RT Journal Article SR Electronic T1 An integrative somatic mutation analysis to identify pathways linked with survival outcomes across 19 cancer types JF bioRxiv FD Cold Spring Harbor Laboratory SP 017582 DO 10.1101/017582 A1 Sunho Park A1 Seung-Jun Kim A1 Donghyeon Yu A1 Samuel Pena-Llopis A1 Jianjiong Gao A1 Jin Suk Park A1 Hao Tang A1 Beibei Chen A1 Jiwoong Kim A1 Jessie Norris A1 Xinlei Wang A1 Min Chen A1 Minsoo Kim A1 Jeongsik Yong A1 Zabi WarDak A1 Kevin S Choe A1 Michael Story A1 Timothy K. Starr A1 Jaeho Cheong A1 Tae Hyun Hwang YR 2015 UL http://biorxiv.org/content/early/2015/04/06/017582.abstract AB Identification of altered pathways that are clinically relevant across human cancers is a key challenge in cancer genomics. We developed a network-based algorithm 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 4,790 cancer patients with 19 different types of malignancies. Our analysis identified cancer-type-specific altered pathways enriched with known cancer-relevant genes and drug targets. Consensus clustering using gene expression datasets that included 4,870 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.