Fujian Medical University Union Hospital, Fujian Medical University, Fuzhou, China.
College of Chinese Traditional Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China.
Acta Oncol. 2023 Aug;62(8):853-860. doi: 10.1080/0284186X.2023.2228991. Epub 2023 Jun 28.
BACKGROUND/PURPOSE: The current study aimed to investigate the correlation between tumor-infiltrating lymphocytes (TILs) and immunotherapy efficacy in patients with advanced non-small cell lung cancer (NSCLC).
Eighty-nine patients with advanced NSCLC who received immune checkpoint inhibitors (ICIs) monotherapy were retrospectively enrolled in this study. The density of TILs in paraffin-embedded pathological tissues taken before receiving ICIs was quantitatively analyzed by immunohistochemical staining. The density of TILs was treated as a dichotomous variable using the median as the cutoff value. The Kaplan-Meier analysis was used to assess survival differences between groups. Univariate and multivariate Cox analyses were applied to screen out independent prognostic factors and further construct a nomogram prediction model to predict survival.
Survival analysis showed that CD8 TILs, CD4 TILs, and IFN-γ Th1 were significant positive indicators for predicting progression-free survival (PFS) and overall survival (OS) ( < 0.05), whereas Foxp3 Treg were a significant negative predictor ( < 0.05). The predictive role of IL-4 Th2 was not apparent in this study and requires further investigation and exploration ( > 0.05). The nomogram prediction model exhibited good discriminative ability, with C-index values of 0.723 (95% CI 0.682-0.764) and 0.793 (95% CI, 0.738-0.848) in the training cohort and validation cohort, respectively. The AUC values indicated that the nomogram prediction model had high predictive value and the calibration curve presented good prediction accuracy.
TILs could predict the efficacy of immunotherapy and may become a promising predictor.
背景/目的：本研究旨在探讨肿瘤浸润淋巴细胞（TILs）与晚期非小细胞肺癌（NSCLC）患者免疫治疗疗效的相关性。
回顾性纳入 89 例接受免疫检查点抑制剂（ICI）单药治疗的晚期 NSCLC 患者。采用免疫组织化学染色法对接受 ICI 治疗前石蜡包埋病理组织中 TILs 的密度进行定量分析。将 TILs 密度作为二分类变量，以中位数为截断值。采用 Kaplan-Meier 分析评估组间生存差异。采用单因素和多因素 Cox 分析筛选独立预后因素，并进一步构建列线图预测模型预测生存。
生存分析显示，CD8 TILs、CD4 TILs 和 IFN-γ Th1 是预测无进展生存期（PFS）和总生存期（OS）的显著阳性指标（ < 0.05），而 Foxp3 Treg 是显著的阴性预测指标（ < 0.05）。IL-4 Th2 的预测作用在本研究中不明显，需要进一步的调查和探索（ > 0.05）。列线图预测模型具有良好的判别能力，在训练队列和验证队列中的 C 指数值分别为 0.723（95%CI 0.682-0.764）和 0.793（95%CI，0.738-0.848）。AUC 值表明，列线图预测模型具有较高的预测价值，校准曲线呈现良好的预测准确性。
TILs 可预测免疫治疗的疗效，可能成为一种有前途的预测指标。
