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

Jing Tang1,2,3, Prson Gautam1, Abhishekh Gupta1,4

  • 11Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.

NPJ Systems Biology and Applications
|July 18, 2019
PubMed

Insights

This study introduces a polypharmacology approach to identify effective drug combinations for triple-negative breast cancer. Combining Aurora B and ZAK kinase inhibitors shows promise for enhanced cancer cell growth inhibition.

Area of Science:

  • Oncology
  • Pharmacology
  • Systems Biology

Background:

  • Cancer cells develop drug resistance through redundant pathways, necessitating combination therapies.
  • Targeting multiple pathways simultaneously can overcome resistance and improve clinical outcomes.

Purpose of the Study:

  • To develop and validate a polypharmacology strategy for predicting synergistic drug combinations in MDA-MB-231 triple-negative breast cancer cells.
  • To identify specific kinase targets whose combined inhibition leads to enhanced anti-cancer effects.

Main Methods:

  • Network pharmacology modeling was used to predict synergistic drug combinations.
  • Kinome-wide drug-target profiles and gene expression data guided target identification.
  • Combinatorial siRNA, CRISPR/Cas9, and drug experiments validated synergistic interactions.
  • Dynamic simulation of signaling networks elucidated the underlying mechanisms.

Main Results:

  • A synergistic interaction between Aurora B and ZAK kinase inhibition was identified.
  • Combined inhibition resulted in enhanced growth inhibition and cytotoxicity of cancer cells.
  • A cross-talk between p53 and p38 pathways was implicated in the observed synergy.

Conclusions:

  • Polypharmacological modeling can systematically identify effective drug combinations for cancer treatment.
  • Targeting Aurora B and ZAK kinases offers a potential therapeutic strategy for triple-negative breast cancer.
  • This approach may lead to clinically actionable and personalized cancer treatment options.
Keywords:
CancerComputational biology and bioinformatics

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