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

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 dru...

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Published in:Bioinformatics 2016-06, Vol.32 (11), p.1643-1651
Main Authors: Park, Sunho, Kim, Seung-Jun, Yu, Donghyeon, Peña-Llopis, Samuel, Gao, Jianjiong, Park, Jin Suk, Chen, Beibei, Norris, Jessie, Wang, Xinlei, Chen, Min, Kim, Minsoo, Yong, Jeongsik, Wardak, Zabi, Choe, Kevin, Story, Michael, Starr, Timothy, Cheong, Jae-Ho, Hwang, Tae Hyun
Format: Article
Language:English
Subjects:
Cancer
DNA Mutational Analysis
Drugs
Gene Regulatory Networks
Genes
Genomics
Hitseq Papers
Humans
Mutation
Mutations
Neoplasms
Pathways
Patients
Stratification
Therapy
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Summary: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. 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. The code is available at: http://www.taehyunlab.org jhcheong@yuhs.ac or taehyun.hwang@utsouthwestern.edu or taehyun.cs@gmail.com Supplementary data are available at Bioinformatics online.
ISSN:1367-4803
1367-4811
1460-2059
DOI:10.1093/bioinformatics/btv692