Gut Metatranscriptomics Based De Novo Assembly Reveals Microbial Signatures Predicting Immunotherapy outcomes in Non-Small Cell Lung Cancer

Abstract

Background: Advanced-stage non-small cell lung cancer (NSCLC), presents significant treatment challenges, with immune checkpoint inhibitor (ICI) combinations being the primary therapeutic option. Emerging evidence highlights the gut microbiome's pivotal role in modulating the efficacy of such immunotherapies. Our study aims to elucidate the association between the gut microbiome and ICI treatment outcomes in NSCLC patients, focusing on identifying metatranscriptomic (MTR) signatures predictive of therapy response. Methods: Utilizing a De Novo Assembly-based MTR analysis on fecal samples from 29 NSCLC patients undergoing ICI therapy, the study segmented patients based on 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) and Support Vector Machine (SVM) machine learning algorithms and comprehensive microbial profiles. Results: There were significant microbial composition variances between the two PFS groups. Notably, 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 (vs short PFS, 66.2% vs. 42.3%, p=0.007). Among the 120 significant DEGs identified, cluster analysis clearly separated a large set of gene clusters more active in patients with short PFS and a smaller set of genes more active in long PFS patients. RF and SVM machine learning models confirmed the predictive validity of the microbial signatures, with ROC AUCs of 0.878 and 0.85, respectively. Furthermore, multivariate analyses incorporating clinical variables such as PD-L1 expression and chemotherapy history underscored the influence of n=6 RNA-based microbial biomarkers on PFS. Conclusion: Using ML models, specific gut microbiome MTR signatures' associate with ICI treated NSCLC outcomes. Expression of specific gene clusters and microbial taxonomy might differentiate long vs short PFS.