Dietary fiber and probiotics influence the gut microbiome and melanoma immunotherapy response

Abstract

Gut bacteria modulate the response to immune checkpoint blockade (ICB) treatment in cancer, but the effect of diet and supplements on this interaction is not well studied. We assessed fecal microbiota profiles, dietary habits, and commercially available probiotic supplement use in melanoma patients and performed parallel preclinical studies. Higher dietary fiber was associated with significantly improved progression-free survival in 128 patients on ICB, with the most pronounced benefit observed in patients with sufficient dietary fiber intake and no probiotic use. Findings were recapitulated in preclinical models, which demonstrated impaired treatment response to anti–programmed cell death 1 (anti–PD-1)–based therapy in mice receiving a low-fiber diet or probiotics, with a lower frequency of interferon-γ–positive cytotoxic T cells in the tumor microenvironment. Together, these data have clinical implications for patients receiving ICB for cancer.

Conflict of interest statement

Competing interests: J.A.W., V.G., and M.C.A. are inventors on patent WO2020106983A1 submitted by the Board of Regents, The University of Texas System, and Institut Gustav Roussy that covers methods and compositions for treating cancer and for predicting a subject’s response to combination checkpoint inhibitor therapy. J.A.W. and V.G. are inventors and C.N.S. is a collaborator on a US patent application (PCT/US17/53.717) submitted by the University of Texas MD Anderson Cancer Center that covers methods to enhance ICB responses by modulating the microbiome. J.A.W. and R.R.J. are inventors on patent WO2020150429A1 submitted by the Board of Regents, The University of Texas System, that covers methods and compositions for treating immune checkpoint inhibitor (ICI)–associated colitis in a subject through the administration of fecal matter from a healthy donor to the subject. R.R.J. is an inventor on patent WO2016086161A1 submitted by the Memorial Sloan Kettering Cancer Center that covers compositions and methods for increasing the abundance of commensal bacteria belonging to the order Clostridiales that are associated with reduced lethal graft-versus-host disease and improved overall survival after bone marrow or hematopoietic stem cell transplant. R.R.J. is an inventor on patent WO2017041039A1 submitted by Memorial Sloan Kettering Cancer Center that covers methods and compositions for reducing the risk of cancer relapse in a subject who has received cancer treatment. J.A.W. reports compensation for speaker’s bureau and honoraria from Imedex, Dava Oncology, Omniprex, Illumina, Gilead, PeerView, Physician Education Resource, Med Immune, and Bristol-Myers Squibb and serves as a consultant and/or advisory board member for Roche/Genentech, Novartis, AstraZeneca, GlaxoSmithKline, Bristol-Myers Squibb, Merck, Biothera Pharmaceuticals, Microbiome DX, and Micronoma. J.A.W. also receives research support from GlaxoSmithKline, Roche/Genentech, Bristol-Myers Squibb, and Novartis. V.G. reports honoraria from Expert Connect and Kansas Society of Clinical Oncology. J.L.M. reports advisory board participation and honoraria from Bristol-Myers Squibb, Merck, and Roche/Genentech. M.C.A. reports advisory board participation, honoraria, and research funding to their institution from MSD Australia, outside the submitted work, and contract research for BMS Australia, outside the current work. A.P.C. is an employee and equity holder at Immunai. A.P.C. serves as an advisory member and holds equity in Vastbiome. G.Mo. is a coinventor on US patents (PCT/US2019/022194, PCT/US2020/029556, and PCT/US2020/046050) not related to the content of this paper. M.I.R. reports paid consultant position for AMGEN and paid consultant and advisory board membership for MERCK. I.C.G. reports research support from Bristol-Myers Squibb and Merck. I.C.G. serves as a consultant for Bristol-Myers Squibb, Array, and Novartis. R.N.A. receives research funding from Merck, Bristol-Myers Squibb, Genentech, Novartis, and Iovance. J.E.G. is a consultant and/or is on the advisory board for Merck, Regeneron, Syndex, Novartis, and Bristol-Myers Squibb, unrelated to the content of this work. R.R.J. is an advisor and holds equity in Seres Therapeutics and Kaleido Biosciences; serves on the advisory board of MaaT Pharma, LISCure Biosciences, and Prolacta Biosciences; and consults for Davolterra, Merck, Microbiome DX, and Karius. C.H. is on the scientific advisory board for Seres Therapeutics and Empress Therapeutics. M.K.W. is on the advisory boards of Merck, Pfizer, Bristol Myers Squibb, Regeneron, EMD-Serono, ExiCure, Castle Biosciences, and Adagene. H.A.T. is a consultant for BMS, Merck, Novartis, Genentech, Eisai, Iovance, Karyopharm, and Pfizer and reports research funding to institution from BMS, Merck, Novartis, Genentech, GSK, and Dragonfly. A.D. serves as a consultant for Nektar, MultiVir, Idera, Array, and Bristol-Myers Squibb. P.S. reports consulting or stock ownership or advisory board for Achelois, Adaptive Biotechnologies, Affini-T, Apricity, BioAtla, BioNTech, Candel Therapeutics, Catalio, Codiak, Constellation, Dragonfly, Earli, Enable Medicine, Glympse, Hummingbird, ImaginAb, Infinity Pharma, Jounce, JSL Health, Lava Therapeutics, Lytix, Marker, Oncolytics, PBM Capital, Phenomic AI, Polaris Pharma, Sporos, Time Bioventures, Trained Therapeutix, Two Bear Capital, and Venn Biosciences. J.P.A. reports consulting, stock ownership, or advisory board membership for Achelois, Adaptive Biotechnologies, Apricity, BioAtla, BioNTech, Candel Therapeutics, Codiak, Dragonfly, Earli, Enable Medicine, Hummingbird, ImaginAb, Jounce, Lava Therapeutics, Lytix, Marker, PBM Capital, Phenomic AI, Polaris Pharma, Time Bioventures, Trained Therapeutix, Two Bear Capital, and Venn Biosciences. M.A.D. has been a consultant to Roche/Genentech, Array, Pfizer, Novartis, BMS, GSK, Sanofi-Aventis, Vaccinex, Apexigen, Eisai, and ABM Therapeutics, and he has been the principal investigator of research grants to MD Anderson from Roche/Genentech, GSK, Sanofi-Aventis, Merck, Myriad, and Oncothyreon. P.H. is on the scientific advisory board for Dragonfly and Immatics. M.I.R. is on the melanoma advisory board for Merck and is a paid consultant for AMGEN and Merck. S.P.P. reports institutional clinical trial support from NCI, Merck, and Bristol Myers Squibb during the conduct of the study; institutional clinical trial support from Reata Pharmaceuticals, Novartis, Deciphera, Provectus Biopharmaceuticals, Foghorn Therapeutics, TriSalus Life Sciences, and Seattle Genetics; advisory board honoraria from Castle Biosciences and TriSalus Life Sciences; honoraria as Peer Discussion Group Leader for Merck; and honoraria for service as Chair of International Data Monitoring Committee for Immunocore, outside the submitted work.

