TY - JOUR T1 - An integrative somatic mutation analysis to identify pathways linked with survival outcomes across 19 cancer types JF - bioRxiv DO - 10.1101/017582 SP - 017582 AU - Sunho Park AU - Seung-Jun Kim AU - Donghyeon Yu AU - Samuel Pena-Llopis AU - Jianjiong Gao AU - Jin Suk Park AU - Hao Tang AU - Beibei Chen AU - Jiwoong Kim AU - Jessie Norris AU - Xinlei Wang AU - Min Chen AU - Minsoo Kim AU - Jeongsik Yong AU - Zabi WarDak AU - Kevin S Choe AU - Michael Story AU - Timothy K. Starr AU - Jaeho Cheong AU - Tae Hyun Hwang Y1 - 2015/01/01 UR - http://biorxiv.org/content/early/2015/04/06/017582.abstract N2 - 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. ER -