Objective We used an informatics approach to identify and validate genes whose expression is unique to hepatic stellate cells and assessed the prognostic capability of their expression in cirrhosis.
Design We defined a hepatic stellate cell gene signature by comparing stellate, immune and hepatic transcriptome profiles. We then created a prognostic index using a combination of hepatic stellate cell signature expression and clinical variables. This signature was derived in a retrospective–prospective cohort of hepatitis C-related early-stage cirrhosis (prognostic index derivation set) and validated in an independent retrospective cohort of patients with postresection hepatocellular carcinoma (HCC). We then examined the association between hepatic stellate cell signature expression and decompensation, HCC development, progression of Child–Pugh class and survival.
Results The 122-gene hepatic stellate cell signature consists of genes encoding extracellular matrix proteins and developmental factors and correlates with the extent of fibrosis in human, mouse and rat datasets. Importantly, association of clinical prognostic variables with overall survival was improved by adding the signature; we used these results to define a prognostic index in the derivation set. In the validation set, the same prognostic index was associated with overall survival. The prognostic index was associated with decompensation, HCC and progression of Child–Pugh class in the derivation set, and HCC recurrence in the validation set.
Conclusions This work highlights the unique transcriptional niche of stellate cells, and identifies potential stellate cell targets for tracking, targeting and isolation. Hepatic stellate cell signature expression may identify patients with HCV cirrhosis or postresection HCC with poor prognosis.
- LIVER CIRRHOSIS
- HEPATIC FIBROSIS
- HEPATIC STELLATE CELL
- LIVER FAILURE
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Significance of this study
What is already known on the subject?
In the setting of chronic liver injury, activated hepatic stellate cells are central mediators of liver fibrosis and cirrhosis.
Patients with cirrhosis have variable progression to liver failure and hepatocellular carcinoma.
Biomarkers derived from stellate cells and/or fibrotic tissue are urgently needed to facilitate clinical trials for antifibrotic agents.
What are the new findings?
We systematically define highly and uniquely expressed stellate cell genes and show that these genes are enriched in human and animal models of liver disease.
There are strong similarities between the hepatic stellate cell signature and renal fibrosis expression changes, but the hepatic stellate cell signature is distinct from pulmonary fibrosis expression changes.
Our approach has identified PCDH7 as a novel extracellular marker of stellate cells. PCDH7 is a candidate marker for stellate cell tracking, targeting and isolation.
Increased expression of hepatic stellate cell signature genes in patients is predictive of clinical outcomes, including decompensation, progression of Child–Pugh class and overall survival. Combining these predictions with clinical factors leads to more accurate predictions than using clinical factors alone.
How might it impact on clinical practice in the foreseeable future?
The hepatic stellate cell signature, alone or in combination with routine clinical measurements, may provide a tool for improved prediction of cirrhosis progression, guiding cirrhosis monitoring or intervention. Additionally, the hepatic stellate cell signature may serve as a surrogate marker of long-term outcome for antifibrotic agents in clinical trials.
Hepatic fibrosis is characterised by progressive deposition of extracellular matrix in patients with chronic liver injury. Among patients with hepatic fibrosis, a significant fraction will progress to cirrhosis, with eventual loss of liver function and an increased risk of hepatocellular carcinoma (HCC).1 ,2 Hepatic stellate cells are the primary cellular mediators of hepatic fibrosis through their transdifferentiation, or activation, from a pericytic, vitamin A-storing cell to a contractile, matrix-producing myofibroblast in response to liver injury and inflammation,3 ,4 and specific abrogation of this response has been validated as an antifibrotic therapy in many experimental models.5 In view of their central role in fibrosis and cirrhosis, the identification of specific hepatic stellate cell markers for use as non-invasive diagnostic markers could greatly facilitate preclinical and clinical development of antifibrotic therapies in patients with liver disease.
