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 organization: Medicum
Institute for Molecular Medicine Finland
Director and Common Matters
Staff Services
University of Helsinki
Research Program in Systems Oncology
Faculty of Medicine
Doctoral Programme in Integrative Life Science
Computational Systems Medicine
Immunobiology Research Program
Department of Diagnostics and Therapeutics
HUSLAB
Department of Medical and Clinical Genetics
University Management
Krister Wennerberg / Principal Investigator
Tero Aittokallio / Principal Investigator
Bioinformatics
Date: 2019-07-08
Language: eng
Number of pages: 11
Belongs to series: npj Systems Biology and Applications
ISSN: 2056-7189
DOI: https://doi.org/10.1038/s41540-019-0098-z
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
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion


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