Predicting Hepatocellular Carcinoma Risk Research Articles

Clinical Utility of the aMAP Score for Predicting Hepatocellular Carcinoma Development in Patients with Chronic Hepatitis B.

This study aimed to evaluate the efficacy of the aMAP score and compare it with other risk scores for predicting hepatocellular carcinoma (HCC) development in Thai patients with chronic hepatitis B (CHB). We retrospectively analyzed patients with CHB between 1 January 2008 and 31 December 2019. Data on demographics, clinical parameters, cirrhosis status, HCC imaging, and alpha fetoprotein surveillance were collected to calculate the aMAP score (0-100) based on age, sex, albumin-bilirubin level, and platelet count. Of the 1060 patients analyzed, 789 were eligible, of whom 51 developed HCC. The cumulative HCC incidences in the low-, moderate-, and high-risk groups at 3, 5, and 10 years were significantly different (log-rank, p < 0.0001). The area under the receiver operating characteristic curves (AUROCs) of the aMAP scores for predicting HCC at 3, 5, and 10 years were 0.748, 0.777, and 0.784, respectively. Among the risk scores, the CU-HCC score had the highest AUROCs (0.823) for predicting 5-year HCC development. The aMAP score is a valuable tool for predicting HCC risk in Thai patients with CHB and can enhance surveillance strategies. However, its performance is inferior to that of the CU-HCC score, suggesting the need for new predictive tools for HCC surveillance.

Evaluation of PAGE-B Score for Hepatocellular Carcinoma Development in Chronic Hepatitis B Patients: Reliability, Validity, and Responsiveness.

Chronic hepatitis B (CHB) constitutes a major global public health issue, affecting millions of individuals. Despite the implementation of robust vaccination programs, the hepatitis B virus (HBV) significantly influences morbidity and mortality rates. CHB emerges as one of the leading causes of hepatocellular carcinoma (HCC), introducing a major challenge in the effective management of CHB patients. Therefore, it is of utmost clinical importance to diligently monitor individuals with CHB who are at high risk of HCC development. While various prognostic scores have been developed for surveillance and screening purposes, their accuracy in predicting HCC risk may be limited, particularly in patients under treatment with nucleos(t)ide analogues. The PAGE-B model, incorporating age, gender, and platelet count, has exhibited remarkable accuracy, validity, and reliability in predicting HCC occurrence among CHB patients receiving HBV treatment. Its predictive performance stands out, whether considered independently or in comparison to alternative HCC risk scoring systems. Furthermore, the introduction of targeted adjustments to the calculation of the PAGE-B score might have the potential to further improve its predictive accuracy. This review aims to evaluate the efficacy of the PAGE-B score as a dependable tool for accurate prediction of the development of HCC in CHB patients. The evidence discussed aims to provide valuable insights for guiding recommendations on HCC surveillance within this specific population.

The hepatocellular carcinoma risk in patients with HBV-related cirrhosis: a competing risk nomogram based on a 4-year retrospective cohort study.

The study aimed to build and validate a competitive risk nomogram to predict the cumulative incidence of hepatocellular carcinoma (HCC) for patients with hepatitis B virus (HBV)-related cirrhosis. A total of 1401 HBV-related cirrhosis patients were retrospectively enrolled from January 1, 2011 to December 31, 2014. Application of 20 times imputation dealt with missing data using multiple imputation by chained equations (MICE). The patients were randomly divided into a training set (n = 1017) and a validation set (n = 384) at a ratio of 3:1. A prediction study was carried out using a competing risk model, where the event of interest was HCC and the competing events were death and liver transplantation, and subdistribution hazard ratios (sHRs) with 95% CIs were reported. The multivariate competing risk model was constructed and validated. There was a negligible difference between the original database and the 20 imputed datasets. At the end of follow-up, the median follow-up time was 69.9 months (interquartile range: 43.8-86.6). There were 31.5% (442/1401) of the patients who developed HCC, with a 5-year cumulative incidence of 22.9 (95%CI, 20.8%-25.2%). The univariate and multivariate competing risk regression and construction of the nomogram were performed in 20 imputed training datasets. Age, sex, antiviral therapy history, hepatitis B e antigen, alcohol drinking history, and alpha-fetoprotein levels were included in the nomogram. The area under receiver operating characteristic curve values at 12, 24, 36, 60, and 96 months were 0.68, 0.69, 0.70, 0.68, and 0.80, and the Brier scores were 0.30, 0.25, 0.23, 0.21, and 0.20 in the validation set. According to the cumulative incidence function, the nomogram effectively screened out high-risk HCC patients from low-risk patients in the presence of competing events (Fine-Gray test p < 0.001). The competitive risk nomogram was allowed to be used for predicting HCC risk in individual patients with liver cirrhosis, taking into account both the association between risk factors and HCC and the modifying effect of competition events on this association.

