Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xian 710072, China.
School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China.
Nucleic Acids Res. 2021 Apr 19;49(7):e37. doi: 10.1093/nar/gkaa1272.
Multiple driver genes in individual patient samples may cause resistance to individual drugs in precision medicine. However, current computational methods have not studied how to fill the gap between personalized driver gene identification and combinatorial drug discovery for individual patients. Here, we developed a novel structural network controllability-based personalized driver genes and combinatorial drug identification algorithm (CPGD), aiming to identify combinatorial drugs for an individual patient by targeting personalized driver genes from network controllability perspective. On two benchmark disease datasets (i.e. breast cancer and lung cancer datasets), performance of CPGD is superior to that of other state-of-the-art driver gene-focus methods in terms of discovery rate among prior-known clinical efficacious combinatorial drugs. Especially on breast cancer dataset, CPGD evaluated synergistic effect of pairwise drug combinations by measuring synergistic effect of their corresponding personalized driver gene modules, which are affected by a given targeting personalized driver gene set of drugs. The results showed that CPGD performs better than existing synergistic combinatorial strategies in identifying clinical efficacious paired combinatorial drugs. Furthermore, CPGD enhanced cancer subtyping by computationally providing personalized side effect signatures for individual patients. In addition, CPGD identified 90 drug combinations candidates from SARS-COV2 dataset as potential drug repurposing candidates for recently spreading COVID-19.
在精准医学中，个体患者样本中的多个驱动基因可能导致对个别药物的耐药性。然而，目前的计算方法尚未研究如何填补个性化驱动基因识别与个体化患者组合药物发现之间的空白。在这里，我们开发了一种新的基于结构网络可控性的个性化驱动基因和组合药物识别算法（CPGD），旨在从网络可控性的角度针对个性化驱动基因来识别个体化患者的组合药物。在两个基准疾病数据集（即乳腺癌和肺癌数据集）上，CPGD 的性能在发现先前已知临床有效组合药物方面优于其他最先进的驱动基因聚焦方法。特别是在乳腺癌数据集上，CPGD 通过测量其相应个性化驱动基因模块的协同作用来评估成对药物组合的协同作用，这些模块受给定靶向个性化驱动基因药物集的影响。结果表明，CPGD 在识别临床有效的配对组合药物方面比现有的协同组合策略表现更好。此外，CPGD 通过为个体患者计算提供个性化的副作用特征来增强癌症亚型分类。此外，CPGD 从 SARS-COV2 数据集中确定了 90 种药物组合候选物，作为最近传播的 COVID-19 的潜在药物再利用候选物。
