Non-Invasive Biomarkers for Immunotherapy in Patients with Hepatocellular Carcinoma: Current Knowledge and Future Perspectives

Role of Gut Microbial Metabolites in the Pathogenesis of Primary Liver Cancers

Hepatobiliary malignancies, which include hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA), are the sixth most common cancers and the third leading cause of cancer-related death worldwide. Hepatic carcinogenesis is highly stimulated by chronic inflammation, defined as fibrosis deposition, and an aberrant imbalance between liver necrosis and nodular regeneration. In this context, the gut-liver axis and gut microbiota have demonstrated a critical role in the pathogenesis of HCC, as dysbiosis and altered intestinal permeability promote bacterial translocation, leading to chronic liver inflammation and tumorigenesis through several pathways. A few data exist on the role of the gut microbiota or bacteria resident in the biliary tract in the pathogenesis of CCA, and some microbial metabolites, such as choline and bile acids, seem to show an association. In this review, we analyze the impact of the gut microbiota and its metabolites on HCC and CCA development and the role of gut dysbiosis as a biomarker of hepatobiliary cancer risk and of response during anti-tumor therapy. We also discuss the future application of gut microbiota in hepatobiliary cancer management.

Epidemiology, pathophysiology and clinical aspects of Hepatocellular Carcinoma in MAFLD patients

Hepatocellular carcinoma (HCC) is undergoing a transformative shift, with metabolic-associated fatty liver disease (MAFLD) emerging as a dominant etiology. Diagnostic criteria for MAFLD involve hepatic steatosis and metabolic dysregulation. Globally, MAFLD prevalence stands at 38.77%, significantly linked to the escalating rates of obesity. Epidemiological data indicate a dynamic shift in the major etiologies of hepatocellular carcinoma (HCC), transitioning from viral to metabolic liver diseases. Besides the degree of liver fibrosis, several modifiable lifestyle risk factors, such as type 2 diabetes, obesity, alcohol use, smoking, and HBV, HCV infection contribute to the pathogenesis of HCC. Moreover  gut microbiota and genetic variants may contribute to HCC development.The pathophysiological link between MAFLD and HCC involves metabolic dysregulation, impairing glucose and lipid metabolism, inflammation and oxidative stress. Silent presentation poses challenges in early MAFLD-HCC diagnosis. Imaging, biopsy, and AI-assisted techniques aid diagnosis, while HCC surveillance in non-cirrhotic MAFLD patients remains debated.ITA.LI.CA. group proposes a survival-based algorithm for treatment based on Barcelona clinic liver cancer (BCLC) algorithm. Liver resection, transplantation, ablation, and locoregional therapies are applied based on the disease stage. Systemic treatments is promising, with initial immunotherapy results indicating a less favorable response in MAFLD-related HCC.Adopting lifestyle interventions and chemopreventive measures with medications, including aspirin, metformin, and statins, constitute promising approaches for the primary prevention of HCC.Prognosis is influenced by multiple factors, with MAFLD-HCC associated with prolonged survival. Emerging diagnostic biomarkers and epigenomic markers, show promising results for early HCC detection in the MAFLD population.

Exploring potential predictive biomarkers through historical perspectives on the evolution of systemic therapies into the emergence of neoadjuvant therapy for the treatment of hepatocellular carcinoma

