Reducing State Conflicts between Network Motifs Synergistically Enhances Cancer Drug Effects and Overcomes Adaptive Resistance

Abstract

Simple Summary The heterogeneous response of cancer cells to targeted drugs is associated with the state transition dynamics of a molecular network. Identifying combinatorial drug targets to compensate for these heterogeneous responses can counteract adaptive resistance in cancer. To achieve this, we developed an algorithm called "merged transition map", which explores essential state transition dynamics to identify combinatorial drug targets. Our analysis showed that drug-induced state conflicts within the molecular regulatory motifs of a network can result in heterogeneous responses. Moreover, we found that addressing these conflicts with additional perturbations can synergistically improve drug efficacy. Compared to other network control algorithms, our approach showed higher performance in drug efficacy of the suggested combinatorial target pairs with reduced computational complexity. Furthermore, by applying the MTM on a Boolean network model, we identified a new target combination that induces apoptosis in gastric cancer, supported by previous experimental data.Abstract Inducing apoptosis in cancer cells is a primary goal in anti-cancer therapy, but curing cancer with a single drug is unattainable due to drug resistance. The complex molecular network in cancer cells causes heterogeneous responses to single-target drugs, thereby inducing an adaptive drug response. Here, we showed that targeted drug perturbations can trigger state conflicts between multi-stable motifs within a molecular regulatory network, resulting in heterogeneous drug responses. However, we revealed that properly regulating an interconnecting molecule between these motifs can synergistically minimize the heterogeneous responses and overcome drug resistance. We extracted the essential cellular response dynamics of the Boolean network driven by the target node perturbation and developed an algorithm to identify a synergistic combinatorial target that can reduce heterogeneous drug responses. We validated the proposed approach using exemplary network models and a gastric cancer model from a previous study by showing that the targets identified with our algorithm can better drive the networks to desired states than those with other control theories. Of note, our approach suggests a new synergistic pair of control targets that can increase cancer drug efficacy to overcome adaptive drug resistance.