Department of Oncology, National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China.
Department of Oncology, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Cancer Immunol Immunother. 2024 Jun 4;73(8):152. doi: 10.1007/s00262-024-03738-x.
Patients treated with immune checkpoint inhibitors (ICIs) are at risk of considerable adverse events, and the ongoing struggle is to accurately identify the subset of patients who will benefit. Lymphocyte subsets play a pivotal role in the antitumor response, this study attempted to combine the absolute counts of lymphocyte subsets (ACLS) with the clinicopathological parameters to construct nomograms to accurately predict the prognosis of advanced non-small cell lung cancer (aNSCLC) patients treated with anti-PD-1 inhibitors.
This retrospective study included a training cohort (n = 200) and validation cohort (n = 100) with aNSCLC patients treated with anti-PD-1 inhibitors. Logistic and Cox regression were conducted to identify factors associated with efficacy and progression-free survival (PFS) respectively. Nomograms were built based on independent influencing factors, and assessed by the concordance index (C-index), calibration curve and receiver operating characteristic (ROC) curve.
In training cohort, lower baseline absolute counts of CD3 (P < 0.001) and CD4 (P < 0.001) were associated with for poorer efficacy. Hepatic metastases (P = 0.019) and lower baseline absolute counts of CD3 (P < 0.001), CD4 (P < 0.001), CD8 (P < 0.001), and B cells (P = 0.042) were associated with shorter PFS. Two nomograms to predict efficacy at 6-week after treatment and PFS at 4-, 8- and 12-months were constructed, and validated in validation cohort. The area under the ROC curve (AUC-ROC) of nomogram to predict response was 0.908 in training cohort and 0.984 in validation cohort. The C-index of nomogram to predict PFS was 0.825 in training cohort and 0.832 in validation cohort. AUC-ROC illustrated the nomograms had excellent discriminative ability. Calibration curves showed a superior consistence between the nomogram predicted probability and actual observation.
We constructed two nomogram based on ACLS to help clinicians screen of patients with possible benefit and make individualized treatment decisions by accurately predicting efficacy and PFS for advanced NSCLC patient treated with anti-PD-1 inhibitors.
接受免疫检查点抑制剂（ICIs）治疗的患者存在发生严重不良反应的风险，目前的研究重点是准确识别可能受益的患者亚群。淋巴细胞亚群在抗肿瘤反应中起着关键作用，本研究试图将淋巴细胞亚群绝对计数（ACLS）与临床病理参数相结合，构建列线图，以准确预测接受抗 PD-1 抑制剂治疗的晚期非小细胞肺癌（aNSCLC）患者的预后。
本回顾性研究纳入了接受抗 PD-1 抑制剂治疗的 aNSCLC 患者的训练队列（n=200）和验证队列（n=100）。采用逻辑回归和 Cox 回归分别识别与疗效和无进展生存期（PFS）相关的因素。基于独立影响因素构建列线图，并通过一致性指数（C-index）、校准曲线和受试者工作特征（ROC）曲线进行评估。
在训练队列中，基线时较低的绝对 CD3 计数（P<0.001）和 CD4 计数（P<0.001）与疗效较差相关。肝转移（P=0.019）和基线时较低的绝对 CD3 计数（P<0.001）、CD4 计数（P<0.001）、CD8 计数（P<0.001）和 B 细胞计数（P=0.042）与较短的 PFS 相关。构建了预测治疗后 6 周疗效和 4、8、12 个月 PFS 的两个列线图，并在验证队列中进行了验证。训练队列中预测反应的 ROC 曲线下面积（AUC-ROC）为 0.908，验证队列中为 0.984。预测 PFS 的列线图的 C-index 在训练队列中为 0.825，在验证队列中为 0.832。AUC-ROC 表明该列线图具有良好的区分能力。校准曲线表明，列线图预测概率与实际观察之间具有良好的一致性。
我们构建了两个基于 ACLS 的列线图，通过准确预测接受抗 PD-1 抑制剂治疗的晚期 NSCLC 患者的疗效和 PFS，帮助临床医生筛选可能受益的患者，并制定个体化的治疗决策。
