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

• DOI: 10.1038/s41467-019-10215-y
• Publication Date: 2019-05-16T10:04:14Z
• Authors: Yuxuan Hu; Chia-hui Chen; Yang-yang Ding; Xiao Wen; Bingbo Wang; Lin Gao; Kai Tan
• Keywords: Science; 0206 medical engineering; Datasets as Topic; 02 engineering and technology; Article; Neoplasms; Antineoplastic Combined Chemotherapy Protocols; Humans; Gene Regulatory Networks; Molecular Targeted Therapy; Protein Interaction Maps; Models, Genetic; Gene Expression Profiling; Q; Computational Biology; Drug Synergism; 3. Good health; Gene Expression Regulation, Neoplastic; HEK293 Cells; A549 Cells; Mutation; MCF-7 Cells; Drug Therapy, Combination; Female; Algorithms; Signal Transduction

AbstractMost 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.