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

Overview of attention for article published in BMC Systems Biology, August 2016
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Title
A simple gene set-based method accurately predicts the synergy of drug pairs
Published in
BMC Systems Biology, August 2016
DOI 10.1186/s12918-016-0310-3
Pubmed ID
Authors

Yu-Ching Hsu, Yu-Chiao Chiu, Yidong Chen, Tzu-Hung Hsiao, Eric Y. Chuang

Abstract

The 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. Based 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. Using 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. Here 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.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 21 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 38%
Student > Ph. D. Student 3 14%
Student > Bachelor 2 10%
Student > Master 1 5%
Professor 1 5%
Other 2 10%
Unknown 4 19%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 38%
Agricultural and Biological Sciences 3 14%
Computer Science 2 10%
Medicine and Dentistry 2 10%
Neuroscience 1 5%
Other 1 5%
Unknown 4 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 September 2016.
All research outputs
#15,383,207
of 22,886,568 outputs
Outputs from BMC Systems Biology
#644
of 1,142 outputs
Outputs of similar age
#216,029
of 338,621 outputs
Outputs of similar age from BMC Systems Biology
#17
of 32 outputs
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So far Altmetric has tracked 1,142 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 32nd percentile – i.e., 32% of its peers scored the same or lower than it.
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