A polygenetic risk score combined with environmental factors better predict susceptibility to hepatocellular carcinoma in Chinese population.

This study aimed to investigate environmental factors and genetic variant loci associated with hepatocellular carcinoma (HCC) in Chinese population and construct a weighted genetic risk score (wGRS) and polygenic risk score (PRS). A case-control study was applied to confirm the single nucleotide polymorphisms (SNPs) and environmental variables linked to HCC in the Chinese population, which had been screened by meta-analyses. wGRS and PRS were built in training sets and validation sets. Area under the curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), Akaike information criterion (AIC), and Bayesian information criterion (BIC) were applied to evaluate the performance of the models. A total of 13 SNPs were included in both risk prediction models. Compared with wGRS, PRS had better accuracy and discrimination ability in predicting HCC risk. The AUC for PRS in combination with drinking history, cirrhosis, HBV infection, and family history of HCC in training sets and validation sets (AUC: 0.86, 95% CI: 0.84-0.89; AUC: 0.85, 95% CI: 0.81-0.89) increased at least 20% than the AUC for PRS alone (AUC: 0.63, 95% CI: 0.60-0.67; AUC: 0.65, 95% CI: 0.60-0.71). A novel model combining PRS with alcohol history, HBV infection, cirrhosis, and family history of HCC could be applied as an effective tool for risk prediction of HCC, which could discriminate at-risk individuals for precise prevention.

Identification of 13 Novel Loci in a Genome-Wide Association Study on Taiwanese with Hepatocellular Carcinoma.

Liver cancer is caused by complex interactions among genetic factors, viral infection, alcohol abuse, and metabolic diseases. We conducted a genome-wide association study and polygenic risk score (PRS) model in Taiwan, employing a nonspecific etiology approach, to identify genetic risk factors for hepatocellular carcinoma (HCC). Our analysis of 2836 HCC cases and 134,549 controls revealed 13 novel associated loci such as the FAM66C gene, noncoding genes, liver-fibrosis-related genes, metabolism-related genes, and HCC-related pathway genes. We incorporated the results from the UK Biobank and Japanese database into our study for meta-analysis to validate our findings. We also identified specific subtypes of the major histocompatibility complex that influence both viral infection and HCC progression. Using this data, we developed a PRS to predict HCC risk in the general population, patients with HCC, and HCC-affected families. The PRS demonstrated higher risk scores in families with multiple HCCs and other cancer cases. This study presents a novel approach to HCC risk analysis, identifies seven new genes associated with HCC development, and introduces a reproducible PRS model for risk assessment.

Serum protein biomarkers for HCC risk prediction in HIV/HBV co-infected people: a clinical proteomic study using mass spectrometry.

HBV coinfection is frequent in people living with HIV (PLWH) and is the leading cause of hepatocellular carcinoma (HCC). While risk prediction methods for HCC in patients with HBV monoinfection have been proposed, suitable biomarkers for early diagnosis of HCC in PLWH remain uncommon. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to examine serum protein alterations in HCC and non-HCC patients with HIV and HBV co-infection. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Disease Ontology (DO) enrichment analysis were performed on the differentially expressed proteins (DEPs). The risk prediction model was created using five-cross-validation and LASSO regression to filter core DEPs. A total of 124 DEPs were discovered, with 95 proteins up-regulated and 29 proteins down-regulated. Extracellular matrix organization and membrane component were the DEPs that were most abundant in the categories of biological processes (BP) and cellular components (CC). Proteoglycans in cancer were one of the top three DEPs primarily enriched in the KEGG pathway, and 60.0% of DEPs were linked to various neoplasms in terms of DO enrichment. Eleven proteins, including GAPR1, PLTP, CLASP2, IGHV1-69D, IGLV5-45, A2M, VNN1, KLK11, ANPEP, DPP4 and HYI, were chosen as the core DEPs, and a nomogram was created to predict HCC risk. In HIV/HBV patients with HCC, several differential proteins can be detected in plasma by mass spectrometry, which can be used as screening markers for early diagnosis and risk prediction of HCC. Monitoring protease expression differences can help in the diagnosis and prognosis of HCC.