There are a growing number of gene expression datasets available that can facilitate the identification of uniquely or differentially expressed genes across multiple tissues and array formats, especially in the liver.6 In the fields of fibrosis and liver disease, increasingly sophisticated genomic approaches are being employed; these studies have combined multiple sources of data to identify HCC biomarkers,7 explore regulators of collagen deposition,8 classify HCC into distinct subclasses9 and establish prognostic gene expression signatures.10 ,11
Several genomic approaches have also been employed to elucidate stellate cell biology. Subtraction cloning12 and microarray13 have identified differentially expressed transcripts during hepatic stellate cell activation and following their inactivation,14 ,15 for example. While these studies have provided valuable insight into stellate cell expression changes in response to injury and regression, no studies have compared stellate cell gene expression with the gene expression profiles of other cell types in the liver or between fibrotic tissues in different organs.
To identify novel stellate cell surface or prognostic markers, we leveraged the rapidly expanding availability of cell and tissue expression profiles to identify transcripts specifically expressed in stellate cells. We have generated a hepatic stellate cell gene expression signature that correlates with progressive liver disease in patients and animal models, and have used this signature to identify novel cell surface markers of stellate cells. Importantly, this expression signature correlates with patient prognosis in chronic liver disease, further validating both the hepatic stellate cell signature and the role of hepatic stellate cells in chronic liver disease. We have also used this signature to determine which tissue expression profiles are similar between hepatic, renal and pulmonary fibrosis. We anticipate that the stellate cell gene signature will be informative for future stellate cell targeting and isolation. Additionally, this signature should facilitate biomarker development, enhancing our ability to assess fibrosis stage and response to therapies.
Identification of the hepatic stellate cell signature
To define the stellate cell gene expression signature, 98 platform-matched transcriptome datasets covering 17 liver cell and tissue types were obtained from previously published work13 and the National Center for Biotechnology Information Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo) (see online supplementary table S1 and figure S1). Raw scan data were obtained and converted to normalised data using the Robust Multi-array Average algorithm16 and quantile normalisation, implemented in the GenePattern genomic analysis toolkit (http://www.broadinstitute.org/genepattern/)17 ExpressionFileCreator module. Multiple probes corresponding to a gene were collapsed into a median value and subsequently labelled with an official gene symbol provided by the NCBI Entrez Gene database. Genes were included in the hepatic stellate cell signature if their median expression in either quiescent or activated stellate cells was at least twice as high as the highest expression in all other cell and tissue types. Mouse and rat genes were mapped to orthologous human genes based on the NCBI HomoloGene database (release 68), and rat and mouse genes with no known human homologous relationships were excluded from subsequent analysis. Unsupervised hierarchical clustering (Pearson correlation, average-linkage method) was performed on the discovery set using the GenePattern HierarchicalClustering module. Validation datasets, generated on microarrays from a range of species and platforms, were also obtained from the GEO database. Prenormalised gene expression measurements from each study were used for the analysis summarised in online supplementary figure S1.
Cell surface marker discovery
Candidate hepatic stellate cell surface markers were selected from hepatic stellate cell signature genes by integrating two independent databases of protein subcellular localisation: Gene Ontology cellular component (geneontology.org/page/cellular-component-ontology-guidelines) and the Mammalian Protein Localization Database (LOCATE, locate.imb.uq.edu.au),18 with use of terms summarised in online supplementary table S4.
Molecular pathway analysis
Association of the hepatic stellate cell signature with gene ontology terms (geneontology.org) was assessed using the hypergeometric test implemented in the DAVID (V.6.7) suite of functional annotation tools (david.abcc.ncifcrf.gov).19 ,20 Bonferroni-corrected p values less than 0.05 were regarded as a statistically significant association. Gene Set Enrichment Analysis (GSEA) was performed as previously published.21 ,22 Gene interaction networks were generated by submitting unranked leading edge gene subsets to the Ingenuity Pathway Analysis core analysis (http://www.ingenuity.com).
Association of hepatic stellate cell signature expression with overall survival (primary clinical outcome) was examined in two cohorts with early cirrhosis and minimal hepatic comorbidities: a retrospective–prospective cohort of patients with HCV-related, early-stage (Child–Pugh class A) cirrhosis10 (prognostic index derivation set), and an independent retrospective cohort of consecutively enrolled patients with curatively resected early-stage HCC followed up for a median of 7.8 years11 (prognostic index validation set). For the prognostic index derivation set, patients were included if they were diagnosed with histologically confirmed liver cirrhosis, but excluded if they had a history of hepatic decompensation, HCC or death. For the prognostic index validation set, patients were included if they were treated with surgery for primary HCC between 1990 and 2001, but excluded if no outcome data were available, or if no formalin-fixed, paraffin-embedded tumour and adjacent tissue were available. Additional details regarding patient enrolment, diagnosis, follow-up and treatment protocols can be found in their respective publications.
