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Fecal microbiota transplantation plus anti-PD-1 immunotherapy in advanced melanoma: a phase I trial

Nature Medicine volume 29, pages 2121–2132 (2023)Cite this article

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An Author Correction to this article was published on 03 November 2023

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Abstract

Fecal microbiota transplantation (FMT) represents a potential strategy to overcome resistance to immune checkpoint inhibitors in patients with refractory melanoma; however, the role of FMT in first-line treatment settings has not been evaluated. We conducted a multicenter phase I trial combining healthy donor FMT with the PD-1 inhibitors nivolumab or pembrolizumab in 20 previously untreated patients with advanced melanoma. The primary end point was safety. No grade 3 adverse events were reported from FMT alone. Five patients (25%) experienced grade 3 immune-related adverse events from combination therapy. Key secondary end points were objective response rate, changes in gut microbiome composition and systemic immune and metabolomics analyses. The objective response rate was 65% (13 of 20), including four (20%) complete responses. Longitudinal microbiome profiling revealed that all patients engrafted strains from their respective donors; however, the acquired similarity between donor and patient microbiomes only increased over time in responders. Responders experienced an enrichment of immunogenic and a loss of deleterious bacteria following FMT. Avatar mouse models confirmed the role of healthy donor feces in increasing anti-PD-1 efficacy. Our results show that FMT from healthy donors is safe in the first-line setting and warrants further investigation in combination with immune checkpoint inhibitors. ClinicalTrials.gov identifier NCT03772899.

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Fig. 1: Clinical outcomes of FMT in combination with first-line anti-PD-1 therapy.
Fig. 2: Global gut microbiome profiling and engraftment after FMT.
Fig. 3: Plasma metabolites analysis and immune modifications after FMT.
Fig. 4: Human-to-mouse FMT experiments.

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Data availability

All clinical metadata have been uploaded to the NCBI Sequence Read Archive, BioProject ID PRJNA928744. Patient baseline clinical data are available in Table 1 and within the text. Study-level clinical data from this study (including the protocol) will be made available upon reasonable request from a qualified medical or scientific professional for the specific purpose laid out in that request and may include de-identified individual participant data. The data for this request will be available after a data access agreement has been signed. Requests should be sent to the corresponding author. Patient-related data not included in the paper were generated as part of a clinical trial and are subject to patient confidentiality.

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Acknowledgements

We thank the patients and their families. We thank the Clinical Research Unit (CRU) at the London Regional Cancer Program (LRCP) for their support in running the trial. We thank the staff at the CRCHUM animal facility and the Immunomonitoring core facility at the CRCHUM for their help with the experiments. We acknowledge the support of the Rosalind and Morris Goodman Cancer Institute Research Support, the Single Cell Imaging and Mass Cytometry Analysis Platform and the Histology Core facilities at McGill University. The clinical trial was funded by a grant from the Lotte and John Hecht Memorial Foundation awarded to S.M.V. and J.P.B., a grant from the Division of Medical Oncology at Western University awarded to J.G.L. and S.M.V. and a Canadian Cancer Society Impact grant supported by the Lotte and John Hecht Memorial Foundation awarded to B.R., S.M.V. and A.E. The laboratory of B.R. for ancillary analyses and biobanking was funded by Institute du Cancer de Montréal, Terry Fox Marathon of Hope clinician-scientist award. The laboratory of S.M.V. for ancillary analyses and biobanking was funded by a project grant from the Canadian Institute of Health Research (CIHR) (MOP no. 389137) and a LRCP Catalyst Grant Program, Keith Samitt Translational Research grant. Metagenomics sequencing was funded by ONCOBIOME, project number 825410 (Gut OncoMicrobiome Signatures associated with cancer incidence, prognosis and prediction of treatment response). B.R. received salary support from Fonds de Recherche du Quebec Santé. S.M.V. received salary support from the Ontario Institute of Cancer Research (OICR) and The London Health Sciences Foundation Helen and Andy Spriet funds. M.M. reports salary support from Seerave foundation. A.E. reports support from CIHR, Royal College of Physicians and Surgeons of Canada. Metabolomics studies were performed at the Medical Research Council (MRC)-National Institute of Health Research (NIHR) National Phenome Centre at Imperial College London; this center receives financial support from the MRC and NIHR (grant number MC_PC_12025). B.H.M. is the recipient of an NIHR Academic Clinical Lectureship (CL-2019-21-002). The Division of Digestive Diseases and MRC-NIHR National Phenome Centre at Imperial College London receive financial and infrastructure support from the NIHR Imperial Biomedical Research Centre based at Imperial College Healthcare NHS Trust and Imperial College London. B.D. acknowledges support from an NSERC Postdoctoral Fellowship Award (PDF-558010-2021). K.F.A. acknowledges support from an American Urological Association Research Scholar Award. Cytofluorimetric analyses in the laboratory of S.M.M.H. were funded through a project grant (PJT – 156295) from the CIHR. I.R.W. reports support from the Terry Fox Research Institute (grant 1084), CIHR (PJT-178341 grant), Canada Research Chairs Program, donations from K. J. Baggs and donations from the Rachel and Jason Schwartz Family Foundation. Financial support was also obtained from the Quebec Cancer Consortium and the Ministère de l’Économie et de l’Innovation du Québec through the Fonds d’accélération des collaborations en santé and the Victor Liu McGill Interdisciplinary Initiative in Infection and Immunity (MI4) initiative. This research was enabled, in part, by support provided by Calcul Québec (www.calculquebec.ca), SHARCNET (www.sharcnet.ca) and Compute Canada (www.computecanada.ca). L.D. was supported by RHU5 ANR-21-RHUS-0017 IMMUNOLIFE; EU-H2020, project no. 825410, project ONCOBIOME, Gut OncoMicrobiome Signatures associated with cancer incidence, prognosis and prediction of treatment response; and the SIGN’IT ARC Foundation.

