Abstract 996: <i>In silico</i> modeling and <i>in vitro</i> validation of undefined off-target of drugs in hepatocellular carcinoma
Authors: Esra Sinoplu, Nurcan Tuncbag, Deniz C. Kahraman, Rengul C. Atalay
Journal: Cancer Research (2019)

Abstract Hepatocellular carcinoma (HCC) is the 5th most common and 2nd deadliest cancer worldwide. The molecular mechanism in HCC involves activated cell survival pathways, but there is a lack of the significant oncogenic drivers for targeted therapies. Thus, novel drugs and targets that can be exploited, are required. The pathways reported to be involved in hepatocarcinogenesis were retrieved from KEGG database (HCC, MAPK, calcium, p53, PI3K-Akt, Wnt, TGF-B, NAFLD and apoptosis) and a directed network with 801 nodes was created. In parallel, small molecule inhibitor drugs having at least one target protein in this network were collected from DrugBank database and integrated into the merged network. By the means of in silico perturbation attack strategies, target proteins and their interactions were calculated as the drug effectiveness, changes in the efficiency of the signaling network, in the number of feedback cycles, and in the network, functionality were identified and ranked. Finally, Brigatinib, Regorafenib, Sunitinib, Thalidomide, Pranlukast, Lenvatinib, and ChloroquineP were identified to have off-target effects specific to HCC pathways. The selected drugs along with Sorafenib were then tested and compared for their cytotoxicity on HepG2, Huh7, Mahlavu and SNU475 cells. Except Thalidomide and Pranlukast, selected drugs were significantly bioactive on the cells with IC50 values below 10µM. We then examined the expression of PanCancer gene panel (NanoString) in Mahlavu cells treated with the selected 8 drugs. As expected a number of genes and pathways were differentially altered in the presence of the drugs and multikinase inhibitors Sorafenib and Regorafenib had similar DEG patterns. In gene expression correlation analysis matrix, Sorafenib differed from the rest of the compounds particularly from Brigatinib while highest correlation was observed with Regorafenib. Although Brigatinib differs from other drugs in terms of its targets, it resulted in the highest number of differentially expressed genes. Thalidomide gene profile was very similar to Sorafenib. This data supports the possible exploitation of Thalidomide for liver cancer therapeutics. We then merged and filtered cancer-associated pathways (MAPK, STAT, PI3K, RAS, Cell Cycle, Apoptosis, Hedgehog, Wnt, DNA Damage Control, Transcriptional Regulation, Chromatin Modification, and TGF-B) based on the differentially expressed genes in Mahlavu cells treated with the 8 drugs. Previously reported genes such as APC, AXIN, CTNBB1, CAMK, TGF, NGF, ERBB2 were significantly enriched in the network along with the genes that has not been associated with liver cancer therapeutics. The enriched network analysis can be exploited for both drug repurposing and novel target identification. Citation Format: Esra Sinoplu, Nurcan Tuncbag, Deniz C. Kahraman, Rengul C. Atalay. In silico modeling and in vitro validation of undefined off-target of drugs in hepatocellular carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 996.