A prediction nomogram for hepatitis B virus-associated hepatocellular carcinoma

Background The present study aimed to develop and validate a new nomogram for predicting the incidence of hepatocellular carcinoma (HCC) among chronic hepatitis B (CHB) patients receiving antiviral therapy from real-world data. Methods The nomogram was established based on a real-world retrospective study of 764 patients with HBV from October 2008 to July 2020. A predictive model for the incidence of HCC was developed by multivariable Cox regression, and a nomogram was constructed. The predictive accuracy and discriminative ability of the nomogram were assessed by the concordance index (C-index), calibration curves, and decision curve analysis (DCA). Risk group stratification was performed to assess the predictive capacity of the nomogram. The nomogram was compared to three current commonly used predictive models. Results A total of 764 patients with HBV were recruited for this study. Age, family history of HCC, alcohol consumption, and Aspartate aminotransferase-to-Platelet Ratio Index (APRI) were all independent risk predictors of HCC in CHB patients. The constructed nomogram had good discrimination with a C-index of 0.811. The calibration curve and DCA also proved the reliability and accuracy of the nomogram. Three risk groups (low, moderate, and high) with significantly different prognoses were identified (p < 0.001). The model’s performance was significantly better than that of other risk models. Conclusions The nomogram was superior in predicting HCC risk among CHB patients who received antiviral treatment. The model can be utilized in clinical practice to aid decision-making on the strategy of long-term HCC surveillance, especially for moderate- and high-risk patients.

Hepatocellular Carcinoma Risk Scores from Modeling to Real Clinical Practice in Areas Highly Endemic for Hepatitis B Infection.

Hepatocellular carcinoma (HCC) accounts for the majority of primary liver cancers and represents a global health challenge. Liver cancer ranks third in cancer-related mortality with 830,000 deaths and sixth in incidence with 906,000 new cases annually worldwide. HCC most commonly occurs in patients with underlying liver disease, especially chronic hepatitis B virus (HBV) infection in highly endemic areas. Predicting HCC risk based on scoring models for patients with chronic liver disease is a simple, effective strategy for identifying and stratifying patients to improve the early diagnosis rate and prognosis of HCC. We examined 23 HCC risk scores published worldwide in CHB patients with (n=10) or without (n=13) antiviral treatment. We also described the characteristics of the risk score's predictive performance and application status. In the future, higher predictive accuracy could be achieved by combining novel technologies and machine learning algorithms to develop and update HCC risk score models and integrated early warning and diagnosis systems for HCC in hospitals and communities.

Can Modifications of LR-M Criteria Improve the Diagnostic Performance of Contrast-Enhanced Ultrasound LI-RADS for Small Hepatic Lesions up to 3 cm?

To assess the diagnostic performance of the contrast-enhanced ultrasound liver imaging reporting and data system (CEUS LI-RADS) version 2017 for small hepatic lesions of ≤3 cm before and after changing the LR-M criteria. We retrospectively analyzed the CEUS examination of 179 patients who were at high risk of hepatocellular carcinoma (HCC) with focal hepatic lesions ≤3 cm (194 lesions in total) and evaluated the diagnostic capability of the American College of Radiology and modified CEUS LI-RADS algorithms. Revision of the early washout time to 45 seconds increased the sensitivity of LR-5 in predicting HCC (P = .004), with no significant decrease in specificity (P = .118). It also made better the specificity of LR-M in predicting non-HCC malignancies (P = .001), with no significant decrease in sensitivity (P = .094). However, using within 3 minutes as the criterion for marked washout time improved the LR-5 sensitivity (P < .001) but decreased its specificity (P = .009) in predicting HCC, whereas the specificity of LR-M in predicting non-HCC malignancies increased (P < .001), but the sensitivity decreased (P = .027). CEUS LI-RADS (v2017) is a valid method for predicting HCC risk in high-risk patients. The diagnostic performance of LR-5 and LR-M could boost when the early washout time is revised to 45 seconds.

Development of a nomogram to predict the risk of hepatocellular carcinoma in patients with hepatitis B-related cirrhosis on antivirals.

