Novel, high accuracy models for hepatocellular carcinoma prediction based on longitudinal data and cell-free DNA signatures

Genome-wide analyses of cell-free DNA for therapeutic monitoring of patients with pancreatic cancer

Determining response to therapy for patients with pancreatic cancer can be challenging. We evaluated methods for assessing therapeutic response using cell-free DNA (cfDNA) in plasma from patients with metastatic pancreatic cancer in the CheckPAC trial (NCT02866383). Patients were evaluated before and after initiation of therapy using tumor-informed plasma whole-genome sequencing (WGMAF) and tumor-independent genome-wide cfDNA fragmentation profiles and repeat landscapes (ARTEMIS-DELFI). Using WGMAF, molecular responders had a median overall survival (OS) of 319 days compared to 126 days for nonresponders [hazard ratio (HR) = 0.29, 95% confidence interval (CI) = 0.11-0.79, P = 0.011]. For ARTEMIS-DELFI, patients with low scores after therapy initiation had longer median OS than patients with high scores (233 versus 172 days, HR = 0.12, 95% CI = 0.046-0.31, P < 0.0001). We validated ARTEMIS-DELFI in patients with pancreatic cancer in the PACTO trial (NCT02767557). These analyses suggest that noninvasive mutation and fragmentation-based cfDNA approaches can identify therapeutic response of individuals with pancreatic cancer.

Long-Term Dynamic Changes of Alanine Aminotransferase Levels Are Associated With Liver-Related Events in Nucleos(t)ide Analogue-Treated Chronic Hepatitis B Patients in China

BACKGROUND

The role of alanine aminotransferase (ALT) dynamics during nucleos(t)ide analogue (NA) therapy in chronic hepatitis B (CHB) is unclear. We aimed to evaluate the correlation between ALT dynamics and liver-related events (LRE), and explore the optimal threshold of ALT during NA treatment.

METHODS

We enrolled 18,129 NA-treated patients, comprising 3104 patients from the Search-B study (NCT02167503) and 15,025 patients from a real-world cohort in Hong Kong. Latent-class mixed model (LCMM) was adopted to identify trajectory patterns of ALT during treatment. ALT value at the 95th percentile of the trajectory group with the lowest LRE risk was obtained as the optimal threshold.

RESULTS

During a median follow-up of 53.3 months, 1164 patients developed LRE with a 7-year cumulative incidence of 9.9%. In the Search-B cohort, LCMM recognised 3 trajectory groups with progressively increasing ALT levels, which were positively associated with LRE risk. Subsequently, the optimal thresholds for ALT were obtained as 23 U/L for men and 16 U/L for women. The 7-year cumulative incidence of LRE was 5.5% for ALT ≤ 23 or 16 U/L, significantly lower than that for ALT > 23 or 16 U/L but ≤ 40 U/L (10.8%; aHR = 2.0, p < 0.001), and ALT > 40 U/L (15.1%; aHR = 3.4, p < 0.001). Similarly, in the Hong Kong cohort, ALT > 23 or 16 U/L but < 40 U/L and ALT > 40 U/L also increased the LRE risk, with aHRs of 2.0 (p = 0.003) and 6.1 (p < 0.001), respectively.

CONCLUSION

On-treatment ALT levels were significantly correlated with the prognosis of CHB. ALT ≤ 23 U/L for men and ≤ 16 U/L for women were identified as the optimal thresholds during NA treatment, suggesting that CHB patients should strive for a lower ALT level beyond the traditional normal range.

Discovery and validation of a novel dual-target blood test for the detection of hepatocellular carcinoma across stages from cirrhosis

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most common cancers. Early detection of HCC helps improve the patients' 5-year survival rate. Our goal was to identify superior methylation biomarkers to develop a methylation-specific quantitative PCR (MS‒qPCR) assay.

METHODS

A five-phase case-control study identified HCC methylation biomarkers via capture sequencing, TCGA/RNA-seq filtering, technical (MS-qPCR/Sanger) and biological (quadruplex MS-qPCR) validation. Methylated biomarkers were selected based on differential methylation expression using a tissue discovery cohort (43 HCC, 32 normal) and validated in plasma validation cohorts (Phase 1: 53 HCC, 52 cirrhosis, 20 benign, 50 healthy; Phase 2: 67 HCC, 81 cirrhosis). Then, the final assay's HCC detection performance was compared with existing blood-based surveillance methods.

