Lipidomics reveals immune-related adverse events in NSCLC patients receiving immune checkpoint inhibitor

被引:3
|
作者
Yu, Jia [1 ]
Xiong, Fen [2 ]
Xu, Yingruo [2 ]
Xu, Hanyan [1 ]
Zhang, Xi [2 ]
Gao, Hongchang [1 ,2 ]
Li, Yuping [1 ]
机构
[1] Wenzhou Med Univ, Affiliated Hosp 1, Dept Resp & Crit Care Med, Wenzhou 325035, Peoples R China
[2] Wenzhou Med Univ, Inst Metabon & Med NMR, Sch Pharmaceut Sci, Oujiang Lab, Wenzhou 325035, Peoples R China
关键词
Immune-related adverse events (irAEs); Immune checkpoint inhibitors (ICIs); Lung cancer; Biomarkers; Lipidomic; KILLER T-CELLS; PERIPHERAL-BLOOD; ACTIVATION; DISEASE; RISK;
D O I
10.1016/j.intimp.2023.111412
中图分类号
R392 [医学免疫学]; Q939.91 [免疫学];
学科分类号
100102 ;
摘要
There is a lack of reliable biomarkers to predict and identify the risk of immune-related adverse events (irAEs) in non-small cell lung cancer (NSCLC) patients undergoing immune checkpoint inhibitor (ICI) treatment. This study aims to explore potential biomarkers using lipidomics to identify and predict the risk of irAEs in NSCLC patients receiving ICI treatment. This prospective study enrolled 94 NSCLC patients with IIIB/IV stage NSCLC who underwent first-line chemotherapy in combination with ICI treatment. The prediction cohort consisted of plasma samples collected from 60 patients before ICI treatment, and the occurrence of irAE was monitored within 6 months of initiating first-line ICI therapy. The validation cohort comprised 34 patients, with plasma samples obtained from 15 patients who did not develop irAE at 6 months of ICI treatment and plasma samples collected from 19 irAE patients at the onset of irAE. Through non-targeted lipidomics and semi-targeted lipid quantification analysis, we identify 11 differentially metabolized lipids and further screened these lipids with the area under the curve (AUC) > 0.7 to predict the occurrence of irAEs in NSCLC patients following ICI treatment. The results showed that the biomarker panel consisting of 9 lipids (LPC-18:2, PC-40:6, LPC-22:6, LPC-O-18:0, PS-38:0, PC-38:6, PC-37:6, PC-36:5,LPC-17:0) exhibited a good AUC of 0.859 in the prediction and 0.940 in the validation cohort phase of the receiver operating characteristic curve; The study utilizes plasma lipidomics to develop a rapid and effective prediction model for identifying irAEs in advanced NSCLC patients who treatment with first-line chemotherapy combined with immunotherapy.
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页数:12
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