Hepatocellular carcinoma (HCC), a type of liver cancer, ranks as the sixth most prevalent cancer globally and represents the third leading cause of cancer-related deaths. Approximately half of HCC patients miss the opportunity for curative treatment and are then limited to undergoing systemic therapies. Currently, systemic therapy has entered the era of immunotherapy, particularly with the advent of immune-checkpoint inhibitors (ICIs), which have significantly enhanced outcomes for patients with advanced HCC. Neoadjuvant treatment for HCC has become a possibility-findings from the IMbrave 050 trial indicated that ICIs offer the benefit of recurrence-free survival for high-risk HCC patients post-resection or local ablation. However, only a small fraction of individuals benefit from systemic therapy. Consequently, there is an urgent need to identify predictive biomarkers for treatment response and outcome assessment. This study reviewed the historical progression of systemic therapy for HCC, highlighting notable therapeutic advancements. This study examined the development of systemic therapies involving conventional drugs and clinical trials utilized in HCC treatment, as well as potential predictive biomarkers for advanced and/or locally advanced HCC. Various studies have revealed potential biomarkers in the context of HCC treatment. These include the association of dendritic cells (DCs) with a favorable response to neoadjuvant therapy, the presence of enriched T effector cells and tertiary lymphoid structures, the identification of CD138+ plasma cells, and distinct spatial arrangements of B cells in close proximity to T cells among responders with locally advanced HCC receiving neoadjuvant cabozantinib and nivolumab treatment. Furthermore, pathological response has been associated with intratumoral cellular triads consisting of progenitor CD8+ T cells and CXCL13+ CD4+ T helper cells surrounding mature DCs in patients receiving neoadjuvant cemiplimab for resectable HCC. Despite no widely recognized predictive biomarkers for HCC individualized treatment, we believe neoadjuvant trials hold the most promise in identifying and validating them. This is because they can collect multiple samples from resectable HCC patients across stages, especially with multi-omics, bridging preclinical and clinical gaps.

Identification and validation of immune-related gene signature models for predicting prognosis and immunotherapy response in hepatocellular carcinoma

Background

This study seeks to enhance the accuracy and efficiency of clinical diagnosis and therapeutic decision-making in hepatocellular carcinoma (HCC), as well as to optimize the assessment of immunotherapy response.

Methods

A training set comprising 305 HCC cases was obtained from The Cancer Genome Atlas (TCGA) database. Initially, a screening process was undertaken to identify prognostically significant immune-related genes (IRGs), followed by the application of logistic regression and least absolute shrinkage and selection operator (LASSO) regression methods for gene modeling. Subsequently, the final model was constructed using support vector machines-recursive feature elimination (SVM-RFE). Following model evaluation, quantitative polymerase chain reaction (qPCR) was employed to examine the gene expression profiles in tissue samples obtained from our cohort of 54 patients with HCC and an independent cohort of 231 patients, and the prognostic relevance of the model was substantiated. Thereafter, the association of the model with the immune responses was examined, and its predictive value regarding the efficacy of immunotherapy was corroborated through studies involving three cohorts undergoing immunotherapy. Finally, the study uncovered the potential mechanism by which the model contributed to prognosticating HCC outcomes and assessing immunotherapy effectiveness.

Results

SVM-RFE modeling was applied to develop an OS prognostic model based on six IRGs (CMTM7, HDAC1, HRAS, PSMD1, RAET1E, and TXLNA). The performance of the model was assessed by AUC values on the ROC curves, resulting in values of 0.83, 0.73, and 0.75 for the predictions at 1, 3, and 5 years, respectively. A marked difference in OS outcomes was noted when comparing the high-risk group (HRG) with the low-risk group (LRG), as demonstrated in both the initial training set (P <0.0001) and the subsequent validation cohort (P <0.0001). Additionally, the SVMRS in the HRG demonstrated a notable positive correlation with key immune checkpoint genes (CTLA-4, PD-1, and PD-L1). The results obtained from the examination of three cohorts undergoing immunotherapy affirmed the potential capability of this model in predicting immunotherapy effectiveness.

Conclusions

The HCC predictive model developed in this study, comprising six genes, demonstrates a robust capability to predict the OS of patients with HCC and immunotherapy effectiveness in tumor management.

Dendritic cell-derived exosome (DEX) therapy for digestive system cancers: Recent advances and future prospect

