An integrative somatic mutation analysis to identify pathways linked with survival outcomes across 19 cancer types
Author(s) -
Sunho Park,
Seung-Jun Kim,
Donghyeon Yu,
Samuel PeñaLlopis,
Jianjiong Gao,
Jin Suk Park,
Beibei Chen,
Jessie Norris,
Xinlei Wang,
Min Chen,
MinSoo Kim,
Jeongsik Yong,
Zabi Wardak,
Kevin S. Choe,
Michael D. Story,
Timothy K. Starr,
JaeHo Cheong,
Tae Hyun Hwang
Publication year - 2015
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btv692
Subject(s) - somatic cell , mutation , biology , computational biology , genetics , cancer , germline mutation , survival analysis , medicine , gene
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.
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