Patients with compensated hepatitis B-related cirrhosis receiving antivirals are at the risk of hepatocellular carcinoma (HCC). This study aimed to develop and validate a nomogram for predicting the incidence of HCC in patients with hepatitis-B related cirrhosis. A total of 632 patients with compensated hepatitis-B related cirrhosis treated with entecavir or tenofovir between August 2010 and July 2018 were enrolled. Cox regression analysis was used to identify independent risk factors for HCC and a nomogram was developed using these factors. The area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analyses were used to evaluate the nomogram performance. The results were validated in an external cohort (n = 324). In the multivariate analysis, age per 10 years, neutrophil-lymphocyte ratio > 1.6, and platelet count < 86×109/L were independent predictors of HCC occurrence. A nomogram was developed to predict HCC risk using these three factors (ranging from 0 to 20). The nomogram showed better performance (AUC: 0.83) than that of the established models (all P < 0.05). The 3-year cumulative HCC incidences in the low- (scores < 4), medium- (4-10), and high-risk (> 10) subgroups were 0.7%, 4.3%, and 17.7%, respectively, in the derivation cohort, and 1.2%, 3.9%, and 17.8%, respectively, in the validation cohort. The nomogram showed good discrimination and calibration in estimating HCC risk in patients with hepatitis-B related cirrhosis on antivirals. High-risk patients with a score > 10 points require close surveillance.

Detection of DNA adduct γ-OHPdG in circulating tumor cells (CTCs) and its use as a prognostic biomarker in patients with hepatocellular carcinoma (HCC).

594 Background: A blood-based biomarker to predict the risk of hepatocellular carcinoma (HCC), and its recurrence is needed. Previously, we showed that γ-OHPdG, a mutagenic DNA adduct formed by lipid peroxidation, in liver biopsies from HCC patients, as detected by IHC, is inversely associated with overall survival (p&lt;0.0001) and recurrence-free survival (p&lt;0.007) after surgical resection. This finding suggests that γ-OHPdG may serve as a prognostic biomarker of HCC and its recurrence. A non-invasive method to detect γ-OHPdG is needed. Liquid biopsy is preferred over tissue biopsy because it is non-invasive, allows repeated sampling, lower risk to patient, and lower medical care cost. We developed a non-invasive method for detecting and quantifying γ-OHPdG using CTCs from HCC patient. Methods: The method involved following steps. First, CTCs from blood samples were isolated using a RosetteSep CD45 Depletion Cocktail and Ficoll Paque-based method. Isolated cells were identified as liver CTCs using asialoglycoprotein receptor 1 (ASGPR1), a cell surface protein expressed solely on the surface of hepatic cells, and γ-OHPdG by immunocytochemistry. The percentage of ASGPR1 and γ-OHPdG-positive stained CTCs and γ-OHPdG staining intensity was quantified using Metamorph software. The staining intensities in livers CTCs will be compared with that found in liver biopsies by IHC. Results: To determine the sensitivity and specificity of testing -γ-OHPdG level in circulating tumor cells using an-anti γ-OHPdG antibody, we showed that the proportion of γ-OHPdG-positively stained cells and staining intensity increased in HepG2 liver cancer cells upon acrolein treatment at increasing concentration. Furthermore, when the HepG2 was used to spike blood of healthy volunteers’, the recovery rate of γ-OHPdG positivity was &gt; 50-60%. CTCs from 32 HCC patients, detected at a positivity rate of ~97%, were tested for γ-OHPdG levels using the method developed in this project. The clinical factors including demographics, risk factors, alpha-fetoprotein (AFP), HCC size, multifocality, vascular invasion, extrahepatic metastasis have been correlated with the levels of γ-OHPdG in CTCs. Conclusions: The CTC method developed for detecting and quantifying γ-OHPdG warrant validations as a prognostic biomarker for predicting HCC risk and recurrence in clinical trials.