RESULTS

Two methylated genes, OSR2 and TSPYL5, and a novel internal reference gene, SDF4, were identified and developed into an MS‒qPCR assay named Qliver. Qliver had an AUC of 0.955 (95% CI: 0.924-0.987) for distinguishing HCC patients from non-HCC patients in the Phase 1 plasma cohort, with a sensitivity of 88.68% (95% CI: 76.97%-95.73%) and a specificity of 89.34% (95% CI: 82.47%-94.20%), and 0.958 (95% CI: 0.927-0.989) for distinguishing HCC patients from cirrhosis patients in the Phase 2 plasma cohort, with a sensitivity of 88.06% (95% CI: 77.82%-94.70%) and a specificity of 92.59% (95% CI: 84.57%-97.23%). For the Phase 1 plus Plasma 2 cohort, Qliver had an AUC of at least 0.958 for detecting HCC in healthy individuals, cirrhosis patients and patients with benign liver diseases, which was superior to that of the GALAD score (AUC: 0.777 to 0.849). For BCLC stage 0 and A HCC patients, the sensitivity of Qliver ranged from 62.50% (95% CI: 24.49%-91.48%) to 72.73% (39.03%-93.98%), with a specificity of 90%. Overall, Qliver was superior to the AFP, AFP-L3, DCP and the GALAD score in terms of cirrhosis history, tumor stage, tumor size and tumor count.

CONCLUSIONS

Qliver demonstrated superior performance in detecting HCC compared with currently widely used blood biomarkers, suggesting its potential clinical benefit in HCC surveillance in high-risk populations.

Reduction of Bacteroides fragilis in Gut Microbiome of Chronic Hepatitis B Patients Promotes Liver Injury

In chronic hepatitis B (CHB) patients under antiviral treatment, liver injury, as evidenced by elevated alanine transaminase (ALT), is associated with unfavorable outcomes and needs effective treatment. The interaction between gut microbiota and liver injury in CHB patients remains unclear. Using a case-control design, 28 cases with elevated ALT and 28 matched controls with normal ALT were randomly selected from CHB patients with viral control. Clinical characteristics were comparable between groups. Metagenomic sequencing revealed that Bacteroides fragilis was decreased in cases and exhibited the greatest disparity between cases and controls. Mice colonized by gut microbiota from cases exhibited more severe liver damage in both LPS-induced and MCD diet-induced liver injury models, and had a lower abundance of B. fragilis compared to mice colonized by gut microbiota from controls. Oral gavage of B. fragilis improved both LPS-induced and MCD diet-induced liver injury. Metabolomics analysis revealed that the levels of 7-Ketolithocholic acid (7-Keto-LCA) were positively correlated with B. fragilis and significantly increased in the cultural supernatant of B. fragilis. Consistently, 7-Keto-LCA exerted protective effects against both LPS-induced and MCD diet-induced liver damage. Targeting gut microbiota might be a promising therapeutic treatment for alleviation residual liver inflammation in CHB patients with viral control.

The Current Role of Circulating Cell-Free DNA in the Management of Hepatocellular Carcinoma

Circulating cell-free DNA (cfDNA) has emerged as a compelling candidate of liquid biopsy markers for the diagnosis and prognosis of several cancers. We systematically reviewed data on the role of cfDNA markers in the diagnosis, prognosis and treatment of hepatocellular carcinoma (HCC). Early studies suggested that levels of circulating cfDNA, mitochondrial DNA and cfDNA integrity are higher in patients with HCC than chronic liver diseases. In subsequent studies, methylation changes in circulating tumor DNA (ctDNA) as well as cfDNA fragmentation patterns and circulating nucleosomes were found to offer high sensitivity (>60%) and excellent specificity (>90%) for HCC diagnosis. The predictive role of cfDNA markers and ctDNA has been assessed in a few studies including untreated patients with HCC providing promising results for prediction of survival. However, port-hepatectomy detection of cfDNA/ctDNA markers or copy number variation indicators of cfDNA seem to reflect minimum residual disease and thus a high risk for HCC recurrence. The same markers can be useful for prediction after transarterial chemoembolization, radiofrequency ablation, radiotherapy and even systemic therapies. In conclusion, cfDNA markers can be useful in HCC surveillance, improving early diagnosis rates, as well as for monitoring treatment effectiveness and minimal residual disease post-treatment.

