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Stratification and prediction of drug synergy based on target functional similarity
Drug combinations can expand therapeutic options and address cancer’s resistance. However, the combinatorial space is enormous precluding its systematic exploration. Therefore, synergy prediction strategies are essential. We here present an approach to prioritise drug combinations in high-throughput...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7265486/ https://www.ncbi.nlm.nih.gov/pubmed/32487991 http://dx.doi.org/10.1038/s41540-020-0136-x |
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author | Yang, Mi Jaaks, Patricia Dry, Jonathan Garnett, Mathew Menden, Michael P. Saez-Rodriguez, Julio |
author_facet | Yang, Mi Jaaks, Patricia Dry, Jonathan Garnett, Mathew Menden, Michael P. Saez-Rodriguez, Julio |
author_sort | Yang, Mi |
collection | PubMed |
description | Drug combinations can expand therapeutic options and address cancer’s resistance. However, the combinatorial space is enormous precluding its systematic exploration. Therefore, synergy prediction strategies are essential. We here present an approach to prioritise drug combinations in high-throughput screens and to stratify synergistic responses. At the core of our approach is the observation that the likelihood of synergy increases when targeting proteins with either strong functional similarity or dissimilarity. We estimate the similarity applying a multitask machine learning approach to basal gene expression and response to single drugs. We tested 7 protein target pairs (representing 29 combinations) and predicted their synergies in 33 breast cancer cell lines. In addition, we experimentally validated predicted synergy of the BRAF/insulin receptor combination (Dabrafenib/BMS-754807) in 48 colorectal cancer cell lines. We anticipate that our approaches can be used for prioritization of drug combinations in large scale screenings, and to maximize the efficacy of drugs already known to induce synergy, ultimately enabling patient stratification. |
format | Online Article Text |
id | pubmed-7265486 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-72654862020-06-16 Stratification and prediction of drug synergy based on target functional similarity Yang, Mi Jaaks, Patricia Dry, Jonathan Garnett, Mathew Menden, Michael P. Saez-Rodriguez, Julio NPJ Syst Biol Appl Article Drug combinations can expand therapeutic options and address cancer’s resistance. However, the combinatorial space is enormous precluding its systematic exploration. Therefore, synergy prediction strategies are essential. We here present an approach to prioritise drug combinations in high-throughput screens and to stratify synergistic responses. At the core of our approach is the observation that the likelihood of synergy increases when targeting proteins with either strong functional similarity or dissimilarity. We estimate the similarity applying a multitask machine learning approach to basal gene expression and response to single drugs. We tested 7 protein target pairs (representing 29 combinations) and predicted their synergies in 33 breast cancer cell lines. In addition, we experimentally validated predicted synergy of the BRAF/insulin receptor combination (Dabrafenib/BMS-754807) in 48 colorectal cancer cell lines. We anticipate that our approaches can be used for prioritization of drug combinations in large scale screenings, and to maximize the efficacy of drugs already known to induce synergy, ultimately enabling patient stratification. Nature Publishing Group UK 2020-06-02 /pmc/articles/PMC7265486/ /pubmed/32487991 http://dx.doi.org/10.1038/s41540-020-0136-x Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Yang, Mi Jaaks, Patricia Dry, Jonathan Garnett, Mathew Menden, Michael P. Saez-Rodriguez, Julio Stratification and prediction of drug synergy based on target functional similarity |
title | Stratification and prediction of drug synergy based on target functional similarity |
title_full | Stratification and prediction of drug synergy based on target functional similarity |
title_fullStr | Stratification and prediction of drug synergy based on target functional similarity |
title_full_unstemmed | Stratification and prediction of drug synergy based on target functional similarity |
title_short | Stratification and prediction of drug synergy based on target functional similarity |
title_sort | stratification and prediction of drug synergy based on target functional similarity |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7265486/ https://www.ncbi.nlm.nih.gov/pubmed/32487991 http://dx.doi.org/10.1038/s41540-020-0136-x |
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