Optimal control nodes in disease-perturbed networks as targets for combination therapy

Nat Commun. 2019 May 16;10(1):2180. doi: 10.1038/s41467-019-10215-y.


Most combination therapies are developed based on targets of existing drugs, which only represent a small portion of the human proteome. We introduce a network controllability-based method, OptiCon, for de novo identification of synergistic regulators as candidates for combination therapy. These regulators jointly exert maximal control over deregulated genes but minimal control over unperturbed genes in a disease. Using data from three cancer types, we show that 68% of predicted regulators are either known drug targets or have a critical role in cancer development. Predicted regulators are depleted for known proteins associated with side effects. Predicted synergy is supported by disease-specific and clinically relevant synthetic lethal interactions and experimental validation. A significant portion of genes regulated by synergistic regulators participate in dense interactions between co-regulated subnetworks and contribute to therapy resistance. OptiCon represents a general framework for systemic and de novo identification of synergistic regulators underlying a cellular state transition.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • A549 Cells
  • Algorithms
  • Antineoplastic Combined Chemotherapy Protocols / pharmacology*
  • Antineoplastic Combined Chemotherapy Protocols / therapeutic use
  • Computational Biology / methods*
  • Datasets as Topic
  • Drug Synergism
  • Drug Therapy, Combination / methods
  • Female
  • Gene Expression Profiling
  • Gene Expression Regulation, Neoplastic / drug effects
  • Gene Regulatory Networks / drug effects*
  • Gene Regulatory Networks / genetics
  • HEK293 Cells
  • Humans
  • MCF-7 Cells
  • Models, Genetic
  • Molecular Targeted Therapy / methods
  • Mutation
  • Neoplasms / drug therapy
  • Neoplasms / genetics*
  • Neoplasms / pathology
  • Protein Interaction Maps / drug effects*
  • Protein Interaction Maps / genetics
  • Signal Transduction / drug effects
  • Signal Transduction / genetics