DIGRE: Drug‐Induced Genomic Residual Effect Model for Successful Prediction of Multidrug Effects

Yang, Jichen; Tang, Jing; Li, Yunguo; Zhong, Rui; Wang, T.; Wong, Stephen T.C.; Xiao, Guanghua; Xie, Yang

doi:10.1002/psp4.1

Cited by 36 publications

(31 citation statements)

References 19 publications

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“…One of the computational approaches is devised based on the gene expression profiles achieved from treatments of individual drugs ([7–9]; reviewed in [2]). With the accumulation of gene expression profiles of drug treatments [10–12], the performance of such approach to model the underlying mechanisms of drug treatment can be improved.…”

Section: Introductionmentioning

confidence: 99%

A simple gene set-based method accurately predicts the synergy of drug pairs

et al. 2016

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BackgroundThe advance in targeted therapy has greatly increased the effectiveness of clinical cancer therapy and reduced the cytotoxicity of treatments to normal cells. However, patients still suffer from cancer relapse due to the occurrence of drug resistance. It is of great need to explore potential combinatorial drug therapy since individual drug alone may not be sufficient to inhibit continuous activation of cancer-addicted genes or pathways. The DREAM challenge has confirmed the potentiality of computational methods for predicting synergistic drug combinations, while the prediction accuracy can be further improved.MethodsBased on previous reports, we hypothesized the similarity in biological functions or genes perturbed by two drugs can determine their synergistic effects. To test the feasibility of the hypothesis, we proposed three scoring systems: co-gene score, co-GS score, and co-gene/GS score, measuring the similarities in genes with significant expressional changes, enriched gene sets, and significantly changed genes within an enriched gene sets between a pair of drugs, respectively. Performances of these scoring systems were evaluated by the probabilistic c-index (PC-index) devised by the DREAM consortium. We also applied the proposed method to the Connectivity Map dataset to explore more potential synergistic drug combinations.ResultsUsing a gold standard derived by the DREAM consortium, we confirmed the prediction power of the three scoring systems (all P-values < 0.05). The co-gene/GS score achieved the best prediction of drug synergy (PC-index = 0.663, P-value < 0.0001), outperforming all methods proposed during DREAM challenge. Furthermore, a binary classification test showed that co-gene/GS scoring was highly accurate and specific. Since our method is constructed on a gene set-based analysis, in addition to synergy prediction, it provides insights into the functional relevance of drug combinations and the underlying mechanisms by which drugs achieve synergy.ConclusionsHere we proposed a novel and simple method to predict and investigate drug synergy, and validated its efficacy to accurately predict synergistic drug combinations and to comprehensively explore their underlying mechanisms. The method is widely applicable to expression profiles of other drug treatments and is expected to accelerate the realization of precision cancer treatment.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0310-3) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

A simple gene set-based method accurately predicts the synergy of drug pairs

et al. 2016

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“…The gene expression-based methods have been shown great potential in the DREAM7 drug combination challenge, where the participants were asked to predict the degrees of synergy on 91 drug pairs on a B-cell lymphoma cancer cell line [52]. The winning method DIGRE utilized the gene expression signatures between paired drugs to derive a linear regression model where the residual effect can be attributed to a drug synergy score [53]. Using a similar concept, the CMap data has also been applied in a Combinatorial Drug Assembler model where the drug pairs with a higher overlap in their gene expression patterns are predicted to be more synergistic, with a certain level of experimental validations done for non-small cell lung cancer and triple-negative breast cancer [54].…”

Section: Mathematical Modeling For the Prediction Of Drug Combinationsmentioning

confidence: 99%

Informatics Approaches for Predicting, Understanding, and Testing Cancer Drug Combinations

Tang

2017

Methods in Molecular Biology

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Making cancer treatment more effective is one of the grand challenges in our health care system. However, many drugs have entered clinical trials but so far showed limited efficacy or induced rapid development of resistance. We urgently need multi-targeted drug combinations, which shall selectively inhibit the cancer cells and block the emergence of drug resistance. The book chapter focuses on mathematical and computational tools to facilitate the discovery of the most promising drug combinations to improve efficacy and prevent resistance. Data integration approaches that leverage drug-target interactions, cancer molecular features, and signaling pathways for predicting, understanding, and testing drug combinations are critically reviewed.

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“…Among all of those 31 groups participating in the project, Drug-Induced Genomic Residual Effect (DIGRE) model achieved the best performance on prediction of synergistic drug combinations [66]. DIGRE was developed based on the hypothesis that, for two drugs used sequentially, the first drug would change the transcriptome of the treated cell and thus modulate the effect of the other one.…”

Section: Biomolecular Network-based Unsupervised Learning Models Fmentioning

confidence: 99%

Biomolecular Network-Based Synergistic Drug Combination Discovery

Qin

Yang

et al. 2016

BioMed Research International

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Drug combination is a powerful and promising approach for complex disease therapy such as cancer and cardiovascular disease. However, the number of synergistic drug combinations approved by the Food and Drug Administration is very small. To bridge the gap between urgent need and low yield, researchers have constructed various models to identify synergistic drug combinations. Among these models, biomolecular network-based model is outstanding because of its ability to reflect and illustrate the relationships among drugs, disease-related genes, therapeutic targets, and disease-specific signaling pathways as a system. In this review, we analyzed and classified models for synergistic drug combination prediction in recent decade according to their respective algorithms. Besides, we collected useful resources including databases and analysis tools for synergistic drug combination prediction. It should provide a quick resource for computational biologists who work with network medicine or synergistic drug combination designing.

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DIGRE: Drug‐Induced Genomic Residual Effect Model for Successful Prediction of Multidrug Effects

Cited by 36 publications

References 19 publications

A simple gene set-based method accurately predicts the synergy of drug pairs

A simple gene set-based method accurately predicts the synergy of drug pairs

Informatics Approaches for Predicting, Understanding, and Testing Cancer Drug Combinations

Biomolecular Network-Based Synergistic Drug Combination Discovery

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