Host and bacterial urine proteomics might predict treatment outcomes for immunotherapy in advanced non-small cell lung cancer patients DOI Creative Commons
Dávid Dóra, Péter Revisnyei, Alija Pašíć

et al.

Frontiers in Immunology, Journal Year: 2025, Volume and Issue: 16

Published: April 14, 2025

Introduction Urine samples are non-invasive approaches to study potential circulating biomarkers from the host organism. Specific proteins cross bloodstream through intestinal barrier and may also derive gut microbiota. In this study, we aimed evaluate predictive role of bacterial urine extracellular vesicle (EV) proteomes in patients with non-small cell lung cancer (NSCLC) treated anti-PD1 immunotherapy. Methods We analyzed EV proteome 33 advanced-stage NSCLC immunotherapy LC-MS/MS, stratifying according long (>6 months) short (≤6 progression-free survival (PFS). Gut microbial communities on a subcohort 23 were shotgun metagenomics. Internal validation was performed using Random Forest (RF) machine learning (ML) algorithm. RF validated non-linear Bayesian ML model. Gene enrichment, pathway analysis Reactome Ontology databases. Results identified human (n=3513), (n=2647), fungal (n=19), viral (n=4) proteins. 186 showed differential abundance (p<0.05) PFS groups, 101 being significantly more abundant n=85 PFS. found several pathways that enriched (vs PFS). Multivariate Cox regression MPP5, IGKV6-21, NT5E, KRT27 strongly associated PFS, LMAN2, NUTF2, NID1, TNC, IGF1, BCR, GPHN, PPBP strongest association revealed an increased bacterial/host protein ratio is frequent Increased E. coli faecalis positively correlates their metagenomic abundance. model supported reliability predicting for critical (AUC=0.89), accuracy (95%) Bacterial (AUC=0.74). Conclusion To our knowledge, first depict anti-PD1-treated advanced NSCLC.

Language: Английский

Host and bacterial urine proteomics might predict treatment outcomes for immunotherapy in advanced non-small cell lung cancer patients DOI Creative Commons
Dávid Dóra, Péter Revisnyei, Alija Pašíć

et al.

Frontiers in Immunology, Journal Year: 2025, Volume and Issue: 16

Published: April 14, 2025

Introduction Urine samples are non-invasive approaches to study potential circulating biomarkers from the host organism. Specific proteins cross bloodstream through intestinal barrier and may also derive gut microbiota. In this study, we aimed evaluate predictive role of bacterial urine extracellular vesicle (EV) proteomes in patients with non-small cell lung cancer (NSCLC) treated anti-PD1 immunotherapy. Methods We analyzed EV proteome 33 advanced-stage NSCLC immunotherapy LC-MS/MS, stratifying according long (>6 months) short (≤6 progression-free survival (PFS). Gut microbial communities on a subcohort 23 were shotgun metagenomics. Internal validation was performed using Random Forest (RF) machine learning (ML) algorithm. RF validated non-linear Bayesian ML model. Gene enrichment, pathway analysis Reactome Ontology databases. Results identified human (n=3513), (n=2647), fungal (n=19), viral (n=4) proteins. 186 showed differential abundance (p<0.05) PFS groups, 101 being significantly more abundant n=85 PFS. found several pathways that enriched (vs PFS). Multivariate Cox regression MPP5, IGKV6-21, NT5E, KRT27 strongly associated PFS, LMAN2, NUTF2, NID1, TNC, IGF1, BCR, GPHN, PPBP strongest association revealed an increased bacterial/host protein ratio is frequent Increased E. coli faecalis positively correlates their metagenomic abundance. model supported reliability predicting for critical (AUC=0.89), accuracy (95%) Bacterial (AUC=0.74). Conclusion To our knowledge, first depict anti-PD1-treated advanced NSCLC.

Language: Английский

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