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Author notes

  1. These authors contributed equally: Bertrand Routy; John G. Lenehan.

Authors and Affiliations

  1. Research Center of the Centre Hospitalier de l’Université de Montréal, Montréal (CRCHUM), Montreal, Quebec, Canada

    Bertrand Routy, Rahima Jamal, Meriem Messaoudene, Cecilia Hes, Karl Belanger, Corentin Richard, Pamela Thebault, Arielle Elkrief & Rejean Lapointe

  2. Hematology-Oncology Division, Department of Medicine, Centre Hospitalier de l’Université de Montréal (CHUM), Montreal, Quebec, Canada

    Bertrand Routy, Rahima Jamal & Karl Belanger

  3. Department of Oncology, Western University, London, Ontario, Canada

    John G. Lenehan, Scott Ernst, Diane Logan, Rene Figueredo & Saman Maleki Vareki

  4. Lawson Health Research Institute, London, Ontario, Canada

    John G. Lenehan, Kait F. Al, S.M. Mansour Haeryfar, Michael S. Silverman, Jeremy P. Burton & Saman Maleki Vareki

  5. Lady Davis Institute of the Jewish General Hospital, Segal Cancer Centre, Montreal, Quebec, Canada

    Wilson H. Miller Jr & Khashayar Esfahani

  6. Departments of Oncology and Medicine, McGill University, Montreal, Quebec, Canada

    Wilson H. Miller Jr & Khashayar Esfahani

  7. Department of Microbiology & Immunology, Western University, London, Ontario, Canada

    Brendan A. Daisley, Kait F. Al, Marina Ninkov, S.M. Mansour Haeryfar, Michael S. Silverman & Jeremy P. Burton

  8. Canadian Centre for Human Microbiome and Probiotics Research, London, Ontario, Canada

    Brendan A. Daisley, Kait F. Al & Jeremy P. Burton

  9. Peter Brojde Lung Cancer Center, Jewish General Hospital, Montreal, Quebec, Canada

    Cecilia Hes

  10. Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, St Mary’s Hospital Campus, Imperial College London, London, UK

    Laura Martinez-Gili, Julian R. Marchesi & Benjamin H. Mullish

  11. Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK

    Laura Martinez-Gili

  12. Department of Computational, Cellular and Integrative Biology, University of Trento, Trento, Italy

    Michal Punčochář, Gianmarco Piccinno, Federica Armanini, Federica Pinto & Nicola Segata

  13. Department of Pathology and Laboratory Medicine, Western University, London, Ontario, Canada

    Mithunah Krishnamoorthy & Saman Maleki Vareki

  14. Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, National Phenome Centre, Imperial College London, London, UK

    Panteleimon Takis

  15. Section of Bioanalytical Chemistry, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK

    Panteleimon Takis

  16. Rosalind and Morris Goodman Cancer Institute, McGill University, Montréal, Quebec, Canada

    Jamie Magrill, LeeAnn Ramsay & Ian R. Watson

  17. Gustave Roussy Cancer Campus, Villejuif, France

    Lisa Derosa

  18. Cancer Medicine Department, Gustave Roussy, Villejuif, France

    Lisa Derosa

  19. Institut National de la Santé Et et de la Recherche Médicale (INSERM) U1015, ClinicObiome, Equipe Labellisée—28 Ligue Nationale contre le Cancer, Villejuif, France

