Transcriptomics-Based Drug Repurposing Approach Identifies Novel Drugs against Sorafenib-Resistant Hepatocellular Carcinoma

Abstract

Objective: Hepatocellular carcinoma (HCC) is frequently diagnosed in patients with late-stage disease who are ineligible for curative surgical therapies. The majority of patients become resistant to sorafenib, the only approved first-line therapy for advanced cancer, underscoring the need for newer, more effective drugs. The purpose of this study is to expedite identification of novel drugs against sorafenib resistant (SR)-HCC. Methods: We employed a transcriptomics-based drug repurposing method termed connectivity mapping using gene signatures from in vitro-derived SR Huh7 HCC cells. For proof of concept validation, we focused on drugs that were FDA-approved or under clinical investigation and prioritized two anti-neoplastic agents (dasatinib and fostamatinib) with targets associated with HCC. We also prospectively validated predicted gene expression changes in drug-treated SR Huh7 cells as well as identified and validated the targets of Fostamatinib in HCC. Results: Dasatinib specifically reduced the viability of SR-HCC cells that correlated with up-regulated activity of SRC family kinases, its targets, in our SR-HCC model. However, fostamatinib was able to inhibit both parental and SR HCC cells in vitro and in xenograft models. Ingenuity pathway analysis of fostamatinib gene expression signature from LINCS predicted JAK/STAT, PI3K/AKT, ERK/MAPK pathways as potential targets of fostamatinib that were validated by Western blot analysis. Fostamatinib treatment reversed the expression of genes that were deregulated in SR HCC. Conclusion: We provide proof of concept evidence for the validity of this drug repurposing approach for SR-HCC with implications for personalized medicine.

Keywords: dasatinib; drug repurposing; drug resistance; fostamatinib; hepatocellular carcinoma; sorafenib.

Figure 1

Overview of computational drug repurposing workflow and drug target network. Gene expression signatures of experimental models of hepatocellular carcinoma (HCC) sorafenib resistance were (i) assessed for prognostic significance, and (ii) queried against gene expression signatures characterizing drug perturbations in the HepG2 cell line contained in the Library of Integrated Network-based Cellular Signatures (LINCS) database. Connectivity scores were calculated by the rank-based, non-parametric weighted Kolgomorov-Smirnov (KS) statistic. Drugs with negative connectivity scores (i.e., anti-correlated) represent those that are hypothesized to reverse HCC sorafenib resistance gene signature. Drug candidates were further prioritized based on FDA approval/clinical investigation status, known anti-neoplastic activity and literature evidence for drug target genes associated with HCC. Two drugs were subsequently selected for in vitro validation.

Figure 2

Sorafenib resistance gene signatures—prognostic significance and drug repurposing candidates. (A) Percentage of primary tumor samples in The Cancer Genome Atlas (TCGA) liver hepatocellular carcinoma (LIHC) dataset containing the four sorafenib resistance gene signatures (SR+). (B) Kaplan–Meier plot of overall survival of TCGA LIHC patients with primary tumors harboring the Huh7-R-A7 SR gene signature (n = 181 SR+) and those with primary tumors containing the inverse SR gene signature (n = 190 SR-). Heatmap visualization of hierarchical clustering analysis of connectivity scores for LINCS drugs derived from gene expression profiles of (C) HCC cell lines (n = 18) from the CellMiner database and (D) HCC sorafenib resistance (SR) experimental models.

Figure 3

Validation of predicted drug candidates for action against sorafenib resistant HCC. Increased sensitivity of sorafenib-resistant Huh7 cells to dasatinib (A) and fostamatinib (B), as measured 48 h post-treatment using CellTiter-Glo viability assay. Growth of sorafenib-resistant Huh7 cells is inhibited by dasatinib (C) and fostamatinib (D), alone and in combination with sorafenib, as measured by colony formation assay 2 weeks post-treatment.

Figure 4

Mechanism of action of predicted drug candidates. (A) Enhanced phosphorylation of the Src family of kinases in sorafenib-resistant cells relative to sensitive cells, as measured by the Proteome Profiler Human Phospho-Kinase Array (* p < 0.05, ** p < 0.010, two-tailed t test) at 48 h. (B) Ingenuity pathway analysis of gene expression profile from fostamatinib-treated HepG2 cells reveals significantly altered pathways.

Figure 5

In vivo efficacy of fostamatinib and signaling pathway. (A) NSG mice bearing subcutaneous tumors of MHCCLM3 cells were treated with vehicle or fostamatinib (Fos) for 4 weeks. Body weights of tumor-bearing mice (BW) and size of the tumor (TS) during treatment, as well as pictures of tumors and tumor weights at the end of the treatment, are provided. (B) Western blot analysis of indicated proteins and quantification of phosphorylated protein normalized to total protein in Huh7 and Huh7-SR cells treated with vehicle (DMSO) or fostamatinib (Fos). Uncropped Western blots are shown in Figure S7. * Indicates a statistically significant difference between the two groups at p = 0.05.

Figure 6

Impact of clinical and demographic factors on drug repurposing candidates and sorafenib resistance gene signature. (A) Heatmap visualization of hierarchical clustering analysis of connectivity scores for LINCS drugs derived from gene expression profiles of HCC patient tumor datasets from the GEO database representing distinct etiologies. (B) Proportions of SR+ and SR- patient primary HCC tumors in the TCGA LIHC dataset across five HCC etiologies (chi-square test). Proportions of HCC patients with primary SR+ and SR- tumors in the TCGA LIHC dataset across (C) stage based on TNM classification (chi-square test p value reported), (D) Child–Pugh class (chi-square test p value reported), (E) patient race (chi-square test p value reported) and (F) patient gender (Fisher exact test p value reported). Note the mean age at diagnosis for SR+ and SR− patients is 59.5 years and 59.4 years, respectively. Abbreviations: AI = alcohol-induced, HBV = hepatitis B virus, HCV = hepatitis C virus, NAFLD = non-alcoholic fatty liver disease, Hem = hemochromatosis.