在402例晚期NSCLC患者中,ROC分析确定了基线和动态NLR和dNLR临界值来预测ICI结果

S. Novello, G. Migliaretti, D. Galetta, F. Tabbò, M. Montrone, A. Alemanni, S. Genestroni, I. Stura, T. Vavalà, R. Buosi, H. S. Soto Parra, A. Lunghi, G. Borra, G. Numico, P. Pizzutilo, A. Mariniello, S. Carnio
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背景:中性粒细胞与淋巴细胞比率(NLR)和衍生中性粒细胞与-(白细胞减去中性粒细胞)比率(dNLR)已被提出作为免疫检查点抑制剂(ICI)应答的可能生物标志物。然而,在非小细胞肺癌(NSCLC)的研究中,主要基于先前关于黑色素瘤的报道,已经使用了各种NLR和/或dNLR切断。方法:在这项意大利多中心回顾性研究中,对IV期非小细胞肺癌(NSCLC)患者的NLR、dNLR、血小板与淋巴细胞比值、白蛋白和乳酸脱氢酶(LDH)进行了纵向评估。主要目的是利用受试者工作特征(ROC)曲线评估基线参数是否能预测对ICI的反应。次要终点是评估NLR和dNLR的动态变化是否也能预测疗效。结果:收集并分析402例患者资料。在所考虑的基线参数中,根据ROC分析,NLR和dNLR是最合适的生物标志物,也确定了有意义的截止值(NLR = 2.46;dNLR = 1.61)。低比例的患者报告了显著改善的结果,就总生存而言(NLR的p = 0.0003;无进展生存期(NLR = 0.0004;dNLR的p = 0.005)。在Cox回归模型中证实了NLR和dNLR作为反应的独立生物标志物的作用。当评估NLR和dNLR从基线到第3周期的动态变化时,NLR≥1.04和dNLR≥0.41的下降也预测了反应。结论:在我们的队列中,我们证实了NLR和dNLR,可以通过外周血轻松评估,可以预测基线和ICI开始后早期的反应。对于基线和动态评估,我们使用ROC曲线确定了具有临床意义的截断值。
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ROC Analysis Identifies Baseline and Dynamic NLR and dNLR Cut-Offs to Predict ICI Outcome in 402 Advanced NSCLC Patients
Background: Neutrophil-to-Lymphocyte Ratio (NLR) and derived Neutrophils-to-(Leukocytes minus neutrophils) Ratio (dNLR) have been proposed as possible biomarkers of response to immune checkpoint inhibitors (ICI). However, in non-small cell lung cancer (NSCLC) studies, various NLR and/or dNLR cut-offs have been used, manly based on previous reports on melanoma. Methods: In this Italian multicenter retrospective study, NLR, dNLR, platelet-to-lymphocyte ratio, albumin, and lactate dehydrogenase (LDH) were longitudinally assessed in patients with stage IV non-small cell lung cancer (NSCLC) treated with ICI. The primary objective was to evaluate if baseline parameters predicted response to ICI, using Receiver Operating Characteristic (ROC) curves. Secondary endpoint was to evaluate if dynamic changing of NLR and dNLR also predicted response. Results: Data of 402 patients were collected and analyzed. Among the baseline parameters considered, NLR and dNLR were the most appropriate biomarkers according to the ROC analyses, which also identified meaningful cut-offs (NLR = 2.46; dNLR = 1.61). Patients with low ratios reported a significantly improved outcome, in terms of overall survival (p = 0.0003 for NLR; p = 0.0002 for dNLR) and progression free survival (p = 0.0004 for NLR; p = 0.005 for dNLR). The role of NLR and dNLR as independent biomarkers of response was confirmed in the Cox regression model. When assessing NLR and dNLR dynamics from baseline to cycle 3, a decrease ≥1.04 for NLR and ≥0.41 for dNLR also predicted response. Conclusions in our cohort, we confirmed that NLR and dNLR, easily assessable on peripheral blood, can predict response at baseline and early after ICI initiation. For both baseline and dynamic assessment, we identified clinically meaningful cut-offs, using ROC curves.
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