    Lisa Derosa

  20. Université Paris-Saclay, Ile-de-France, France

    Lisa Derosa

  21. Department of Medicine, Division of Infectious Diseases, Western University, London, Ontario, Canada

    Seema Nair Parvathy & Michael S. Silverman

  22. Division of Infectious Diseases, St Joseph’s Health Care, London, Ontario, Canada

    Seema Nair Parvathy & Michael S. Silverman

  23. Department of Biochemistry, McGill University, Montréal, Quebec, Canada

    Ian R. Watson

  24. Département de Médecine, Faculté de Médecine, Université de Montréal, Montréal, Quebec, Canada

    Rejean Lapointe

  25. Department of Medicine, Division of Clinical Immunology and Allergy, Western University, London, Ontario, Canada

    S.M. Mansour Haeryfar

  26. Department of Surgery, Division of General Surgery, Western University, London, Ontario, Canada

    S.M. Mansour Haeryfar

  27. Departments of Gastroenterology and Hepatology, St Mary’s Hospital, Imperial College Healthcare NHS Trust, London, UK

    Benjamin H. Mullish

  28. Department of Medical Biophysics, Western University, London, Ontario, Canada

    Saman Maleki Vareki

  29. Ontario Institute of Cancer Research, Toronto, Ontario, Canada

    Saman Maleki Vareki

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  1. Bertrand Routy
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Contributions

S.M.V. conceived the study. S.M.V. and J.G.L. designed the trial and co-wrote the trial protocol. S.M.V. and B.R. designed and supervised translational studies. J.G.L., W.H.M., R.J., S.E., D.L., K.B. and K.E. recruited and/or treated patients. M.M., B.A.D., C.H., K.F.A., L.M.G., M.P., C.R., M.N., G.P., F.A., F.P., M.K., R.F., P. Thebault, P. Takis, J.M., L.R., L.D., J.R.M., A.E., I.R.W., R.L., N.S., S.M.M.H., B.H.M. and J.P.B. collected and/or analyzed the data. S.N.P. and M.S.S. prepared and conducted FMT. B.R., J.G.L. and S.M.V. co-wrote the first draft. All authors provided comments and approved the paper.

Corresponding author

Correspondence to Saman Maleki Vareki.

Ethics declarations

Competing interests

B.R. reports grants from Kaleido, Vedanta, Surface and Da Volterra outside the submitted work, as well as consulting fees from BMS, AstraZeneca, Merck and Da Volterra. He is also the co-founder of Science Curebiota. B.H.M. has received consultancy fees from Finch Therapeutics Group and Ferring Pharmaceuticals, outside of the submitted work. R.J. reports grants from BMS, Merck and Iovance Biotherapeutics as well as consulting fees from BMS and Merck. M.K. is an employee at Pfizer. L.D. had consulting and advisory roles for BMS, Sanofi and EverImmune and was supported by the Philantropia Fondation Gustave Roussy. J.R.M. has received consultancies from Enterobiotix and Cultech outside of the submitted work. S.M.V. is a director of the board of IMV. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Medicine thanks Christian Jobin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Saheli Sadanand, in collaboration with the Nature Medicine team.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1

a. Flow diagram indicating the number of patients screened, enrolled in the study, and who received the combination of FMT and anti-PD-1. One patient completed 24 months of pembrolizumab. Seven patients discontinued anti-PD-1 therapy due to progression and five patients discontinued treatment due to grade 3 immune-related adverse events. One patient discontinued anti-PD-1due to undiagnosed cardiac amyloidosis. Six patients remained on anti-PD-1 therapy at data cut off. All 20 patients were evaluable for safety and clinical outcome. b. Baseline characteristics information of the three healthy donors. c. Kaplan Meier curve representation of the progression-free survival, and d. overall survival at data cut-off with a median follow-up of 20.7 months.

Extended Data Fig. 2

a. Beta-diversity ordination plot of 16S rRNA gene sequencing data showing baseline Aitchison distances between the 19 patients and healthy donors at S1. b. All metagenomics results were obtained from 61 samples from baseline n = 12 R patients and n = 6 NR patients. Longitudinal samples available for n = 10 R and n = 6 NR. Metagenomic sequencing with representation of the alpha-diversity measured by observed species over time from S1 to S4 including n = 18 patients.* p < 0.05.

Extended Data Fig. 3

a. Jaccard dissimilarity index between matching donor and patient measured with metagenomics data separated by outcomes 6 NR vs 10R from S1 to S4. b. 16S rRNA results from 19 patients, Aitchison distance between the patients and their marching donor over time. c. Variability of strain engraftment rate from the donor over time and d. Bray-Curtis dissimilarity rate representation for patients based on alpha-diversity n = 16 patients. e, f. Similarly, strain engraftment rate and Bray-Curtis dissimilarity rates when segregating for body mass index (BMI) below or above the median, respectively n = 16 patients. g. Spearman correlation between baseline alpha-diversity (Shannon index) and BMI. * p < 0.05; ** p < 0.01; *** p < 0.001. S1: Baseline, S2: one week S3: one month, S4 three months after FMT; R: responder, NR: nonresponder; BMI: body mass index.

