Dataset Information

An integrative somatic mutation analysis to identify pathways linked with survival outcomes across 19 cancer types.

ABSTRACT:

Motivation

Identification of altered pathways that are clinically relevant across human cancers is a key challenge in cancer genomics. Precise identification and understanding of these altered pathways may provide novel insights into patient stratification, therapeutic strategies and the development of new drugs. However, a challenge remains in accurately identifying pathways altered by somatic mutations across human cancers, due to the diverse mutation spectrum. We developed an innovative approach to integrate somatic mutation data with gene networks and pathways, in order to identify pathways altered by somatic mutations across cancers.

Results

We applied our approach to The Cancer Genome Atlas (TCGA) dataset of somatic mutations in 4790 cancer patients with 19 different types of tumors. Our analysis identified cancer-type-specific altered pathways enriched with known cancer-relevant genes and targets of currently available drugs. To investigate the clinical significance of these altered pathways, we performed consensus clustering for patient stratification using member genes in the altered pathways coupled with gene expression datasets from 4870 patients from TCGA, and multiple independent cohorts confirmed that the altered pathways could be used to stratify patients into subgroups with significantly different clinical outcomes. Of particular significance, certain patient subpopulations with poor prognosis were identified because they had specific altered pathways for which there are available targeted therapies. These findings could be used to tailor and intensify therapy in these patients, for whom current therapy is suboptimal.

Availability and implementation

The code is available at: http://www.taehyunlab.org

Contact

jhcheong@yuhs.ac or taehyun.hwang@utsouthwestern.edu or taehyun.cs@gmail.com

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Park S

PROVIDER: S-EPMC4892411 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

An integrative somatic mutation analysis to identify pathways linked with survival outcomes across 19 cancer types.

Park Sunho S Kim Seung-Jun SJ Yu Donghyeon D Peña-Llopis Samuel S Gao Jianjiong J Park Jin Suk JS Chen Beibei B Norris Jessie J Wang Xinlei X Chen Min M Kim Minsoo M Yong Jeongsik J Wardak Zabi Z Choe Kevin K Story Michael M Starr Timothy T Cheong Jae-Ho JH Hwang Tae Hyun TH

Bioinformatics (Oxford, England) 20151203 11

<h4>Motivation</h4>Identification of altered pathways that are clinically relevant across human cancers is a key challenge in cancer genomics. Precise identification and understanding of these altered pathways may provide novel insights into patient stratification, therapeutic strategies and the development of new drugs. However, a challenge remains in accurately identifying pathways altered by somatic mutations across human cancers, due to the diverse mutation spectrum. We developed an innovati ...[more]

PMID: 26635139

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Dataset Information

An integrative somatic mutation analysis to identify pathways linked with survival outcomes across 19 cancer types.

Motivation

Results

Availability and implementation

Contact

Supplementary information

Publications

An integrative somatic mutation analysis to identify pathways linked with survival outcomes across 19 cancer types.

Similar Datasets

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