Jaeger, Savina, Min, Junxia, Nigsch, Florian, Camargo, Miguel, Hutz, Janna, Cornett, Allen, Cleaver, Stephen, Buckler, Alan and Jenkins, Jeremy (2014) Causal Network Models for Predicting Compound Targets and Driving Pathways in Cancer. Journal of Biomolecular Screening, 19 (5). pp. 791-802. ISSN 1087-05711552-454X

Abstract

Gene expression data is often used to infer pathways regulating transcriptional responses. For example, differentially expressed genes (DEGs) induced by compound treatment can help characterize hits from phenotypic screens, either by correlation with known drug signatures or by pathway enrichment. However, pathway enrichment is typically computed with DEGs rather than ‘upstream’ nodes that are potentially causal of ‘downstream’ changes. Here we present graph-based models to predict causal targets using compound-microarray data. We test several approaches to traversing network topology for interactions of varying confidence levels. We found that larger, less-canonical networks outperformed linear canonical interactions. In addition, combining network topology scoring methods with a consensus minimum-rank score beat individual methods and could highly rank compound targets among all network nodes. Importantly, pathway enrichment using causal nodes rather than DEGs recovers relevant pathways more often. To extend our validation, we used integrated datasets from the The Cancer Genome Atlas to define driving pathways in triple-negative breast cancer. Critical pathways were uncovered, including EGFR/PI3K/AKT/MAPK growth pathway and ATR/p53/BRCA DNA damage pathway, as well as unexpected pathways, such as TGF/WNT cytoskeleton remodeling, TNFR/IAP apoptosis, and IL12-induced IFN-gamma production. Overall, our approach can bridge transcriptional profiles to compound targets and driving pathways in cancer.

Item Type:	Article
Additional Information:	For the former NIBR postdoc, Janna Hutz, who is a middle author, this was not part of her main project but rather a side piece of work with Savina
Keywords:	causal modeling, networks, target prediction
Date Deposited:	13 Oct 2015 13:13
Last Modified:	04 Jul 2016 23:46
URI:	https://oak.novartis.com/id/eprint/10982