School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi, 710071, China.
Division of Oncology and Center for Childhood Cancer Research, 4004 CTRB, Children's Hospital of Philadelphia, 3501 Civic Center Boulevard, Philadelphia, PA, 19104, USA.
Nat Commun. 2019 May 16;10(1):2180. doi: 10.1038/s41467-019-10215-y.
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.
大多数联合治疗方法都是基于现有药物的靶点开发的，而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，用于从头鉴定协同调节剂作为联合治疗的候选药物。这些调节剂在疾病中对失调基因施加最大的控制，但对未受干扰的基因施加最小的控制。使用来自三种癌症类型的数据，我们表明，预测的调节剂中有 68%是已知的药物靶点，或者在癌症发展中具有关键作用。预测的调节剂中缺乏与副作用相关的已知蛋白。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以及实验验证的支持。受协同调节剂调控的基因的很大一部分参与了共同调控子网络之间的密集相互作用，并导致治疗耐药性。OptiCon 代表了一种系统的和从头鉴定细胞状态转变背后协同调节剂的通用框架。
