Hepatocellular carcinoma chemoprevention by targeting the angiotensin-converting enzyme and EGFR transactivation.

Hepatocellular carcinoma (HCC) is a leading cause of death among cirrhotic patients, for which chemopreventive strategies are lacking. Recently, we developed a simple human cell-based system modeling a clinical prognostic liver signature (PLS) predicting liver disease progression and HCC risk. In a previous study, we applied our cell-based system for drug discovery and identified captopril, an approved angiotensin converting enzyme (ACE) inhibitor, as a candidate compound for HCC chemoprevention. Here, we explored ACE as a therapeutic target for HCC chemoprevention. Captopril reduced liver fibrosis and effectively prevented liver disease progression toward HCC development in a diethylnitrosamine (DEN) rat cirrhosis model and a diet-based rat model for nonalcoholic steatohepatitis-induced (NASH-induced) hepatocarcinogenesis. RNA-Seq analysis of cirrhotic rat liver tissues uncovered that captopril suppressed the expression of pathways mediating fibrogenesis, inflammation, and carcinogenesis, including epidermal growth factor receptor (EGFR) signaling. Mechanistic data in liver disease models uncovered a cross-activation of the EGFR pathway by angiotensin. Corroborating the clinical translatability of the approach, captopril significantly reversed the HCC high-risk status of the PLS in liver tissues of patients with advanced fibrosis. Captopril effectively prevents fibrotic liver disease progression toward HCC development in preclinical models and is a generic and safe candidate drug for HCC chemoprevention.

Molecular signatures of long-term hepatocellular carcinoma risk in nonalcoholic fatty liver disease.

Prediction of hepatocellular carcinoma (HCC) risk is an urgent unmet need in patients with nonalcoholic fatty liver disease (NAFLD). In cohorts of 409 patients with NAFLD from multiple global regions, we defined and validated hepatic transcriptome and serum secretome signatures predictive of long-term HCC risk in patients with NAFLD. A 133-gene signature, prognostic liver signature (PLS)-NAFLD, predicted incident HCC over up to 15 years of longitudinal observation. High-risk PLS-NAFLD was associated with IDO1+ dendritic cells and dysfunctional CD8+ T cells in fibrotic portal tracts along with impaired metabolic regulators. PLS-NAFLD was validated in independent cohorts of patients with NAFLD who were HCC naïve (HCC incidence rates at 15 years were 22.7 and 0% in high- and low-risk patients, respectively) or HCC experienced (de novo HCC recurrence rates at 5 years were 71.8 and 42.9% in high- and low-risk patients, respectively). PLS-NAFLD was bioinformatically translated into a four-protein secretome signature, PLSec-NAFLD, which was validated in an independent cohort of HCC-naïve patients with NAFLD and cirrhosis (HCC incidence rates at 15 years were 37.6 and 0% in high- and low-risk patients, respectively). Combination of PLSec-NAFLD with our previously defined etiology-agnostic PLSec-AFP yielded improved HCC risk stratification. PLS-NAFLD was modified by bariatric surgery, lipophilic statin, and IDO1 inhibitor, suggesting that the signature can be used for drug discovery and as a surrogate end point in HCC chemoprevention clinical trials. Collectively, PLS/PLSec-NAFLD may enable NAFLD-specific HCC risk prediction and facilitate clinical translation of NAFLD-directed HCC chemoprevention.

Atorvastatin favorably modulates a clinical hepatocellular carcinoma risk gene signature.

Lipophilic but not hydrophilic statins have been shown to be associated with reduced risk for hepatocellular carcinoma (HCC) in patients with chronic viral hepatitis. We investigated differential actions of lipophilic and hydrophilic statins and their ability to modulate a clinical prognostic liver signature (PLS) predicting HCC risk in patients with liver disease. Hepatitis C virus (HCV)-infected Huh7.5.1 cells, recently developed as a model to screen HCC chemopreventive agents, were treated with lipophilic statins (atorvastatin and simvastatin) and hydrophilic statins (rosuvastatin and pravastatin), and then analyzed by RNA sequencing and PLS. Lipophilic statins, particularly atorvastatin, more significantly suppressed the HCV-induced high-risk pattern of PLS and genes in YAP and AKT pathway implicated in fibrogenesis and carcinogenesis, compared with the hydrophilic statins. While atorvastatin inhibited YAP activation through the mevalonate pathway, the distinctive AKT inhibition of atorvastatin was mediated by stabilizing truncated retinoid X receptor alpha, which has been known to enhance AKT activation, representing a target for HCC chemoprevention. In addition, atorvastatin modulated the high-risk PLS in an in vitro model of nonalcoholic fatty liver disease (NAFLD). Conclusion: Atorvastatin distinctively inhibits YAP and AKT activation, which are biologically implicated in HCC development, and attenuates a high-risk PLS in an in vitro model of HCV infection and NAFLD. These findings suggest that atorvastatin is the most potent statin to reduce HCC risk in patients with viral and metabolic liver diseases.

