基于肿瘤免疫微环境和外周血 T 细胞表型鉴定和验证用于胃癌预后的新型生存预测模型。
Identification and validation of a novel survival prediction model based on the T-cell phenotype in the tumor immune microenvironment and peripheral blood for gastric cancer prognosis.
机构信息
Department of Gastroenterology, Tangdu Hospital, The Air Force Military Medical University, Xi'an, 710032, Shaanxi, China.
State Key Laboratory of Cancer Biology and Institute of Digestive Diseases, The Air Force Military Medical University, Xi'an, China.
出版信息
J Transl Med. 2023 Feb 3;21(1):73. doi: 10.1186/s12967-023-03922-0.
BACKGROUND
The correlation and difference in T-cell phenotypes between peripheral blood lymphocytes (PBLs) and the tumor immune microenvironment (TIME) in patients with gastric cancer (GC) is not clear. We aimed to characterize the phenotypes of CD8 T cells in tumor infiltrating lymphocytes (TILs) and PBLs in patients with different outcomes and to establish a useful survival prediction model.
METHODS
Multiplex immunofluorescence staining and flow cytometry were used to detect the expression of inhibitory molecules (IMs) and active markers (AMs) in CD8TILs and PBLs, respectively. The role of these parameters in the 3-year prognosis was assessed by receiver operating characteristic analysis. Then, we divided patients into two TIME clusters (TIME-A/B) and two PBL clusters (PBL-A/B) by unsupervised hierarchical clustering based on the results of multivariate analysis, and used the Kaplan-Meier method to analyze the difference in prognosis between each group. Finally, we constructed and compared three survival prediction models based on Cox regression analysis, and further validated the efficiency and accuracy in the internal and external cohorts.
RESULTS
The percentage of PD-1CD8TILs, TIM-3CD8TILs, PD-L1CD8TILs, and PD-L1CD8PBLs and the density of PD-L1CD8TILs were independent risk factors, while the percentage of TIM-3CD8PBLs was an independent protective factor. The patients in the TIME-B group showed a worse 3-year overall survival (OS) (HR: 3.256, 95% CI 1.318-8.043, P = 0.006), with a higher density of PD-L1CD8TILs (P < 0.001) and percentage of PD-1CD8TILs (P = 0.017) and PD-L1CD8TILs (P < 0.001) compared to the TIME-A group. The patients in the PBL-B group showed higher positivity for PD-L1CD8PBLs (P = 0.042), LAG-3CD8PBLs (P < 0.001), TIM-3CD8PBLs (P = 0.003), PD-L1CD4PBLs (P = 0.001), and LAG-3CD4PBLs (P < 0.001) and poorer 3-year OS (HR: 0.124, 95% CI 0.017-0.929, P = 0.015) than those in the PBL-A group. In our three survival prediction models, Model 3, which was based on the percentage of TIM-3CD8PBLs, PD-L1CD8TILs and PD-1CD8TILs, showed the best sensitivity (0.950, 0.914), specificity (0.852, 0.857) and accuracy (κ = 0.787, P < 0.001; κ = 0.771, P < 0.001) in the internal and external cohorts, respectively.
CONCLUSION
We established a comprehensive and robust survival prediction model based on the T-cell phenotype in the TIME and PBLs for GC prognosis.
背景
胃癌（GC）患者外周血淋巴细胞（PBLs）和肿瘤免疫微环境（TIME）中 T 细胞表型的相关性和差异尚不清楚。我们旨在描述不同结局患者肿瘤浸润淋巴细胞（TILs）和 PBLs 中 CD8 T 细胞的表型，并建立一个有用的生存预测模型。
方法
采用多重免疫荧光染色和流式细胞术分别检测 CD8 TILs 和 PBLs 中抑制性分子（IMs）和活性标志物（AMs）的表达。通过接受者操作特征分析评估这些参数对 3 年预后的作用。然后，我们根据多变量分析的结果，通过无监督层次聚类将患者分为两个 TIME 簇（TIME-A/B）和两个 PBL 簇（PBL-A/B），并使用 Kaplan-Meier 方法分析每组之间的预后差异。最后，我们基于 Cox 回归分析构建并比较了三个生存预测模型，并在内部和外部队列中进一步验证了其效率和准确性。
结果
PD-1CD8TILs、TIM-3CD8TILs、PD-L1CD8TILs 和 PD-L1CD8PBLs 的百分比以及 PD-L1CD8TILs 的密度是独立的危险因素，而 TIM-3CD8PBLs 的百分比是独立的保护因素。TIME-B 组患者的 3 年总生存率（OS）较差（HR：3.256，95%CI 1.318-8.043，P = 0.006），PD-L1CD8TILs 的密度（P < 0.001）和 PD-1CD8TILs（P = 0.017）和 PD-L1CD8TILs（P < 0.001）的百分比均高于 TIME-A 组。PBL-B 组患者的 PD-L1CD8PBLs（P = 0.042）、LAG-3CD8PBLs（P < 0.001）、TIM-3CD8PBLs（P = 0.003）、PD-L1CD4PBLs（P = 0.001）和 LAG-3CD4PBLs（P < 0.001）的阳性率更高，3 年 OS 更差（HR：0.124，95%CI 0.017-0.929，P = 0.015）。在我们的三个生存预测模型中，基于 TIM-3CD8PBLs、PD-L1CD8TILs 和 PD-1CD8TILs 的百分比的模型 3 显示出最佳的灵敏度（0.950、0.914）、特异性（0.852、0.857）和准确性（κ = 0.787，P < 0.001；κ = 0.771，P < 0.001），在内部和外部队列中分别。
结论
我们基于 GC 预后的 TIME 和 PBLs 中 T 细胞表型建立了一个全面而稳健的生存预测模型。
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