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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
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.
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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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