Association of the stellate cell signature with development of hepatic decompensation (bleeding varices, ascites, hepatic encephalopathy, hepatorenal syndrome and infection), progression of Child–Pugh class and HCC development were evaluated in the discovery set, and association with HCC recurrence was evaluated in the validation set. In the validation set, recurrent HCC tumours were confirmed to be clonally independent from resected primary HCC tumours: poor prognosis in this cohort is attributable to underlying liver cirrhosis, and not to dissemination or metastasis of resected primary tumours.
For each patient, relative overexpression of the hepatic stellate cell signature was measured by a gene set enrichment score based on the Kolmogorov–Smirnov (KS) statistic, and patients in each cohort were stratified into hepatic stellate cell signature positive and negative groups based on a 75th percentile cut-off value. Association of the groups with clinical outcomes was evaluated by Kaplan–Meier curve, log-rank test, univariable Cox regression modelling, and multivariable Cox regression modelling. To assess whether adding the stellate signature to baseline clinical data (bilirubin greater than 1 mg/dL and platelets count less than 100 000/mm3) improved the discrimination and overall fit of the Cox regression model, we calculated Harrell's c-statistic (a measure of the predictive power of the model ranging from 0.5 to 1) for each model, and compared the two nested models with a likelihood ratio test (assessing whether overall fit of the model is significantly improved by adding the extra variable). MELD and FIB4 scores were approximated for each patient and tested for correlation to outcomes using multivariable Cox regression modelling. For FIB4 score calculation, aspartate aminotransferase(AST) levels were not available, so alanine aminotransferase was substituted for AST. Additionally for MELD score calculation, international normalized ratio was not available and was assumed to be 1 for all patients. All data analysis was performed using the R statistical language (http://www.r-project.org) and GenePattern.
Based on our previously generated multivariate Cox regression model for overall survival in the HCV cohort (discovery cohort), we developed a prognostic index by linearly combining the variables weighted with regression coefficients from the multivariable Cox regression models in the prognostic index derivation set. Tertile values of the index in the derivation set were used as cut-offs to classify patients into high, intermediate and low risk groups. This prognostic model was tested in the discovery cohort and validated in the HCC cohort (validation cohort) with identical definitions and cut-offs. To evaluate whether the hepatic stellate cell signature improved prediction of clinical outcomes, we compared the c-statistic improvement and 95% CI of a predictive model using bilirubin and platelet count alone, with a model using bilirubin, platelet count and hepatic stellate cell signature expression. A c-statistic above 0.7 would be considered the lower limit for clinical utility.
The Hep3B, Huh7, LX2, Hep1-6, TSEC, and JS1 cells were cultured at 37°C and 5% CO2 in Dulbecco's modification of Eagle's medium (high glucose, sodium pyruvate-free) (Invitrogen) supplemented with 10% fetal bovine serum and penicillin/streptomycin.
Quantitative reverse transcriptase PCR
RNA was extracted from adherent cell lines using the RNEasy Mini kit (Qiagen). Equimolar concentrations of RNA were converted to cDNA (Clontech Cat. 639549). Copy number was assessed by SYBR green qPCR on the Roche Lightcycler 480 platform. Human PCDH7 expression was measured using the 5′-TTGTGGGAGCAGGAGACAAC-3′ forward and 5′-CTCTG CAGTGACCCCTGATG-3′ reverse primers, which yielded a 154 base pair amplification product. Mouse PCDH7 expression was measured using the 5′-TCCACTCCCAGAGGACAACT-3′ forward and 5′-GGCTGGCTCTTCTTCCTCTC-3′ reverse primers, which yielded a 198 base pair amplification product.
