iCAGES: integrated CAncer GEnome Score for comprehensively prioritizing driver genes in personal cancer genomes

被引：34

作者：

Dong, Chengliang ^{[1
,2
]}

Guo, Yunfei ^{[1
,2
]}

Yang, Hui ^{[1
,3
]}

He, Zeyu ^{[4
]}

Liu, Xiaoming ^{[5
,6
]}

Wang, Kai ^{[1
,7
]}

机构：

[1] Univ Southern Calif, Zilkha Neurogenet Inst, Los Angeles, CA 90089 USA

[2] Univ Southern Calif, Dept Prevent Med, Biostat Grad Program, Los Angeles, CA 90089 USA

[3] Univ Southern Calif, Neurosci Grad Program, Los Angeles, CA 90089 USA

[4] NYU, Dept Comp Sci, New York, NY 10012 USA

[5] Univ Texas Hlth Sci Ctr Houston, Human Genet Ctr, Houston, TX 77030 USA

[6] Univ Texas Hlth Sci Ctr Houston, Div Epidemiol Human Genet & Environm Sci, Houston, TX 77030 USA

[7] Columbia Univ, Inst Genom Med, 630 W 168th St,Room 11-451, New York, NY 10032 USA

来源：

GENOME MEDICINE | 2016年 / 8卷

关键词：

Cancer genomics; Machine learning; Precision medicine; Precision oncology; TCGA; HIGH-THROUGHPUT ANNOTATION; SOMATIC MUTATIONS; KRAS MUTATIONS; PREDICTION; VARIANTS; RESOURCE; IDENTIFICATION; EVOLUTIONARY; CONSEQUENCES; LANDSCAPE;

D O I：

10.1186/s13073-016-0390-0

中图分类号：

Q3 [遗传学];

学科分类号：

071007 ; 090102 ;

摘要：

Cancer results from the acquisition of somatic driver mutations. Several computational tools can predict driver genes from population-scale genomic data, but tools for analyzing personal cancer genomes are underdeveloped. Here we developed iCAGES, a novel statistical framework that infers driver variants by integrating contributions from coding, non-coding, and structural variants, identifies driver genes by combining genomic information and prior biological knowledge, then generates prioritized drug treatment. Analysis on The Cancer Genome Atlas (TCGA) data showed that iCAGES predicts whether patients respond to drug treatment (P = 0.006 by Fisher's exact test) and long-term survival (P = 0.003 from Cox regression).

引用

页数：22

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