Metformin reduces hepatocellular carcinoma incidence after successful antiviral therapy in patients with diabetes and chronic hepatitis C in Taiwan

Diabetes mellitus (DM) is known to increase the risk of hepatocellular carcinoma (HCC) among individuals with chronic hepatitis C (CHC). We aimed to evaluate whether metformin reduces HCC risk among individuals with DM and CHC after successful antiviral therapy. Individuals with CHC who achieved a sustained virological response (SVR) after interferon-based therapy were enrolled in a large-scale, multicenter cohort in Taiwan (T-COACH). Cases of HCC at least 1 year after SVR were identified through linkage to the catastrophic illness and cancer registry databases. Of 7,249 individuals with CHC enrolled in the study, 781 (10.8%) had diabetes and 647 (82.8%) were metformin users. During a median follow-up of 4.4 years, 227 patients developed new-onset HCC. The 5-year cumulative HCC incidence was 10.9% in non-metformin users and 2.6% in metformin users, compared to 3.0% in individuals without DM (adjusted hazard ratio [aHR] 2.83; 95% CI 1.57-5.08 and aHR 1.46; 95% CI 0.98-2.19, respectively). Cirrhosis was the most important factor significantly associated with higher HCC risk in Cox regression analysis, followed by DM non-metformin use, older age, male sex, and obesity; whereas hyperlipidemia with statin use was associated with a lower HCC risk. Using the two most crucial risk factors, cirrhosis and DM non-metformin use, we constructed a simple risk model that could predict HCC risk among individuals with CHC after SVR. Metformin use was shown to reduce the risk of all liver-related complications. Metformin use greatly reduced HCC risk after successful antiviral therapy in individuals with diabetes and CHC. A simple risk stratification model comprising cirrhosis and DM non-metformin use could predict long-term outcomes in individuals with CHC after SVR. The current study provides evidence that metformin could reduce hepatocellular carcinoma (HCC) incidence after successful antiviral therapy among those with diabetes and chronic hepatitis C in a large-scale nationwide cohort study. Although successful antiviral therapy greatly reduces HCC risk in individuals with chronic hepatitis C, those with cirrhosis, diabetes, obesity, and the elderly remain at high risk of HCC development. We demonstrated that a simple risk model composed of two crucial unfavorable factors, cirrhosis and diabetes without metformin use, predicts the risk of HCC and major liver-related complications after successful antiviral therapy in individuals with chronic hepatitis C. Metformin use is highly recommended for individuals with diabetes and chronic hepatitis C after viral eradication to reduce the risk of HCC.

The Glu69Asp Polymorphism of EME1 Gene is Associated with an Increased Risk of Hepatocellular Carcinoma in Guangxi Population, China.

BackgroundThe dysfunction of Essential meiotic endonuclease 1 homolog 1 (EME1) can lead to genomic instability and tumorigenesis. Single nucleotide polymorphisms (SNPs) in the EME1 gene have been reported to be associated with the risk of several cancers, but its association with hepatocellular carcinoma (HCC) has not been investigated. This study aimed to determine the association between EME1 SNPs and the risk of HCC.MethodsThis study included 645 HCC patients and 649 healthy controls from a Guangxi population of Southern China, and genotyped three functional SNPs (Glu69Asp: rs3760413A>C, Ile350Thr: rs12450550T>C, and rs11868055A>G) of the EME1 gene utilizing the Agena MassARRAY platform.ResultsThe rs3760413C variant genotypes (AC+CC: Glu/Asp+Asp/Asp) conferred a 1.419-fold risk of HCC compared to the AA (Glu/Glu) genotype (adjusted OR = 1.419, 95% CI = 1.017–1.980), and the allele C increased the risk of HCC in a dose-dependent manner (Ptrend = 0.017). Moreover, the effects of the rs3760413C variant genotypes were more pronounced in individuals who drank pond/ditch water (adjusted OR = 3.956, 95% CI = 1.413–11.076) than in those who never drank (P = 0.033). We further observed that a potential carcinogen microcystin-LR induced more DNA oxidative damages in peripheral blood mononuclear cells from the carriers of rs3760413C variant genotypes than those from the subjects with AA genotype (P = 0.006). A nomogram was also constructed combining the rs3760413A>C polymorphism and environmental risk factors for predicting HCC risk with a good discriminatory ability (concordance index = 0.892, 95% CI: 0.874–0.911) and good calibration (mean absolute error = 0.005).ConclusionOur data suggest that the Glu69Asp missense polymorphism (rs3760413) of EME1 gene is associated with the risk of HCC, which may be a susceptible biomarker of HCC in the Guangxi population.

Untargeted plasma metabolomics for risk prediction of hepatocellular carcinoma: A prospective study in two Chinese cohorts.