Study on SARS-CoV-2 infection in middle-aged and elderly population infected with hepatitis virus: a cohort study in a rural area of northeast China

Background

To investigate the symptoms, the level of antibody, the progression of liver disease after SARS-CoV-2 infection in middle-aged and elderly population infected with hepatitis virus.

Methods

The study was based on a cohort of high-risk liver cancer and the participants was recruited in April 2023. Blood sample were collected and information was obtained through questionnaires. Data on reinfection was obtained by follow-up until July 31, 2023. The SARS-CoV-2-specific neutralizing antibody and IgG were measured.

Results

A total of 599 participants infected with hepatitis virus were included and the mean age was 61.3 ± 7.4 years. The SARS-CoV-2 infection rate was 94.7%. Among the infected, 132 were asymptomatic, 435 were symptomatic, no severe cases occurred. Four months after infection, no difference was in liver function and aMAP score between the infected and uninfected. The infected had higher seropositivity rates of both antibodies than the uninfected (neutralizing antibody: uninfected: 93.7%, infected: 99.6%; IgG: uninfected: 59.4%, infected: 98.9%). The levels of both antibodies in the symptomatic were higher than those the asymptomatic and the uninfected (neutralizing antibody: uninfected: 0.75 AU/mL, asymptomatic: 15.46 AU/mL, symptomatic: 24.76 AU/mL; IgG: uninfected: 15.10 AU/mL, asymptomatic: 263.84 AU/mL, symptomatic: 291.83 AU/mL). By July 31, 2023, the incidence of reinfection was 17.5%.

Conclusions

Although the infection rate of SARS-CoV-2 was high, no severe cases occurred. Omicron infection may not aggravate progression of hepatitis. Four months after infection, the population showed high positivity rate in neutralizing antibody and IgG. Monitoring of virus mutations and targeted prevention and care strategies is crucial for vulnerable populations.

Long-term outcome and risk stratification in compensated advanced chronic liver disease after HCV-cure

BACKGROUND AND AIMS

Around 750,000 patients per year will be cured of HCV infection until 2030. Those with compensated advanced chronic liver disease remain at risk for hepatic decompensation and de novo HCC. Algorithms have been developed to stratify risk early after cure; however, data on long-term outcomes and the prognostic utility of these risk stratification algorithms at later time points are lacking.

APPROACH AND RESULTS

We retrospectively analyzed a cohort of 2335 patients with compensated advanced chronic liver disease (liver stiffness measurement≥10 kPa) who achieved HCV-cure by interferon-free therapies from 15 European centers (median age 60.2±11.9 y, 21.1% obesity, 21.2% diabetes).During a median follow-up of 6 years, first hepatic decompensation occurred in 84 patients (3.6%, incidence rate: 0.74%/y, cumulative incidence at 6 y: 3.2%); 183 (7.8%) patients developed de novo HCC (incidence rate: 1.60%/y, cumulative incidence at 6 y: 8.3%), with both risks being strictly linear over time.Baveno VII criteria to exclude (FU-liver stiffness measurement <12 kPa and follow-up platelet count >150 g/L) or rule-in (FU-liver stiffness measurement ≥25 kPa) clinically significant portal hypertension (CSPH) stratified the risk of hepatic decompensation with proportional hazards. Estimated probability of CSPH discriminated patients developing versus not developing hepatic decompensation in the gray zone (ie, patients meeting none of the above criteria).Published HCC risk stratification algorithms identified high-incidence and low-incidence groups; however, the size of the latter group varied substantially (9.9%-69.1%). A granular "HCC-sustained virologic response" model was developed to inform an individual patient's HCC risk after HCV-cure.

CONCLUSIONS

In patients with compensated advanced chronic liver disease, the risks of hepatic decompensation and HCC remain constant after HCV-cure, even in the long term (>3 y). One-time post-treatment risk stratification based on noninvasive criteria provides important prognostic information that is maintained during long-term follow-up, as the hazards remain proportional over time.