Peroxidasin Deficiency Re-programs Macrophages Toward Pro-fibrolysis Function and Promotes Collagen Resolution in Liver.

BACKGROUND & AIMS: During liver fibrosis, tissue repair mechanisms replace necrotic tissue with highly stabilized extracellular matrix proteins. Extracellular matrix stabilization influences the speed of tissue recovery. Here, we studied the expression and function of peroxidasin (PXDN), a peroxidase that uses hydrogen peroxide to cross-link collagen IV during liver fibrosis progression and regression. METHODS: Mouse models of liver fibrosis and cirrhosis patients were analyzed for the expression of PXDN in liver and serum. Pxdn-/- and Pxdn+/+ mice were either treated with carbon tetrachloride for 6 weeks to generate toxin-induced fibrosis or fed with a choline-deficient L-amino acid-defined high-fat diet for 16 weeks to create nonalcoholic fatty liver disease fibrosis. Liver histology, quantitative real-time polymerase chain reaction, collagen content, flowcytometry and immunostaining of immune cells, RNA-sequencing, and liver function tests were analyzed. In vivo imaging of liver reactive oxygen species (ROS) was performed using a redox-active iron complex, Fe-PyC3A. RESULTS: In human and mouse cirrhotic tissue, PXDN is expressed by stellate cells and is secreted into fibrotic areas. In patients with nonalcoholic fatty liver disease, serum levels of PXDN increased significantly. In both mouse models of liver fibrosis, PXDN deficiency resulted in elevated monocyte and pro-fibrolysis macrophage recruitment into fibrotic bands and caused decreased accumulation of cross-linked collagens. In Pxdn-/- mice, collagen fibers were loosely organized, an atypical phenotype that is reversible upon macrophage depletion. Elevated ROS in Pxdn-/- livers was observed, which can result in activation of hypoxic signaling cascades and may affect signaling pathways involved in macrophage polarization such as TNF-a via NF-kB. Fibrosis resolution in Pxdn-/- mice was associated with significant decrease in collagen content and improved liver function. CONCLUSION: PXDN deficiency is associated with increased ROS levels and a hypoxic liver microenvironment that can regulate recruitment and programming of pro-resolution macrophages. Our data implicate the importance of the liver microenvironment in macrophage programming during liver fibrosis and suggest a novel pathway that is involved in the resolution of scar tissue.

Molecular Signature Predictive of Long-Term Liver Fibrosis Progression to Inform Antifibrotic Drug Development.

BACKGROUND & AIMS: There is a major unmet need to assess the prognostic impact of antifibrotics in clinical trials because of the slow rate of liver fibrosis progression. We aimed to develop a surrogate biomarker to predict future fibrosis progression. METHODS: A fibrosis progression signature (FPS) was defined to predict fibrosis progression within 5 years in patients with hepatitis C virus and nonalcoholic fatty liver disease (NAFLD) with no to minimal fibrosis at baseline (n = 421) and was validated in an independent NAFLD cohort (n = 78). The FPS was used to assess response to 13 candidate antifibrotics in organotypic ex vivo cultures of clinical fibrotic liver tissues (n = 78) and cenicriviroc in patients with nonalcoholic steatohepatitis enrolled in a clinical trial (n = 19, NCT02217475). A serum protein-based surrogate FPS was developed and tested in a cohort of compensated cirrhosis patients (n = 122). RESULTS: A 20-gene FPS was defined and validated in an independent NAFLD cohort (adjusted odds ratio, 10.93; area under the receiver operating characteristic curve, 0.86). Among computationally inferred fibrosis-driving FPS genes, BCL2 was confirmed as a potential pharmacologic target using clinical liver tissues. Systematic ex vivo evaluation of 13 candidate antifibrotics identified rational combination therapies based on epigallocatechin gallate, which were validated for enhanced antifibrotic effect in ex vivo culture of clinical liver tissues. In patients with nonalcoholic steatohepatitis treated with cenicriviroc, FPS modulation was associated with 1-year fibrosis improvement accompanied by suppression of the E2F pathway. Induction of the PPARα pathway was absent in patients without fibrosis improvement, suggesting a benefit of combining PPARα agonism to improve the antifibrotic efficacy of cenicriviroc. A 7-protein serum protein-based surrogate FPS was associated with the development of decompensation in cirrhosis patients. CONCLUSION: The FPS predicts long-term fibrosis progression in an etiology-agnostic manner, which can inform antifibrotic drug development.

