Hepatocyte–Stellate Cell Cross-Talk in the Liver Engenders a Permissive Inflammatory Microenvironment That Drives Progression in Hepatocellular Carcinoma
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Published OnlineFirst March 14, 2012; DOI: 10.1158/0008-5472.CAN-11-3317 Cancer Microenvironment and Immunology Research Hepatocyte–Stellate Cell Cross-Talk in the Liver Engenders a Permissive Inflammatory Microenvironment That Drives Progression in Hepatocellular Carcinoma Cedric Coulouarn1,2, Anne Corlu1,2, Denise Glaise1,2, Isabelle Guénon1,2, Snorri S. Thorgeirsson3, and Bruno Clément1,2 Abstract Many solid malignant tumors arise on a background of inflamed and/or fibrotic tissues, features that are found in more than 80% hepatocellular carcinomas (HCC). Activated hepatic stellate cells (HSC) play a critical role in fibrogenesis associated with HCC onset and progression, yet their functional impact on hepatocyte fate remains largely unexplored. Here, we used a coculture model to investigate the cross-talk between hepatocytes (human hepatoma cells) and activated human HSCs. Unsupervised genome-wide expression profiling showed that hepatocyte–HSC cross-talk is bidirectional and results in the deregulation of functionally relevant gene networks. Notably, coculturing increased the expression of proinflammatory cytokines and modified the phenotype of hepatocytes toward motile cells. Hepatocyte–HSC cross-talk also generated a permissive proangiogenic micro- environment, particularly by inducing VEGFA and matrix metalloproteinase (MMP)9 expression in HSCs. An integrative genomic analysis revealed that the expression of genes associated with hepatocyte–HSC cross-talk correlated with HCC progression in mice and was predictive of a poor prognosis and metastasis propensity in human HCCs. Interestingly, the effects of cross-talk on migration and angiogenesis were reversed by the histone deacetylase inhibitor trichostatin A. Our findings, therefore, indicate that the cross-talk between hepatoma cells and activated HSCs is an important feature of HCC progression, which may be targeted by epigenetic modulation. Cancer Res; 72(10); 2533–42. Ó2012 AACR. Introduction tosis, and maintain epithelial cell polarity and differentiation Extensive evidence from genetics, genomics, and cell (4). In cancer, the microenvironment, which is also referred biology showed that cancer onset and progression is not to as stroma, experiences drastic changes including the only determined by tumor cells but also influenced by the recruitment and the activation of stromal cells and the microenvironment (1–3). Microenvironment is a complex remodeling of ECM. Importantly, coevolution of tumor cells system, which largely consists of extracellular matrix (ECM) with their microenvironment during tumorigenesis suggests – fl proteins and proteoglycans, soluble factors, and small sig- that tumor stroma cross-talk may likely in uence the phe- naling molecules such as cytokines and chemokines, along notype of tumor cells and may provide a selective pressure with a variety of cell types such as fibroblasts, immune for tumor initiation, progression, and metastasis (1, 5, 6). cells, and endothelial cells. Under normal conditions, the Hepatocellular carcinoma (HCC) is the most common pri- microenvironment constitutes an important modulator of mary tumor of the liver. The incidence of HCC is increasing in epithelium cell fate and a barrier to cell transformation (4). many countries and its prognosis is typically poor. Thus, with Dynamic communications between the epithelium and 550,000 cases newly diagnosed and 600,000 deaths annually, the microenvironment notably modulate cell growth, apop- HCC ranks among the deadliest forms of human malignancies worldwide (7). The remodeling of liver microenvironment is a hallmark of HCC pathogenesis (8). Indeed, more than 80% Authors' Affiliations: 1INSERM, UMR991, Liver Metabolisms and Cancer; HCCs develop in the setting of chronic hepatitis, fibrosis, and 2University of Rennes 1, Rennes, France; and 3Laboratory of Experimental cirrhosis, conditions in which inflammation and ECM depo- Carcinogenesis, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, Maryland sition profoundly alter the hepatic microenvironment (9). However, the functional impact of the disrupted microenvi- Note: Supplementary data for this article are available at Cancer Research Online (http://cancerres.aacrjournals.org/). ronment on hepatocyte biology remains poorly understood (10). Over the last decade, high-throughput genomic studies Corresponding Author: Cedric Coulouarn, INSERM, UMR991, Pontch- aillou University Hospital, 2 rue Henri Le Guilloux, Rennes F-35033, France. provided important insights to classify HCCs at a molecular Phone: 33-223233-881; Fax: 33-299540-137; E-mail: level and to identify deregulated gene networks and signaling [email