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Deep graph embedding for prioritizing synergistic anticancer drug combinations
Drug combinations are frequently used for the treatment of cancer patients in order to increase efficacy, decrease adverse side effects, or overcome drug resistance. Given the enormous number of drug combinations, it is cost- and time-consuming to screen all possible drug pairs experimentally. Curre...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Research Network of Computational and Structural Biotechnology
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7052513/ https://www.ncbi.nlm.nih.gov/pubmed/32153729 http://dx.doi.org/10.1016/j.csbj.2020.02.006 |
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author | Jiang, Peiran Huang, Shujun Fu, Zhenyuan Sun, Zexuan Lakowski, Ted M. Hu, Pingzhao |
author_facet | Jiang, Peiran Huang, Shujun Fu, Zhenyuan Sun, Zexuan Lakowski, Ted M. Hu, Pingzhao |
author_sort | Jiang, Peiran |
collection | PubMed |
description | Drug combinations are frequently used for the treatment of cancer patients in order to increase efficacy, decrease adverse side effects, or overcome drug resistance. Given the enormous number of drug combinations, it is cost- and time-consuming to screen all possible drug pairs experimentally. Currently, it has not been fully explored to integrate multiple networks to predict synergistic drug combinations using recently developed deep learning technologies. In this study, we proposed a Graph Convolutional Network (GCN) model to predict synergistic drug combinations in particular cancer cell lines. Specifically, the GCN method used a convolutional neural network model to do heterogeneous graph embedding, and thus solved a link prediction task. The graph in this study was a multimodal graph, which was constructed by integrating the drug-drug combination, drug-protein interaction, and protein–protein interaction networks. We found that the GCN model was able to correctly predict cell line-specific synergistic drug combinations from a large heterogonous network. The majority (30) of the 39 cell line-specific models show an area under the receiver operational characteristic curve (AUC) larger than 0.80, resulting in a mean AUC of 0.84. Moreover, we conducted an in-depth literature survey to investigate the top predicted drug combinations in specific cancer cell lines and found that many of them have been found to show synergistic antitumor activity against the same or other cancers in vitro or in vivo. Taken together, the results indicate that our study provides a promising way to better predict and optimize synergistic drug pairs in silico. |
format | Online Article Text |
id | pubmed-7052513 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Research Network of Computational and Structural Biotechnology |
record_format | MEDLINE/PubMed |
spelling | pubmed-70525132020-03-09 Deep graph embedding for prioritizing synergistic anticancer drug combinations Jiang, Peiran Huang, Shujun Fu, Zhenyuan Sun, Zexuan Lakowski, Ted M. Hu, Pingzhao Comput Struct Biotechnol J Short Survey Drug combinations are frequently used for the treatment of cancer patients in order to increase efficacy, decrease adverse side effects, or overcome drug resistance. Given the enormous number of drug combinations, it is cost- and time-consuming to screen all possible drug pairs experimentally. Currently, it has not been fully explored to integrate multiple networks to predict synergistic drug combinations using recently developed deep learning technologies. In this study, we proposed a Graph Convolutional Network (GCN) model to predict synergistic drug combinations in particular cancer cell lines. Specifically, the GCN method used a convolutional neural network model to do heterogeneous graph embedding, and thus solved a link prediction task. The graph in this study was a multimodal graph, which was constructed by integrating the drug-drug combination, drug-protein interaction, and protein–protein interaction networks. We found that the GCN model was able to correctly predict cell line-specific synergistic drug combinations from a large heterogonous network. The majority (30) of the 39 cell line-specific models show an area under the receiver operational characteristic curve (AUC) larger than 0.80, resulting in a mean AUC of 0.84. Moreover, we conducted an in-depth literature survey to investigate the top predicted drug combinations in specific cancer cell lines and found that many of them have been found to show synergistic antitumor activity against the same or other cancers in vitro or in vivo. Taken together, the results indicate that our study provides a promising way to better predict and optimize synergistic drug pairs in silico. Research Network of Computational and Structural Biotechnology 2020-02-15 /pmc/articles/PMC7052513/ /pubmed/32153729 http://dx.doi.org/10.1016/j.csbj.2020.02.006 Text en © 2020 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Short Survey Jiang, Peiran Huang, Shujun Fu, Zhenyuan Sun, Zexuan Lakowski, Ted M. Hu, Pingzhao Deep graph embedding for prioritizing synergistic anticancer drug combinations |
title | Deep graph embedding for prioritizing synergistic anticancer drug combinations |
title_full | Deep graph embedding for prioritizing synergistic anticancer drug combinations |
title_fullStr | Deep graph embedding for prioritizing synergistic anticancer drug combinations |
title_full_unstemmed | Deep graph embedding for prioritizing synergistic anticancer drug combinations |
title_short | Deep graph embedding for prioritizing synergistic anticancer drug combinations |
title_sort | deep graph embedding for prioritizing synergistic anticancer drug combinations |
topic | Short Survey |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7052513/ https://www.ncbi.nlm.nih.gov/pubmed/32153729 http://dx.doi.org/10.1016/j.csbj.2020.02.006 |
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