Surveillance for Hepatocellular Carcinoma

This article reviews surveillance for the detection of early stage hepatocellular carcinoma, covering the rationale for surveillance, optimal selection of persons needing surveillance, methods and frequency of screening, strategies for addressing barriers to surveillance, and trends for future improvement in surveillance leading to more effective cancer control and improved patient outcomes. The importance of integrating liver cancer surveillance as a core component of national public health programs is emphasized. The impact of emerging technologies for identifying persons at risk, stratifying individual risk to improve the cost-effectiveness of surveillance programs, and improving the performance, accessibility, and convenience of surveillance are discussed.

Development and Validation of a PIVKA-II-Based Model for HCC Risk Stratification in Patients With HCV-Related Cirrhosis Successfully Treated With DAA

BACKGROUND AND AIMS

Patients with hepatitis C virus (HCV)-related cirrhosis with sustained virological response (SVR) to direct-acting antivirals (DAA) remain at risk of developing hepatocellular carcinoma (HCC). Recently, serum protein induced by vitamin K absence or antagonist-II (PIVKA-II) has shown promising results as an HCC-predictive biomarker. We aimed to develop and validate a PIVKA-II-based model for HCC risk stratification in cirrhotic patients with SVR to DAA.

METHODS

A total of 1220 consecutive patients (Turin, n = 531; Pisa, n = 335; Milan, n = 354) with HCV-related cirrhosis treated with DAA were included in the study. Patients were retrospectively allocated to the training cohort (Turin+Pisa; median follow-up [FU] 39, 22-55 months; incident HCC: 93 [10.7%]) and validation cohort (Milan; median FU 49.0, 35.0-52.0 months; incident HCC: 19 [5.4%]). Serum PIVKA-II levels were measured using the LumipulseG system (Fujirebio, Japan) at SVR12 (Turin and Pisa cohorts) or the end of treatment (Milan cohort).

RESULTS

Using Cox regression analysis, a model including PIVKA-II combined with age, sex, ALT, AST, γGT, platelet count, albumin and total bilirubin was derived from the training cohort (C-index = 0.72). In the validation cohort, the model showed a C-index of 0.71 with an area under the curve of 0.84 for identifying patients who developed HCC during the first 12 months of FU. When patients were grouped into three risk categories, the cumulative incidence of HCC was 2.7%, 4.0% and 14.3% in the low-, medium- and high-risk groups, respectively (p < 0.001). Notably, no HCC occurred within 3 years of FU in the low-risk group.

CONCLUSIONS

Our PIVKA-II-based model showed satisfactory accuracy for HCC risk stratification and may represent a valuable tool for implementing risk-based surveillance protocols in patients with HCV-related cirrhosis with SVR to DAA.

Lenvatinib and tislelizumab versus atezolizumab and bevacizumab in combination with TAE-HAIC for unresectable hepatocellular carcinoma with high tumor burden: a multicenter retrospective cohort study

BACKGROUND

Systemic and locoregional combination therapy has demonstrated promising outcomes for unresectable hepatocellular carcinoma (HCC); However, the best combination option remains unknown. This study compared the efficacy and safety of lenvatinib and tislelizumab versus atezolizumab and bevacizumab in combination with transarterial embolization (TAE) plus hepatic artery infusion chemotherapy (HAIC) for unresectable HCC with high tumor burden.

METHODS

This multicenter retrospective cohort study enrolled treatment-naive patients with unresectable HCC treated with TAE-HAIC plus lenvatinib and tislelizumab (THLP group) or TAE-HAIC plus atezolizumab and bevacizumab (THTA group). The primary endpoint was overall survival (OS). Secondary endpoints included progression-free survival (PFS), tumor response, and adverse events (AEs). Propensity score matching (PSM) was performed to reduce bias.

RESULTS

Of the 240 patients enrolled, 153 and 51 patients were assigned to the THLP and THTA groups, respectively after PSM (3:1). The THLP group showed a longer median OS (22 months vs. 18.2 months; P = 0.412), whereas the median PFS was longer in the THTA group (8.1 months vs. 7 months; P = 0.723), with statistically insignificant intergroup differences. No statistical differences were observed in objective response rate (RECIST 1.1: 33.9 vs. 31.4%; mRECIST: 77.1% vs. 74.5%; P = 0.635), disease control rate (RECIST 1.1: 88.9% vs. 92.2; mRECIST: 92.2% vs. 94.1%; P = 0.716), and in grade 3/4 AEs. Gastrointestinal hemorrhage rate was significantly higher in the THTA group (9.1% vs. 1.6%; P = 0.007). All AEs were controllable and no treatment-related grade 5 AEs occurred.