Tumor-mediated immunosuppression is a fundamental obstacle to the development of dendritic cell (DC)-based cancer vaccines, which despite their ability to stimulate host anti-tumor CD8 T cell immunity, have not been able to generate meaningful therapeutic responses. Exosomes are inactive membrane vesicles that are nanoscale in size and are produced by the endocytic pathway. They are essential for intercellular communication. Additionally, DC-derived exosomes (DEXs) contained MHC class I/II (MHCI/II), which is frequently complexed with antigens and co-stimulatory molecules and is therefore able to prime CD4 and CD8 T cells that are specific to particular antigens. Indeed, vaccines with DEXs have been shown to exhibit better anti-tumor efficacy in eradicating tumors compared to DC vaccines in pre-clinical models of digestive system tumors. Also, there is room for improvement in the tumor antigenic peptide (TAA) selection process. DCs release highly targeted exosomes when the right antigenic peptide is chosen, which could aid in the creation of DEX-based antitumor vaccines that elicit more targeted immune responses. Coupled with their resistance to tumor immunosuppression, DEXs-based cancer vaccines have been heralded as the superior alternative cell-free therapeutic vaccines over DC vaccines to treat digestive system tumors. In this review, current studies of DEXs cancer vaccines as well as potential future directions will be deliberated.

Efficacy of immunotherapy in hepatocellular carcinoma: Does liver disease etiology have a role?

The systemic treatment of hepatocellular carcinoma (HCC) is changing rapidly. After a decade of tyrosine kinase inhibitors (TKIs), as the only therapeutic option for the treatment of advanced HCC, in the last few years several phase III trials demonstrated the efficacy of immune checkpoint inhibitors (ICIs). The combination of the anti-PD-L1 atezolizumab and the anti-vascular endothelial growth factor (VEGF) bevacizumab demonstrated the superiority over sorafenib and currently represents the standard of care treatment for advanced HCC. In addition, the combination of durvalumab (an anti-PD-L1) and tremelimumab (an anti-CTLA4) proved to be superior to sorafenib, and in the same trial durvalumab monotherapy showed non-inferiority compared to sorafenib. However, early reports suggest an influence of HCC etiology in modulating the response to these drugs. In particular, a lower effectiveness of ICIs has been suggested in patients with non-viral HCC (in particular non-alcoholic fatty liver disease). Nevertheless, randomized controlled trials available to date have not been stratified for etiology and data suggesting a possible impact of etiology in the outcome of patients managed with ICIs derive from subgroup not pre-specified analyses. In this review, we aim to examine the potential impact of HCC etiology on the response to immunotherapy regimens for HCC.

Three molecular subtypes and a five-gene signature for hepatocellular carcinoma based on m7G-related classification

BACKGROUND

The current research investigated the heterogeneity of hepatocellular carcinoma (HCC) based on the expression of N7-methylguanosine (m7G)-related genes as a classification model and developed a risk model predictive of HCC prognosis, key pathological behaviors and molecular events of HCC.

METHODS

The RNA sequencing data of HCC were extracted from The Cancer Genome Atlas (TCGA)-live cancer (LIHC) database, hepatocellular carcinoman database (HCCDB) and Gene Expression Omnibus database, respectively. According to the expression level of 29 m7G-related genes, a consensus clustering analysis was conducted. The least absolute shrinkage and selection operator (LASSO) regression analysis and COX regression algorithm were applied to create a risk prediction model based on normalized expression of five characteristic genes weighted by coefficients. Tumor microenvironment (TME) analysis was performed using the MCP-Counter, TIMER, CIBERSORT and ESTIMATE algorithms. The Tumor Immune Dysfunction and Exclusion algorithm was applied to assess the responses to immunotherapy in different clusters and risk groups. In addition, patient sensitivity to common chemotherapeutic drugs was determined by the biochemical half-maximal inhibitory concentration using the R package pRRophetic.

RESULTS

Three molecular subtypes of HCC were defined based on the expression level of m7G-associated genes, each of which had its specific survival rate, genomic variation status, TME status and immunotherapy response. In addition, drug sensitivity analysis showed that the C1 subtype was more sensitive to a number of conventional oncolytic drugs (including paclitaxel, imatinib, CGP-082996, pyrimethamine, salubrinal and vinorelbine). The current five-gene risk prediction model accurately predicted HCC prognosis and revealed the degree of somatic mutations, immune microenvironment status and specific biological events.

CONCLUSION

In this study, three heterogeneous molecular subtypes of HCC were defined based on m7G-related genes as a classification model, and a five-gene risk prediction model was created for predicting HCC prognosis, providing a potential assessment tool for understanding the genomic variation, immune microenvironment status and key pathological mechanisms during HCC development.

