Dong Liyuan, Ma Yue, Cao Guang, Chen Dongze, Dong Fengxiao, Jiao Xi, Cao Yanshuo, Liu Chang, Wang Yanni, Zhuo Na, Wang Fengyuan, Guo Yixuan, Dai Tingting, Zhang Shuwei, Jiao Hao, Zou Xingyue, Li Jian, Shen Lin, He Zhonghu, Zhang Yanqiao, Lu Zhihao
Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Gastrointestinal Oncology, Peking University Cancer Hospital & Institute, Beijing, 100142, China.
Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, 150081, China.
Cancer Immunol Immunother. 2025 Feb 25;74(4):112. doi: 10.1007/s00262-025-03963-y.
Immune checkpoint inhibitors (ICIs) benefit only a subset of patients in advanced esophageal squamous cell carcinoma (ESCC). Our study aims to develop and validate a clinically accessible model to better identify those who may respond to ICIs.
This study enrolled advanced ESCC patients treated with ICIs at Peking University Cancer Hospital from January 14, 2016, to January 26, 2024 for the training cohort and at Harbin Medical University Cancer Hospital between January 10, 2019, and July 6, 2022 for the validation cohort. Combined positive score (CPS) was recorded to assess the predictive value of programmed cell death ligand-1 (PD-L1). Baseline clinical and laboratory characteristics were identified as predictors through Akaike information criterion (AIC) and Cox proportional hazards regression. The prediction model underwent internal validation through bootstrapping and was externally validated in the validation cohort.
The training cohort consisted of 430 patients, while the validation cohort included 184 patients. PD-L1 expression failed to discriminate survival outcomes. The prediction model incorporates 10 variables: stage, bone metastasis, line of therapy, treatment, lactate dehydrogenase, carcinoembryonic antigen, carbohydrate antigen 199, systemic immune-inflammation index, lymphocyte count and prognostic nutritional index. The model achieved a C-index of 0.725 in the training cohort, 0.722 following bootstrapping, and 0.691 in the external validation cohort. An interactive online prediction tool ( https://escc-survival.shinyapps.io/shiny_app/ ) was subsequently developed.
This is the first large-scale, real-world model for individualized survival prediction for advanced ESCC patients treated with ICIs, offering a practical tool for optimizing clinical decisions.
免疫检查点抑制剂（ICI）仅使晚期食管鳞状细胞癌（ESCC）患者中的一部分受益。我们的研究旨在开发并验证一种临床可用的模型，以更好地识别可能对ICI有反应的患者。
本研究纳入了2016年1月14日至2024年1月26日期间在北京大学肿瘤医院接受ICI治疗的晚期ESCC患者作为训练队列，以及2019年1月10日至2022年7月6日期间在哈尔滨医科大学附属肿瘤医院接受治疗的患者作为验证队列。记录联合阳性评分（CPS）以评估程序性细胞死亡配体1（PD-L1）的预测价值。通过赤池信息准则（AIC）和Cox比例风险回归确定基线临床和实验室特征作为预测因子。预测模型通过自举法进行内部验证，并在验证队列中进行外部验证。
训练队列包括430例患者，验证队列包括184例患者。PD-L1表达未能区分生存结果。预测模型纳入了10个变量：分期、骨转移、治疗线数、治疗方式、乳酸脱氢酶、癌胚抗原、糖类抗原199、全身免疫炎症指数、淋巴细胞计数和预后营养指数。该模型在训练队列中的C指数为0.725，自举后为0.722，在外部验证队列中为0.691。随后开发了一个交互式在线预测工具（https://escc-survival.shinyapps.io/shiny_app/）。
这是首个针对接受ICI治疗的晚期ESCC患者进行个体化生存预测的大规模真实世界模型，为优化临床决策提供了实用工具。
