NoMAS: A Computational Approach to Find Mutated Subnetworks Associated With Survival in Genome-Wide Cancer Studies

NoMAS: A Computational Approach to Find Mutated Subnetworks Associated With Survival in Genome-Wide Cancer Studies Federico Altieri1, Tommy V. Hansen2 and Fabio Vandin1* 1Department of Information Engineering, University of Padova, Padova, Italy 2Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark Next-generation sequencing technologies allow to measure somatic mutations in a large number of patients from the same cancer type: one of the main goals in their analysis is the identification of mutations associated with clinical parameters. The identification of such relationships is hindered by extensive genetic heterogeneity in tumors, with different genes mutated in different patients, due, in part, to the fact that genes and mutations act in the context of pathways: it is therefore crucial to study mutations in the context of interactions among genes. In this work we study the problem of identifying subnetworks of a large gene-gene interaction network with mutations associated with survival time. We formally define the associated computational problem by using a score for subnetworks based on the log-rank statistical test to compare the survival of two given populations. We propose a novel approach, based on a new algorithm, called Network of Mutations Associated with Survival (NoMAS) to find subnetworks of a large interaction network whose mutations are associated with survival time. NoMAS is based on the color-coding tec...
Source: Frontiers in Genetics - Category: Genetics & Stem Cells Source Type: research

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