Biomarkers predicting the efficacy of immune checkpoint inhibitors in hepatocellular carcinoma

In recent years, immune checkpoint inhibitors (ICIs) have emerged as a transformative approach in treating advanced hepatocellular carcinoma (HCC). Despite their success, challenges persist, including concerns about their effectiveness, treatment costs, frequent occurrence of treatment-related adverse events, and tumor hyperprogression. Therefore, it is imperative to identify indicators capable of predicting the efficacy of ICIs treatment, enabling optimal patient selection to maximize clinical benefits while minimizing unnecessary toxic side effects and economic losses. This review paper categorizes prognostic biomarkers of ICIs treatment into the following categories: biochemical and cytological indicators, tumor-related markers, imaging and personal features, etiology, gut microbiome, and immune-related adverse events (irAEs). By organizing these indicators systematically, we aim to guide biomarker exploration and inform clinical treatment decisions.

Biomarkers for immunotherapy of hepatocellular carcinoma

Immune-checkpoint inhibitors (ICIs) are now widely used for the treatment of patients with advanced-stage hepatocellular carcinoma (HCC). Two different ICI-containing regimens, atezolizumab plus bevacizumab and tremelimumab plus durvalumab, are now approved standard-of-care first-line therapies in this setting. However, and despite substantial improvements in survival outcomes relative to sorafenib, most patients with advanced-stage HCC do not derive durable benefit from these regimens. Advances in genome sequencing including the use of single-cell RNA sequencing (both of tumour material and blood samples), as well as immune cell identification strategies and other techniques such as radiomics and analysis of the microbiota, have created considerable potential for the identification of novel predictive biomarkers enabling the accurate selection of patients who are most likely to derive benefit from ICIs. In this Review, we summarize data on the immunology of HCC and the outcomes in patients receiving ICIs for the treatment of this disease. We then provide an overview of current biomarker use and developments in the past 5 years, including gene signatures, circulating tumour cells, high-dimensional flow cytometry, single-cell RNA sequencing as well as approaches involving the microbiome, radiomics and clinical markers. Novel concepts for further biomarker development in HCC are then discussed including biomarker-driven trials, spatial transcriptomics and integrated 'big data' analysis approaches. These concepts all have the potential to better identify patients who are most likely to benefit from ICIs and to promote the development of new treatment approaches.

Advances in Histological and Molecular Classification of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a primary liver cancer characterized by hepatocellular differentiation. HCC is molecularly heterogeneous with a wide spectrum of histopathology. The prognosis of patients with HCC is generally poor, especially in those with advanced stages. HCC remains a diagnostic challenge for pathologists because of its morphological and phenotypic diversity. However, recent advances have enhanced our understanding of the molecular genetics and histological subtypes of HCC. Accurate diagnosis of HCC is important for patient management and prognosis. This review provides an update on HCC pathology, focusing on molecular genetics, histological subtypes, and diagnostic approaches.

Case report: Predictability of clinical response and rejection risk after immune checkpoint inhibition in liver transplantation

Background

The approval of Atezolizumab / Bevacizumab therapy (Atezo/Bev) in 2020 opened up a promising new treatment option for patients with end-stage hepatocellular carcinoma (HCC). However, liver transplant (LTx) patients with HCC are still denied this therapy owing to concerns about ICI-induced organ rejection and lack of regulatory approval.

Methods

A prospective observational study at a tertiary liver transplant centre monitored the compassionate, off-label use of Atezo/Bev in a single, stable LTx recipient with non-resectable HCC recurrence. Close clinical, laboratory and immunological monitoring of the patient was performed throughout a four-cycle Atezo/Bev treatment. Measured parameters were selected after a systematic review of the literature on predictive markers for clinical response and risk of graft rejection caused by ICI therapy.

