Identification of undefined off-targets of novel drugs and drug combinations with network-based in silico modeling and in vitro validation in hepatocellular carcinoma

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2022-06-24

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Sinoplu, Esra
Tunçbağ, Nurcan
Atalay, Rengül

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Introduction Hepatocellular carcinoma (HCC) is one of the most common and deadliest cancer worldwide. It is highly resistant to conventional chemotherapies due to the hyperactivation of many critical survival signaling pathways, and targeted agents can extend the patient’s survival for a few months. Furthermore, HCC patients have limited chemotherapeutics due to impaired liver functions. Thus, it is crucial to identify drugs and drug combinations for repurposing in HCC treatment. Material and Methods The pathways involved in hepatocarcinogenesis were used for the generation of a directed network, in which small molecule inhibitor drugs having at least one target protein were also integrated. The target proteins and their interactions were calculated; the functionality of the network was identified. Top nine ranked drugs were selected to validate their bioactivities on HCC cell lines in vitro by NCI-SRB assay. Transcriptomic analysis was done by Nanostring nCounter system using the PanCancer Pathways panel. Synergistic drug combinations were determined by analyzing NCI-SRB results using SynergyFinder. The molecular mechanisms induced by single drugs and drug combinations were studied by cell cycle assay, cell death assay, and western blotting. Results and Discussions Amrinone, Thalidomide, Chloroquine, Sunitinib, Pranlukast, Pseudoephedrine, Brigatinib, Lenvatinib, and Regorafenib were ranked as the first nine potent drugs. Except for Thalidomide, Pranlukast, Amrinone, Pseudoephedrine, the selected drugs, especially Brigatinib and Sunitinib, were remarkably bioactive on the HCC cells. Several cancer-related genes and pathways were altered in the presence of drugs, with Brigatinib having the highest number of differentially expressed genes (DEGs). The most enriched signaling pathways were Wnt-beta catenin, cell cycle, and MAPK. Brigatinib differed from the rest of the drugs in the pathway scoring matrix. Apart from the previously reported genes, HDAC1, HDAC2, HDAC4, FOS, DAXX, LEF1, RELA, and RUNX, etc., that were not associated with liver cancer therapeutics before were significantly enriched in the network. The bioactive drugs induced apoptosis involving Akt pathway inactivation due to cell cycle arrest in G0/G1 phase. Furthermore, combinations of Brigatinib with Amrinone, Sunitinib or Regorafenib, and Sunitinib with Pseudoephedrine or Chloroquine had synergistic effects on HCC cells. Conclusion Our results have shown that our network-based in silico model can be exploited for drug repurposing and novel target identification.

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https://www.eacr.org/user_uploads/files/2022-Event-Websites/EACR_2022/EACR_2022_Abstract_Book.pdf
https://hdl.handle.net/11511/107248

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Graduate School of Informatics, Conference / Seminar