Current Trends and Advancements in the Management of Hepatocellular Carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) remains a significant global health burden with a high mortality rate. Over the past 40 years, significant progress has been achieved in the prevention and management of HCC. SUMMARY: Hepatitis B vaccination programs, the development of direct acting antiviral drugs for Hepatitis C, and effective surveillance strategies provide a profound basis for the prevention of HCC. Advanced surgery and liver transplantation along with local ablation techniques potentially offer cure for the disease. Also, just recently, the introduction of immunotherapy opened a new chapter in systemic treatment. Finally, the introduction of the BCLC classification system for HCC, clearly defining patient groups and assigning reasonable treatment options, has standardized treatment and become the basis of almost all clinical trials for HCC. With this review, we provide a comprehensive overview of the evolving landscape of HCC management and also touch on current challenges. KEY MESSAGE: A comprehensive and multidisciplinary approach is crucial for effective HCC management. Continued research and clinical trials are imperative to further enhance treatment options and will ultimately reduce the global burden of this devastating disease.

Prevention in Hepatology.

The prevention of liver disease has improved significantly in the last few decades, to the point that it can now be considered a true success story. The wide variety of interventions, including comprehensive vaccination strategies, novel medications, lifestyle changes, and even preventive surgeries, have reduced the morbidity and mortality of chronic liver diseases. However, the prevalence of chronic liver diseases is increasing worldwide. Currently, fatty liver disease alone is estimated to be present in as much as 30% of the adult population. Furthermore, there is a trend towards increasing incidences of chronic hepatitis B, and a global lack of success in efforts to eliminate chronic hepatitis C. Thus, improving and efficiently rolling out existing and successful prevention strategies for chronic liver diseases will play an essential role in healthcare throughout the upcoming decades. In this review, we summarize the current options and concepts for preventing chronic liver diseases, highlight their limitations, and provide an outlook on probable future developments to improve awareness, integrated care, and the analysis of big data.

Impact of concomitant cardiovascular medications on overall survival in patients with liver cirrhosis.

OBJECTIVES OF THE ARTICLE: Liver cirrhosis is the end-stage liver disease associated with poor prognosis and cardiovascular comorbidity could significantly impact mortality of cirrhotic patients. We conducted a large, retrospective study to investigate the survival impact of cardiovascular co-medications in patients with liver cirrhosis. MATERIALS AND METHODS: A study-specific R package was processed on the local databases of partner institutions within the Observational Health Data Sciences and Informatics consortium, namely Columbia University, New York City (NYC), USA and Ajou University School of Medicine (AUSOM), South Korea. Patients with cirrhosis diagnosed between 2000 and 2020 were included. Final analysis of the anonymous survival data was performed at Medical Faculty Mannheim, Heidelberg University. RESULTS: We investigated a total of 32,366 patients with liver cirrhosis. Our data showed that administration of antiarrhythmics amiodarone or digoxin presented as a negative prognostic indicator (p = 0.000 in both cohorts). Improved survival was associated with angiotensin-converting enzyme inhibitor ramipril (p = 0.005 in NYC cohort, p = 0.075 in AUSOM cohort) and angiotensin II receptor blocker losartan (p = 0.000 in NYC cohort, p = 0.005 in AUSOM cohort). Non-selective beta blocker carvedilol was associated with a survival advantage in the NYC (p = 0.000) cohort but not in the AUSOM cohort (p = 0.142). Patients who took platelet inhibitor clopidogrel had a prolonged overall survival compared to those without (p = 0.000 in NYC cohort, p = 0.003 in AUSOM cohort). CONCLUSION: Concomitant cardiovascular medications are associated with distinct survival difference in cirrhotic patients. Multidisciplinary management is needed for a judicious choice of proper cardiovascular co-medications in cirrhotic patients.

Efficacy of Co-Medications in Patients with Alcoholic Liver Disease.

BACKGROUND: Alcoholic liver disease (ALD) is still increasing and leads to acute liver injury but also liver cirrhosis and subsequent complications such as liver failure or hepatocellular carcinoma (HCC). As most patients fail to achieve alcohol abstinence, it is essential to identify alternative treatment options in order to improve the outcome of ALD patients. METHODS: Evaluating two large cohorts of patients with ALD from the USA and Korea with a total of 12,006 patients, we investigated the effect on survival of aspirin, metformin, metoprolol, dopamine, and dobutamine drugs in patients with ALD between 2000 and 2020. Patient data were obtained through the "The Observational Health Data Sciences and Informatics consortium," an open-source, multi-stakeholder, and interdisciplinary collaborative effort. RESULTS: The use of aspirin (p = 0.000, p = 0.000), metoprolol (p = 0.002, p = 0.000), and metformin (p = 0.000, p = 0.000) confers a survival benefit for both AUSOM- and NY-treated cohorts. Need of catecholamines dobutamine (p = 0.000, p = 0.000) and dopamine (p = 0.000, p = 0.000) was strongly indicative of poor survival. ß-Blocker treatment with metoprolol (p = 0.128, p = 0.196) or carvedilol (p = 0.520, p = 0.679) was not shown to be protective in any of the female subgroups. CONCLUSION: Overall, our data fill a large gap in long-term, real-world data on patients with ALD, confirming an impact of metformin, acetylsalicylic acid, and ß-blockers on ALD patient's survival. However, gender and ethnic background lead to diverse efficacy in those patients.

