Hepatocellular Carcinoma: Surveillance, Diagnosis, Evaluation and Management

Efficacy and safety of apatinib plus immune checkpoint inhibitors and transarterial chemoembolization for the treatment of advanced hepatocellular carcinoma

PURPOSE

The evidence of apatinib plus immune checkpoint inhibitors (ICIs) and transarterial chemoembolization (TACE) for treating advanced hepatocellular carcinoma (HCC) is limited. This study aimed to compare the treatment efficacy and safety of apatinib plus ICIs and TACE with apatinib plus TACE in these patients.

METHODS

This study retrospectively enrolled 90 patients with advanced HCC treated with apatinib plus TACE (A-TACE group, n = 52) or apatinib plus ICIs and TACE (IA-TACE group, n = 38).

RESULTS

The objective response rate was numerically higher in IA-TACE group compared with A-TACE group without statistical significance (57.9% vs. 36.5%, P = 0.055). Disease control rate was not different between groups (86.8% vs. 76.9%, P = 0.248). Progression-free survival (PFS) was improved in IA-TACE group compared with A-TACE group (P = 0.018). The median PFS (95% confidence interval) was 12.5 (8.7-16.3) months in IA-TACE group and 8.5 (5.6-11.4) months in A-TACE group. Overall survival (OS) was also prolonged in IA-TACE group compared with A-TACE group (P = 0.007). The median OS (95% confidence interval) was 21.1 (15.8-26.4) months in IA-TACE group and 14.3 (11.5-17.1) months in A-TACE group. By multivariate Cox regression model, IA-TACE was independently associated with prolonged PFS (hazard ratio = 0.539, P = 0.038) and OS (hazard ratio = 0.447, P = 0.025). Most adverse events were not different between groups. Only the incidence of reactive cutaneous capillary endothelial proliferation was higher in IA-TACE group compared with A-TACE group (10.5% vs. 0.0%, P = 0.029).

CONCLUSION

Apatinib plus ICIs and TACE may be an effective and safe treatment for patients with advanced HCC, but further large-scale studies are needed for verification.

Multiomics profiling of buffy coat and plasma unveils etiology-specific signatures in hepatocellular carcinoma

BACKGROUND/AIMS

Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality worldwide. Despite identification of several biomarkers for HCC diagnosis, challenges such as low sensitivity and intratumoral heterogeneity have impeded early detection, highlighting the need for etiology-specific blood biomarkers.

METHODS

We generated whole-transcriptome sequencing (WTS) and targeted proteome data from buffy coat and plasma samples from HCC patients. By integrating etiological information on viral infection, we investigated the etiology-specific gene expression landscape at the blood level. Validation of differentially expressed genes (DEGs) was performed using publicly available RNA-seq datasets and qRT‒PCR with AUC analyses.

RESULTS

Differential expression analyses with multiomics data revealed distinct gene expression profiles between HBV-associated HCC and nonviral HCC, indicating the presence of etiology-specific blood biomarkers. The identified DEGs were validated across multiple independent datasets, underscoring their utility as biomarkers. Additionally, single-cell RNA-seq analysis of HCC confirmed differences in DEG expression across distinct immune cell types.

CONCLUSION

Our buffy coat WTS data and plasma proteome data may serve as reliable sources for identifying etiology-specific blood biomarkers of HCC and might contribute to discovery of therapeutic targets for HCC across different etiologies.

Prognostic Impact of Metabolic Syndrome and Steatotic Liver Disease in Hepatocellular Carcinoma Using Machine Learning Techniques

Metabolic dysfunction-associated steatotic liver disease (MASLD) currently represents the predominant cause of chronic liver disease and is closely linked to a significant increase in the risk of hepatocellular carcinoma (HCC), even in the absence of liver cirrhosis. In this retrospective multicenter study, machine learning (ML) methods were employed to investigate the relationship between metabolic profile and prognosis at diagnosis in a total of 219 HCC patients. The eXtreme Gradient Boosting (XGB) method demonstrated superiority in identifying mortality predictors in our patients. Etiology was the most determining prognostic factor followed by Barcelona Clinic Liver Cancer (BCLC) and Eastern Cooperative Oncology Group (ECOG) classifications. Variables related to the development of hepatic steatosis and metabolic syndrome, such as elevated levels of alkaline phosphatase (ALP), uric acid, obesity, alcohol consumption, and high blood pressure (HBP), had a significant impact on mortality prediction. This study underscores the importance of metabolic syndrome as a determining factor in the progression of HCC secondary to MASLD. The use of ML techniques provides an effective tool to improve risk stratification and individualized therapeutic management in these patients.

