Optimal control nodes in disease-perturbed networks as targets for combination therapy
Optimal control nodes in disease-perturbed networks as targets for combination therapy
复制标题
疾病扰动网络中的最佳控制节点作为联合治疗的目标
DOI：
10.1038/s41467-019-10215-y
复制
发表时间：
2019-05-16
影响因子：
16.6
通讯作者：
Tan, Kai
中科院分区：
文献类型：
--
作者：
Hu, Yuxuan;Chen, Chia-hui;Tan, Kai
大多数联合疗法是基于现有药物的靶点开发的，这些靶点仅代表人类蛋白质组的一小部分。我们引入了一种基于网络可控性的方法 OptiCon，用于从头识别协同调节剂作为联合治疗的候选者。这些调节因子共同对疾病中失调的基因施加最大程度的控制，但对未受干扰的基因施加最小程度的控制。使用三种癌症类型的数据，我们表明 68% 的预测调节因子要么是已知的药物靶标，要么在癌症发展中发挥关键作用。预测的与副作用相关的已知蛋白质的调节因子已被耗尽。预测的协同作用得到了疾病特异性和临床相关的合成致死相互作用和实验验证的支持。受协同调节子调节的基因的很大一部分参与共同调节子网络之间的密集相互作用，并导致治疗抵抗。 OptiCon 代表了系统性和从头识别细胞状态转变背后的协同调节因子的通用框架。
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.