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Hepatocellular carcinoma (HCC) is the most prevalent cancer of the liver and is a leading cause of cancer deaths worldwide. The multikinase inhibitor sorafenib has long been used to treat HCC because it was shown to extend survival by 3 months.
Nevertheless, correlation does not equate to functionality. We used an innovative approach to perform genetic screens in the native environment of the mouse liver to interrogate gene function in the presence or absence of sorafenib, designed to identify potential combination treatments for HCC. We validate a drug combination—sorafenib plus a Liver X Receptor alpha (LXR⍺) agonist—with enhanced killing of HCC in multiple cell lines. LXR⍺ agonists are currently in clinical trials and may have acceptable side effect profiles for patients with advanced HCC.
We engineered a plasmid library of 43 complementary DNA (cDNAs) important in liver growth and function to conduct genetic screening in the livers of Fah−/− mice, a model of liver injury and repopulation.
Fah−/− mice hydrodynamically injected into the tail vein with the plasmid library developed visible HCCs as early as 6 weeks postinjection, which were confirmed to be HCCs linked to plasmids from the library (Figure 1A and Figure A1).
We hypothesized that a screening approach could be used to discover genes that confer sensitivity or resistance to antitumor drug therapy with sorafenib. Therefore, we injected Fah−/− mice with the plasmid library and allowed the mice to form tumors for 6 weeks, then began daily treatment with either 30 mg/kg sorafenib or vehicle as a control for 2 months (Figure 1B and C). Sorafenib treatment significantly reduced the liver weight to body weight ratio and tumor burden compared with vehicle (Figure 1D and Table A1).
To identify genes correlated with sorafenib sensitivity, we analyzed cDNAs present or absent in HCCs that developed in the sorafenib-treated group. Strikingly, the oncogene Myc was linked to all tumors of vehicle- or sorafenib-treated mice. In contrast, the Nr1h3 transgene, which encodes LXR⍺, was present in a number of vehicle-treated tumors as expected by chance but was completely absent from sorafenib-resistant tumors (Figure 1E and F). These results suggested that LXR⍺ activity is incompatible with tumor growth in the presence of sorafenib.
Based on the result of the screen, we asked whether activating LXR⍺ can increase the response to sorafenib. We treated 3 previously established human HCC cell lines (Hep3B and Huh7), a primary human HCC cell line (PGM898), and a murine hepatoma cell line (Hepa1-6) with varying concentrations of the small-molecule LXR⍺ agonists GW3965 and T0901317 and sorafenib (Figure 2A and Figure A2). In each cell line, there was a 30%–50% reduction in cell viability with the combination treatment at a minimal concentration of 5 μM GW3965 and 2 μM sorafenib compared with sorafenib-only treatment, whereas treatment with LXR⍺ agonist alone did not affect cell viability. At these concentrations, the combination treatment led to only a 20% reduction from baseline in a murine hepatocyte-like cell line (Figure A2B), suggesting treatment preferentially affects HCC cells. Taken together, the results indicate that LXR⍺ activation improves the efficacy of sorafenib in multiple HCC cell lines.
We queried the mechanism by which the combination treatment targets HCC by performing RNA sequencing (RNAseq) of Hep3B and Huh7 cells treated with drugs for 24 hours (Figure 2B and Figure A3A). Principal component analysis of the RNAseq data indicated a clear separation between the cell lines and treatment groups (Figure A3B). Differential expression analysis comparing treatment to DMSO control groups showed marked changes in expression with the combination. Among genes altered congruently in both cell lines: 19 genes were differentially expressed in response to GW3965, 190 to sorafenib, and 915 to the combination treatment, reflecting a remarkable synergistic effect on gene expression. We confirmed the specificity of sorafenib and GW3965 treatment by assessing gene expression levels of known markers of drug activity (Figure A3C).
The gene expression changes with combined LXR⍺ agonist and sorafenib treatment were linked to alterations in 472 gene ontology pathways. The top 10 altered pathways, based on gene ratio, are displayed in Figure 2C. The regulation of cell death was the most altered pathway, and cell cycle pathways were also altered (red boxes). When investigating steady-state messenger RNA levels of cell cycle and apoptotic regulators, we found downregulation of many proliferation markers such as various cyclins (CCNE1 and CCND1), minichromosome maintenance complex proteins (MCM2, MCM4, MCM5, and MCM6), proliferating cell nuclear antigen, and cyclin-dependent kinase 2 (Figure 2B). Western blotting confirmed the downregulation of cell cycle regulators Cyclin D1 and proliferating cell nuclear antigen with combination therapy (Figure A3D). Differentially expressed genes associated with apoptosis included (BBC3, PMAIP1, JUN, and DDIT3; Figure 2B). Western blotting confirmed the upregulation of apoptosis regulator JUN with sorafenib and the combination treatment (Figure A3E). In short, combination treatment led to diminished expression of cell cycle genes and upregulation in apoptosis markers compared with sorafenib or LXR agonist alone, highlighting the cooperativity of these compounds.
In summary, our in vivo genetic screen documents that LXRα is a key signal to determine the response to sorafenib of MYC-driven tumors in a model of liver injury and repopulation. We show that LXR⍺ agonists enhance the vulnerability of HCC cell cultures to sorafenib. The beneficial action of LXR⍺ agonism we reported here is in agreement with LXR⍺ expression correlating with better HCC prognosis
Recent preclinical assays in mice of LXR⍺ agonists and sorafenib support the benefit of combinatorial treatment and showed enhanced antitumor activity in both xenograft- and oncogene-driven hydrodynamic tail vein injection models.
Further studies are needed to identify HCC subgroups that may benefit most from this combination. Collectively, our studies document how in vivo genetic screening in combination with drug therapies could identify cancer drug dependency and susceptibility patterns, which could be scaled to high-throughput screens.
Conflicts of Interest: The authors disclose no conflicts.
Funding: Grant support: National Institute of Diabetes and Digestive Diseases (NIDDK) grants R01-DK102667 to K.H.K., K08-DK106478 to K.J.W., and K01-DK102868 to A.M.Z. Molecular Pathology and Imaging Core and Host Microbial Analytic and Repository Core of the Penn Center for Molecular Studies in Digestive and Liver Disease (P30-DK50306).
Ethical Statement: The corresponding author, on behalf of all authors, jointly and severally, certifies that their institution has approved the protocol for any investigation involving humans or animals and that all experimentation was conducted in conformity with ethical and humane principles of research.
Data Transparency Statement: Data, analytic methods, and study materials will be made available to other researchers on request to the corresponding author. The RNA-seq data set is available on the Gene Expression Omnibus (GEO, accession #GSE151412).