Network pharmacology modeling identifies synergistic Aurora B and ZAK interaction in triple-negative breast cancer

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http://hdl.handle.net/10138/307149

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Tang , J , Gautam , P , Gupta , A , He , L , Timonen , S , Akimov , Y , Wang , W , Szwajda , A , Jaiswal , A , Turei , D , Yadav , B , Kankainen , M , Saarela , J , Saez-Rodriguez , J , Wennerberg , K & Aittokallio , T 2019 , ' Network pharmacology modeling identifies synergistic Aurora B and ZAK interaction in triple-negative breast cancer ' , npj Systems Biology and Applications , vol. 5 , no. 1 , 20 . https://doi.org/10.1038/s41540-019-0098-z

Title: Network pharmacology modeling identifies synergistic Aurora B and ZAK interaction in triple-negative breast cancer
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
Contributor: University of Helsinki, Medicum
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Doctoral Programme in Integrative Life Science
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Computational Systems Medicine
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Immunobiology Research Program
University of Helsinki, HUSLAB
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Krister Wennerberg / Principal Investigator
University of Helsinki, Tero Aittokallio / Principal Investigator
Date: 2019-07-08
Language: eng
Number of pages: 11
Belongs to series: npj Systems Biology and Applications
ISSN: 2056-7189
URI: http://hdl.handle.net/10138/307149
Abstract: 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.
Subject: 1182 Biochemistry, cell and molecular biology
3122 Cancers
3111 Biomedicine
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