宿主和细菌尿液蛋白质组学可能预测晚期非小细胞肺癌患者免疫治疗的疗效。

Host and bacterial urine proteomics might predict treatment outcomes for immunotherapy in advanced non-small cell lung cancer patients.

作者信息

Dora David, Revisnyei Peter, Pasic Alija, Galffy Gabriella, Dulka Edit, Mihucz Anna, Roskó Brigitta, Szincsak Sara, Iliuk Anton, Weiss Glen J, Lohinai Zoltan

机构信息

Department of Anatomy, Histology and Embryology, Semmelweis University, Budapest, Hungary.

Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics, Budapest, Hungary.

出版信息

Front Immunol. 2025 Apr 14;16:1543817. doi: 10.3389/fimmu.2025.1543817. eCollection 2025.

DOI:10.3389/fimmu.2025.1543817

PMID:40297587

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12035445/

Abstract

INTRODUCTION

Urine samples are non-invasive approaches to study potential circulating biomarkers from the host organism. Specific proteins cross the bloodstream through the intestinal barrier and may also derive from gut microbiota. In this study, we aimed to evaluate the predictive role of the host and bacterial urine extracellular vesicle (EV) proteomes in patients with non-small cell lung cancer (NSCLC) treated with anti-PD1 immunotherapy.

METHODS

We analyzed the urine EV proteome of 33 advanced-stage NSCLC patients treated with anti-PD1 immunotherapy with LC-MS/MS, stratifying patients according to long (>6 months) and short (≤6 months) progression-free survival (PFS). Gut microbial communities on a subcohort of 23 patients were also analyzed with shotgun metagenomics. Internal validation was performed using the Random Forest (RF) machine learning (ML) algorithm. RF was validated with a non-linear Bayesian ML model. Gene enrichment, and pathway analysis of host urine proteins were analyzed using the Reactome and Gene Ontology databases.

RESULTS

We identified human (n=3513), bacterial (n=2647), fungal (n=19), and viral (n=4) proteins. 186 human proteins showed differential abundance (p<0.05) according to PFS groups, 101 being significantly more abundant in patients with short PFS and n=85 in patients with long PFS. We found several pathways that were significantly enriched in patients with short PFS (vs long PFS). Multivariate Cox regression showed that human urine proteins MPP5, IGKV6-21, NT5E, and KRT27 were strongly associated with long PFS, and LMAN2, NUTF2, NID1, TNC, IGF1, BCR, GPHN, and PPBP showed the strongest association with short PFS. We revealed that an increased bacterial/host protein ratio in the urine is more frequent in patients with long PFS. Increased abundance of and proteins in the urine positively correlates with their gut metagenomic abundance. RF ML model supported the reliability in predicting PFS for critical human urine proteins (AUC=0.89), accuracy (95%) and Bacterial proteins (AUC=0.74).

CONCLUSION

To our knowledge, this is the first study to depict the predictive role of the host and bacterial urine proteome in anti-PD1-treated advanced NSCLC.

摘要

引言

尿液样本是研究宿主生物体潜在循环生物标志物的非侵入性方法。特定蛋白质可穿过肠道屏障进入血液循环，也可能来源于肠道微生物群。在本研究中，我们旨在评估宿主和细菌尿液细胞外囊泡（EV）蛋白质组在接受抗PD1免疫治疗的非小细胞肺癌（NSCLC）患者中的预测作用。

方法

我们采用液相色谱-串联质谱（LC-MS/MS）分析了33例接受抗PD1免疫治疗的晚期NSCLC患者的尿液EV蛋白质组，并根据无进展生存期（PFS）长短（>6个月和≤6个月）对患者进行分层。还使用鸟枪法宏基因组学分析了23例患者亚组的肠道微生物群落。使用随机森林（RF）机器学习（ML）算法进行内部验证。RF通过非线性贝叶斯ML模型进行验证。使用Reactome和基因本体数据库对宿主尿液蛋白质进行基因富集和通路分析。

结果

我们鉴定出人类（n=3513）、细菌（n=2647）、真菌（n=19）和病毒（n=4）蛋白质。186种人类蛋白质根据PFS分组显示出丰度差异（p<0.05），其中101种在PFS短的患者中丰度显著更高，85种在PFS长的患者中丰度显著更高。我们发现了几条在PFS短的患者（与PFS长的患者相比）中显著富集的通路。多变量Cox回归显示，人类尿液蛋白质MPP5、IGKV6-21、NT5E和KRT27与PFS长密切相关，而LMAN2、NUTF2、NID1、TNC、IGF1、BCR、GPHN和PPBP与PFS短的关联最强。我们发现，PFS长的患者尿液中细菌/宿主蛋白质比率增加更为常见。尿液中和蛋白质丰度增加与其肠道宏基因组丰度呈正相关。RF ML模型支持关键人类尿液蛋白质（AUC=0.89）、准确率（95%）和细菌蛋白质（AUC=0.74）在预测PFS方面的可靠性。