protected] pathways (11). However, most of gene expression signatures in doi: 10.1158/0008-5472.CAN-11-3317 HCCs have been derived from whole-tumor tissues consisting Ó2012 American Association for Cancer Research. of both the cancer epithelial cells and their surrounding www.aacrjournals.org 2533 Downloaded from cancerres.aacrjournals.org on October 1, 2021. © 2012 American Association for Cancer Research. Published OnlineFirst March 14, 2012; DOI: 10.1158/0008-5472.CAN-11-3317 Coulouarn et al. microenvironment, a strategy which rendered elusive to eval- human SurePrint G3 8 Â 60K pangenomic microarrays (Agilent uate the specific contribution of each compartment. Technologies) as previously described (18). Starting from 150 Activation of hepatic stellate cells (HSC) is a key feature of ng total RNA, amplification yield was 9.7 Æ 0.6 mg cRNA and liver fibrosis and cirrhosis (12). Following liver injury, qui- specific activity was 20.3 Æ 1.3 pmol Cy3 per mg cRNA. Gene escent HSCs become activated and convert into highly expression data were processed using Feature Extraction and proliferative myofibroblast-like cells, which express inflam- GeneSpring softwares (Agilent Technologies) and further ana- matory and fibrogenic mediators responsible for ECM accu- lyzed using R-based BRB-ArrayTools. Briefly, differentially mulation within the microenvironment (12, 13). Therefore, expressed genes were identified by a 2-sample univariate t the present study was specifically designed to address the test and a random variance model (P < 0.01; false discovery rate functional impact and the clinical relevance of the cross-talk < 1%) as described (19). Permutation P values for significant between tumor hepatocytes and activated HSCs. By using a genes were computed on the basis of 10,000 random permuta- coculture model system, genome-wide expression profiling, tions. Class prediction was conducted using 7 algorithms and and functional assays, we showed that the cross-talk misclassification rate was computed using a leave-one-out between hepatocytes and activated HSCs (i) is bidirectional, cross-validation method (20). Clustering analysis was done (ii) induces an alteration of hepatocyte phenotype toward using Cluster 3.0 and TreeView 1.6 using uncentered correla- migration, (iii) generates a permissive proinflammatory and tion and average linkage options. MIAME compliant micro- proangiogenic microenvironment, (iv) is predictive of a poor array data have been deposited into Gene Expression Omnibus prognosis in human HCC, and (v) could be targeted by (GEO) database (GSE32565). epigenetic modulation. Data mining and integrative genomics Materials and Methods Gene annotation was based on Gene Ontology and enrich- ment for specific biologic functions or canonical pathways was Cell lines and coculture experiments evaluated using FuncAssociate 2.0 program (21). Ingenuity HepaRG and HuGB cell lines were established in our labo- Pathway Analysis (IPA) was used to examine the functional ratory and maintained as previously described (14, 15). association between differentially expressed genes and to HepaRG cells were grown in William's E medium supplemen- generate the highest significant gene networks (Ingenuity). m ted with 10% FBS, 100 U/mL penicillin, 100 g/mL strepto- Relevant networks were identified using the scoring system m m mycin, 5 g/mL insulin, and 50 mol/L hydrocortisone hemi- provided by IPA. Gene set enrichment analysis (GSEA) was succinate. Differentiation of HepaRG from progenitors to conducted by using the Java tool developed at the Broad mature well-differentiated hepatocytes was achieved in 4 Institute (Cambridge, MA) as previously described (22). Unsu- weeks by culturing the cells in the supplemented medium in pervised GSEA was done with the whole C2 collection of presence of 2% dimethyl sulfoxide (DMSO) for the last 2 weeks curated gene sets from the molecular signatures database as previously described (ref. 16; Supplementary Fig. S1A). All (MSigDB). Enrichment score was determined after 1,000 per- experiments hereinafter referred to as HepaRG were carried mutations. Connectivity map algorithm was used to link gene out using mature hepatocytes selectively isolated by mild expression signatures with putative therapeutic molecules trypsinization from DMSO-treated cultures. LX2 cells (Supple- (23). Integration of genomic data was conducted as previously mentary Fig. S1B) were provided by S.L. Friedman (Mount Sinai described (20) using publicly available gene expression data School of Medicine, New York, NY) and were maintained in sets downloaded from GEO. supplemented Dulbecco's Modified Eagles' Media (DMEM) as described (17). HepaRG–LX2 cocultures were conducted in Real-time reverse transcriptase PCR serum- and DMSO-free William's E medium using 6-well plates Expression of relevant genes was measured by quantita- and 1-mm