3D Visualization, Skeletonization and Branching Analysis of Blood Vessels in Angiogenesis.

Angiogenesis is the process of new blood vessels growing from existing vasculature. Visualizing them as a three-dimensional (3D) model is a challenging, yet relevant, task as it would be of great help to researchers, pathologists, and medical doctors. A branching analysis on the 3D model would further facilitate research and diagnostic purposes. In this paper, a pipeline of vision algorithms is elaborated to visualize and analyze blood vessels in 3D from formalin-fixed paraffin-embedded (FFPE) granulation tissue sections with two different staining methods. First, a U-net neural network is used to segment blood vessels from the tissues. Second, image registration is used to align the consecutive images. Coarse registration using an image-intensity optimization technique, followed by finetuning using a neural network based on Spatial Transformers, results in an excellent alignment of images. Lastly, the corresponding segmented masks depicting the blood vessels are aligned and interpolated using the results of the image registration, resulting in a visualized 3D model. Additionally, a skeletonization algorithm is used to analyze the branching characteristics of the 3D vascular model. In summary, computer vision and deep learning is used to reconstruct, visualize and analyze a 3D vascular model from a set of parallel tissue samples. Our technique opens innovative perspectives in the pathophysiological understanding of vascular morphogenesis under different pathophysiological conditions and its potential diagnostic role.

Comparative Response of HCC Cells to TKIs: Modified in vitro Testing and Descriptive Expression Analysis.

Introduction: Although the treatment paradigm for hepatocellular carcinoma (HCC) has recently shifted in favour of checkpoint inhibitor (CPI)-based treatment options, the tyrosine kinase inhibitors (TKI) currently approved for the treatment of HCC are expected to remain the cornerstone of HCC treatment alone or in combination with CPIs. Despite considerable research efforts, no biomarker capable of predicting the response to specific TKIs has been validated. Thus, personalized approaches to HCC may aid in determining optimal treatment lines for 2nd and 3rd lines. To identify new biomarkers, we examined differential sensitivity and investigated potential transcriptomic predictors. Methods: To this aim, the sensitivity of nine HCC cell lines to sorafenib, lenvatinib, regorafenib, and cabozantinib was evaluated by a prolonged treatment scheme to determine their respective growth rate inhibition concentrations (GR50). Subgroups discriminated by GR50 values underwent differential expression and gene set enrichment analysis (GSEA). Results: The nine cell lines showed broadly different sensitivities to different TKIs. GR50 values of sorafenib and regorafenib clustered closer in all cell lines, whereas treatments with lenvatinib and cabozantinib showed diversified GR50 values. GSEA showed the activation of specific pathways in sensitive vs non-sensitive cell lines. A signature consisting of 14 biomarkers (GAGE12H, GJB6, PTCHD3, PRH1-PRR4, C6orf222, HBB, C17orf99, GOLGA6A, CRYAA, CCL23, RP11-347C12.3, RP11-514O12.4, FAM180B, and TMPRSS4) discriminates the cell lines' response into three distinct treatment profiles: 1) equally sensible to sorafenib, regorafenib and cabozantinib, 2) sensible to lenvatinib, and 3) more sensible to regorafenib than sorafenib. Conclusion: We observed diverse responses to either of the four TKIs. Subgroup analysis of TKI effectiveness showed distinct transcriptomic profiles and signaling pathways associated with responsiveness. This prompts more extensive studies to explore and validate pharmacogenomic and transcriptomic strategies for a personalized treatment approach, particularly after the failure of CPI treatment.

Prognostic Cancer Gene Expression Signatures: Current Status and Challenges.

Current staging systems of cancer are mainly based on the anatomical extent of disease. They need refinement by biological parameters to improve stratification of patients for tumor therapy or surveillance strategies. Thanks to developments in genomic, transcriptomic, and big-data technologies, we are now able to explore molecular characteristics of tumors in detail and determine their clinical relevance. This has led to numerous prognostic and predictive gene expression signatures that have the potential to establish a classification of tumor subgroups by biological determinants. However, only a few gene signatures have reached the stage of clinical implementation so far. In this review article, we summarize the current status, and present and future challenges of prognostic gene signatures in three relevant cancer entities: breast cancer, colorectal cancer, and hepatocellular carcinoma.