUNCTIONAL IMPACT; GENOME; IDENTIFICATION; HETEROGENEITY; PREDICTION; LANDSCAPE;

D O I：

10.1093/bib/bbu039

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

Technological advances in next-generation sequencing have uncovered a wide spectrum of aberrations in cancer genomes. The extreme diversity in cancer mutations necessitates computational approaches to differentiate between the 'drivers' with vital function in cancer progression and those nonfunctional 'passengers'. Although individual driver mutations are routinely identified, mutational profiles of different tumors are highly heterogeneous. There is growing consensus that pathways rather than single genes are the primary target of mutations. Here we review extant bioinformatics approaches to identifying oncogenic drivers at different mutational levels, highlighting the strategies for discovering driver pathways and networks from cancer mutation data. These approaches will help reduce the mutation complexity, thus providing a simplified picture of cancer.

引用

页码：413 / 428

页数：16

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