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

BACKGROUND: 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 poten...

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Autores principales: Hsu, Yu-Ching, Chiu, Yu-Chiao, Chen, Yidong, Hsiao, Tzu-Hung, Chuang, Eric Y.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5009556/
https://www.ncbi.nlm.nih.gov/pubmed/27585722
http://dx.doi.org/10.1186/s12918-016-0310-3
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author Hsu, Yu-Ching
Chiu, Yu-Chiao
Chen, Yidong
Hsiao, Tzu-Hung
Chuang, Eric Y.
author_facet Hsu, Yu-Ching
Chiu, Yu-Chiao
Chen, Yidong
Hsiao, Tzu-Hung
Chuang, Eric Y.
author_sort Hsu, Yu-Ching
collection PubMed
description BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The 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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spelling pubmed-50095562016-09-08 A simple gene set-based method accurately predicts the synergy of drug pairs Hsu, Yu-Ching Chiu, Yu-Chiao Chen, Yidong Hsiao, Tzu-Hung Chuang, Eric Y. BMC Syst Biol Research BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-016-0310-3) contains supplementary material, which is available to authorized users. BioMed Central 2016-08-26 /pmc/articles/PMC5009556/ /pubmed/27585722 http://dx.doi.org/10.1186/s12918-016-0310-3 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Hsu, Yu-Ching
Chiu, Yu-Chiao
Chen, Yidong
Hsiao, Tzu-Hung
Chuang, Eric Y.
A simple gene set-based method accurately predicts the synergy of drug pairs
title A simple gene set-based method accurately predicts the synergy of drug pairs
title_full A simple gene set-based method accurately predicts the synergy of drug pairs
title_fullStr A simple gene set-based method accurately predicts the synergy of drug pairs
title_full_unstemmed A simple gene set-based method accurately predicts the synergy of drug pairs
title_short A simple gene set-based method accurately predicts the synergy of drug pairs
title_sort simple gene set-based method accurately predicts the synergy of drug pairs
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5009556/
https://www.ncbi.nlm.nih.gov/pubmed/27585722
http://dx.doi.org/10.1186/s12918-016-0310-3
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