Frozen liver tissue sections were prepared from mice treated for 6 weeks with 10% CCl4 (intraperitoneal injection three times per week). The sections were fixed with ice-cold acetone for 5 min, permeabilised with 0.2% Triton X-100 in PBS for 15 min, rinsed three times with phosphate- buffered saline and blocked with normal goat serum diluted 1:20 or with 5% BSA/PBS for 30 min. Staining was performed with primary mouse McAb to PCDH7 or rabbit PcAb to Desmin (abcam, Cambridge, Massachusetts, USA) at 4°C overnight. Primary antibody detection was performed with Alexa Fluor 488 -conjugated goat antirabbit immunoglobulin G, E or Alexa Fluor 647-conjugated goat antimouse. The slides were mounted by ProLong Gold antifade reagent with 4’,6-diamidino-2-phenylindole (Invitrogen) and examined using a Zeiss Axiophot microscope (Zeiss).
Development of the hepatic stellate cell signature
To identify genes highly and specifically expressed in stellate cells, expression profiles representing all liver cell subsets were subtracted from hepatic stellate cell profiles. These datasets were derived from a series of mouse-based transcriptome profiling studies, which was enabled by the extensive availability of isolated liver cell type datasets. Whole liver, hepatocyte and fetal liver profiles were included, as well as representative epithelial, endothelial, myeloid and erythroid lineages (figure 1A and see online supplementary table S1). In the global transcriptome space, samples clustered together according to cell type, as opposed to study or dataset, supporting the presence of robust transcriptional programmes specific to each liver cell type (figure 1B). A total of 122 genes were identified as highly and uniquely expressed in stellate cells (figure 1C). As expected, several canonical hepatic stellate cell markers were highly expressed in the stellate cell samples, but excluded from the hepatic stellate cell signature due to concurrent expression in other cell or tissue types (see online supplementary figure S2). The 122-gene hepatic stellate cell signature was highly enriched with extracellular matrix remodelling and related molecular pathways (see online supplementary figure S3 and table S2).
Upregulation of the hepatic stellate cell signature in human and animal models of fibrosis
To validate the stellate cell gene expression signature for its relevance to cirrhosis-related phenotypes, we tested for hepatic stellate cell signature enrichment in ten different datasets, representing a variety of liver disease aetiologies in humans, mice and rats (see online supplementary table S3). Hepatic stellate cell signature expression was strikingly induced in both human cirrhotic livers, compared with healthy normal livers (figure 1D), and fibrotic non-alcoholic fatty liver disease (NAFLD) livers compared with non-fibrotic NAFLD livers (figure 1E). The hepatic stellate cell signature was also significantly associated with the presence of inflammation in livers from patients with NAFLD (figure 1F). In each of the human datasets examined, the diseased population had significantly higher hepatic stellate cell signature enrichment compared with the normal population (figure 1D–I). We examined gene ontology enrichment within the leading edge of each GSEA, and the 10 most statistically significant networks are listed in online supplementary figure S3. The same leading edge genes were also subject to ingenuity pathway analysis, with the highest ranking interaction networks shown in online supplementary figures S4 and S6. These networks implicate central involvement of many canonical stellate cell factors, including tumour growth factor-β (TGF-β), platelet-derived growth factor receptor alpha (PDGFRA), PDGFRB, interleukin-1 (IL-1), focal adhesion kinase (FAK), collagen and matrix metalloproteinases, in the regulation of hepatic stellate cell signature genes. Of particular interest to us is the regulation of TAGLN by NOTCH, FAK, TGF-β, and PDGF signalling, which may be a novel final common pathway of profibrotic signalling. Additionally, the regulation of collagen expression by TNXB and ADAMTS2 highlights them as possible targets for antifibrotic therapies.
