Gao Hengxing, Zou Xuexue, Wang Jing, Zhou Jiejun, Fan Meng, Chen Mingwei
Department of Respiratory and Critical Care Medicine, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Department of Radiology, Binzhou Medical University Hospital, Binzhou, China.
J Thorac Dis. 2023 Oct 31;15(10):5307-5318. doi: 10.21037/jtd-23-523. Epub 2023 Sep 11.
Recent studies have shown that immune checkpoint inhibitors (ICIs) targeting programmed cell death-ligand 1 (PD-L1) have potential benefits in patients with non-small cell lung cancer (NSCLC) subgroups, while the clinicopathological characteristics associated with PD-L1 expression have not been well established. The purpose of this study was to detect the expression level of PD-L1 in tumor tissues of patients with advanced lung adenocarcinoma (ADC) and analyze its possible relationship with clinicopathological characteristics, so as to identify the predictors of PD-L1 expression.
This retrospective study was conducted by analyzing the clinicopathological and imaging characteristics of hospitalized advanced lung ADC patients with PD-L1 available data and admitted to the respiratory department of our hospital. The expression level of PD-L1 in fresh-frozen tumor tissue samples of 136 advanced ADC patients was analyzed by immunohistochemistry. The patients were divided into positive and negative groups based on a cut-off of 1% PD-L1 expression level. Subsequently, the significant correlation between PD-L1 levels and clinicopathological features were evaluated. The predictive performance of clinicopathological characteristics on PD-L1 expression was evaluated and the optimal cut-off values were identified by plotting the receiver operating characteristic (ROC) curve.
The expression level of PD-L1 was related to sex, clinical stage, serum carcinoembryonic antigen (CEA), neuron specific enolase (NSE), white blood cell (WBC), and tumor (T) and metastasis (M) stage. Multivariate logistic regression analysis showed the CEA, NSE, T stage, and WBC were independent predictors of PD-L1 positive expression in lung ADC patients. The ROC curve suggested the model combining CEA with NSE [area under the curve (AUC) =0.815] could better predict the expression levels of PD-L1. The optimal cut-off values for identifying advanced lung ADC patients with PD-L1 positive were CEA ≤13.38 ng/mL and NSE ≤42.35 ng/mL, with sensitivity and specificity of 85.4% and 55.6%, and 92.7% and 32.1%, respectively.
Some commonly used clinicopathological features are related to the histological expression of PD-L1. The serum CEA, NSE, T stage, and WBC values can be used as indicators to predict the expression level of PD-L1 in advanced lung ADC, and are used as predictors to evaluate the efficacy of ICIs before treatment.
近期研究表明,靶向程序性细胞死亡配体1(PD-L1)的免疫检查点抑制剂(ICI)在非小细胞肺癌(NSCLC)亚组患者中具有潜在益处,而与PD-L1表达相关的临床病理特征尚未完全明确。本研究旨在检测晚期肺腺癌(ADC)患者肿瘤组织中PD-L1的表达水平,并分析其与临床病理特征的可能关系,以确定PD-L1表达的预测因素。
本回顾性研究通过分析我院呼吸科收治的有PD-L1可用数据的住院晚期肺ADC患者的临床病理和影像特征进行。采用免疫组织化学法分析136例晚期ADC患者新鲜冷冻肿瘤组织样本中PD-L1的表达水平。根据PD-L1表达水平1%的临界值将患者分为阳性和阴性组。随后,评估PD-L1水平与临床病理特征之间的显著相关性。评估临床病理特征对PD-L1表达的预测性能,并通过绘制受试者工作特征(ROC)曲线确定最佳临界值。
PD-L1的表达水平与性别、临床分期、血清癌胚抗原(CEA)、神经元特异性烯醇化酶(NSE)、白细胞(WBC)以及肿瘤(T)和转移(M)分期有关。多因素逻辑回归分析显示,CEA、NSE、T分期和WBC是肺ADC患者PD-L1阳性表达的独立预测因素。ROC曲线表明,将CEA与NSE相结合的模型[曲线下面积(AUC)=0.815]能更好地预测PD-L1的表达水平。识别PD-L1阳性晚期肺ADC患者的最佳临界值为CEA≤13.38 ng/mL和NSE≤42.35 ng/mL,敏感性和特异性分别为85.4%和55.6%,以及92.7%和32.1%。
一些常用的临床病理特征与PD-L1组织学表达有关。血清CEA、NSE、T分期和WBC值可作为预测晚期肺ADC中PD-L1表达水平的指标,并作为预测因素在治疗前评估ICI的疗效。