Results

19 articles describing 20 unique predictive biomarkers were identified. The most promising negative prognostic factors were the baseline values and dynamic course of IL-6, alpha-fetoprotein (AFP) and the AFP/CRP ratio. The frequency of regulatory T cells (Treg) reportedly correlates with the success of ICI therapy. PD-L1 and CD28 expression level with the allograft, peripheral blood CD4+ T cell numbers and Torque Teno Virus (TTV) titre may predict risk of LTx rejection following ICI therapy. No relevant side effects or acute rejection occurred during Atezo/Bev therapy; however, treatment did not prevent tumor progression. Absence of PD-L1 expression in pre-treatment liver biopsies, as well as a progressive downregulation of CD28 expression by CD4+ T cells during therapy, correctly predicted absence of rejection. Furthermore, increased IL-6 and AFP levels after starting therapy, as well as a reduction in blood Treg frequency, correctly anticipated a lack of therapeutic response.

Conclusion

Atezo/Bev therapy for unresectable HCC in stable LTx patients remains a controversial strategy because it carries a high-risk of rejection and therapeutic response rates are poorly defined. Although previously described biomarkers of rejection risk and therapeutic response agreed with clinical outcomes in the described case, these immunological parameters are difficult to reliably interpret. Clearly, there is an important unmet need for standardized assays and clinically validated cut-offs before we use these biomarkers to guide treatment decisions for our patients.

Perspective on the Role of Gut Microbiome in the Treatment of Hepatocellular Carcinoma with Immune Checkpoint Inhibitors

Background and Objectives: Hepatocellular carcinoma (HCC) is the leading cause of liver cancer worldwide and has a high mortality rate. Its incidence has increased due to metabolic-associated liver disease (MAFLD) epidemics. Liver transplantation and surgery remain the most resolute measures. Despite the optimistic use of multi-kinase inhibitors, namely sorafenib, the co-existence of chronic liver disease made the response rate low in these patients. Immune checkpoint inhibitors (ICIs) have become a promising hope for certain advanced solid tumors and, also, for advanced HCC. Unfortunately, a large cohort of patients with HCC fail to respond to immunotherapy. Materials and Methods: We conducted a narrative search on the main medical databases for original articles, reviews, meta-analyses, randomized clinical trials, and case series using the following keywords and acronyms and their associations: hepatocellular carcinoma, immunotherapy, checkpoint inhibitors, gut microbiota, and fecal microbiota transplantation. Results: ICIs are a promising and sufficiently safe treatment option for HCC. In detail, they have significantly improved survival and prognosis in these patients vs. sorafenib. Although there are several highlighted mechanisms of resistance, the gut microbiota signature can be used both as a response biomarker and as an effect enhancer. Practically, probiotic dose-finding and fecal microbiota transplantation are the weapons that can be used to increase ICI's treatment-response-reducing resistance mechanisms. Conclusion: Immunotherapy has been a significant step-up in HCC treatment, and gut microbiota modulation is an effective liaison to increase its efficacy.

A novel tumor 4-driver gene signature for the prognosis of hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC), the main type of liver cancer, is the second most lethal tumor worldwide, with a 5-year survival rate of only 18%. Driver genes facilitate cancer cell growth and spread in the tumor microenvironment. Here, a comprehensive driver gene signature for the prognosis of HCC was developed.

Methods

HCC driver genes were analyzed comprehensively to develop a better prognostic signature. The dataset of HCC patients included mRNA sequencing data and clinical information from the TCGA, the ICGC, and the Guangxi Medical University Cancer Hospital cohorts. First, LASSO was performed to develop a prognostic signature for differentially expressed driver genes in the TCGA cohort. Then, the robustness of the signature was assessed using survival and time-dependent ROC curves. Furthermore, independent predictors were determined using univariate and multivariate Cox regression analyses. Stepwise multi-Cox regression analysis was employed to identify significant variables for the construction of a nomogram that predicts survival rates. Functional analysis by Spearman correlation analysis, enrichment analysis (GO, KEGG, and GSEA), and immunoassay (ssGSEA and xCell) were performed.

Result

A 4-driver gene signature (CLTC, DNMT3A, GMPS, and NRAS) was successfully constructed and showed excellent predictive efficiency in three cohorts. The nomogram indicated high predictive accuracy for the 1-, 3-, and 5-year prognoses of HCC patients, which included clinical information and risk score. Enrichment analysis revealed that driver genes were involved in regulating oncogenic processes, including the cell cycle and metabolic pathways, which were associated with the progression of HCC. ssGSEA and xCell showed differences in immune infiltration and the immune microenvironment between the two risk groups.