CONCLUSIONS

TAE-HAIC plus lenvatinib and tislelizumab or TAE-HAIC plus atezolizumab and bevacizumab showed similar outcomes for unresectable HCC with high tumor burden, and manageable safety. The results need further validation.

Assessing the Predictive Accuracy of the aMAP Risk Score for Hepatocellular Carcinoma (HCC): Diagnostic Test Accuracy and Meta-analysis

Purpose

We aimed to perform a meta-analysis with the intention of evaluating the reliability and test accuracy of the aMAP risk score in the identification of HCC.

Methods

A systematic search was performed in PubMed, Scopus, Cochrane, Embase, and Web of Science databases from inception to September 2023, to identify studies measuring the aMAP score in patients for the purpose of predicting the occurrence or recurrence of HCC. The meta-analysis was performed using the meta package in R version 4.1.0. The diagnostic accuracy meta-analysis was conducted using Meta-DiSc software.

Results

Thirty-five studies 102,959 participants were included in the review. The aMAP score was significantly higher in the HCC group than in the non-HCC group, with a mean difference of 6.15. When the aMAP score is at 50, the pooled sensitivity, specificity, negative likelihood ratio, and positive likelihood ratio with 95% CI was 0.961 (95% CI 0.936, 0.976), 0.344 (95% CI 0.227, 0.483), 0.114 (95% CI 0.087, 0.15), and 1.464 (95% CI 1.22, 1.756), respectively. At a cutoff value of 60, the pooled sensitivity, specificity, negative likelihood ratio, and positive likelihood ratio with 95% CI was 0.594 (95% CI 0.492, 0.689), 0.816 (95% CI 0.714, 0.888), 0.497 (95% CI 0.418, 0.591), and 3.235 (95% CI 2.284, 4.582), respectively.

Conclusion

The aMAP score is a reliable, accurate, and easy-to-use tool for predicting HCC patients of all stages, including early-stage HCC. Therefore, the aMAP score can be a valuable tool for surveillance of HCC patients and can help to improve early detection and reduce mortality.

Biomarkers for Immunotherapy Efficacy in Advanced Hepatocellular Carcinoma: A Comprehensive Review

Hepatocellular carcinoma (HCC), the most common primary liver malignancy and the sixth most common cancer globally, remains fatal for many patients with inappropriate responses to treatment. Recent advancements in immunotherapy have transformed the treatment landscape for advanced HCC. However, variability in patient responses to immunotherapy highlights the need for biomarkers that can predict treatment outcomes. This manuscript comprehensively reviews the evolving role of biomarkers in immunotherapy efficacy, spanning from blood-derived indicators-alpha-fetoprotein, inflammatory markers, cytokines, circulating tumor cells, and their DNA-to tissue-derived indicators-programmed cell death ligand 1 expression, tumor mutational burden, microsatellite instability, and tumor-infiltrating lymphocytes. The current body of evidence suggests that these biomarkers hold promise for improving patient selection and predicting immunotherapy outcomes. Each biomarker offers unique insights into disease biology and the immune landscape of HCC, potentially enhancing the precision of treatment strategies. However, challenges such as methodological variability, high costs, inconsistent findings, and the need for large-scale validation in well-powered two-arm trial studies persist, making them currently unsuitable for integration into standard care. Addressing these challenges through standardized techniques and implementation of further studies will be critical for the future incorporation of these biomarkers into clinical practice for advanced HCC.

High accuracy model for HBsAg loss based on longitudinal trajectories of serum qHBsAg throughout long-term antiviral therapy

OBJECTIVE

Hepatitis B surface antigen (HBsAg) loss is the optimal outcome for patients with chronic hepatitis B (CHB) but this rarely occurs with currently approved therapies. We aimed to develop and validate a prognostic model for HBsAg loss on treatment using longitudinal data from a large, prospectively followed, nationwide cohort.

DESIGN

CHB patients receiving nucleos(t)ide analogues as antiviral treatment were enrolled from 50 centres in China. Quantitative HBsAg (qHBsAg) testing was prospectively performed biannually per protocol. Longitudinal discriminant analysis algorithm was used to estimate the incidence of HBsAg loss, by integrating clinical data of each patient collected during follow-up.

