Table12_Network-based prediction of anti-cancer drug combinations.DOCX

Drug combinations have emerged as a promising therapeutic approach in cancer treatment, aimed at overcoming drug resistance and improving the efficacy of monotherapy regimens. However, identifying effective drug combinations has traditionally been time-consuming and often dependent on chance discoveries. Therefore, there is an urgent need to explore alternative strategies to support experimental research. In this study, we propose network-based prediction models to identify potential drug combinations for 11 types of cancer. Our approach involves extracting 55,299 associations from literature and constructing human protein interactomes for each cancer type. To predict drug combinations, we measure the proximity of drug-drug relationships within the network and employ a correlation clustering framework to detect functional communities. Finally, we identify 61,754 drug combinations. Furthermore, we analyze the network configurations specific to different cancer types and identify 30 key genes and 21 pathways. The performance of these models is subsequently assessed through in vitro assays, which exhibit a significant level of agreement. These findings represent a valuable contribution to the development of network-based drug combination design strategies, presenting potential solutions to overcome drug resistance and enhance cancer treatment outcomes.

药物联合疗法已成为癌症治疗中极具前景的治疗策略，旨在克服单药治疗耐药性并提升单一疗法的疗效。然而，传统上筛选有效药物联合疗法耗时耗力，且往往依赖偶然发现。因此，亟需探索辅助实验研究的新型替代策略。本研究构建了基于网络的预测模型，用于识别11种癌症的潜在药物联合疗法。研究过程中，我们从文献中提取了55,299条关联信息，并针对每种癌症类型构建了人类蛋白质相互作用组（protein interactomes）。为预测药物联合疗法，我们通过量化网络内药物-药物关联的邻近性，并采用相关聚类框架来检测功能社群。最终共筛选得到61,754种潜在药物联合疗法。此外，本研究还针对不同癌症类型的网络特征进行了分析，共识别出30个关键基因与21条信号通路。随后通过体外实验对模型性能进行了验证，结果显示二者具有显著的一致性。本研究成果为基于网络的药物联合疗法设计策略的发展提供了重要参考，有望为克服癌症耐药性、提升癌症治疗效果提供新的解决方案。