Characterization of metabolic perturbation prior to hepatocellular carcinoma (HCC) may deepen the understanding of causal pathways and identify novel biomarkers for early prevention. We conducted two 1:1 matched nested case-control studies (108 and 55 pairs) to examine the association of plasma metabolome (profiled using LC-MS) with the risk of HCC based on two prospective cohorts in China. Differential metabolites were identified by paired t tests and orthogonal partial least-squares discriminant analysis (OPLS-DA). Weighted gene coexpression network analysis (WGCNA) was performed to classify metabolites into modules for identifying biological pathways involved in hepatocarcinogenesis. We assessed the risk predictivity of metabolites using multivariable logistic regression models. Among 612 named metabolites, 44 differential metabolites were identified between cases and controls, including 12 androgenic/progestin steroid hormones, 8 bile acids, 10 amino acids, 6 phospholipids, and 8 others. These metabolites were associated with HCC in the multivariable logistic regression analyses, with odds ratios ranging from 0.19 (95% confidence interval [CI]: 0.11-0.35) to 5.09 (95% CI: 2.73-9.50). WGCNA including 612 metabolites showed 8 significant modules related to HCC risk, including those representing metabolic pathways of androgen and progestin, primary and secondary bile acids, and amino acids. A combination of 18 metabolites of independent effects showed the potential to predict HCC risk, with an AUC of 0.87 (95% CI: 0.82-0.92) and 0.86 (95% CI: 0.80-0.93) in the training and validation sets, respectively. In conclusion, we identified a panel of plasma metabolites that could be implicated in hepatocellular carcinogenesis and have the potential to predict HCC risk.

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.

Abstract 6235: Detection of γ-OHPdG in circulating liver cells and its potential use as a prognostic biomarker in patients with hepatocellular carcinoma recurrence

Abstract Background: Primary liver cancer is the fourth most common cause of cancer-related death. Resection is the most widely used potentially curative treatment for patients with early-stage hepatocellular carcinoma (HCC). Recurrence within 2 years occurs in 30-50% of patients and is a major cause of mortality. A biomarker to predict the risk of HCC and its recurrence is urgently needed. Our studies showed that hepatic levels of γ-OHPdG, an endogenous adduct from lipid peroxidation, closely correlate with hepatocarcinogenesis in various independent animal models, including an obesity model. Importantly, we observed that higher levels of γ-OHPdG in liver specimens from HCC patients who underwent surgical resection were strongly associated with poor survival (p &amp;lt; 0.0001) as well as with poor recurrence-free survival (RFS) (p = 0.007). These finding suggest that γ-OHPdG may serve as a prognostic biomarker of HCC and its recurrence. A non-invasive method to detect γ-OHPdG is highly desirable. There is a surge of interest in using circulating liver cells (CLCs) from patient blood to diagnose cancer stages and predict treatment outcomes, metastatic potential, and recurrence. We developed a non-invasive method for detecting and quantifying γ-OHPdG using CLCs from patient blood. Method: The method was developed by following steps: (i) isolate CLCs from blood samples using a RosetteSep™ Human CD45 Depletion Cocktail and Ficoll Paque, (ii) detect γ-OHPdG by immunocytochemistry, (iii) quantify the images of γ-OHPdG-positive stained CLCs, and (iv) compare with that found in liver biopsies by immunohistochemistry. Results: To determine the specificity of the anti-γ-OHPdG antibody developed previously in our laboratory, we showed that the proportion of γ-OHPdG-positively stained cells and staining intensity increased in HepG2 liver cancer cells upon acrolein treatment. Liver cells were co-stained with asialoglycoprotein receptor 1 (ASGPR1), a cell surface protein expressed solely on the surface of hepatic cells. CLC γ-OHPdG was quantified using Metamorph (BioVision)-based journal software, developed to quantify the percentage of ASGPR1-stained cells that also stain positively, and with what level of intensity, for γ-OHPdG. We then applied this method to 35 HCC patients’ blood samples. This method detects the number of ASGPR1 stained CLC and determines the cellular localization of γ-OHPdG, allowing the quantification of γ-OHPdG in these cells. While the nuclear γ-OHPdG reflects DNA damage, adducts in both the nucleus and the cytosol will be evaluated against IHC staining using biopsies and other clinical endpoints, such as disease stage and recurrence. Conclusion: The CLC method descried above for detecting and quantifying γ-OHPdG can be potentially used to validate this adduct as a prognostic biomarker for predicting HCC risk and recurrence in clinical trials. Citation Format: Monika Aggarwal, Zizhao Zhu, Sophie Gould, Peter Johnson, Samira Beheshtian, Hongbin Fang, Bhaskar Kallakury, Susette C. Mueller, Aiwu Ruth He, Fung-Lung Chung. Detection of γ-OHPdG in circulating liver cells and its potential use as a prognostic biomarker in patients with hepatocellular carcinoma recurrence [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6235.