PCDH7 is a novel hepatic stellate cell surface marker
We explored whether genes in the hepatic stellate cell signature encode novel cell surface markers of stellate cells, which would make them particularly appealing as candidate therapeutic targets. To identify this subset of cell surface transcripts within the stellate cell expression signature, we intersected the hepatic stellate cell signature with LOCATE and gene ontology groups that are known to be on the cell surface, on the plasma membrane, at cell junctions or in the extracellular matrix (figure 2A, see online supplementary table S4). This approach yielded several genes previously identified as stellate cell or myofibroblast markers, including DDR2,23 EDNRA,24 EDNRB,25 EREG26 and IL1R1.27 Additionally, this approach highlighted PCDH7, a member of the protocadherin family28 as a candidate stellate cell surface marker. In the hepatic stellate cell signature discovery data, PCDH7 is highly expressed in both activated and quiescent stellate cells. It decreases slightly in activated stellate cells but remains substantially higher than in other cell and tissue types examined (figure 2B). We performed qRT-PCR to compare absolute expression of PCDH7 in immortalised stellate cell lines (LX2, JS1), sinusoidal endothelial cells (TSEC) and hepatocytes (HepG2, Hep3B, Huh7, Hep1-6). PCDH7 expression was enriched in immortalised stellate cells, compared with immortalised hepatocytes and sinusoidal endothelial cells (figure 2C). We then performed protein characterisation by immunofluorescence, observing sinusoidal staining, localising with desmin, for PCDH7 in fibrotic mouse livers (figure 2D–E).
Hepatic stellate cell gene signature in fibrosis models
We next assessed the extent of stellate cell enrichment in mouse and rat fibrosis models. For most validation sets, the mean enrichment score varied substantially between diseased and control groups, but statistical significance was not reached due to the low number of samples (see online supplementary figure S7). However, GSEA demonstrated highly significant hepatic stellate cell signature enrichment in the mouse bile duct ligation, mouse unilateral ureteral obstruction, rat bile duct ligation, and rat diethylnitrosamine models (table 1). Interestingly, strong association was observed between the hepatic stellate cell signature and the renal unilateral ureteral obstruction dataset, while no association was present between the hepatic stellate cell signature and the lung bleomycin dataset; this suggests that liver fibrosis is genomically more closely related to renal fibrosis than to lung fibrosis.
The hepatic stellate cell signature expression is associated with patient prognosis in two human cirrhosis cohorts
To further validate the hepatic stellate cell signature, we explored the association between hepatic stellate cell signature expression and long-term clinical outcomes in cirrhotic cohorts of patients with resected HCC11 and patients with hepatitis C infected cirrhosis.10 Hepatic stellate cell signature expression was calculated for the resected HCC (figure 3A) and hepatitis C (figure 3B) patient cohorts. Patients were ordered by hepatic stellate cell signature expression (figure 3C, D), and the top quartile was considered as a separate group in outcomes analysis. In the resected HCC cohort, patients with high hepatic stellate cell signature expression had significantly reduced survival and increased tumour recurrence (figure 3E, F). In the hepatitis C cirrhosis cohort, patients with high hepatic stellate cell signature expression had significantly poorer liver-related outcomes, such as Child–Pugh class progression and hepatic decompensation, as well as decreased survival (figure 3G–I). There was no significant difference in progression to HCC in the hepatitis C cohort (figure 3J).
For the hepatitis C cirrhosis cohort, associations between high bilirubin (>1.0 mg/dL), low platelet count (<100 000/mm3) and patient outcome have been previously described.10 To exclude the possibility that the hepatic stellate cell signature expression is a surrogate for either of these clinical features, we performed multivariable Cox regression to assess the independent contribution of each factor. For overall survival and progression of Child–Pugh class, high hepatic stellate cell signature expression was independently correlated with outcomes (table 2). We also examined prognostic association between outcomes and albumin, FIB4 score, and MELD score. Although there was an association in univariate analysis of MELD and albumin levels with survival in the index derivation cohort, in multivariable Cox regression only bilirubin, platelet count and hepatic stellate cell signature expression were significantly associated with survival (see online supplementary table S5). This is most likely because all patients in our prognostic index derivation set have earlier stage disease with limited clinical variable variation—most clinical variables were within normal reference ranges. Using the hepatic stellate cell signature and clinical data together improved prognostic model fit and discrimination in the prognostic index derivation set (p=0.0015 for death and p=0.0043 for Child–Pugh class progression respectively; see online supplementary table S6). Despite being significantly associated in univariable analysis, clinical variables and the hepatic stellate cell signature marker were not independently predictive of decompensation (table 2). In the prognostic index validation set, the stellate signature was significantly associated with mortality in multivariable analysis, although the hepatic stellate cell signature was not associated with HCC development, consistent with the univariable results (table 2). Since associations between gene expression and patient outcomes have been previously published in these same datasets, we checked for overlap between our hepatic stellate cell signature and the previously published prognostic signature. Only three genes overlapped between the previously published 186-gene prognostic signature and the 122-gene hepatic stellate cell signature, indicating that substantially different sets of genes are being examined (see online supplementary figure S8).
