Network pharmacology modeling identifies synergistic Aurora B and ZAK interaction in triple-negative breast cancer
Cancer cells with heterogeneous mutation landscapes and extensive functional redundancy easily develop resistance to monotherapies by emerging activation of compensating or bypassing pathways. To achieve more effective and sustained clinical responses, synergistic interactions of multiple druggable...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6614366/ https://www.ncbi.nlm.nih.gov/pubmed/31312514 http://dx.doi.org/10.1038/s41540-019-0098-z |
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author | Tang, Jing Gautam, Prson Gupta, Abhishekh He, Liye Timonen, Sanna Akimov, Yevhen Wang, Wenyu Szwajda, Agnieszka Jaiswal, Alok Turei, Denes Yadav, Bhagwan Kankainen, Matti Saarela, Jani Saez-Rodriguez, Julio Wennerberg, Krister Aittokallio, Tero |
author_facet | Tang, Jing Gautam, Prson Gupta, Abhishekh He, Liye Timonen, Sanna Akimov, Yevhen Wang, Wenyu Szwajda, Agnieszka Jaiswal, Alok Turei, Denes Yadav, Bhagwan Kankainen, Matti Saarela, Jani Saez-Rodriguez, Julio Wennerberg, Krister Aittokallio, Tero |
author_sort | Tang, Jing |
collection | PubMed |
description | Cancer cells with heterogeneous mutation landscapes and extensive functional redundancy easily develop resistance to monotherapies by emerging activation of compensating or bypassing pathways. To achieve more effective and sustained clinical responses, synergistic interactions of multiple druggable targets that inhibit redundant cancer survival pathways are often required. Here, we report a systematic polypharmacology strategy to predict, test, and understand the selective drug combinations for MDA-MB-231 triple-negative breast cancer cells. We started by applying our network pharmacology model to predict synergistic drug combinations. Next, by utilizing kinome-wide drug-target profiles and gene expression data, we pinpointed a synergistic target interaction between Aurora B and ZAK kinase inhibition that led to enhanced growth inhibition and cytotoxicity, as validated by combinatorial siRNA, CRISPR/Cas9, and drug combination experiments. The mechanism of such a context-specific target interaction was elucidated using a dynamic simulation of MDA-MB-231 signaling network, suggesting a cross-talk between p53 and p38 pathways. Our results demonstrate the potential of polypharmacological modeling to systematically interrogate target interactions that may lead to clinically actionable and personalized treatment options. |
format | Online Article Text |
id | pubmed-6614366 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-66143662019-07-16 Network pharmacology modeling identifies synergistic Aurora B and ZAK interaction in triple-negative breast cancer Tang, Jing Gautam, Prson Gupta, Abhishekh He, Liye Timonen, Sanna Akimov, Yevhen Wang, Wenyu Szwajda, Agnieszka Jaiswal, Alok Turei, Denes Yadav, Bhagwan Kankainen, Matti Saarela, Jani Saez-Rodriguez, Julio Wennerberg, Krister Aittokallio, Tero NPJ Syst Biol Appl Article Cancer cells with heterogeneous mutation landscapes and extensive functional redundancy easily develop resistance to monotherapies by emerging activation of compensating or bypassing pathways. To achieve more effective and sustained clinical responses, synergistic interactions of multiple druggable targets that inhibit redundant cancer survival pathways are often required. Here, we report a systematic polypharmacology strategy to predict, test, and understand the selective drug combinations for MDA-MB-231 triple-negative breast cancer cells. We started by applying our network pharmacology model to predict synergistic drug combinations. Next, by utilizing kinome-wide drug-target profiles and gene expression data, we pinpointed a synergistic target interaction between Aurora B and ZAK kinase inhibition that led to enhanced growth inhibition and cytotoxicity, as validated by combinatorial siRNA, CRISPR/Cas9, and drug combination experiments. The mechanism of such a context-specific target interaction was elucidated using a dynamic simulation of MDA-MB-231 signaling network, suggesting a cross-talk between p53 and p38 pathways. Our results demonstrate the potential of polypharmacological modeling to systematically interrogate target interactions that may lead to clinically actionable and personalized treatment options. Nature Publishing Group UK 2019-07-08 /pmc/articles/PMC6614366/ /pubmed/31312514 http://dx.doi.org/10.1038/s41540-019-0098-z Text en © The Author(s) 2019 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 Tang, Jing Gautam, Prson Gupta, Abhishekh He, Liye Timonen, Sanna Akimov, Yevhen Wang, Wenyu Szwajda, Agnieszka Jaiswal, Alok Turei, Denes Yadav, Bhagwan Kankainen, Matti Saarela, Jani Saez-Rodriguez, Julio Wennerberg, Krister Aittokallio, Tero Network pharmacology modeling identifies synergistic Aurora B and ZAK interaction in triple-negative breast cancer |
title | Network pharmacology modeling identifies synergistic Aurora B and ZAK interaction in triple-negative breast cancer |
title_full | Network pharmacology modeling identifies synergistic Aurora B and ZAK interaction in triple-negative breast cancer |
title_fullStr | Network pharmacology modeling identifies synergistic Aurora B and ZAK interaction in triple-negative breast cancer |
title_full_unstemmed | Network pharmacology modeling identifies synergistic Aurora B and ZAK interaction in triple-negative breast cancer |
title_short | Network pharmacology modeling identifies synergistic Aurora B and ZAK interaction in triple-negative breast cancer |
title_sort | network pharmacology modeling identifies synergistic aurora b and zak interaction in triple-negative breast cancer |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6614366/ https://www.ncbi.nlm.nih.gov/pubmed/31312514 http://dx.doi.org/10.1038/s41540-019-0098-z |
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