Gut metatranscriptomics based de novo assembly reveals microbial signatures predicting immunotherapy outcomes in non-small cell lung cancer

Dóra Dávid; Király Péter; Somodi Csenge; Ligeti Balázs; Dulka Edit; Gálffy Gabriella; Lohinai Zoltán: Gut metatranscriptomics based de novo assembly reveals microbial signatures predicting immunotherapy outcomes in non-small cell lung cancer.
JOURNAL OF TRANSLATIONAL MEDICINE, 22 (1). ISSN 1479-5876 (2024)

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Folyóiratcikk

Szerző azonosítók:

Név	ORCID	MTMT szerző azonosító
Dóra Dávid		10050618
Király Péter		10084062
Somodi Csenge		10090151
Ligeti Balázs		10034554
Dulka Edit
Gálffy Gabriella		10003448
Lohinai Zoltán	0000-0002-6442-9530	10044743

Absztrakt (kivonat):

Background Advanced-stage non-small cell lung cancer (NSCLC) poses treatment challenges, with immune checkpoint inhibitors (ICIs) as the main therapy. Emerging evidence suggests the gut microbiome significantly influences ICI efficacy. This study explores the link between the gut microbiome and ICI outcomes in NSCLC patients, using metatranscriptomic (MTR) signatures. Methods We utilized a de novo assembly-based MTR analysis on fecal samples from 29 NSCLC patients undergoing ICI therapy, segmented according to progression-free survival (PFS) into long (> 6 months) and short (<= 6 months) PFS groups. Through RNA sequencing, we employed the Trinity pipeline for assembly, MMSeqs2 for taxonomic classification, DESeq2 for differential expression (DE) analysis. We constructed Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) machine learning (ML) algorithms and comprehensive microbial profiles. Results We detected no significant differences concerning alpha-diversity, but we revealed a biologically relevant separation between the two patient groups in beta-diversity. Actinomycetota was significantly overrepresented in patients with short PFS (vs long PFS, 36.7% vs. 5.4%, p < 0.001), as was Euryarchaeota (1.3% vs. 0.002%, p = 0.009), while Bacillota showed higher prevalence in the long PFS group (66.2% vs. 42.3%, p = 0.007), when comparing the abundance of corresponding RNA reads. Among the 120 significant DEGs identified, cluster analysis clearly separated a large set of genes more active in patients with short PFS and a smaller set of genes more active in long PFS patients. Protein Domain Families (PFAMs) were analyzed to identify pathways enriched in patient groups. Pathways related to DNA synthesis and Translesion were more enriched in short PFS patients, while metabolism-related pathways were more enriched in long PFS patients. E. coli-derived PFAMs dominated in patients with long PFS. RF, SVM and XGBoost ML models all confirmed the predictive power of our selected RNA-based microbial signature, with ROC AUCs all greater than 0.84. Multivariate Cox regression tested with clinical confounders PD-L1 expression and chemotherapy history underscored the influence of n = 6 key RNA biomarkers on PFS. Conclusion According to ML models specific gut microbiome MTR signatures' associate with ICI treated NSCLC outcomes. Specific gene clusters and taxa MTR gene expression might differentiate long vs short PFS.

Folyóirat címe:

JOURNAL OF TRANSLATIONAL MEDICINE

Megjelenés éve:

2024

Kötet:

Szám:

ISSN:

1479-5876

Intézmény:

Pázmány Péter Katolikus Egyetem

Kar:

Információs Technológiai és Bionikai Kar (2013.07.-)