Given the prognostic association of the stellate cell signature, we developed a prognostic index combining bilirubin, platelet count and hepatic stellate cell signature expression. In the prognostic index derivation set (n=216), 88 subjects (n=41%) were classified as low risk, 103 (48%) were classified as intermediate risk and 25 (12%) were classified as high risk. The high-risk group was associated with shorter overall survival (HR 8.00, p<0.0001), more frequent decompensation (HR 2.9, p=0.0027) and faster Child–Pugh class progression (HR 6.5, p<0.0001). Association with HCC development was not significant (HR=2.2, p=0.068) in this cohort (table 3, figure 4A–C). Adding the hepatic stellate cell signature improved the model c-statistic (and 95% CI) from 0.66 (0.59 to 0.74) to 0.70 (0.62 to 0.78) for overall survival, 0.61 (0.54 to 0.68) to 0.62 (0.55 to 0.69) for decompensation and 0.68 (0.61 to 0.75) to 0.70 (0.63 to 0.78) for progression of Child–Pugh class.
In the prognostic index validation cohort (n=82), 42 subjects (51%) were classified as low risk, 30 (37%) were classified as intermediate risk and 10 (12%) were classified as high risk using cut-off values predefined in the derivation set. The high-risk group was associated with shorter overall survival (HR=3.9, p=0.0077) but not with HCC recurrence (HR=1.7, p=0.20) (table 3, figure 4D). Addition of hepatic stellate cell signature expression improved the model c-statistic (and 95% CI) from 0.58 (0.48 to 0.68) to 0.62 (0.51 to 0.72) for death and 0.51 (0.44 to 0.59) to 0.54 (0.46 to 0.62) for HCC recurrence.
We have used a bioinformatics approach to identify a group of genes that are specifically expressed by hepatic stellate cells. Furthermore, we show that these genes correlate with stellate cell abundance and patient outcomes and contain transcripts that encode a novel cell surface marker of stellate cells.
The selection strategy for hepatic stellate cell signature genes in the discovery set yielded several different types of genes. Although all genes in the hepatic stellate cell signature were highly enriched in stellate cells, some were substantially higher in quiescent stellate cells, others were substantially higher in activated stellate cells and some were equally expressed between quiescent and activated stellate cells. We initially explored using separate subsets of the hepatic stellate cell signature to capture this difference. We expected higher correlation between gene expression and fibrotic phenotype for the set of activated stellate cell genes. Instead, we found that no subgroup had a substantially stronger correlation with phenotype (see online supplementary table S7). In addition, almost every gene in the signature was enriched in several disease phenotypes (see online supplementary table S8). These results suggest that the hepatic stellate cell signature enrichment we observe in diseased phenotypes is a result of increased stellate cell number rather than changes in stellate cell activation. This is important because it implies that stellate cell proliferation is a dominant driving factor for hepatic fibrosis. Hepatic stellate cell proliferation during stellate cell activation was described when the cell type was defined,29 ,30 ,31 but stellate cell activation and proliferation have seldom been mechanistically uncoupled. Instead, stellate cell number is generally normalised to measure changes in RNA and protein expression. In addition to therapeutic approaches that limit stellate cell activation, our data indicate that strategies limiting hepatic stellate cell proliferation merit strong consideration as potential antifibrotic therapies.
Since stellate cells comprise a minority of the cell volume in the liver, stellate cell gene expression changes are difficult to detect in whole liver expression profiles. By examining genes that are uniquely expressed in stellate cells—the genes in the hepatic stellate cell signature—tracking of stellate cell genes in whole liver samples becomes tractable. This allows us to rapidly assess stellate cell abundance in any mouse, rat, or human liver sample, provided that a microarray or RNA-seq procedure has been performed. This approach will be especially useful for characterising novel genetic or aetiological models of liver injury.