A human liver cell-based system modeling a clinical prognostic liver signature for therapeutic discovery.

Chronic liver disease and hepatocellular carcinoma (HCC) are life-threatening diseases with limited treatment options. The lack of clinically relevant/tractable experimental models hampers therapeutic discovery. Here, we develop a simple and robust human liver cell-based system modeling a clinical prognostic liver signature (PLS) predicting long-term liver disease progression toward HCC. Using the PLS as a readout, followed by validation in nonalcoholic steatohepatitis/fibrosis/HCC animal models and patient-derived liver spheroids, we identify nizatidine, a histamine receptor H2 (HRH2) blocker, for treatment of advanced liver disease and HCC chemoprevention. Moreover, perturbation studies combined with single cell RNA-Seq analyses of patient liver tissues uncover hepatocytes and HRH2+, CLEC5Ahigh, MARCOlow liver macrophages as potential nizatidine targets. The PLS model combined with single cell RNA-Seq of patient tissues enables discovery of urgently needed targets and therapeutics for treatment of advanced liver disease and cancer prevention.

A blood-based prognostic liver secretome signature and long-term hepatocellular carcinoma risk in advanced liver fibrosis.

BACKGROUND: Accurate non-invasive prediction of long-term hepatocellular carcinoma (HCC) risk in advanced liver fibrosis is urgently needed for cost-effective HCC screening; however, this currently remains an unmet need. METHODS: A serum-protein-based prognostic liver secretome signature (PLSec) was bioinformatically derived from previously validated hepatic transcriptome signatures and optimized in 79 patients with advanced liver fibrosis. We independently validated PLSec for HCC risk in 331 cirrhosis patients with mixed etiologies (validation set 1 [V1]) and thereafter developed a score with clinical prognostic variables. The score was then validated in two independent cohorts: validation set 2 (V2): 164 patients with advanced liver fibrosis due to hepatitis C virus (HCV) infection cured after direct-acting antiviral therapy; validation set 3 (V3): 146 patients with advanced liver fibrosis with successfully-treated HCC and cured HCV infection. FINDINGS: An 8-protein blood-based PLSec recapitulated transcriptome-based hepatic HCC risk status. In V1, PLSec was significantly associated with incident HCC risk (adjusted hazard ratio [aHR], 2.35; 95% confidence interval [CI], 1.30-4.23). A composite score with serum alpha-fetoprotein (PLSec-AFP) was defined in V1, and validated in V2 (adjusted odds ratio, 3.80 [95%CI, 1.66-8.66]) and V3 (aHR, 3.08 [95%CI, 1.78-5.31]; c-index, 0.74). PLSec-AFP outperformed AFP alone (Brier score, 0.165 vs. 0.186 in V2; 0.196 vs. 0.206 in V3, respectively). CONCLUSIONS: The blood-based PLSec-AFP can accurately stratify patients with advanced liver fibrosis for long-term HCC risk and thereby guide risk-based tailored HCC screening.

Omics-derived hepatocellular carcinoma risk biomarkers for precision care of chronic liver diseases.

Precise hepatocellular carcinoma (HCC) risk prediction will play increasingly important roles with the contemporary HCC etiologies, that is, non-alcoholic fatty liver disease and resolved hepatitis C virus infection. Because the HCC incidence rate in this emerging patient population is relatively low (~1% per year), identification of a subset of patients at the highest risk is critical to concentrate the effort and resources of regular HCC screening to those who most need it. Omics profiling has been derived using several candidate HCC risk biomarkers, which could refine HCC screening by enabling individual risk-based personalized or risk-stratified patient management. Various types of biomolecules have been explored as sources of information to predict HCC risk at various time horizons. Germline DNA polymorphisms likely reflect race/ethnicity- and/or etiology-specific susceptibility to HCC development or chronic liver disease progression toward carcinogenesis. Transcriptomic dysregulations in the diseased liver capture functional molecular status supporting oncogenesis such as inflammatory pathway and myofibroblast activation. Circulating nucleic acids, proteins, and metabolites could serve as less-invasive measures of molecular HCC risk. Characterization of gut microbiota could also inform HCC risk estimation. Each biomarker could have its niche of clinical application depending on logistics of use, performance, and costs with a goal to eventually improve patient prognosis as a part of the whole algorithm of chronic liver disease management.

Restricted immunological and cellular pathways are shared by murine models of chronic alcohol consumption.

