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Gut metatranscriptomics based de novo assembly reveals microbial signatures predicting immunotherapy outcomes in
David Dora1, Peter Kiraly2, Csenge Somodi3
1Department of Anatomy, Histology, and Embryology, Semmelweis University, Budapest, Hungary.
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.
Insights
Specific gut microbiome metatranscriptomic (MTR) signatures correlate with immune checkpoint inhibitor (ICI) treatment outcomes in non-small cell lung cancer (NSCLC). These MTR signatures, including specific taxa and gene expression patterns, can predict progression-free survival (PFS).
Area of Science:
- Microbiome Research
- Cancer Genomics
- Immunotherapy
Background:
- Advanced non-small cell lung cancer (NSCLC) treatment relies heavily on immune checkpoint inhibitors (ICIs).
- Emerging research indicates a significant role for the gut microbiome in modulating ICI efficacy.
- Understanding these microbial influences is crucial for optimizing NSCLC treatment strategies.
Purpose of the Study:
- To investigate the association between gut microbiome metatranscriptomic (MTR) signatures and treatment outcomes in NSCLC patients receiving ICIs.
- To identify specific microbial taxa and gene expression patterns that predict progression-free survival (PFS).
Main Methods:
- Fecal samples from 29 NSCLC patients undergoing ICI therapy were analyzed using de novo assembly-based MTR.
- RNA sequencing and differential gene expression (DE) analysis were performed.
- Machine learning models (Random Forest, SVM, XGBoost) were employed to assess the predictive power of microbial signatures.
Main Results:
- While alpha-diversity showed no significant differences, beta-diversity revealed distinct microbial profiles between long and short PFS groups.
- Specific bacterial phyla (Actinomycetota, Euryarchaeota, Bacillota) and their RNA expression levels differed significantly between groups.
- Machine learning models demonstrated high predictive accuracy (ROC AUC > 0.84) for PFS based on RNA-based microbial signatures.
- Six key RNA biomarkers were identified as influential on PFS, independent of PD-L1 expression and chemotherapy history.
Conclusions:
- Metatranscriptomic (MTR) signatures of the gut microbiome are associated with ICI treatment outcomes in NSCLC patients.
- Specific microbial gene expression patterns and taxa can differentiate between patients with long and short PFS.
- These findings highlight the potential of microbiome-based biomarkers for predicting ICI response in NSCLC.
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