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

Sunho Park, Seung Jun Kim, Donghyeon Yu, Samuel Peña-Llopis, Jianjiong Gao, Jin Suk Park, Beibei Chen, Jessie Norris, Xinlei Wang, Min Chen, Minsoo Kim, Jeongsik Yong, Zabi Wardak, Kevin Choe, Michael Story, Timothy Starr, Jae Ho Cheong, Tae Hyun Hwang

Research output: Contribution to journalArticle

11 Citations (Scopus)

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. Supplementary information: Supplementary data are available at Bioinformatics online.

Original languageEnglish (US)
Pages (from-to)1643-1651
Number of pages9
JournalBioinformatics
Volume32
Issue number11
DOIs
StatePublished - Jun 1 2016

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Pathway
Cancer
Mutation
Genes
Survival
Neoplasms
Therapy
Atlases
Atlas
Bioinformatics
Stratification
Gene expression
Pharmaceutical Preparations
Tumors
Drugs
Genome
Availability
Gene
Gene Regulatory Networks
Neoplasm Genes

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computational Theory and Mathematics
  • Computer Science Applications
  • Computational Mathematics
  • Statistics and Probability

Cite this

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

In: Bioinformatics, Vol. 32, No. 11, 01.06.2016, p. 1643-1651.

Research output: Contribution to journalArticle

Park, S, Kim, SJ, Yu, D, Peña-Llopis, S, Gao, J, Park, JS, Chen, B, Norris, J, Wang, X, Chen, M, Kim, M, Yong, J, Wardak, Z, Choe, K, Story, M, Starr, T, Cheong, JH & Hwang, TH 2016, 'An integrative somatic mutation analysis to identify pathways linked with survival outcomes across 19 cancer types', Bioinformatics, vol. 32, no. 11, pp. 1643-1651. https://doi.org/10.1093/bioinformatics/btv692
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. / An integrative somatic mutation analysis to identify pathways linked with survival outcomes across 19 cancer types. In: Bioinformatics. 2016 ; Vol. 32, No. 11. pp. 1643-1651.
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