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

Yuxuan Hu,Kai Tan,Chia-Hui Chen,Yang-Yang Ding,Bingbo Wang,Lin Gao,Xiao Wen

doi:10.1038/s41467-019-10215-y

Abstract

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.

Highlights

Most combination therapies are developed based on targets of existing drugs, which only represent a small portion of the human proteome
A structural control configuration (SCC) of a network characterizes a topological skeleton for controlling the dynamics of the network from any initial state to any desired final state using a minimal set of driver nodes[14,20]
Compared to VIPER and ranking-system of anti-cancer synergy (RACS), we found that Optimal control region (OCR) controlled by optimal control nodes (OCNs) pairs predicted by OptiCon have significantly higher enrichment (Wilcoxon test p-values < 0.05) for both experimentally derived (Fig. 3d–f) and clinically relevant synthetic lethal interactions (Fig. 3g–i)

Summary

Introduction

Most combination therapies are developed based on targets of existing drugs, which only represent a small portion of the human proteome. A number of computational methods have been developed for discovering combination therapeutic targets[6,7], including network-based approaches[8,9,10] These network-based approaches are motivated by the observation that multiple disease relevant genes, rather than a single gene, often interact within a complex gene network, resulting in a disease phenotype and drug resistance. A representative network-based method is the ranking-system of anti-cancer synergy (RACS) algorithm[9] It predicts synergistic anticancer drugs using drugtreated transcriptome profiles and protein–protein interaction network and KEGG pathways. Using a synergy score that combines both genetic mutation and gene functional interaction information, OptiCon identifies a set of synergistic OCNs as key regulators in the disease-perturbed network, which can serve as candidate targets for combination therapy

Methods

Results

Conclusion