Although the hepatic stellate cell signature is independently predictive of patient outcomes, translating it into a clinical test would require further development. The prognostic index c-statistic for overall survival, the primary outcome, was 0.70—this is the threshold generally regarded as clinically useful. The dependence on liver biopsy and cost of measuring the expression of 122 genes are obstacles to adoption, but do not preclude their future clinical implementation. Nonetheless, we primarily view this analysis as a validation of the hepatic stellate cell signature. Additionally, we hope that study of individual genes in the signature will help us uncover fundamental aetiological drivers of chronic liver disease.
For easier clinical implementation, we explored whether subsets of the hepatic stellate cell signature were equally predictive of outcomes. We discovered that smaller gene subsets (31–55 genes, instead of 122) were similarly associated with overall survival (see online supplementary table S9). The reduced 55 gene signature is a promising candidate clinical biomarker for evaluation in future studies.
The novel cell surface markers uncovered in our analysis have several potential applications. From an experimental perspective, isolation of pure stellate cell populations has been an ongoing challenge for the field. Use of these markers for magnetic activated cell sorting (MACS) or flow cytometry would generate a purer population of isolated stellate cells. Additionally, the promoter of each gene in the hepatic stellate cell signature is potentially useful to drive stellate-targeted deletion of genes in animal models. From a clinical perspective, novel stellate cell surface markers could be used to target pharmacologic agents to stellate cells. Additionally, secreted members of the hepatic stellate cell signature may represent surrogate serum markers of fibrosis.
Our method for identifying stellate cell surface markers proved to be extremely robust. Out of the six cell surface markers computationally identified, five of them were previously described, and we identified an additional novel marker, PCDH7, which we have validated as a specific cell surface marker of stellate cells. PCDH7 is a transmembrane protocadherin with seven cadherin repeats on its extracellular domain.28 Although there is a paucity of previously published work on PCDH7, protocadherins typically function in signal transduction and cell–cell recognition. These characteristics make PCDH7 an interesting molecule not only for its targetable, cell-surface location, but also as a possible regulator of hepatic stellate cell fibrogenesis.
Another major goal of our study was to seek a correlation between the hepatic stellate cell signature and fibrotic responses in other tissues. Specifically, we compared the signature with tissues from murine renal fibrosis (unilateral urethral obstruction) and murine lung fibrosis (bleomycin). The renal fibrosis model correlated very strongly with the hepatic stellate cell signature, while the lung fibrosis model did not (table 1). This correlation suggests a stronger similarity between hepatic and renal fibrosis than with pulmonary fibrosis. This is consistent with our understanding of fibrosis pathogenesis among the three organs, as the biology of fibrosis in liver and kidney is more akin to each other through the presence of kidney and liver pericytes (ie, stellate cells), whereas the cellular source(s) of fibrogenic cells in the lung is less certain. This is also consistent with the divergent clinical course of fibrotic disease in lung, which is typically more aggressive than that of liver and kidney fibrosis.
In summary, our work establishes a novel and clinically relevant platform for unbiased discovery of novel stellate cell genes. These genes correlate with clinical outcomes in human liver disease and may contribute to fibrosis in vivo. This creates a unique opportunity to systematically uncover novel fibrosis biology, identify candidate drug targets and define novel candidate biomarkers that correlate with disease outcomes.
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- Data supplement 1 - Online supplement
Correction notice This article has been corrected since it published Online First. The surname for author Massimo Iavarone has been corrected and the abstract amended.
Contributors DYZ wrote the manuscript and performed all experiments not otherwise described below. NG performed the computational analyses in table 3 and figure 4. JG performed the immunofluorescence staining in figure 2. M-cT, H-IC and CA performed antibody optimisation and pilot studies for the PCDH7 staining in figure 2. AS, MI, MC, MK, HK, AV and JML collected the outcomes data referenced in figures 3 and 4. YH and SLF are the principal investigators directly supervising DYZ, NG, JG, M-cT, H-IC and CA.
Funding To SLF: NIH-DK056621, NIH-AA020709; To YH: NIH-DK099558, Irma T Hirschl Trust, Dr Harold and Golden Lamport Research Award, To Icahn School of Medicine Medical Scientist Training Program: NIH-GM007280
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
Data sharing statement All data generated by work presented in the manuscript is provided in the manuscript or supplementary information.
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