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.
诱导癌细胞凋亡是抗癌治疗的主要目标，但由于耐药性的存在，单一药物治愈癌症难以实现。癌细胞中复杂的分子网络导致对单靶点药物产生异质性反应，从而引发适应性药物应答。本研究发现，靶向药物扰动可触发分子调控网络内多稳态基序间的状态冲突，导致异质性药物反应。然而，我们揭示通过适当调控这些基序间的连接分子，能够协同最小化异质性反应并克服耐药性。我们提取了靶节点扰动驱动的布尔网络基本细胞响应动态，并开发出一种算法以识别能够减少异质性药物反应的协同组合靶点。通过典型网络模型和既往研究中的胃癌模型验证，本研究所提算法识别的靶点相较于其他控制理论能更有效地驱动网络至预期状态。值得注意的是，该方法提出了一对新型协同控制靶点，可增强抗癌药物疗效以克服适应性耐药。
肿瘤(癌症)患者之家