DrugComboRanker: drug combination discovery based on target network analysis

By Lei Huang, Fuhai Li, J. Sheng, Xiaofeng Xia, Jinwen Ma, M. Zhan, Stephen T. C. Wong. 2014

158 citations recorded by the source corpus.

Motivation: Currently there are no curative anticancer drugs, and drug resistance is often acquired after drug treatment. One of the reasons is that cancers are complex diseases, regulated by multiple signaling pathways and cross talks among the pathways. It is expected that drug combinations can reduce drug resistance and improve patients’ outcomes. In clinical practice, the ideal and feasible drug combinations are combinations of existing Food and Drug Administration-approved drugs or bioactive compounds that are already used on patients or have entered clinical trials and passed safety tests. These drug combinations could directly be used on patients with less concern of toxic effects. However, there is so far no effective computational approach to search effective drug combinations from the enormous number of possibilities. Results: In this study, we propose a novel systematic computational tool DrugComboRanker to prioritize synergistic drug combinations and uncover their mechanisms of action. We first build a drug functional network based on their genomic profiles, and partition the network into numerous drug network communities by using a Bayesian non-negative matrix factorization approach. As drugs within overlapping community share common mechanisms of action, we next uncover potential targets of drugs by applying a recommendation system on drug communities. We meanwhile build disease-specific signaling networks based on patients’ genomic profiles and interactome data. We then identify drug combinations by searching drugs whose targets are enriched in the complementary signaling modules of the disease signaling network. The novel method was evaluated on lung adenocarcinoma and endocrine receptor positive breast cancer, and compared with other drug combination approaches. These case studies discovered a set of effective drug combinations top ranked in our prediction list, and mapped the drug targets on the disease signaling network to highlight the mechanisms of action of the drug combinations. Availability and implementation: The program is available on request. Contact: stwong@tmhs.org

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

Systems biology approaches for advancing the discovery of effective drug combinations (2015, methodology): “Huang et al. used drug genomic profiles from the Connectivity Map database to construct a drug functional network and then grouped drugs into modules with similar transcriptional responses [62].”
A review of connectivity map and computational approaches in pharmacogenomics (2017, background): “[99] proposed a novel systematic computational approach called DrugComboRanker to find synergistic drug combinations and to uncover their MoA.”
Predicting combinative drug pairs towards realistic screening via integrating heterogeneous features (2017, background): “…drug-target network related to the disease-associated genes [8]; and (3) drugs sharing no target or independent signaling mechanisms could be combined, if they have the active functional targets, which are of high-degree and closely connected in the disease-related protein interaction network [9].”
Combining genomic and network characteristics for extended capability in predicting synergistic drugs for cancer (2015, unknown): “The drugs in the remaining 17 pairs were purchased and experimentally validated using the human MCF7 cell line (Supplementary Note 1).”
Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities (2018, background): “Drug-drug interactions are defined through the concepts of synergy and antagonism [283, 284] and are quantified biologically by measuring the dose-effect curves [285, 286] or cell viability [287, 288, 289, 290, 280, 291, 292].”