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Abstract 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

IntroductionChanges in the human gut microbiome have been linked to various chronic diseases, including chronic obstructive pulmonary disease (COPD). While substantial knowledge is available on the genomic features of fecal communities, little is known about the microbiome’s transcriptional activity. Here, we analyzed the metatranscriptomic (MTR) abundance of MetaCyc pathways, SuperPathways, and protein domain families (PFAM) represented by the gut microbiome in a cohort of non-small cell lung cancer (NSCLC) patients with- or without COPD comorbidity.MethodsFecal samples of 40 NSCLC patients with- or without COPD comorbidity were collected at the time of diagnosis. Data was preprocessed using the Metaphlan3/Humann3 pipeline and BioCyc© to identify metabolic SuperPathways. LEfSe analysis was conducted on Pathway- and PFAM abundance data to determine COPD- and non-COPD-related clusters.ResultsKey genera Streptococcus, Escherichia, Gemella, and Lactobacillus were significantly more active transcriptionally compared to their metagenomic presence. LEfSe analysis identified 11 MetaCyc pathways that were significantly overrepresented in patients with- and without COPD comorbidity. According to Spearman’s rank correlation, Smoking PY showed a significant negative correlation with Glycolysis IV, Purine Ribonucleoside Degradation and Glycogen Biosynthesis I, and a significant positive correlation with Superpathway of Ac-CoA Biosynthesis and Glyoxylate cycle, whereas forced expiratory volume in the first second (FEV1) showed a significant negative correlation with Glycolysis IV and a significant positive correlation with Glycogen Biosynthesis I. Furthermore, COPD patients showed a significantly increased MTR abundance in ~60% of SuperPathways, indicating a universally increased MTR activity in this condition. FEV1 showed a significant correlation with SuperPathways Carbohydrate degradation, Glycan biosynthesis, and Glycolysis. Taxonomic analysis suggested a more prominent MTR activity from multiple Streptococcus species, Enterococcus (E.) faecalis, E. faecium and Escherichia (E.) coli than expected from their metagenomic abundance. Multiple protein domain families (PFAMs) were identified as more associated with COPD, E. faecium, E.coli, and Streptococcus salivarius, contributing the most to these PFAMs.ConclusionMetatranscriptome analysis identified COPD-related subsets of lung cancer with potential therapeutic relevance.

Due to the high variance in response rates concerning anti-PD1 immunotherapy (IT), there is an unmet need to discover innovative biomarkers to predict immune checkpoint inhibitor (ICI)-efficacy. Our study included 62 Caucasian advanced-stage non-small cell lung cancer (NSCLC) patients treated with anti-PD1 ICI. Gut bacterial signatures were evaluated by metagenomic sequencing and correlated with progression-free survival (PFS), PD-L1 expression and other clinicopathological parameters. We confirmed the predictive role of PFS-related key bacteria with multivariate statistical models (Lasso- and Cox-regression) and validated on an additional patient cohort (n = 60). We find that alpha-diversity showed no significant difference in any comparison. However, there was a significant difference in beta-diversity between patients with long- (>6 months) vs. short (≤6 months) PFS and between chemotherapy (CHT)-treated vs. CHT-naive cases. Short PFS was associated with increased abundance of Firmicutes (F) and Actinobacteria phyla, whereas elevated abundance of Euryarchaeota was specific for low PD-L1 expression. F/Bacteroides (F/B) ratio was significantly increased in patients with short PFS. Multivariate analysis revealed an association between Alistipes shahii, Alistipes finegoldii, Barnesiella visceriola, and long PFS. In contrast, Streptococcus salivarius, Streptococcus vestibularis, and Bifidobacterium breve were associated with short PFS. Using Random Forest machine learning approach, we find that taxonomic profiles performed superiorly in predicting PFS (AUC = 0.74), while metabolic pathways including Amino Acid Synthesis and Fermentation were better predictors of PD-L1 expression (AUC = 0.87). We conclude that specific metagenomic features of the gut microbiome, including bacterial taxonomy and metabolic pathways might be suggestive of ICI efficacy and PD-L1 expression in NSCLC patients.

Abstract Candida species overgrowth in the human gut is considered a prerequisite for invasive candidiasis, but our understanding of gut bacteria promoting or restricting this overgrowth is still limited. By integrating cross-sectional mycobiome and shotgun metagenomics data from the stool of 75 male and female cancer patients at risk but without systemic candidiasis, bacterial communities in high Candida samples display higher metabolic flexibility yet lower contributional diversity than those in low Candida samples. We develop machine learning models that use only bacterial taxa or functional relative abundances to predict the levels of Candida genus and species in an external validation cohort with an AUC of 78.6–81.1%. We propose a mechanism for intestinal Candida overgrowth based on an increase in lactate-producing bacteria, which coincides with a decrease in bacteria that regulate short chain fatty acid and oxygen levels. Under these conditions, the ability of Candida to harness lactate as a nutrient source may enable Candida to outcompete other fungi in the gut.

Impaired exercise tolerance and lung function is a marker for increased mortality in lung cancer patients undergoing lung resection surgery. Recent data suggest that the gut-lung axis regulates systemic metabolic and immune functions, and microbiota might alter exercise tolerance. Here, we aimed to evaluate the associations between gut microbiota and outcomes in lung cancer patients who underwent lung resection surgery. We analysed stool samples, from 15 early-stage lung cancer patients, collected before and after surgical resection using shotgun metagenomic and Internal Transcribed Spacer (ITS) sequencing. We analysed microbiome and mycobiome associations with post-surgery lung function and cardiopulmonary exercise testing (CPET) to assess the maximum level of work achieved. There was a significant difference, between pre- and post-surgical resection samples, in microbial community functional profiles and several species from Alistipes and Bacteroides genus, associated with the production of SCFAs, increased significantly in abundance. Interestingly, an increase in VO2 coincides with an increase in certain species and the "GABA shunt" pathway, suggesting that treatment outcome might improve by enriching butyrate-producing species. Here, we revealed associations between specific gut bacteria, fungi, and their metabolic pathways with the recovery of lung function and exercise capacity.