Figures

Fig. 1.. Profiles of gut microbiota in patients with melanoma and associations with outcomes on therapy.

(A) Schema of study design. (B) Box plots comparing the relative abundance of anti–PD-1 response–associated taxa from Gopalakrishnan et al. (4) with a newly recruited cohort (n = 132) of anti–PD-1–treated patients (P = 0.036 and P = 0.018, respectively, for Ruminococcaceae and Faecalibacterium by Wilcoxon rank sum test). Patients included in the prior study were excluded from this analysis. (C) Volcano plot depicting pairwise comparisons of relative abundances of bacterial taxa. The y axis displays the −log10 false discovery rate (FDR)–corrected P value (dashed line, q < 0.1), and the x axis shows the log2 fold change comparing 193 R and 100 NR patients with systemic therapy across the full cohort, including patients from the prior study (by Wilcoxon rank sum test with FDR correction per level). (D) Heatmap of scaled relative abundances [parts per million (PPM)] of bacteria belonging to order Clostridiales and family Ruminococcaceae in pre- and post-FMT samples of anti–PD-1 refractory metastatic melanoma FMT recipients who responded to FMT + anti–PD-1 in Davar et al. (20) [National Center for Biotechnology Information (NCBI) accession no. PRJNA672867]. Number of days from FMT are depicted on the top of each heatmap column, with post-FMT values being the geometric mean days of all post-FMT time points for that patient. The geometric mean of relative abundances of post-FMT samples from each patient were calculated as the single post-FMT mean relative abundance. The exception is patient PT−18−0018, who received two FMTs (denoted by an asterisk). The first post-FMT column for this patient reflects the geometric mean of samples leading up to the second FMT event. (E) Heatmap of scaled relative abundances of bacteria belonging to order Clostridiales and family Ruminococcaceae in pre- and post-FMT samples of anti–PD-1 refractory metastatic melanoma FMT recipients who responded to FMT + anti–PD-1 in Baruch et al. (19) (NCBI accession no. PRJNA678737). Number of days from FMT are depicted on the top of each heatmap column, with post-FMT values being the geometric mean days of all post-FMT time points for that patient. The geometric mean of relative abundances of post-FMT samples from each patient were calculated as the single post-FMT mean relative abundance.

Fig. 2.. Effect of probiotic supplement use in patients and in preclinical models of melanoma immunotherapy.

