Hepatocellular Carcinoma Surveillance and Staging

Risk stratification and early detection biomarkers for precision HCC screening

Hepatocellular carcinoma (HCC) mortality remains high primarily due to late diagnosis as a consequence of failed early detection. Professional societies recommend semi-annual HCC screening in at-risk patients with chronic liver disease to increase the likelihood of curative treatment receipt and improve survival. However, recent dynamic shift of HCC etiologies from viral to metabolic liver diseases has significantly increased the potential target population for the screening, whereas annual incidence rate has become substantially lower. Thus, with the contemporary HCC etiologies, the traditional screening approach might not be practical and cost-effective. HCC screening consists of (i) definition of rational at-risk population, and subsequent (ii) repeated application of early detection tests to the population at regular intervals. The suboptimal performance of the currently available HCC screening tests highlights an urgent need for new modalities and strategies to improve early HCC detection. In this review, we overview recent developments of clinical, molecular, and imaging-based tools to address the current challenge, and discuss conceptual framework and approaches of their clinical translation and implementation. These encouraging progresses are expected to transform the current "one-size-fits-all" HCC screening into individualized precision approaches to early HCC detection and ultimately improve the poor HCC prognosis in the foreseeable future.

Single-nucleus RNA sequencing of pre-malignant liver reveals disease-associated hepatocyte state with HCC prognostic potential

Current approaches to staging chronic liver diseases have limited utility for predicting liver cancer risk. Here, we employed single-nucleus RNA sequencing (snRNA-seq) to characterize the cellular microenvironment of healthy and pre-malignant livers using two distinct mouse models. Downstream analyses unraveled a previously uncharacterized disease-associated hepatocyte (daHep) transcriptional state. These cells were absent in healthy livers but increasingly prevalent as chronic liver disease progressed. Copy number variation (CNV) analysis of microdissected tissue demonstrated that daHep-enriched regions are riddled with structural variants, suggesting these cells represent a pre-malignant intermediary. Integrated analysis of three recent human snRNA-seq datasets confirmed the presence of a similar phenotype in human chronic liver disease and further supported its enhanced mutational burden. Importantly, we show that high daHep levels precede carcinogenesis and predict a higher risk of hepatocellular carcinoma development. These findings may change the way chronic liver disease patients are staged, surveilled, and risk stratified.

HCC-Mark: a simple non-invasive model based on routine parameters for predicting hepatitis C virus related hepatocellular carcinoma

BACKGROUND

Early detection of hepatocellular carcinoma (HCC) is crucial in providing more effective therapies. As routine laboratory variables are readily accessible, this study aimed to develop a simple non-invasive model for predicting hepatocellular cancer.

METHODS

Two groups of patients were recruited: an estimation group (n = 300) and a validation group (n = 625). Each comprised two categories: hepatocellular cancer and liver cirrhosis. Logistic regression analyses and receiver operating characteristic (ROC) curves were used to develop and validate the HCC-Mark model comprising AFP, high-sensitivity C-reactive protein, albumin and platelet count. This model was tested in cancer patients classified by the Barcelona Clinic Liver Cancer (BCLC), Cancer of Liver Italian Program (CLIP) and Okuda systems, and was compared with other non-invasive models for predicting hepatocellular cancer.

RESULTS

HCC-Mark produced a ROC AUC of 0.89 (95% CI 0.85-0.90) for discriminating hepatocellular carcinoma from liver cirrhosis in the estimation group and 0.90 (0.86-0.90) in the validation group (both p < 0.0001). This AUC exceeded all other models, that had AUCs from 0.41 to 0.81. AUCs of HCC-Mark for discriminating patients with a single focal lesion, absent macrovascular invasion, tumour size <2 cm, BCLC (0-A), CLIP (0-1) and Okuda (stage Ι) from cirrhotic patients were 0.88 (0.85-0.90), 0.87 (0.85-0.89), 0.89 (0.85-0.93), 0.87 (0.84-0.89), 0.85 (0.82-0.87) and 0.86 (0.83-0.89), respectively (all p < 0.0001).

CONCLUSION

HCC-Mark is an accurate and validated model for the detection of hepatocellular cancer and certain of its clinical features.

Hepatic Stellate Cells and Hepatocarcinogenesis

Hepatic stellate cells (HSCs) are a significant component of the hepatocellular carcinoma (HCC) tumor microenvironment (TME). Activated HSCs transform into myofibroblast-like cells to promote fibrosis in response to liver injury or chronic inflammation, leading to cirrhosis and HCC. The hepatic TME is comprised of cellular components, including activated HSCs, tumor-associated macrophages, endothelial cells, immune cells, and non-cellular components, such as growth factors, proteolytic enzymes and their inhibitors, and other extracellular matrix (ECM) proteins. Interactions between HCC cells and their microenvironment have become topics under active investigation. These interactions within the hepatic TME have the potential to drive carcinogenesis and create challenges in generating effective therapies. Current studies reveal potential mechanisms through which activated HSCs drive hepatocarcinogenesis utilizing matricellular proteins and paracrine crosstalk within the TME. Since activated HSCs are primary secretors of ECM proteins during liver injury and inflammation, they help promote fibrogenesis, infiltrate the HCC stroma, and contribute to HCC development. In this review, we examine several recent studies revealing the roles of HSCs and their clinical implications in the development of fibrosis and cirrhosis within the hepatic TME.