全身炎症反应指数（SIRI）可预测接受免疫治疗的晚期非小细胞肺癌患者的生存情况并构建列线图模型。

The systemic inflammation response index (SIRI) predicts survival in advanced non-small cell lung cancer patients undergoing immunotherapy and the construction of a nomogram model.

作者信息

Tang Chunhan, Zhang Min, Jia Hongyuan, Wang Tianlei, Wu Hongwei, Xu Ke, Ren Tao, Liang Long

机构信息

Clinical Medical College, Chengdu Medical College, Chengdu, Sichuan, China.

Department of Oncology, The First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan, China.

出版信息

Front Immunol. 2024 Dec 24;15:1516737. doi: 10.3389/fimmu.2024.1516737. eCollection 2024.

DOI:10.3389/fimmu.2024.1516737

PMID:39776905

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11703897/

Abstract

BACKGROUND

Inflammation and immune evasion are associated with tumorigenesis and progression. The Systemic Inflammation Response Index (SIRI) has been proposed as a pre-treatment peripheral blood biomarker. This study aims to compare the relationship between SIRI, various serum biomarkers, and the prognosis of NSCLC patients before and after treatment.

METHODS

A retrospective study was conducted on advanced NSCLC patients treated with anti-PD-1 drugs from December 2018 to September 2021. Peripheral blood markers were measured pre- and post-treatment, and hazard ratios were calculated to assess the association between serum biomarkers and progression-free survival (PFS) and overall survival (OS). Kaplan-Meier curves and Cox proportional hazards models were employed for survival analysis. A nomogram model was built based on multivariate Cox proportional hazards regression analysis using the R survival package, with internal validation via the bootstrap method (1,000 resamples). Predictive performance was expressed using the concordance index (C-index), and calibration plots illustrated predictive accuracy.The application value of the model was evaluated by decision curve analysis (DCA).

RESULTS

Among 148 advanced NSCLC patients treated with PD-1 inhibitors, the median PFS was 12.9 months (range: 5.4-29.2 months), and the median OS was 19.9 months (range: 9.6-35.2 months). Univariate analysis identified pre- and post-treatment SIRI, mGRIm-Score, and PNI as independent prognostic factors for both PFS and OS (p < 0.05). Multivariate analysis demonstrated that high post-SIRI and post-mGRIm-Score independently predicted poor PFS (P < 0.001, P = 0.004) and OS (P = 0.048, P = 0.001). The C-index of the nomogram model for OS was 0.720 (95% CI: 0.693-0.747) and for PFS was 0.715 (95% CI: 0.690-0.740). Internal validation via bootstrap resampling (B = 1,000) showed good agreement between predicted and observed OS and PFS at 1, 2, and 3 years, as depicted by calibration plots.

CONCLUSION

SIRI is an important independent predictor of early progression in advanced NSCLC patients treated with PD-1 inhibitors and may assist in identifying patients who will benefit from PD-1 inhibitors therapy in routine clinical practice.

摘要

背景

炎症和免疫逃逸与肿瘤发生及进展相关。全身炎症反应指数（SIRI）已被提议作为一种治疗前外周血生物标志物。本研究旨在比较SIRI、各种血清生物标志物与非小细胞肺癌（NSCLC）患者治疗前后预后的关系。

方法

对2018年12月至2021年9月接受抗PD - 1药物治疗的晚期NSCLC患者进行回顾性研究。在治疗前后测量外周血标志物，并计算风险比以评估血清生物标志物与无进展生存期（PFS）和总生存期（OS）之间的关联。采用Kaplan - Meier曲线和Cox比例风险模型进行生存分析。基于多变量Cox比例风险回归分析，使用R生存软件包构建列线图模型，并通过自抽样法（1000次重复抽样）进行内部验证。使用一致性指数（C - index）表示预测性能，并通过校准图说明预测准确性。通过决策曲线分析（DCA）评估模型的应用价值。

结果

在148例接受PD - 1抑制剂治疗的晚期NSCLC患者中，中位PFS为12.9个月（范围：5.4 - 29.2个月），中位OS为19.9个月（范围：9.6 - 35.2个月）。单因素分析确定治疗前后的SIRI、改良粒细胞免疫球蛋白评分（mGRIm - Score）和预后营养指数（PNI）为PFS和OS的独立预后因素（p < 0.05）。多因素分析表明，治疗后高SIRI和高mGRIm - Score独立预测不良PFS（P < 0.001，P = 0.004）和OS（P = 0.048，P = 0.001）。OS列线图模型的C - index为0.720（95% CI：0.693 - 0.747），PFS列线图模型的C - index为0.715（95% CI：0.690 - 0.740）。通过自抽样重采样（B = 1000）进行的内部验证显示，在校准图中，预测的和观察到的1年、2年和3年OS及PFS之间具有良好的一致性。