This article is part of the supplement: Selected articles from the Eleventh Asia Pacific Bioinformatics Conference (APBC 2013): Bioinformatics
Discovery and analysis of consistent active sub-networks in cancers
1 NICTA, Victoria Laboratory and Department of Computing and Information Systems, University of Melbourne, Parkville, Vic 3010, Australia
2 Metabolomics, Population Studies and Profiling, Baker IDI Heart and Diabetes Institute, Melbourne, Vic 3004, Australia
3 Cell Cycle & Cancer Genetics, Peter MacCallum Cancer Centre, Melbourne, Vic 3002, Australia
4 Department of Pathology, School of Medicine, University of Melbourne, Parkville, Vic 3010, Australia
5 Faculty of Medicine in Galilee, Bar Ilan University, Israel
BMC Bioinformatics 2013, 14(Suppl 2):S7 doi:10.1186/1471-2105-14-S2-S7Published: 21 January 2013
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, ESR1, MYC, E2F2, PGR, BCL2 and CCND1 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 IL6 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.