RESULTS

In total, 6792 CHB patients who had initiated antiviral treatment 41.3 (IQR 7.6-107.6) months before enrolment and had median qHBsAg 2.9 (IQR 2.3-3.3) log10IU/mL at entry were analysed. With a median follow-up of 65.6 (IQR 51.5-84.7) months, the 5-year cumulative incidence of HBsAg loss was 2.4%. A prediction model integrating all qHBsAg values of each patient during follow-up, designated GOLDEN model, was developed and validated. The AUCs of GOLDEN model were 0.981 (95% CI 0.974 to 0.987) and 0.979 (95% CI 0.974 to 0.983) in the training and external validation sets, respectively, and were significantly better than those of a single qHBsAg measurement. GOLDEN model identified 8.5%-10.4% of patients with a high probability of HBsAg loss (5-year cumulative incidence: 17.0%-29.1%) and was able to exclude 89.6%-91.5% of patients whose incidence of HBsAg loss is 0. Moreover, the GOLDEN model consistently showed excellent performance among various subgroups.

CONCLUSION

The novel GOLDEN model, based on longitudinal qHBsAg data, accurately predicts HBsAg clearance, provides reliable estimates of functional hepatitis B virus (HBV) cure and may have the potential to stratify different subsets of patients for novel anti-HBV therapies.

Early warning of hepatocellular carcinoma in cirrhotic patients by three-phase CT-based deep learning radiomics model: a retrospective, multicentre, cohort study

BACKGROUND

The diagnosis of hepatocellular carcinoma (HCC) often experiences latency, ultimately leading to unfavorable patient outcomes due to delayed therapeutic interventions. Our study is designed to develop and validate a model that employs triple-phase computerized tomography (CT)-based deep learning radiomics and clinical variables for early warning of HCC in patients with cirrhosis.

METHODS

We studied 1858 patients with cirrhosis primarily from the PreCar cohort (NCT03588442) between June 2018 and January 2020 at 11 centres, and collected triple-phase CT images and laboratory results 3-12 months prior to HCC diagnosis or non-HCC final follow-up. Using radiomics and deep learning techniques, early warning model was developed in the discovery cohort (n = 924), and then validated in an internal validation cohort (n = 231), and an external validation cohort from 10 external centres (n = 703).

FINDINGS

We developed a hybrid model, named ALARM model, which integrates deep learning radiomics with clinical variables, enabling early warning of the majority of HCC cases. The ALARM model effectively predicted short-term HCC development in cirrhotic patients with area under the curve (AUC) of 0.929 (95% confidence interval 0.918-0.941) in the discovery cohort, 0.902 (0.818-0.987) in the internal validation cohort, and 0.918 (0.898-0.961) in the external validation cohort. By applying optimal thresholds of 0.21 and 0.65, the high-risk (n = 221, 11.9%) and medium-risk (n = 433, 23.3%) groups, which covered 94.4% (84/89) of the patients who developed HCC, had significantly higher rates of HCC occurrence compared to the low-risk group (n = 1204, 64.8%) (24.3% vs 6.4% vs 0.42%, P < 0.001). Furthermore, ALARM also demonstrated consistent performance in subgroup analysis.

INTERPRETATION

The novel ALARM model, based on deep learning radiomics with clinical variables, provides reliable estimates of short-term HCC development for cirrhotic patients, and may have the potential to improve the precision in clinical decision-making and early initiation of HCC treatments.

FUNDING

This work was supported by National Key Research and Development Program of China (2022YFC2303600, 2022YFC2304800), and the National Natural Science Foundation of China (82170610), Guangdong Basic and Applied Basic Research Foundation (2023A1515011211).

Metabolomics biomarkers of hepatocellular carcinoma in a prospective cohort of patients with cirrhosis

Background & Aims

The effectiveness of surveillance for hepatocellular carcinoma (HCC) in patients with cirrhosis is limited, due to inadequate risk stratification and suboptimal performance of current screening modalities.

Methods

We developed a multicenter prospective cohort of patients with cirrhosis undergoing surveillance with MRI and applied global untargeted metabolomics to 612 longitudinal serum samples from 203 patients. Among them, 37 developed HCC during follow-up.

