CDA: Combinatorial Drug Discovery Using Transcriptional Response Modules

Abstract

BackgroundAnticancer therapies that target single signal transduction pathways often fail to prevent proliferation of cancer cells because of overlapping functions and cross-talk between different signaling pathways. Recent research has identified that balanced multi-component therapies might be more efficacious than highly specific single component therapies in certain cases. Ideally, synergistic combinations can provide 1) increased efficacy of the therapeutic effect 2) reduced toxicity as a result of decreased dosage providing equivalent or increased efficacy 3) the avoidance or delayed onset of drug resistance. Therefore, the interest in combinatorial drug discovery based on systems-oriented approaches has been increasing steadily in recent years.MethodologyHere we describe the development of Combinatorial Drug Assembler (CDA), a genomics and bioinformatics system, whereby using gene expression profiling, multiple signaling pathways are targeted for combinatorial drug discovery. CDA performs expression pattern matching of signaling pathway components to compare genes expressed in an input cell line (or patient sample data), with expression patterns in cell lines treated with different small molecules. Then it detects best pattern matching combinatorial drug pairs across the input gene set-related signaling pathways to detect where gene expression patterns overlap and those predicted drug pairs could likely be applied as combination therapy. We carried out in vitro validations on non-small cell lung cancer cells and triple-negative breast cancer (TNBC) cells. We found two combinatorial drug pairs that showed synergistic effect on lung cancer cells. Furthermore, we also observed that halofantrine and vinblastine were synergistic on TNBC cells.ConclusionsCDA provides a new way for rational drug combination. Together with phExplorer, CDA also provides functional insights into combinatorial drugs. CDA is freely available at http://cda.i-pharm.org.

A review of connectivity map and computational approaches in pharmacogenomics

“…This seems to be particularly appropriate for complex disorders such as cancer, as cancer cells possess compensatory mechanisms to overcome perturbations occurring at the individual signaling pathway level by means of, e.g. mutations of key receptors or cross-talk between pathways [98].…”

“…For instance, Lee et al [98] developed the Combinatorial Drug Assembler as a genomic and bioinformatics system by using gene expression profiling to target multiple signaling pathways for a combinatorial drug discovery. The method performs an expression search against a signaling pathway to compare gene expression profiles of patient samples (or cell lines) as input signature, with the expression patterns of the sample treated with different small molecules.…”

In-Silico Methodologies for Cancer Multidrug Optimization

“…AstraZeneca-Sanger Drug Combination DREAM Challenge was launched using 85 cancer cell lines and 11,759 drug combination screening for 118 drugs [8]. The predictive models were designed to differentiate synergistic, additive and antagonistic combinations and predict new synergistic combinations in silico [9][10][11][12][13][14][15][16][17].…”

“…Lee et al [13] applied hypergeometric and Kolmogorov-Smirnov statistical tests to the prediction of drug combinations. They hypothesized that drugs that regulate two different disease-specific pathways could be synergistic.…”

Computational Approaches and Pharmacogenomics Data Resources for Drug Repositioning

“…Figure 3 shows the targets of Dasatinib obtained from STITCH. In addition to single drug repositioning, synergistic drug combinations can also be repositioned computationally using the network-based approach, e.g., DrugComboRanker and GenSynNET (37, 38), as well as other approaches, e.g., Combinatorial Drug Assembler (CDA) (42,43), DrugPairSeeker (DPS) (44).…”