Immune-related adverse events are a potent predictor of post-transplant rejection in HCC: a multicentre retrospective cohort study

医学 回顾性队列研究 不利影响 队列研究 内科学 队列 梅德林 比例危险模型 重症监护医学 年轻人 风险评估 移植物排斥 临床试验 儿科 前瞻性队列研究 外科 并发症 肿瘤科
作者
Jun Fang,Siyi Zhong,Tielong Wang,Kang He,Andre Mu,Meiching Ong,Yimou Lin,Zebin Zhu,Ning Wang,Jian-Cheng Wu,Zhihao Wang,Li Zhao,Feng Gao,Li Zhuang,Zhiyong Guo,Shusen Zheng,Hao Li,Shugeng Zhang,Qi Ling
出处
期刊:Gut [BMJ]
卷期号:75 (6): 1201-1210 被引量:7
标识
DOI:10.1136/gutjnl-2025-336719
摘要

Background Immune checkpoint inhibitors (ICIs) enable successful hepatocellular carcinoma (HCC) downstaging or bridging for liver transplantation (LT) but increase allograft rejection risk. Although immune-related adverse events (irAEs) reflect immune activation during ICI therapy, their association with post-transplant rejection remains unclear. Objective We aimed to identify the risk factors of rejection, focusing on irAEs. Design This national multicentre retrospective study included 209 adult HCC patients who received pretransplant ICIs from 2018 to 2024. Logistic regression was performed to identify rejection factors and build a predictive model. A prospective observational cohort of 23 HCC patients was enrolled to explore the association between peripheral blood immunophenotype and irAEs. Results Among 209 patients, 36 (17.2%) experienced rejection with a median time of 10 days post-LT. Multivariate analysis identified irAEs as the strongest predictor of rejection, with an OR of 9.170 (p<0.001). Other independent risk factors were recipient age <40 years (OR=3.028, p=0.049) and washout period <30 days (OR=3.071, p=0.018). The resulting predictive model achieved an area under the curve of 0.788. Moreover, patients with irAEs had significantly increased numbers of peripheral blood CD8 + T cells and higher levels of IFN-α and TNF-α versus those without (all p<0.05). Conclusions This study identified irAEs as a potent predictor of allograft rejection in HCC patients receiving pretransplant ICIs, potentially driven by heightened immune activation. The predictive model may help stratify patients at high risk of rejection.
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