Immunotherapy in hepatocellular carcinoma: How does underlying liver disease influence therapeutic strategy and outcomes?

Roth, Gaël S.; Villeret, François; Decaens, Thomas; Merle, Philippe; Nahon, Pierre

doi:10.1111/liv.15504

Cited by 8 publications

(10 citation statements)

References 66 publications

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“…Targeted therapies and immune therapy approaches targeting mutation-associated neoantigens are promising strategies for HCC, especially in non-surgical treatment ( Roth et al, 2022 ). Immunotherapy can greatly activate the autoimmunity of HCC patients but the therapeutic performance in clinical trials remain unsatisfactory till now ( Chen et al, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

Identification of novel prognostic biomarkers in the TF-enhancer-target regulatory network in hepatocellular carcinoma and immune infiltration analysis

Yan

Shao

et al. 2023

Front. Genet.

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Background: Hepatocellular carcinoma (HCC) remains notorious for its high malignancy, poor prognosis and high mortality. The exploration of novel therapeutic agents for HCC has remained challenging due to its complex aetiology. Therefore, it is necessary to elucidate the pathogenesis and mechanism of HCC for clinical intervention.Methods: We collected data from several public data portals and systematically analysed the association between transcription factors (TFs), eRNA-associated enhancers and downstream targets. We next filtered the prognostic genes and established a novel prognosis-related nomogram model. Moreover, we explored the potential mechanisms of the identified prognostic genes. The expression level was validated by several ways.Results: We first constructed a significant TF-enhancer-target regulatory network and identified DAPK1 as a coregulatory differentially expressed prognosis-related gene. We combined common clinicopathological factors and built a prognostic nomogram model for HCC. We found that our regulatory network was correlated with the processes of synthesizing various substances. Moreover, we explored the role of DAPK1 in HCC and found that it was associated with immune cell infiltration and DNA methylation. Several immunostimulators and targeting drugs could be promising immune therapy targets. The tumor immune microenvironment was analyzed. Finally, the lower DAPK1 expression in HCC was validated via the GEO database, UALCAN cohort, and qRT-PCR.Conclusion: In conclusion, we established a significant TF-enhancer-target regulatory network and identified downregulated DAPK1 as an important prognostic and diagnostic gene in HCC. Its potential biological functions and mechanisms were annotated using bioinformatics tools.

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Section: Discussionmentioning

confidence: 99%

Identification of novel prognostic biomarkers in the TF-enhancer-target regulatory network in hepatocellular carcinoma and immune infiltration analysis

Yan

Shao

et al. 2023

Front. Genet.

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“…Accordingly, the ALBI score is also expected to predict the response to immunotherapy [209]. According to the Imbrave150 exploratory analysis [210], the response to atezolizumab/bevacizumab was better than the response to sorafenib in patients with ALBI grade 1 compared to patients with ALBI grade 2. A subgroup analysis of the HIMALAYA trial [7] demonstrated that durvalumab/tremelimumab was superior to sorafenib in patients with ALBI grades 1 and 2; however, this difference did not reach statistical significance.…”

Section: Performance Status and Liver Functionmentioning

confidence: 99%

Predictive Biomarkers for Immune-Checkpoint Inhibitor Treatment Response in Patients with Hepatocellular Carcinoma

Lee

et al. 2023

IJMS

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Hepatocellular carcinoma (HCC) has one of the highest mortality rates among solid cancers. Late diagnosis and a lack of efficacious treatment options contribute to the dismal prognosis of HCC. Immune checkpoint inhibitor (ICI)-based immunotherapy has presented a new milestone in the treatment of cancer. Immunotherapy has yielded remarkable treatment responses in a range of cancer types including HCC. Based on the therapeutic effect of ICI alone (programmed cell death (PD)-1/programmed death-ligand1 (PD-L)1 antibody), investigators have developed combined ICI therapies including ICI + ICI, ICI + tyrosine kinase inhibitor (TKI), and ICI + locoregional treatment or novel immunotherapy. Although these regimens have demonstrated increasing treatment efficacy with the addition of novel drugs, the development of biomarkers to predict toxicity and treatment response in patients receiving ICI is in urgent need. PD-L1 expression in tumor cells received the most attention in early studies among various predictive biomarkers. However, PD-L1 expression alone has limited utility as a predictive biomarker in HCC. Accordingly, subsequent studies have evaluated the utility of tumor mutational burden (TMB), gene signatures, and multiplex immunohistochemistry (IHC) as predictive biomarkers. In this review, we aim to discuss the current state of immunotherapy for HCC, the results of the predictive biomarker studies, and future direction.

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“…Local therapy, immunotherapy, and targeted therapy are the main treatments for patients with advanced HCC ( Wang et al, 2022b ). Studies have shown that the degree of liver cirrhosis and liver function status affect the long-term efficacy of systemic anti-tumor therapy and transcatheter arterial chemoembolization (TACE) ( Lencioni et al, 2016 ; Roth et al, 2023 ). Therefore, evaluating the level of liver cirrhosis in HCC patients is of great significance.…”

Section: Introductionmentioning

confidence: 99%

A decision tree model to predict liver cirrhosis in hepatocellular carcinoma patients: a retrospective study

Zhou,

Chen,

Sun

et al. 2023

PeerJ

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Background The severity of liver cirrhosis in hepatocellular carcinoma (HCC) patients is essential for determining the scope of surgical resection. It also affects the long-term efficacy of systemic anti-tumor therapy and transcatheter arterial chemoembolization (TACE). Non-invasive tools, including aspartate aminotransferase to platelet ratio index (APRI), fibrosis-4 (FIB-4), and γ-glutamyl transferase to platelet ratio (GPR), are less accurate in predicting cirrhosis in HCC patients. We aimed to build a novel decision tree model to improve diagnostic accuracy of liver cirrhosis. Patients and Methods The Mann-Whitney U test, χ2 test, and multivariate logistic regression analysis were used to identify independent cirrhosis predictors. A decision tree model was developed using machine learning algorithms in a training cohort of 141 HCC patients. Internal validation was conducted in 99 HCC patients. The diagnostic accuracy and calibration of the established model were evaluated using receiver operating characteristic (ROC) and calibration curves, respectively. Results Sex and platelet count were identified as independent cirrhosis predictors. A decision tree model integrating imaging-reported cirrhosis, APRI, FIB-4, and GPR was established. The novel model had an excellent diagnostic performance in the training and validation cohorts, with area under the curve (AUC) values of 0.853 and 0.817, respectively. Calibration curves and the Hosmer-Lemeshow test showed good calibration of the novel model. The decision curve analysis (DCA) indicated that the decision tree model could provide a larger net benefit to predict liver cirrhosis. Conclusion Our developed decision tree model could successfully predict liver cirrhosis in HCC patients, which may be helpful in clinical decision-making.

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Immunotherapy in hepatocellular carcinoma: How does underlying liver disease influence therapeutic strategy and outcomes?

Cited by 8 publications

References 66 publications

Identification of novel prognostic biomarkers in the TF-enhancer-target regulatory network in hepatocellular carcinoma and immune infiltration analysis

Identification of novel prognostic biomarkers in the TF-enhancer-target regulatory network in hepatocellular carcinoma and immune infiltration analysis

Predictive Biomarkers for Immune-Checkpoint Inhibitor Treatment Response in Patients with Hepatocellular Carcinoma

A decision tree model to predict liver cirrhosis in hepatocellular carcinoma patients: a retrospective study

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