Facilitating the design of combination therapy in cancer using multipartite network models: Emphasis on acute myeloid leukemia

From the drug discovery perspective, combination therapy is recommended in cancer due to efficiency and safety compared to the common cytotoxic and single-targeted monotherapies. However, identifying effective drug combinations is time- and cost-consuming. Here, we offer a novel strategy of predicting potential drug combinations and patient subclasses by constructing multipartite networks using drug response data on patient samples. In the present study, we used Beat AML and GDSC, two comprehensive datasets based on patient-derived and cell line-based samples, to show the potential of multipartite network modeling in cancer combinatorial therapy. We used the median values of cell viability to compare drug potency and reconstruct a weighted bipartite network, which models the interaction of drugs and biological samples. Then, clusters of network communities were identified in two projected networks based on the topological structure of networks. Chemical structures, drug-target networks, protein-protein interactions, and signaling networks were used to corroborate the intra-cluster homogeneity. We further leveraged the community structures within the drug-based multipartite networks to discover effective multi-targeted drug combinations, and the synergy levels which were supported with more evidence using the DrugComb and the ALMANAC databases. Furthermore, we confirmed the potency of selective combinations of drugs against monotherapy in vitro experiment using three acute myeloid leukemia (AML) cell lines. Taken together, this study presents an innovative data-driven strategy based on multipartite networks to suggest potential drug combinations to improve treatment of AML.