Estimating functional coupling between cancer gene sub-networks using novel interaction measures. (2016)

• Record Type: Journal Article
• Authors: Mallavarapu, Rama Srikanth; Ahn, Tae-Jin; Mukherjee, Subhankar; Bopardikar, Ajit S.; Agarwal, Garima; Park, Taesung
• Is Part Of: International journal of data mining and bioinformatics. Volume 15:Number 1(2016)
• Page Start: 10
• Page End: 21
• Publication Date: 2016
• Subjects: cancer gene sub-networks -- graph theory -- sub-network analysis -- genomic pathway analysis -- data mining -- functional coupling -- cancer genes -- interaction measures -- breast cancer -- patient stratification -- bioinformatics; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

Abstract

Cancer is a heterogeneous disease in that a single therapy may not be suitable for every patient. This is because, even within a single cancer, gene-level aberrations vary across patients. However, when genes are viewed at sub-network level, it is observed that these aberrations occur in sub-networks that are conserved across patients. Investigation of cancer gene sub-networks is therefore expected to yield greater insights. Also, multiple sub-networks could be involved in propagation of cancer and therefore coupling between them is of great importance. We present a method to measure and quantify coupling between two given sub-networks. For this we present interaction measures based on connecting genes and common neighbourhoods between sub-networks. We demonstrate this method using breast cancer focused around MET pathway and discuss functional coupling of various associated sub-networks. Studies based on such methods will play an important role in patient stratification and development of effective targeted therapies.