Discovery and analysis of consistent active sub-networks in cancers

BMC Bioinformatics

Proceedings

Lorey Smith¹  Patrick Humbert¹  Izhak Haviv²  James Bailey³  Peter J Stuckey³  Raj K Gaire⁴ 

[1] Cell Cycle & Cancer Genetics, Peter MacCallum Cancer Centre, 3002, Melbourne, Vic, Australia;Department of Pathology, School of Medicine, University of Melbourne, 3010, Parkville, Vic, Australia;Faculty of Medicine in Galilee, Bar Ilan University, Israel;NICTA, Victoria Laboratory and Department of Computing and Information Systems, University of Melbourne, 3010, Parkville, Vic, Australia;NICTA, Victoria Laboratory and Department of Computing and Information Systems, University of Melbourne, 3010, Parkville, Vic, Australia;Metabolomics, Population Studies and Profiling, Baker IDI Heart and Diabetes Institute, 3004, Melbourne, Vic, Australia;

关键词: Interaction Network;    Mixed Integer Programming;    Differentially Express Gene;    Intrinsic Subtype;    Breast Cancer Dataset;   

DOI  :  10.1186/1471-2105-14-S2-S7

来源: Springer

【 摘 要 】

Gene expression profiles can show significant changes when genetically diseased cells are compared with non-diseased cells. Biological networks are often used to identify active subnetworks (ASNs) of the diseases from the expression profiles to understand the reason behind the observed changes. Current methodologies for discovering ASNs mostly use undirected PPI networks and node centric approaches. This can limit their ability to find the meaningful ASNs when using integrated networks having comprehensive information than the traditional protein-protein interaction networks. Using appropriate scoring functions to assess both genes and their interactions may allow the discovery of better ASNs.In this paper, we present CASNet, which aims to identify better ASNs using (i) integrated interaction networks (mixed graphs), (ii) directions of regulations of genes, and (iii) combined node and edge scores. We simplify and extend previous methodologies to incorporate edge evaluations and lessen their sensitivity to significance thresholds. We formulate our objective functions using mixed integer programming (MIP) and show that optimal solutions may be obtained.We compare the ASNs obtained by CASNet and similar other approaches to show that CASNet can often discover more meaningful and stable regulatory ASNs. Our analysis of a breast cancer dataset finds that the positive feedback loops across 7 genes, AR, ESR 1, MYC, E 2F 2, PGR, BCL 2 and CCND 1 are conserved across the basal/triple negative subtypes in multiple datasets that could potentially explain the aggressive nature of this cancer subtype. Furthermore, comparison of the basal subtype of breast cancer and the mesenchymal subtype of glioblastoma ASNs shows that an ASN in the vicinity of IL 6 is conserved across the two subtypes. This result suggests that subtypes of different cancers can show molecular similarities indicating that the therapeutic approaches in different types of cancers may be shared.

【 授权许可】

CC BY   
© Gaire et al.; licensee BioMed Central Ltd. 2013