Circulating Biomarkers Predict Immunotherapeutic Response in Hepatocellular Carcinoma Using a Machine Learning Method.

IF 4.2 3区 医学 Q2 ONCOLOGY Journal of Hepatocellular Carcinoma Pub Date : 2024-10-30 eCollection Date: 2024-01-01 DOI:10.2147/JHC.S474593
Zhiyan Dai, Chao Chen, Ziyan Zhou, Mingzhen Zhou, Zhengyao Xie, Ziyao Liu, Siyuan Liu, Yiqiang Chen, Jingjing Li, Baorui Liu, Jie Shen
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Abstract

Background: 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.

Methods: 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.

Results: 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 (P = 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 (P = 0.026; HR 0.39; 95% CI 0.11-1.37).

Conclusion: Circulating biomarkers are useful in predicting therapeutic outcomes and can help in making clinical decisions regarding the use of ICI therapy.

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循环生物标记物利用机器学习方法预测肝细胞癌的免疫治疗反应
背景: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 疗法的反应率很低(约为 30%)。因此,需要一种方法来预测患者是否能从 ICI 治疗中获益。本研究旨在设计一种基于循环指标的分类器,以识别适合接受 ICI 治疗的患者: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。通过评估17种血清生物标志物的水平和患者的基线特征,利用随机森林法找出与生存获益相关的有意义的循环指标。然后构建预后模型,预测患者治疗后的生存期:本研究共纳入 369 名患者(平均年龄 56 岁,中位随访时间 373 天)。在 17 个循环生物标志物中,精心挑选了 11 个构建分类器。接收者操作特征(ROC)分析得出的曲线下面积(AUC)为 0.724。值得注意的是,被归入低风险组的患者预后更乐观(P = 0.0079;HR,0.43;95% CI 0.21-0.87)。为了提高疗效,我们纳入了 11 项临床特征。扩展模型纳入了 12 个循环指标和 5 个临床特征。改进后的分类器的AUC提高到了0.752。与高风险组相比,低风险组患者的总生存率更高(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,并有助于做出使用 ICI 治疗的临床决策。
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来源期刊
CiteScore
0.50
自引率
2.40%
发文量
108
审稿时长
16 weeks
期刊最新文献
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