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

doi:10.1093/bioinformatics/btv692

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

Bioinformatics. 2016 Jun 1;32(11):1643-51. doi: 10.1093/bioinformatics/btv692. Epub 2015 Dec 3.

Authors

Sunho Park¹, Seung-Jun Kim², Donghyeon Yu³, Samuel Peña-Llopis⁴, Jianjiong Gao⁵, Jin Suk Park¹, Beibei Chen¹, Jessie Norris¹, Xinlei Wang⁶, Min Chen⁷, Minsoo Kim¹, Jeongsik Yong⁸, Zabi Wardak⁹, Kevin Choe⁹, Michael Story⁹, Timothy Starr¹⁰, Jae-Ho Cheong¹¹, Tae Hyun Hwang¹²

Affiliations

¹ Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA.
² Department of Computer Science and Electrical Engineering, University of Maryland at Baltimore County, Baltimore, MD, USA.
³ Department of Statistics, Keimyung University, Daegu, South Korea.
⁴ Internal Medicine and Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.
⁵ Center for Molecular Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
⁶ Department of Statistical Science, Southern Methodist University, Dallas, TX, USA.
⁷ Department of Mathematical Sciences, University of Texas at Dallas, Dallas, TX, USA.
⁸ Department of Biochemistry, Molecular Biology and Biophysics, Obstetrics, Gynecology & Women's Health, University of Minnesota Twin Cities, Minneapolis, MN, USA.
⁹ Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA, Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
¹⁰ Genetics, Cell Biology, University of Minnesota Twin Cities, Minneapolis, MN, USA, Masonic Cancer Center, University of Minnesota Twin Cities, Minneapolis, MN, USA.
¹¹ Department of Surgery, Yonsei University College of Medicine, Seoul, South Korea and Open NBI Convergence Technology Research Laboratory, Yonsei University College of Medicine, Seoul, South Korea.
¹² Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA, Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.

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.

MeSH terms

DNA Mutational Analysis
Gene Regulatory Networks
Genomics
Humans
Mutation*
Neoplasms*

Abstract

MeSH terms

Grants and funding