Conclusion

The 4-driver gene signature is closely associated with the survival prediction of HCC and is expected to provide new insights into targeted therapy for HCC patients.

The upregulation of CLGN in hepatocellular carcinoma is potentially regulated by hsa-miR-194-3p and associated with patient progression

Background

Patients with hepatocellular carcinoma (HCC) have poor prognosis, especially in advanced stages. Targeted therapy is the main treatment for advanced HCC patients, but the optimal targets for HCC remain poorly understood. The main purpose of this study was to identify potential novel prognostic markers and therapeutic targets.

Methods

Firstly, differentially expressed genes (DEGs) in HCC were identified from the Gene Expression Omnibus (GEO) database. The expression, significance in prognosis, and potential mechanisms of DEGs were analyzed using GEPIA, TIMER, HPA, Kaplan Meier Plotter, CBioPortal, miRWalk, TargetScan, and ENCORI databases. Immunohistochemical staining was used to determine the protein expression levels of potential candidate genes.

Results

The mRNA levels of MND1, STXBP6, and CLGN were significantly increased in HCC (p< 0.01). HCC patients with elevated CLGN mRNA levels had poorer overall survival (OS), disease-free survival (DFS), progression-free survival (PFS), and disease-specific survival (DSS) (p < 0.05). Higher MND1 mRNA levels significantly correlated with poorer DFS in HCC patients (p< 0.05). However, there was no significant correlation between STXBP6 expression and prognosis of HCC (p> 0.05). Further analysis revealed that patients with elevated CLGN mRNA expression in advanced pathology stages had poorer prognosis (p< 0.01). In addition, CLGN protein levels were elevated in HCC compared to their levels in normal tissues. The mRNA levels of CLGN had no significant correlation with the abundance of six common tumor infiltrating lymphocytes in HCC (COR < 0.5). Moreover, the mutation rate of CLGN was less than 1% in HCC patients (10/1089). Finally, the expression level of hsa-miR-194-3p in HCC was significantly lower than that in normal tissues (p < 0.05), and prognosis of HCC with low expression of hsa-miR-194 was poor (p < 0.05).

Conclusion

The upregulation of CLGN in HCC is significantly associated with poor patient prognosis, especially in the advanced stages, and may be regulated by hsa-miR-194-3p. These findings suggest that CLGN may be closely related to the progression of HCC, and is a potential therapeutic target and prognostic indicator for patients with advanced HCC.

Prediction of early treatment response to the combination therapy of TACE plus lenvatinib and anti-PD-1 antibody immunotherapy for unresectable hepatocellular carcinoma: Multicenter retrospective study

Background and aim

The purpose of this study was to investigate and validate the efficacy of a nomogram model in predicting early objective response rate (ORR) in u-HCC patients receiving a combination of TACE, Lenvatinib, and anti-PD-1 antibody treatment after 3 months (triple therapy).

Method

This study included 169 u-HCC cases from five different hospitals. As training cohorts (n = 102), cases from two major centers were used, and external validation cohorts (n = 67) were drawn from the other three centers. The clinical data and contrast-enhanced MRI characteristics of patients were included in this retrospective study. For evaluating MRI treatment responses, the modified revaluation criteria in solid tumors (mRECIST) were used. Univariate and multivariate logistic regression analyses were used to select relevant variables and develop a nomogram model. Our as-constructed nomogram was highly consistent and clinically useful, as confirmed by the calibration curve and decision curve analysis (DCA); an independent external cohort also calibrated the nomogram.

Results

The ORR was 60.7% and the risk of early ORR was independently predicted by AFP, portal vein tumor thrombus (PVTT), tumor number, and size in both the training (C-index = 0.853) and test (C-index = 0.731) cohorts. The calibration curve revealed that the nomogram-predicted values were consistent with the actual response rates in both cohorts. Furthermore, DCA indicated that our developed nomogram performed well in clinical settings.

Conclusion

The nomogram model accurately predicts early ORR achieved by triple therapy in u-HCC patients, which aids in individual decision-making and modifying additional therapies for u-HCC cases.