Murine models of chronic alcohol consumption are frequently used to investigate alcoholic liver injury and define new therapeutic targets. Lieber-DeCarli diet (LD) and Meadows-Cook diet (MC) are the most accepted models of chronic alcohol consumption. It is unclear how similar these models are at the cellular, immunologic, and transcriptome levels. We investigated the common and specific pathways of LD and MC models. Livers from LD and MC mice were subjected to histologic changes, hepatic leukocyte population, hepatic transcripts level related to leukocyte recruitment, and hepatic RNA-seq analysis. Cross-species comparison was performed using the alcoholic liver disease (ALD) transcriptomic public dataset. Despite LD mice have increased liver injury and steatosis by alcohol exposure, the number of CD45+ cells were reduced. Opposite, MC mice have an increased number of monocytes/liver by alcohol. The pattern of chemokine gradient, adhesion molecules, and cytokine transcripts is highly specific for each model, not shared with advanced human alcoholic liver disease. Moreover, hepatic RNA-seq revealed a limited and restricted number of shared genes differentially changed by alcohol exposure in these 2 models. Thus, mechanisms involved in alcohol tissue injury are model-dependent at multiple levels and raise the consideration of significant pathophysiological diversity of human alcoholic liver injury.

MPIC: Molecular Prognostic Indicators in Cirrhosis Database for Clinical Context-Specific in Silico Prognostic Biomarker Validation.

Prognostic biomarkers are vital in the management of progressive chronic diseases such as liver cirrhosis, affecting 1-2% of the global population and causing over 1 million deaths every year. Despite numerous candidate biomarkers in literature, the costly and lengthy process of validation hampers their clinical translation. Existing omics databases are not suitable for in silico validation due to the ignorance of critical factors, i.e., study design, clinical context of biomarker application, and statistical power. To address the unmet need, we have developed the Molecular Prognostic Indicators in Cirrhosis (MPIC) database as a representative example of an omics database tailored for prognostic biomarker validation. MPIC consists of (i) a molecular and clinical database structured by defined disease context and specific clinical outcome and annotated with employed study design and anticipated statistical power by disease domain experts, (ii) a bioinformatics analysis engine for user-provided gene-signature- or gene-based prognostic prediction, and (iii) a user interface for interactive exploration of relevant clinical cohort/scenario and assessment of significance and reliability of the result for prognostic prediction. MPIC assists cost-effective prognostic biomarker development by facilitating the process of validation, and will transform the care of chronic diseases such as cirrhosis. MPIC is freely available at www.mpic-app.org. The website is implemented in Java, Apache, and MySQL with all major browsers supported.

Use of big data in drug development for precision medicine: an update.

INTRODUCTION: Big-data-driven drug development resources and methodologies have been evolving with ever-expanding data from large-scale biological experiments, clinical trials, and medical records from participants in data collection initiatives. The enrichment of biological- and clinical-context-specific large-scale data has enabled computational inference more relevant to real-world biomedical research, particularly identification of therapeutic targets and drugs for specific diseases and clinical scenarios. AREAS COVERED: Here we overview recent progresses made in the fields: new big-data-driven approach to therapeutic target discovery, candidate drug prioritization, inference of clinical toxicity, and machine-learning methods in drug discovery. EXPERT OPINION: In the near future, much larger volumes and complex datasets for precision medicine will be generated, e.g., individual and longitudinal multi-omic, and direct-to-consumer datasets. Closer collaborations between experts with different backgrounds would also be required to better translate analytic results into prognosis and treatment in the clinical practice. Meanwhile, cloud computing with protected patient privacy would become more routine analytic practice to fill the gaps within data integration along with the advent of big-data. To conclude, integration of multitudes of data generated for each individual along with techniques tailored for big-data analytics may eventually enable us to achieve precision medicine.

GIGSEA: genotype imputed gene set enrichment analysis using GWAS summary level data.

Summary: level data of GWAS becomes increasingly important in post-GWAS data mining. Here, we present GIGSEA (Genotype Imputed Gene Set Enrichment Analysis), a novel method that uses GWAS summary statistics and eQTL to infer differential gene expression and interrogate gene set enrichment for the trait-associated SNPs. By incorporating empirical eQTL of the disease relevant tissue, GIGSEA naturally accounts for factors such as gene size, gene boundary, SNP distal regulation and multiple-marker regulation. The weighted linear regression model was used to perform the enrichment test, properly adjusting for imputation accuracy, model incompleteness and redundancy in different gene sets. The significance level of enrichment is assessed by the permutation test, where matrix operation was employed to dramatically improve computation speed. GIGSEA has appropriate type I error rates, and discovers the plausible biological findings on the real data set. Availability and implementation: GIGSEA is implemented in R, and freely available at www.github.com/zhushijia/GIGSEA. Supplementary information: Supplementary data are available at Bioinformatics online.