Validation of the PAGE-B score to predict hepatocellular carcinoma risk in caucasian chronic hepatitis B patients on treatment.

BACKGROUNDSeveral risk scores have been developed to predict hepatocellular carcinoma (HCC) risk in chronic hepatitis B (CHB) patients. The majority of risk scores are based on pretreatment variables that are no longer considered risk factors for HCC development due to the suppression of hepatitis B virus replication early in the course of potent antiviral treatment in most patients. The PAGE-B score, which is based on platelet levels, age and sex, has been shown to accurately predict HCC risk in CHB patients on antiviral treatment in various populations.AIMWe aimed to evaluate the PAGE-B score in predicting HCC risk in Turkish CHB patients on antiviral treatment.METHODSIn this study, we recruited 742 CHB patients who had been treated with tenofovir disoproxil fumarate or entecavir for ≥ 1 year. Risk groups were determined according to the PAGE-B scores as follows: ≤ 9, low; 10-17, moderate and ≥ 18, high. The cumulative HCC incidences in each risk group were computed using Kaplan-Meier analysis and were compared using the log-rank test. The accuracy of the PAGE-B score in predicting HCC risk was evaluated using a time-dependent area under the receiver operating characteristic (AUROC) curve at all study time points. Univariate and multivariate logistic regression analyses were used to assess the risk factors for HCC development.RESULTSThe mean follow-up time was 54.7 ± 1.2 mo. HCC was diagnosed in 26 patients (3.5%). The cumulative HCC incidences at 1, 3, 5 and 10 years were 0%, 0%, 0% and 0.4% in the PAGE-B low-risk group; 0%, 1.2%, 1.5% and 2.1% in the PAGE-B moderate-risk group; and 5%, 11.7%, 12.5%, and 15% in the PAGE-B high-risk group, respectively (log-rank P < 0.001). The AUROCs of the PAGE-B score in the prediction of HCC development at 1, 3, 5 and 10 years were 0.977, 0.903, 0.903 and 0.865, respectively. In the multivariable analysis, older age, male sex, lower platelet levels, presence of cirrhosis, and absence of alanine aminotransferase normalization at month 6 were associated with HCC development (all P < 0.05).CONCLUSIONThe PAGE-B score is a practical tool to predict HCC risk in Turkish patients with CHB and may be helpful to improve surveillance strategies.

Hepatocellular Carcinoma Risk Prediction in the NIH-AARP Diet and Health Study Cohort: A Machine Learning Approach.

BackgroundPrediction of hepatocellular carcinoma (HCC) development in persons with known risk factors remain a challenge and is an urgent unmet need, considering projected increases in HCC incidence and mortality in the US. We aimed to use machine learning techniques to identify a set of demographic, lifestyle, and health history information that can be used simultaneously for population-level HCC risk prediction.MethodsData from 377,065 participants of the NIH-AARP Diet and Health Study, among whom 647 developed HCC over 16 years of follow-up, were analyzed. The sample was randomly divided into independent training (60%) and validation (40%) sets. We evaluated 123 participant characteristics and tested 15 different machine learning algorithms for robustness in predicting HCC risk. Separately, we evaluated variables selected from multivariable logistic regression for risk prediction.ResultsThe random under-sampling boosting (RUSBoost) algorithm performed best during model testing. Fourteen participant characteristics were selected for risk prediction based on differences between cases and controls (Bonferroni-corrected p-values <0.0004) and from the most frequently used variables in the initial two decision trees of the RUSBoost learner trees. A predictive model based on the 14 variables had an AUC of 0.72 (sensitivity=0.68, specificity=0.63) and independent validation AUC of 0.65 (sensitivity=0.68, specificity=0.63). A subset of 9 variables identified through logistic regression also had an AUC of 0.72 (sensitivity=0.67, specificity=0.63) and independent validation AUC of 0.65 (sensitivity=0.70, specificity=0.61).ConclusionPopulation-level HCC risk prediction can be performed with a machine learning-based algorithm and could inform strategies for improving HCC risk reduction in at-risk groups.