Biosensing of Alpha-Fetoprotein: A Key Direction toward the Early Detection and Management of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is currently one of the most prevalent cancers worldwide. Associated risk factors include, but are not limited to, cirrhosis and underlying liver diseases, including chronic hepatitis B or C infections, excessive alcohol consumption, nonalcoholic fatty liver disease (NAFLD), and exposure to chemical carcinogens. It is crucial to detect this disease early on before it metastasizes to adjoining parts of the body, worsening the prognosis. Serum biomarkers have proven to be a more accurate diagnostic tool compared to imaging. Among various markers such as nucleic acids, circulating genetic material, proteins, enzymes, and other metabolites, alpha-fetoprotein (AFP) is a protein marker primarily used to diagnose HCC. However, current methods need a large sample and carry a high cost, among other challenges, which can be improved using biosensing technology. Early and accurate detection of AFP can prevent severe progression of the disease and ensure better management of HCC patients. This review sheds light on HCC development in the human body. Afterward, we outline various types of biosensors (optical, electrochemical, and mass-based), as well as the most relevant studies of biosensing modalities for non-invasive monitoring of AFP. The review also explains these sensing platforms, detection substrates, surface modification agents, and fluorescent probes used to develop such biosensors. Finally, the challenges and future trends in routine clinical analysis are discussed to motivate further developments.

Bayesian modelling of time series data (BayModTS)-a FAIR workflow to process sparse and highly variable data

MOTIVATION

Systems biology aims to better understand living systems through mathematical modelling of experimental and clinical data. A pervasive challenge in quantitative dynamical modelling is the integration of time series measurements, which often have high variability and low sampling resolution. Approaches are required to utilize such information while consistently handling uncertainties.

RESULTS

We present BayModTS (Bayesian modelling of time series data), a new FAIR (findable, accessible, interoperable, and reusable) workflow for processing and analysing sparse and highly variable time series data. BayModTS consistently transfers uncertainties from data to model predictions, including process knowledge via parameterized models. Further, credible differences in the dynamics of different conditions can be identified by filtering noise. To demonstrate the power and versatility of BayModTS, we applied it to three hepatic datasets gathered from three different species and with different measurement techniques: (i) blood perfusion measurements by magnetic resonance imaging in rat livers after portal vein ligation, (ii) pharmacokinetic time series of different drugs in normal and steatotic mice, and (iii) CT-based volumetric assessment of human liver remnants after clinical liver resection.

AVAILABILITY AND IMPLEMENTATION

The BayModTS codebase is available on GitHub at https://github.com/Systems-Theory-in-Systems-Biology/BayModTS. The repository contains a Python script for the executable BayModTS workflow and a widely applicable SBML (systems biology markup language) model for retarded transient functions. In addition, all examples from the paper are included in the repository. Data and code of the application examples are stored on DaRUS: https://doi.org/10.18419/darus-3876. The raw MRI ROI voxel data were uploaded to DaRUS: https://doi.org/10.18419/darus-3878. The steatosis metabolite data are published on FairdomHub: 10.15490/fairdomhub.1.study.1070.1.

Recent Advances in Liver Tissue Engineering as an Alternative and Complementary Approach for Liver Transplantation

Acute and chronic liver diseases cause significant morbidity and mortality worldwide, affecting millions of people. Liver transplantation is the primary intervention method, replacing a non-functional liver with a functional one. However, the field of liver transplantation faces challenges such as donor shortage, postoperative complications, immune rejection, and ethical problems. Consequently, there is an urgent need for alternative therapies that can complement traditional transplantation or serve as an alternative method. In this review, we explore the potential of liver tissue engineering as a supplementary approach to liver transplantation, offering benefits to patients with severe liver dysfunctions.