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

Facilitating the design of combination therapy for cancer using multipartite network models: Emphasis on acute myeloid leukaemia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Facilitating the design of combination therapy for cancer using multipartite network models: Emphasis on acute myeloid leukaemia Mohieddin Jafari, Mehdi Mirzaie, Jie Bao, Farnaz Barneh, Shuyu Zheng, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-577256/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Apr, 2022 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract From the drug discovery perspective, combination therapy is recommended for cancer treatment due to its 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 for predicting potential drug combinations and patient subclasses by constructing multipartite networks using drug-response data on patient samples. In this 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 modelling in combinatorial cancer therapy. We used the median values of cell viability to compare drug potency and reconstruct a weighted bipartite network that 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 the networks. Chemical structures, drug-target networks, protein– protein interactions, and signalling 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 synergy levels, which were supported with more evidence using the DrugComb and ALMANAC databases. Furthermore, we confirmed the potency of selective combinations of drugs against monotherapy in in vitro experiments using three acute myeloid leukaemia (AML) cell lines. Taken together, this study presents an innovative data-driven strategy based on multipartite networks to suggest potential drug combinations to improve AML treatment. Cancer Biology Oncology Bioinformatics Computational Biology drug therapy cancer treatments myeloid leukaemia Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryFig1.pdf Supplementary Figure 1 SupplementaryFig2.pdf Supplementary Figure 2 Cite Share Download PDF Status: Published Journal Publication published 19 Apr, 2022 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-577256","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":31930228,"identity":"b909ce9c-51e1-4a58-b540-6bcf5dd7797c","order_by":0,"name":"Mohieddin 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