Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
Prinses Maxima Center for Pediatric Oncology, 3584 CS Utrecht, Utrech, the Netherlands.
Nat Commun. 2022 Apr 19;13(1):2128. doi: 10.1038/s41467-022-29793-5.
Combination therapy is preferred over single-targeted monotherapies for cancer treatment due to its efficiency and safety. However, identifying effective drug combinations costs time and resources. We propose a method for identifying potential drug combinations by bipartite network modelling of patient-related drug response data, specifically the Beat AML dataset. The median of cell viability is used as a drug potency measurement to reconstruct a weighted bipartite network, model drug-biological sample interactions, and find the clusters of nodes inside two projected networks. Then, the clustering results are leveraged to discover effective multi-targeted drug combinations, which are also supported by more evidence using GDSC and ALMANAC databases. The potency and synergy levels of selective drug combinations are corroborated against monotherapy in three cell lines for acute myeloid leukaemia in vitro. In this study, we introduce a nominal data mining approach to improving acute myeloid leukaemia treatment through combinatorial therapy.
由于其效率和安全性，联合治疗优于单一靶向的单药治疗，用于癌症治疗。然而，确定有效的药物组合需要时间和资源。我们提出了一种通过对与患者相关的药物反应数据进行二分网络建模来识别潜在药物组合的方法，特别是针对 Beat AML 数据集。细胞活力的中位数被用作药物效力的测量指标，以重建加权二分网络，模拟药物-生物样本相互作用，并找到两个投影网络内部的节点簇。然后，利用聚类结果发现有效的多靶点药物组合，并用 GDSC 和 ALMANAC 数据库中的更多证据进行支持。针对体外急性髓系白血病，在三种细胞系中对选择性药物组合的效力和协同水平进行了与单药治疗的对比。在这项研究中，我们引入了一种名义数据挖掘方法，通过组合治疗来改善急性髓系白血病的治疗效果。