Multicentre external validation of the GES score for predicting HCC risk in Japanese HCV patients who achieved SVR following DAAs

AbstractA simple score combining clinical and biochemical parameters (general evaluation score (GES)) has shown value in predicting hepatocellular carcinoma (HCC) risk after hepatitis C virus (HCV) eradication in Egyptian patients with HCV genotype 4. We aimed to apply the GES to predict HCC risk in Japanese HCV patients who achieved sustained virological response (SVR) following direct‐acting antivirals (DAAs). This multicentre retrospective cohort study included 187 HCV patients without a history of HCC treatment who achieved SVR. The GES was calculated using pre‐ and post‐treatment data. The median age of the patients was 66 years; 49% were male, 89% had cirrhosis and 69% had HCV genotype 1. During the mean 36‐month follow‐up, 19 (10.2%) developed HCC. Regarding the pretreatment scores, 75 (40.1%), 58 (31.0%) and 54 (28.9%) patients had low‐, intermediate‐ and high‐risk scores, respectively. The 4‐year cumulative incidence (CumI) was 1.64% in the low‐risk group, 2.82% in the intermediate‐risk group and 6.88% in the high‐risk group (log‐rank P = .029). In patients with cirrhosis, 60 (36.1%), 57 (34.3%) and 49 (29.5%) had low‐, intermediate‐ and high‐risk scores respectively. The 4‐year CumI was 0.98% in the low‐risk group, 2.86% in the intermediate‐risk group and 6.67% in the high‐risk group (log‐rank P = .02). The GES calculated with pretreatment data was more useful than that calculated with post‐treatment data (Harrell's C statistic: 0.670 vs 0.587). This tool incorporates changes over time to estimate variations in HCC risk and could help identify low‐risk patients for whom HCC surveillance can be discontinued.

Comparative performance of risk prediction models for hepatitis B-related hepatocellular carcinoma in the United States

Guidelines recommend hepatocellular carcinoma (HCC) surveillance in patients with chronic HBV infection. Several HCC risk prediction models are available to guide surveillance decisions, but their comparative performance remains unclear. Using a retrospective cohort of patients with HBV treated with nucleos(t)ide analogues at 130 Veterans Administration facilities between 9/1/2008 and 12/31/2018, we calculated risk scores from 10 HCC risk prediction models (REACH-B, PAGE-B, m-PAGE-B, CU-HCC, HCC-RESCUE, CAMD, APA-B, REAL-B, AASL-HCC, RWS-HCC). We estimated the models' discrimination and calibration. We calculated HCC incidence in risk categories defined by the reported cut-offs for all models. Of 3,101 patients with HBV (32.2% with cirrhosis), 47.0% were treated with entecavir, 40.6% tenofovir, and 12.4% received both. During a median follow-up of 4.5 years, 113 patients developed HCC at an incidence of 0.75/100 person-years. AUC values for 3-year HCC risk were the highest for RWS-HCC, APA-B, REAL-B, and AASL-HCC (all >0.80). Of these, 3 (APA-B, RWS-HCC, REAL-B) incorporated alpha-fetoprotein. AUC values for the other models ranged from 0.73 for PAGE-B to 0.79 for CAMD and HCC-RESCUE. Of the 7 models with AUC >0.75, only APA-B was poorly calibrated. In total, 10-20% of the cohort was deemed low-risk based on the published cut-offs. None of the patients in the low-risk groups defined by PAGE-B, m-PAGE-B, AASL-HCC, and REAL-B developed HCC during the study timeframe. In this national cohort of US-based patients with HBV on antiviral treatment, most models performed well in predicting HCC risk. A low-risk group, in which no cases of HCC occurred within a 3-year timeframe, was identified by several models (PAGE-B, m-PAGE-B, CAMD, AASL-HCC, REAL-B). Further studies are warranted to examine whether these patients could be excluded from HCC surveillance. Risk prediction models for hepatocellular carcinoma (HCC) in patients infected with hepatitis B virus (HBV) could guide HCC surveillance decisions. In this large cohort of US-based patients receiving treatment for HBV, most published models discriminated between those who did or did not develop HCC, although the RWS-HCC, REAL-B, and AASL-HCC performed the best. If confirmed in future studies, these models could help identify a low-risk subset of patients on antiviral treatment who could be excluded from HCC surveillance.