Dietary pattern and the corresponding gut microbiome in response to immunotherapy in Thai patients with advanced non-small cell lung cancer (NSCLC)

Abstract

Gut microbiota is considered a key player modulating the response to immune checkpoint inhibitors (ICI) in cancer. The effects of dietary pattern on this interaction is not well-studied. A prospective multicenter cohort of 95 patients with advanced non-small cell lung cancer (NSCLC) undergoing ICI therapy were enrolled. Stool shotgun metagenomic sequencing was performed. Three-day dietary patterns before ICI were assessed. Patients were categorized as hyperprogressive disease (HPD) if they exhibited a time to treatment failure of less than 2 months. All others were categorized as non-hyperprogressive disease (non-HPD). The correlation between dietary patterns, gut microbiome, and response to ICI therapy was analyzed. In the multivariate analysis, a high abundance of Firmicutes unclassified and the Ruminococcaceae family correlated with a significantly diminished progression-free survival (PFS) with an HR of 2.40 [P = 0.006] and 4.30 [P = 0.005], respectively. More specifically, within the subset of NSCLC patients treated solely with ICI therapy, a high abundance of Intestinimonas and the Enterobacteriaceae family were associated with substantially reduced PFS with an HR of 2.61 [P = 0.02] and HR 3.34 [P = 0.005], respectively. In our comprehensive dietary pattern analysis, the HPD group showed increased consumption of cholesterol, sodium, and fats beyond recommended levels compared to the non-HPD group. This group also displayed a tendency towards higher food pattern scores characterized by a high intake of fat and dairy products. Our study revealed a distinct association between the gut microbiome composition and treatment outcomes. The overall composition of diet might be related to ICI therapeutic outcomes.

Keywords: Anti-PD-1/PD-L1 immunotherapy; Diet; Gut microbiome; Immune checkpoints inhibitors; Non-small cell lung cancer; Nutrition.

Fig. 1

Diversity of gut microbiome in response to ICI therapy. Comparison of alpha diversity of the gut microbiome was conducted using richness (observed indices) and diversity (Shannon indices) by the Mann-Whitney U rank sum test. (A). Taxonomic profiles at phylum level of the gut microbiome of the study cohort according to response of treatment; HPD (hyper-progressive disease) and non-HPD (non-hyper-progressive disease) (B). Principal coordinate analysis of gut microbiome by response using Bray-Curtis metrics pairwise dissimilarities. Red and blue dots represented HPD and non-HPD group, respectively. The gray sphere represented the coefficient of significant taxa of our cohort (C). Diverse eigenvector plot of top 5 most abundance at species level according to response status; HPD vs. non-HPD (D and E).

Fig. 2

Taxonomic cladogram from LEfSe showing differences in gut microbiome taxa. The circle radiating inside-out demonstrated the classification from the phylum to the genus. Dot size is proportional to the abundance of the taxon. Red and blue dots denoted the core bacterial populations in each respective group (A). Linear discriminant analysis (LDA) scores computed for differentially abundant taxa in the gut microbiomes of HPD (red) and non-HPD (blue). Length indicates the effect size associated with a taxon (B). Pairwise comparisons by Mann-Whitney U rank sum test of abundances of metagenomic species identified by metagenomic WGS sequencing for HPD (red) and non-HPD (blue) (C). *p-value < 0.05; ** p-value < 0.01; *** p-value < 0.005.

Fig. 3

Progression-free survival of overall cohort according to abundance of top hits gut microbiomes is shown. Using median relative abundance as the cut-off level, Kaplan-Meier analysis of high abundance (black) or low abundance (blue) of Firmicutes unclassified genus (P = 0.02) (A), Alphaproteobacteria family (GGB6612) (P = 0.02) (B) and Ruminococcaceae family (GGB9730) (P < 0.001) (C) is shown. For subset of ICIs monotherapy (68.4%), we found differences in gut microbiome composition related to outcome between ICI monotherapy vs. overall cohort. Abundance of Intestinimonas genus (P = 0.04) (D), Ruthenibacterium genus (P = 0.01) (E), and Enterobacteriaceae family (P = 0.0005) (F) were significantly correlated with PFS.

Fig. 4

Correlation matrix shows paired correlations of all food items. Red color indicates positive correlations, and blue color indicates negative correlations (A). Dietary patterns derived from factor analysis using 15 food groups. Food pattern 3 factor loading which predominate fat (0.67) and daily products (0.88) is shown (C). Boxplots show the individual scores of dietary pattern 3 for HPD (red) and non-HPD (blue). The HPD group demonstrated a trend non-significantly higher scores in the food pattern 3 compared to the non-HPD groups by Wilcoxon test (p-value = 0.07).