(A) Kaplan-Meier plot comparing progression-free survival intervals by probiotic use among patients who received ICB (n = 158; P = 0.29 by log-rank test). (B) Experimental design of studies in germ-free (GF) mice that received FMT from a complete responder (CR) donor combined with probiotic 1, probiotic 2, or sterile water control before tumor injection [2.5 × 105 to 8 × 105 BRAFV600E/PTEN−/− (BP) tumor cells] and treatment with anti–PD-L1. Time is in days relative to tumor injection [day 0 (D0)]. PO, per orem; s.c, subcutaneous; IP, intraperitoneal. (C) Mouse tumor growth curves comparing volume of tumors in mice who received probiotics or sterile water control (n = 4 to 5 per group); probiotic 1 versus probiotic 2 versus sterile water control. Data are means ± SEM tumor volume. All P values are from a likelihood ratio test in a linear mixed model (P = 0.04 Bifidobacterium longum 35624–based probiotic 1 versus control; P = 0.01 Lactobacillus rhamnosus GG–based probiotic 2 versus control). *P < 0.05. (D) Box plots comparing alpha diversity of the gut microbiome, as measured by the inverse Simpson index in mice treated with control, probiotic 1 (Bifidobacterium longum 35624–based), or probiotic 2 (Lactobacillus rhamnosus GG–based) (pairwise P values compared with control were calculated by Wilcoxon rank sum test). Fecal samples were collected for microbiome analysis (via metagenomic sequencing) from tumor-bearing mice before the anti–PD-L1 therapy (n =7 to 8 per group), mimicking baseline sample collection from patients. (E) Ordination plot by t-distributed uniform manifold approximation and projection (t-UMAP) by Bray-Curtis distance, demonstrating compositional differences of the gut microbiome in mice treated with sterile water control, probiotic 1 (Bifidobacterium longum 35624–based), or probiotic 2 (Lactobacillus rhamnosus GG–based) [permutational multivariate analysis of variance (PERMANOVA) P = 0.036]. (F and G) Pairwise comparisons of sterile water control versus probiotic 1 (Bifidobacterium longum 35624–based) or control versus probiotic 2 (Lactobacillus rhamnosus GG–based) groups (n = 6 per group) via supervised analysis with manual gating for either frequency of IFN-γ+ CD8+ T cells in tumors (percent total tumor CD8+ T cells) (P = 0.03, P = 0.03) (F) or frequency of IFN-γ+ CD4+ T cells in tumors (percent total tumor CD4+ T cells) (P = 0.26, P = 0.10) (G). (H) Unsupervised analysis of flow cytometry data showing density t-distributed stochastic neighbor embedding (t-SNE) plot of tumor-infiltrating immune cells overlaid with color-coded clusters, with an equal number of CD45+ infiltrating leukocytes for each treatment group (control, probiotic 1, and probiotic 2).

Fig. 3.. Effect of dietary fiber intake in patients and in preclinical models of melanoma immunotherapy.

(A) Kaplan-Meier plot comparing progression-free survival intervals by dietary fiber intake among patients who received ICB (n = 128; P = 0.047 by log-rank test). (B) Kaplan-Meier plot comparing progression-free survival intervals by combined dietary fiber and probiotic status among patients who received ICB (n = 123; overall P across four groups = 0.11; P for sufficient dietary fiber intake + no probiotics use versus else = 0.015; both by log-rank test). (C) Experimental design of studies in C57BL/6 SPF mice that received either a high-fiber or low-fiber diet at inoculation of M3 (HCmel1274) melanoma cells (1 × 106 tumor cells) and were then treated with anti–PD-1 or isotype control. Time is in days relative to tumor injection. (D) M3 melanoma growth kinetics of control (high-fiber) diet (circles) or low-fiber (fiber-free) diet (squares) treated four times with intraperitoneal injection of anti–PD-1 antibody (dark green) or of isotype control (Iso Ctrl) (light green). Data are means ± SEM of tumor volume from one representative experiment (n = 5 per group). All P values are from a likelihood ratio test in a linear mixed model (isotype control and high fiber, P = 0.69; anti–PD-1 and high fiber, P = 0.02; anti–PD-1 and low fiber versus isotype control and low fiber, P = 0.08). *P < 0.05. (E) t-UMAP plot comparing the gut microbiome (via shotgun metagenomic sequencing of fecal samples) of mice by treatment and diet group from two experiments (n = 4 to 5 per group) using Bray-Curtis distances (PERMANOVA P < 0.0001) at experimental day 16. (F) Heatmap of gene expression of flow-sorted CD45+ tumor-infiltrating immunocytes in mice fed high-versus low-fiber diets and treated with anti–PD-1 or isotype control. (G) Gene set enrichment analysis depicting pathways enriched in high-fiber diet mice treated with anti–PD-1 versus isotype control which were not differentially expressed by treatment in low-fiber diet mice.