PO.CL01.02 · 临床研究
血浆代谢组学分析预测局部晚期NSCLC对新辅助免疫化疗的反应
Plasma metabolomic profiling predicts response to neoadjuvant immunochemotherapy in locally advanced NSCLC
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
背景：虽然免疫检查点抑制剂（ICI）在非小细胞肺癌（NSCLC）中显示出显著获益，但个体间疗效差异很大。PD-L1表达和肿瘤突变负荷（TMB）等既有生物标志物的预测价值有限，凸显了对更可靠的预测性生物标志物这一关键未满足需求。为此，我们的研究旨在发现可预测局部晚期NSCLC患者对新辅助免疫化疗治疗反应的血浆代谢组学生物标志物。
方法：在2023年11月至2025年7月期间，前瞻性纳入71例接受新辅助免疫化疗的局部晚期NSCLC患者。根据最佳总体缓解（BOR），将患者分为良好缓解（GR）组（n=38，部分缓解）和有限缓解（LR）组（n=33，疾病稳定或进展）。血浆采样包括71份基线样本、50份第一周期后样本和39份第二周期后样本。使用多因素logistic分析评估临床病理特征与临床反应之间的关联。使用液相色谱-串联质谱（LC-MS/MS）对所有血浆样本进行非靶向代谢组学分析，以识别预测性代谢生物标志物。
结果：新辅助治疗后，22例患者接受手术切除，病理完全缓解（pCR）率为36.4%（8/22）。多因素分析确定鳞状细胞癌组织学和肿瘤细胞PD-L1表达>50%为新辅助免疫化疗良好缓解的预测因素。血浆代谢组学分析检测到3,756种代谢物。GR组中酰基肉碱甘油酯和羟丙酰基肉碱水平显著较高，而LR组则富集甘氨胆酸、磷脂酰胆碱和牛磺胆酸。KEGG通路分析表明差异代谢物涉及初级胆汁酸生物合成和胆固醇代谢。使用机器学习，我们整合了基线、第一周期后和第二周期后的血浆代谢组学特征，开发了一个用于pCR预测的15特征GLMNet模型，其曲线下面积（AUC）达到0.906。
结论：动态血浆代谢组学特征是预测NSCLC新辅助免疫化疗结局的有前景的非侵入性生物标志物。这些发现为利用代谢组学对患者进行分层并优化个性化治疗策略提供了依据。
查看英文原文 English abstract
Background: While immune checkpoint inhibitors (ICIs) have demonstrated significant benefit in non-small cell lung cancer (NSCLC), the efficacy varies substantially among individuals. Established biomarkers like PD-L1 expression and tumor mutational burden (TMB) offer limited predictive value, underscoring the critical unmet need for more reliable predictive biomarkers. To address this, our study aimed to discover predictive plasma metabolomic biomarkers for treatment response to neoadjuvant immunochemotherapy in patients with locally advanced NSCLC.
Methods: Between November 2023 and July 2025, 71 patients with locally advanced NSCLC receiving neoadjuvant immunochemotherapy were prospectively enrolled. According to best overall response (BOR), patients were classified into the good response (GR) group (n=38, partial response) and the limited response (LR) group (n=33, stable or progressive disease). Plasma sampling included 71 baseline samples, 50 samples after cycle one and 39 samples after cycle two. The association between clinicopathological features and clinical response was assessed using multivariate logistic analysis. Untargeted metabolomic profiling of all plasma samples was conducted using liquid chromatography-tandem mass spectrometry (LC-MS/MS) to identify predictive metabolic biomarkers.
Results: After neoadjuvant therapy, surgical resection in 22 patients revealed a pathological complete response (pCR) rate of 36.4% (8/22). Multivariate analysis identified squamous cell carcinoma histology and tumor cell PD-L1 expression >50% as predictors of good response to neoadjuvant immunochemotherapy. Plasma metabolomic profiling detected 3,756 metabolites. The GR group exhibited significantly higher levels of glycerol ester of acylcarnitine and hydroxypropionylcarnitine, whereas the LR group was enriched in glycocholic acid, phosphatidylcholine and taurocholic acid. KEGG pathway analysis indicated that the differential metabolites were involved in primary bile acid biosynthesis and cholesterol metabolism. Using machine learning, we integrated the baseline, post-cycle one and post-cycle two plasma metabolomic profiles to develop a 15-signature GLMNet model for pCR prediction, which achieved an area under curve (AUC) of 0.906.
Conclusion: Dynamic plasma metabolomic signatures are promising non-invasive biomarkers for predicting outcomes to neoadjuvant immunochemotherapy in NSCLC. These findings provide a rationale for leveraging metabolomics to stratify patients and optimize personalized treatment strategies.
利益披露 Disclosure
Y. Wang, None..
H. Shi, None..
H. Gu, None..
W. Jiang, None..
L. Tian, None..
F. Wei, None..
D. Zheng, None..
H. Xu, None..
T. Xiao, None.