Mani, K. M. et al. A systems biology approach to prediction of oncogenes and perturbation targets in B cell lymphomas. Mol. Syst. Biol. 4, 169-178

Department of Biomedical Informatics (DBMI), Columbia University, New York, NY 10032, USA.
Molecular Systems Biology 02/2008; 4(1). DOI: 10.1038/msb.2008.2
Source: PubMed


The computational identification of oncogenic lesions is still a key open problem in cancer biology. Although several methods have been proposed, they fail to model how such events are mediated by the network of molecular interactions in the cell. In this paper, we introduce a systems biology approach, based on the analysis of molecular interactions that become dysregulated in specific tumor phenotypes. Such a strategy provides important insights into tumorigenesis, effectively extending and complementing existing methods. Furthermore, we show that the same approach is highly effective in identifying the targets of molecular perturbations in a human cellular context, a task virtually unaddressed by existing computational methods. To identify interactions that are dysregulated in three distinct non-Hodgkin's lymphomas and in samples perturbed with CD40 ligand, we use the B-cell interactome (BCI), a genome-wide compendium of human B-cell molecular interactions, in combination with a large set of microarray expression profiles. The method consistently ranked the known gene in the top 20 (0.3%), outperforming conventional approaches in 3 of 4 cases.

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    • "DeMAND was shown to be highly robust to network and sample variability. More importantly, unlike previous methods (di Bernardo et al., 2005; Mani et al., 2008), DeMAND can reliably predict compound MoA using as few as six control and six perturbation samples. This allows unprecedented applicability of the methods to elucidate MoA for novel developmental compounds within specific cellular contexts of interest, including in vivo. "
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    ABSTRACT: Genome-wide identification of the mechanism of action (MoA) of small-molecule compounds characterizing their targets, effectors, and activity modulators represents a highly relevant yet elusive goal, with critical implications for assessment of compound efficacy and toxicity. Current approaches are labor intensive and mostly limited to elucidating high-affinity binding target proteins. We introduce a regulatory network-based approach that elucidates genome-wide MoA proteins based on the assessment of the global dysregulation of their molecular interactions following compound perturbation. Analysis of cellular perturbation profiles identified established MoA proteins for 70% of the tested compounds and elucidated novel proteins that were experimentally validated. Finally, unknown-MoA compound analysis revealed altretamine, an anticancer drug, as an inhibitor of glutathione peroxidase 4 lipid repair activity, which was experimentally confirmed, thus revealing unexpected similarity to the activity of sulfasalazine. This suggests that regulatory network analysis can provide valuable mechanistic insight into the elucidation of small-molecule MoA and compound similarity. Copyright © 2015 Elsevier Inc. All rights reserved.
    Full-text · Article · Jul 2015 · Cell
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    • "The ceRNA network developed by Sumazin et al.6 was downloaded from the publication website. We used an approach similar to the IDEA algorithm40 developed by Mani et al. to analyze the perturbation of the ceRNA network coupled with 3′UTR APA dynamics. Briefly, MIs between ceRNA gene pairs were first calculated using all prostate cancer samples (MIall). "
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    ABSTRACT: Competing endogenous RNA (ceRNA) interactions form a multilayered network that regulates gene expression in various biological pathways. Recent studies have demonstrated novel roles of ceRNA interactions in tumorigenesis, but the dynamics of the ceRNA network in cancer remain unexplored. Here, we examine ceRNA network dynamics in prostate cancer from the perspective of alternative cleavage and polyadenylation (APA) and reveal the principles of such changes. Analysis of exon array data revealed that both shortened and lengthened 3'UTRs are abundant. Consensus clustering with APA data stratified cancers into groups with differing risks of biochemical relapse and revealed that a ceRNA subnetwork enriched with cancer genes was specifically dysregulated in high-risk cancers. The novel connection between 3'UTR shortening and ceRNA network dysregulation was supported by the unusually high number of microRNA response elements (MREs) shared by the dysregulated ceRNA interactions and the significantly altered 3'UTRs. The dysregulation followed a fundamental principle in that ceRNA interactions connecting genes that show opposite trends in expression change are preferentially dysregulated. This targeted dysregulation is responsible for the majority of the observed expression changes in genes with significant ceRNA dysregulation and represents a novel mechanism underlying aberrant oncogenic expression.
    Full-text · Article · Jun 2014 · Scientific Reports
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    • "In the cancer research field, the gene network framework has been applied successfully to identify dysregulated pathways due to oncogenic lesions [11]; a similar approach could be used to identify molecular targets of a small molecule. An algorithm named Interaction Dysregulation Enrichment Analysis (IDEA) has been recently proposed [11]. "
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    ABSTRACT: Network-based drug discovery aims at harnessing the power of networks to investigate the mechanism of action of existing drugs, or new molecules, in order to identify innovative therapeutic treatments. In this review, we describe some of the most recent advances in the field of network pharmacology, starting with approaches relying on computational models of transcriptional networks, then moving to protein and signaling network models and concluding with "drug networks". These networks are derived from different sources of experimental data, or literature-based analysis, and provide a complementary view of drug mode of action. Molecular and drug networks are powerful integrated computational and experimental approaches that will likely speed up and improve the drug discovery process, once fully integrated into the academic and industrial drug discovery pipeline.
    Full-text · Article · Dec 2013 · BMC Systems Biology
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