Yaa Kyeremateng

Immune checkpoint inhibitors (ICI) have transformed the treatment of advanced non–small cell lung cancer (NSCLC), yet a large proportion of patients do not respond. The lung microbiome is a key modulator of antitumor immunity; however, the functional contribution of active microbial communities and their interaction with host transcriptional programs in shaping ICI outcomes remain poorly defined. In this study, we prospectively analyzed bronchoalveolar lavage (BAL) samples from 71 patients with NSCLC prior to ICI initiation. We profiled the microbiome and host transcriptome in the BAL to look for microbial and host signatures in the lower airways associated with treatment response. We found that enrichment of the lower airway microbiome with oral commensals was associated with disease progression and worse prognosis of patients treated with ICIs. Metatranscriptomics-derived microbial clustering revealed distinct functional profiles. The microbial cluster enriched with oral commensals, which was only present among patients with poor response to ICIs, had differential regulation of pathways involved in the metabolism of short-chain fatty acids, methane, and pyruvate. This cluster also correlated with host immune pathways including upregulation of interferon signaling, neutrophil degranulation, and mitophagy, all of which could contribute to the poor response to immunotherapy. In a preclinical murine model of lung cancer with dysbiosis, inoculating lung tumor–bearing mice with oral commensals increased intratumoral neutrophils and Th17 responses and impaired ICI efficacy. Our findings support the concept that microbial communities in the lower airways influence tumor–immune interactions via defined metabolic–immune axes. Significance: Transcriptionally active lower airway microbial communities are linked clinically and preclinically to immunotherapy response in NSCLC, identifying microbial activity as a functional interface with the tumor microenvironment and a potential therapeutic target.

Supplementary Table S5 provides the statistical analysis of fecal bile acids, with t-test results followed by adjustment using the Benjamini and Hochberg (B&B) method.

Supplementary Figure S5 shows the growth response of ETBF to a high-fiber diet in APCmin/+ mice. Mice were inoculated with ETBF at day 0 and fed either a low-fiber or high-fiber diet. Fecal samples were collected at designated time points, and the expression of B. fragilis toxin 2 (BFT-2) was measured using qPCR as a surrogate for ETBF abundance. The relative percentage of ETBF expression at each time point was calculated relative to pre-inoculation levels.

Supplementary Figure S3 shows the impact of dietary fiber composition on immune cell subsets in ETBF-inoculated APCmin/+ mice. Flow cytometric (FACS) and CyTOF analyses were conducted to evaluate immune cell populations in the spleen, mesenteric lymph nodes (MLN), and lamina propria lymphocytes (LPLs) 8 weeks post-inoculation. Panels A–C display frequencies of neutrophils, monocytes, myeloid-derived suppressor cells, macrophages, and dendritic cells in the spleen, MLN, and LPLs, respectively. Panel D provides a tSNE plot from CyTOF analysis, highlighting major immune cell clusters in LPLs. Panels E–K and L–R quantify lymphocyte subsets, including Th1, Treg, Th17, CTL, and γδT17 cells in the spleen and MLN, respectively.

Supplementary Figure S8 examines the impact of dietary fiber on fecal bile acid profiles in ETBF-inoculated APCmin/+ mice. Fecal bile acids were analyzed using LC-MS, comparing high-fiber and low-fiber diet groups. Panel A shows primary bile acid profiles in colon contents. Panel B compares secondary bile acid profiles between the two diet groups.

Supplementary Figure S4 shows the effects of dietary fiber on IL-17A-producing RORgt+ Tregs in ETBF-inoculated APCmin/+ mice. Panel A presents flow cytometry analysis of the percentage of IL-17A-producing RORγt+ Tregs in LPLs of mice fed normal chow, low-fiber, or high-fiber diets 8 weeks post-ETBF. Panel B quantifies the percentage of these cells across the diet groups.

Supplementary Table S2 provides a summary of the antibodies used for CyTOF analysis.

Supplementary Table S1 provides experimental replication details, including the number of repeats, groups, and mice per group for each figure panel.

Supplementary Figure S7 shows that how dietary fiber modulates gut microbiota composition and immune landscape in ETBF-inoculated APCmin/+ mice. Mice were fed normal chow, a low-fiber diet, or a high-fiber diet for 8 weeks. Cecal microbiota composition was analyzed using 16S rRNA gene sequencing. Panel A shows alpha diversity based on the Shannon index, with significant differences between groups. Panel B presents beta diversity analysis using PCoA, revealing differences between diet groups. Panel C depicts the relationship between microbiota and immune landscape, as determined by RDA ordination. Significant associations were observed in the RDA model.

Supplementary Table S3 provides the nutritional composition of Chow, Low-fiber, and High-fiber diets, including fuel value, fiber content, and the percentage by weight of protein, carbohydrates, and fat.