Ermin Hodzic1,2, Raunak Shrestha1,3, Kaiyuan Zhu4, Kuoyuan Cheng5, Colin C Collins1,3, S Cenk Sahinalp1,4. 1. Laboratory for Advanced Genome Analysis, Vancouver Prostate Centre, 2660 Oak St, Vancouver, BC, V6H 3Z6, Canada. 2. School of Computing Science, Simon Fraser University, 8888 University Dr, Burnaby, BC, V5A 1S6, Canada. 3. Department of Urologic Sciences, University of British Columbia, 2775 Laurel St, Vancouver, BC, V5Z 1M9, Canada. 4. Department of Computer Science, Indiana University Bloomington, 700 N. Woodlawn Ave, Bloomington, IN, 47408, USA. 5. Center for Bioinformatics and Computational Biology, University of Maryland, 8125 Paint Branch Dr, College Park, MD, 20742, USA.
Abstract
BACKGROUND: Advances in large-scale tumor sequencing have led to an understanding that there are combinations of genomic and transcriptomic alterations specific to tumor types, shared across many patients. Unfortunately, computational identification of functionally meaningful and recurrent alteration patterns within gene/protein interaction networks has proven to be challenging. FINDINGS: We introduce a novel combinatorial method, cd-CAP (combinatorial detection of conserved alteration patterns), for simultaneous detection of connected subnetworks of an interaction network where genes exhibit conserved alteration patterns across tumor samples. Our method differentiates distinct alteration types associated with each gene (rather than relying on binary information of a gene being altered or not) and simultaneously detects multiple alteration profile conserved subnetworks. CONCLUSIONS: In a number of The Cancer Genome Atlas datasets, cd-CAP identified large biologically significant subnetworks with conserved alteration patterns, shared across many tumor samples.
BACKGROUND: Advances in large-scale tumor sequencing have led to an understanding that there are combinations of genomic and transcriptomic alterations specific to tumor types, shared across many patients. Unfortunately, computational identification of functionally meaningful and recurrent alteration patterns within gene/protein interaction networks has proven to be challenging. FINDINGS: We introduce a novel combinatorial method, cd-CAP (combinatorial detection of conserved alteration patterns), for simultaneous detection of connected subnetworks of an interaction network where genes exhibit conserved alteration patterns across tumor samples. Our method differentiates distinct alteration types associated with each gene (rather than relying on binary information of a gene being altered or not) and simultaneously detects multiple alteration profile conserved subnetworks. CONCLUSIONS: In a number of The Cancer Genome Atlas datasets, cd-CAP identified large biologically significant subnetworks with conserved alteration patterns, shared across many tumor samples.
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