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

NPJ Systems Biology and Applications
Jing TangTero Aittokallio


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 targe...Continue Reading


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Oct 12, 2020·Journal of the American Medical Informatics Association : JAMIA·Yejin KimXiaoqian Jiang
Dec 19, 2020·Computational and Structural Biotechnology Journal·Tianduanyi WangTero Aittokallio

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Methods Mentioned

gene knock-downs
interaction predictions

Software Mentioned

SGNS2 ( Stochastic Genetic Network Simulator )

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