Antiviral therapy can effectively suppress irAEs in HBV positive hepatocellular carcinoma treated with ICIs - validation based on multi machine learning

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Immune checkpoint inhibitors show therapeutic activity in hepatitis B-virus positive hepatocellular carcinoma, yet immune-related adverse events remain a key limitation for tumor immunotherapy. A multi-machine learning framework was built using retrospective data from 274 HBV-positive patients receiving PD-1 and/or CTLA-4 blockade, incorporating immune cell measurements to screen irAEs risk factors and construct a clinical prediction model. Models (Lasso, RSF, XGBoost) were validated with resampling and ten-fold cross-validation, with predictive performance assessed using decision curve analysis.

LanguageEnglish

CategoryResearch & Report

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Pages13

Reading Time33 min

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Background

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TYPE Original Research PUBLISHED 27 January 2025 DOI 10.3389/fimmu.2024.1516524 OPEN ACCESS EDITED BY Pengpeng Zhang, Nanjing Medical University, China REVIEWED BY Ge Zhang, The First Afﬁliated Hospital of Zhengzhou University, China Le Qu, Nanjing University, China *CORRESPONDENCE Zibing Wang [zlyywzb2118@zzu.edu.cn](zlyywzb2118@zzu.edu.cn) RECEIVED 24 October 2024 ACCEPTED 30 December 2024 PUBLISHED 27 January 2025 CITATION Pan S and Wang Z (2025) Antiviral therapy can effectively suppress irAEs in HBV positive hepatocellular carcinoma treated with ICIs: validation based on multi machine learning. Front. Immunol. 15:1516524 . doi: 10.3389/fimmu.2024.1516524 COPYRIGHT © 2025 Pan and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Antiviral therapy can effectively suppress irAEs in HBV positive hepatocellular carcinoma treated with ICIs: validation based on multi machine learning Shuxian Pan and Zibing Wang* Department of Immunotherapy, The Afﬁliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China Background: Immune checkpoint inhibitors have proven efﬁcacy against hepatitis B-virus positive hepatocellular. However, Immunotherapy-related adverse reactions are still a major challenge faced by tumor immunotherapy, so it is urgent to establish new methods to effectively predict immunotherapyrelated adverse reactions. Objective: Multi-machine learning model were constructed to screen the risk factors for irAEs in ICIs for the treatment of HBV-related hepatocellular and build a prediction model for the occurrence of clinical IRAEs. Methods: Data from 274 hepatitis B virus positive tumor patients who received PD-1 or/and CTLA4 inhibitor treatment and had immune cell detection results were collected from Henan Cancer Hospital for retrospective analysis. Models were established using Lasso, RSF (RandomForest), and xgBoost, with ten-fold cross-validation and resampling methods used to ensure model reliability. The impact of inﬂuencing factors on irAEs (immune-related adverse events) was validated using Decision Curve Analysis (DCA) . Both uni/multivariable analysis were accomplished by Chi-square/Fisher’s exact tests. The accuracy of the model is veriﬁed in the DCA curve. Results: A total of 274 HBV-related liver cancer patients were enrolled in the study. Predictive models were constructed using three machine learning algorithms to analyze and statistically evaluate clinical characteristics, including immune cell data. The accuracy of the Lasso regression model was 0 . 864, XGBoost achieved 0. 903, and RandomForest reached 0 .961. Resampling internal validation revealed that RandomForest had the highest recall rate (AUC = 0 . 892) . Based on machine learning-selected indicators, antiviral therapy and The HBV DNA copy number showed a signiﬁcant correlation with both the occurrence and severity of irAEs. Antiviral therapy notably reduced the incidence of IRAEs and may modulate these events through regulation of B cells. The DCA model also demonstrated strong predictive performance. Effective control of viral load through antiviral therapy signiﬁcantly mitigates the occurrence of irAEs. Frontiers in Immunology 01 [frontiersin.org](frontiersin.org) Conclusion: ICIs show therapeutic potential in the treatment of HBV-HCC. Following antiviral therapy, the incidence of severe irAEs decreases. Even in cases where viral load control is incomplete, continuous antiviral treatment can still mitigate the occurrence of irAEs. KEYWORDS hepatocel

FAQs

What problem does this study address in HBV-positive hepatocellular carcinoma treated with ICIs?

The study focuses on preventing immune-related adverse events (irAEs), which remain a major challenge despite the efficacy of immune checkpoint inhibitors.

How were the prediction models constructed and validated?

A retrospective cohort of 274 HBV-positive patients was used. Lasso, RSF, and XGBoost models were trained with ten-fold cross-validation and resampling, and predictive value was assessed with decision curve analysis.

Which factors were associated with the occurrence and severity of irAEs?

Machine-learning-selected indicators showed that antiviral therapy and HBV DNA copy number were significantly correlated with both the occurrence and severity of irAEs.

What effect did antiviral therapy have on irAEs after ICI treatment?

Antiviral therapy reduced the incidence of severe irAEs and may modulate these events through regulation of B cells; viral load control helped mitigate irAEs even when control was incomplete.

Aug 5, 2026

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