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. 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. 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.
Our latest publication in @GigaScience on an algorithm to identify patterns of similarity of molecular alterations between cancer patients. “Combinatorial Detection of Conserved Alteration Patterns for Identifying Cancer Subnetworks” #Bioinformatics https://t.co/V1MgtqOLkL pic.twitter.com/IRcUVtbnIG— Raunak Shrestha (@raunakms) April 11, 2019