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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. 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 identifieddoi:10.1101/017582 fatcat:6qknjho7srfyzmt5663tm4ru2y