Results

We identified 150 metabolites with significant abundance changes in samples collected prior to HCC (Cases) compared to samples from patients who did not develop HCC (Controls). Tauro-conjugated bile acids and gamma-glutamyl amino acids were increased, while acyl-cholines and deoxycholate derivatives were decreased. Seven amino acids including serine and alanine had strong associations with HCC risk, while strong protective effects were observed for N-acetylglycine and glycerophosphorylcholine. Machine learning using the 150 metabolites, age, gender, and PNPLA3 and TMS6SF2 single nucleotide polymorphisms, identified 15 variables giving optimal performance. Among them, N-acetylglycine had the highest AUC in discriminating Cases and Controls. When restricting Cases to samples collected within 1 year prior to HCC (Cases-12M), additional metabolites including microbiota-derived metabolites were identified. The combination of the top six variables identified by machine learning (alpha-fetoprotein, 6-bromotryptophan, N-acetylglycine, salicyluric glucuronide, testosterone sulfate and age) had good performance in discriminating Cases-12M from Controls (AUC 0.88, 95% CI 0.83-0.93). Finally, 23 metabolites distinguished Cases with LI-RADS-3 lesions from Controls with LI-RADS-3 lesions, with reduced abundance of acyl-cholines and glycerophosphorylcholine-related lysophospholipids in Cases.

Conclusions

This study identified N-acetylglycine, amino acids, bile acids and choline-derived metabolites as biomarkers of HCC risk, and microbiota-derived metabolites as contributors to HCC development.

Impact and implications

The effectiveness of surveillance for hepatocellular carcinoma (HCC) in patients with cirrhosis is limited. There is an urgent need for improvement in risk stratification and new screening modalities, particularly blood biomarkers. Longitudinal collection of paired blood samples and MRI images from patients with cirrhosis is particularly valuable in assessing how early blood and imaging markers become positive during the period when lesions are observed to obtain a diagnosis of HCC. We generated a multicenter prospective cohort of patients with cirrhosis under surveillance with contrast MRI, applied untargeted metabolomics on 612 serum samples from 203 patients and identified metabolites associated with risk of HCC development. Such biomarkers may significantly improve early-stage HCC detection for patients with cirrhosis undergoing HCC surveillance, a critical step to increasing curative treatment opportunities and reducing mortality.

A novel stratification scheme combined with internal arteries in CT imaging for guiding postoperative adjuvant transarterial chemoembolization in hepatocellular carcinoma: a retrospective cohort study

BACKGROUND

Although postoperative adjuvant transarterial chemoembolization (PA-TACE) improves survival outcomes in a subset of patients with resected hepatocellular carcinoma (HCC), the lack of reliable biomarkers for patient selection remains a significant challenge. The present study aimed to evaluate whether computed tomography imaging can provide more value for predicting benefits from PA-TACE and to establish a new scheme for guiding PA-TACE benefits.

METHODS

In this retrospective study, patients with HCC who had undergone preoperative contrast-enhanced computed tomography and curative hepatectomy were evaluated. Inverse probability of treatment weight was performed to balance the difference of baseline characteristics. Cox models were used to test the interaction among PA-TACE, imaging features, and pathological indicators. An HCC imaging and pathological classification (HIPC) scheme incorporating these imaging and pathological indicators was established.

RESULTS

This study included 1488 patients [median age, 52 years (IQR, 45-61 years); 1309 male]. Microvascular invasion (MVI) positive, and diameter >5 cm tumors achieved a higher recurrence-free survival (RFS), and overall survival (OS) benefit, respectively, from PA-TACE than MVI negative, and diameter ≤5 cm tumors. Patients with internal arteries (IA) positive benefited more than those with IA-negative in terms of RFS ( P =0.016) and OS ( P =0.018). PA-TACE achieved significant RFS and OS improvements in HIPC3 (IA present and diameter >5 cm, or two or three tumors) patients but not in HIPC1 (diameter ≤5 cm, MVI negative) and HIPC2 (other single tumor) patients. Our scheme may decrease the number of patients receiving PA-TACE by ~36.5% compared to the previous suggestion.

CONCLUSIONS

IA can provide more value for predicting the benefit of PA-TACE treatment. The proposed HIPC scheme can be used to stratify patients with and without survival benefits from PA-TACE.

