Dai Zhiyan, Chen Chao, Zhou Ziyan, Zhou Mingzhen, Xie Zhengyao, Liu Ziyao, Liu Siyuan, Chen Yiqiang, Li Jingjing, Liu Baorui, Shen Jie
Department of Precision Medicine, Nanjing Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, People's Republic of China.
Department of Oncology, Nanjing Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, People's Republic of China.
J Hepatocell Carcinoma. 2024 Oct 30;11:2133-2144. doi: 10.2147/JHC.S474593. eCollection 2024.
Immune checkpoint inhibitor (ICI) therapy is a promising treatment for cancer. However, the response rate to ICI therapy in hepatocellular carcinoma (HCC) patients is low (approximately 30%). Thus, an approach to predict whether a patient will benefit from ICI therapy is required. This study aimed to design a classifier based on circulating indicators to identify patients suitable for ICI therapy.
This retrospective study included HCC patients who received immune checkpoint inhibitor therapy between March 2017 and September 2023 at Nanjing Drum Tower Hospital and Jinling Hospital. The levels of the 17 serum biomarkers and baseline patients' characters were assessed to discern meaningful circulating indicators related with survival benefits using random forest. A prognostic model was then constructed to predict survival of patients after treatment.
A total of 369 patients (mean age 56, median follow-up duration 373 days,) were enrolled in this study. Among the 17 circulating biomarkers, 11 were carefully selected to construct a classifier. Receiver operating characteristic (ROC) analysis yielded an area under the curve (AUC) of 0.724. Notably, patients classified into the low-risk group exhibited a more positive prognosis ( = 0.0079; HR, 0.43; 95% CI 0.21-0.87). To enhance efficacy, we incorporated 11 clinical features. The extended model incorporated 12 circulating indicators and 5 clinical features. The AUC of the refined classifier improved to 0.752. Patients in the low-risk group demonstrated superior overall survival compared with those in the high-risk group ( = 0.026; HR 0.39; 95% CI 0.11-1.37).
Circulating biomarkers are useful in predicting therapeutic outcomes and can help in making clinical decisions regarding the use of ICI therapy.
免疫检查点抑制剂（ICI）疗法是一种很有前景的癌症治疗方法。然而，肝细胞癌（HCC）患者对ICI疗法的反应率较低（约30%）。因此，需要一种方法来预测患者是否会从ICI疗法中获益。本研究旨在设计一种基于循环指标的分类器，以识别适合ICI疗法的患者。
这项回顾性研究纳入了2017年3月至2023年9月期间在南京鼓楼医院和金陵医院接受免疫检查点抑制剂治疗的HCC患者。评估了17种血清生物标志物的水平和患者的基线特征，以使用随机森林识别与生存获益相关的有意义的循环指标。然后构建了一个预后模型来预测患者治疗后的生存情况。
本研究共纳入369例患者（平均年龄56岁，中位随访时间373天）。在17种循环生物标志物中，精心挑选了11种来构建一个分类器。受试者操作特征（ROC）分析得出曲线下面积（AUC）为0.724。值得注意的是，分类为低风险组的患者表现出更积极的预后（P = 0.0079；HR，0.43；95%CI 0.21 - 0.87）。为提高疗效，我们纳入了11项临床特征。扩展模型纳入了12项循环指标和5项临床特征。优化后的分类器的AUC提高到了0.752。低风险组患者的总生存期优于高风险组患者（P = 0.026；HR 0.39；95%CI 0.11 - 1.37）。
循环生物标志物有助于预测治疗结果，并可帮助做出关于使用ICI疗法的临床决策。
