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

Abstract

Zhiyan Dai,1,* Chao Chen,2,3,* Ziyan Zhou,1,2 Mingzhen Zhou,1,2 Zhengyao Xie,1 Ziyao Liu,1 Siyuan Liu,1 Yiqiang Chen,1 Jingjing Li,1 Baorui Liu,2 Jie Shen1,2 1Department of Precision Medicine, Nanjing Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, People’s Republic of China; 2Department of Oncology, Nanjing Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, People’s Republic of China; 3Department of Oncology, Jinling Hospital, Clinical College of Nanjing Medical University, Nanjing, 21002, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jie Shen, Department of precision medicine, Nanjing Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, People’s Republic of China, Email [email protected] Baorui Liu, Department of Oncology, Nanjing Drum Tower Hospital,Clinical College of Nanjing Medical University, 321 Zhongshan Road, Nanjing, 210008, People’s Republic of China, Email [email protected]: 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.Keywords: hepatocellular carcinoma, predictive model, immunotherapy, machine learning

Keywords

• hepatocellular carcinoma
• predictive model
• immunotherapy
• machine learning