Prognostic biomarkers associated with immune checkpoint inhibitors in hepatocellular carcinoma

The treatment of hepatocellular carcinoma (HCC), particularly advanced HCC, has been a serious challenge. Immune checkpoint inhibitors (ICIs) are landmark drugs in the field of cancer therapy in recent years, which have changed the landscape of cancer treatment. In the field of HCC treatment, this class of drugs has shown good therapeutic prospects. For example, atezolizumab in combination with bevacizumab has been approved as first-line treatment for advanced HCC due to significant efficacy. However, sensitivity to ICI therapy varies widely among HCC patients. Therefore, there is an urgent need to search for determinants of resistance/sensitivity to ICIs and to screen biomarkers that can predict the efficacy of ICIs. This manuscript reviews the research progress of prognostic biomarkers associated with ICIs in HCC in order to provide a scientific basis for the development of clinically individualised precision medication regimens.

Machine-learning model comprising five clinical indices and liver stiffness measurement can accurately identify MASLD-related liver fibrosis

BACKGROUND & AIMS

aMAP score, as a hepatocellular carcinoma risk score, is proven to be associated with the degree of chronic hepatitis B-related liver fibrosis. We aimed to evaluate the ability of aMAP score for metabolic dysfunction-associated steatotic liver disease (MASLD; formerly NAFLD)-related fibrosis diagnosis and establish a machine-learning (ML) model to improve the diagnostic performance.

METHODS

A total of 946 biopsy-proved MASLD patients from China and the United States were included in the analysis. The aMAP score, demographic/clinical indices and liver stiffness measurement (LSM) were included in seven ML algorithms to build fibrosis diagnostic models in the training set (N = 703). The performance of ML models was evaluated in the external validation set (N = 125).

RESULTS

The AUROCs of aMAP versus fibrosis-4 index (FIB-4) and aspartate aminotransferase-platelet ratio (APRI) in cirrhosis and advanced fibrosis were (0.850 vs. 0.857 [P = 0.734], 0.735 [P = 0.001]) and (0.759 vs. 0.795 [P = 0.027], 0.709 [P = 0.049]). When using dual cut-off values, aMAP had a smaller uncertainty area and higher accuracy (26.9%, 86.6%) than FIB-4 (37.3%, 85.0%) and APRI (59.0%, 77.3%) in cirrhosis diagnosis. The seven ML models performed satisfactorily in most cases. In the validation set, the ML model comprising LSM and 5 indices (including age, sex, platelets, albumin and total bilirubin used in aMAP calculator), built by logistic regression algorithm (called LSM-plus model), exhibited excellent performance. In cirrhosis and advanced fibrosis detection, the LSM-plus model had higher accuracy (96.8%, 91.2%) than LSM alone (86.4%, 67.2%) and Agile score (76.0%, 83.2%), respectively. Additionally, the LSM-plus model also displayed high specificity (cirrhosis: 98.3%; advanced fibrosis: 92.6%) with satisfactory AUROC (0.932, 0.875, respectively) and sensitivity (88.9%, 82.4%, respectively).

CONCLUSIONS

The aMAP score is capable of diagnosing MASLD-related fibrosis. The LSM-plus model could accurately identify MASLD-related cirrhosis and advanced fibrosis.

Benefit and harm of waiting time in liver transplantation for HCC

Liver transplantation is the most successful treatment for limited-stage HCC. The waiting time for liver transplantation (LT) can be a critical factor affecting the oncological prognosis and outcome of patients with HCC. Efficient strategies to optimize waiting time are essential to maximize the benefits of LT and to reduce the harm of delay in transplantation. The ever-increasing demand for donor livers emphasizes the need to improve the organization of the waiting list for transplantation and to optimize organ availability for patients with and without HCC. Current progress in innovations to expand the donor pool includes the implementation of living donor LT and the use of grafts from extended donors. By expanding selection criteria, an increased number of patients are eligible for transplantation, which necessitates criteria to prevent futile transplantations. Thus, the selection criteria for LT have evolved to include not only tumor characteristics but biomarkers as well. Enhancing our understanding of HCC tumor biology through the analysis of subtypes and molecular genetics holds significant promise in advancing the personalized approach for patients. In this review, the effect of waiting time duration on outcome in patients with HCC enlisted for LT is discussed.