Extended Data Fig. 4

a. Bray-Curtis comparison and b. strain engraftment rate from metagenomic sequences obtained from the MIMic trial (16 patients; 3 donors), Baruch et al.19 (10 patients; 2 donors) and Davar et al.,20 (15 patients; 7 donors) FMT trials. c. Spearman representation between baseline alpha-diversity (Shannon index) and strain engraftment rate for the 3 FMT trials. d. Correlation between baseline alpha-diversity (Shannon index) for each FMT trials and outcome. e. Pooled baseline alpha-diversity (Shannon index) between R and NR combining the n = 43 patients from the 3 FMT trials. * p < 0.05; ** p < 0.01; *** p < 0.001. S1: Baseline, S2: one week S3: one month, S4 three months after FMT; R: responder, NR: nonresponder.

Extended Data Fig. 5

Metagenomic fecal sample analysis a. LefSe representation of bacterial differential abundance between R and NR at S1 n = 18 patients. b. Heatmap of significant bacteria evolution between S1 vs S3 in NR patients (n = 6). Only bacteria with a p < 0.05 difference between both time points are represented. c. Heatmap of differentially abundant taxa (we.ep < 0.05, ALDEx2) in the 16S rRNA gene dataset for R (n = 13), and d. Taxa evolution between S1 and S3 n = 6 NR patients measured by 16S rRNA gene sequencing. e. Metagenomics strain that preferentially engrafted from donors in R and NR at S2 and S3 n = 16 patients. S1: Baseline, S2: one week S3: one month, S4 three months after FMT; R: responder, NR: nonresponder.

Extended Data Fig. 6

a. Volcano plot from METACyc pathways representation between S3 and S1 in NR (left panel) and R (right panel). b. Regression coefficient of serum bile acids measured by UHPLC–MS between S2 and S1 on 19 patients. c. Serum succinate and lactic acid levels measured by 1H-NMR on 13R and 6 NR over time. Thick lines represent mean-group average levels while thin lines represent individual trajectories. * p < 0.05; ** p < 0.01.

Extended Data Fig. 7

a. Representative Delaunay graphs of 3 CyTOF images showing NR with low immune infiltration (patient 623-1), R with high immune infiltration (patient 803-1), and R with low immune infiltration (patient 623-4). b. Stacked bar plots showing the proportion of each cell type in each patient. Melanoma and other cell types are excluded for visualization purposes. c. Boxplot comparing immune cell proportions in 3 NR and 9 R. P-values calculated from two-sided Wilcoxon rank-sum test. d Boxplot comparing contact enrichment scores between melanoma and immune cells. Higher scores indicate cell contacts occur more frequently than expected by chance. P values calculated from two-sided Wilcoxon rank-sum test. Antigen-Experienced cytotoxic T cells (Tc.ae), Naive cytotoxic T cells (Tc.naive), Antigen-experienced T helper cells (Th.ae), Naive T helper cells (Th.naive).

Extended Data Fig. 8

a. Flow cytometry representation of unsupervised t-SNE panels of circulating immune cells present from n = 13 R and n = 6 NR over time from S1–S4. Each population is color-coded. b. Supervised t-SNE panel on CD8+ T cells representation from S1–S4 between R and NR. ICOS+ populations are represented in red. c. Flow cytometric analysis of peripheral blood MAIT cell frequency over time between R and NR. d. Frequency of PD-1+ MAIT cells at S3 between 11 R and 5 NR. MAIT cells were identified as CD3+MR1-5-OP-RU-loaded tetramer positive population. MR1-6-FP-loaded tetramers were used to set control gates. * p < 0.05.

Extended Data Fig. 9

a. Pooled analysis of tumor growth size (left panel) and tumor size at sacrifice (right panel) of antibiotic-treated mice bearing MCA-205 treated with sequential anti-PD-1 or control IsoPD-1 after receiving FMT from one donor. Experiment was performed for the 3 donors. Each circle represents one animal, n = 15. b. Similar experimental setting was performed in B16-OVA cells in antibiotic-treated mice with FMT from 1 NR S1 and 1 NR S3. Each square represents one animal at the time of sacrifice n = 5. c. Tumor size at sacrifice in MCA-205 model from Fig. 4a,b, where one circle represents the mean value of one FMT performed on 5 mice at the time of sacrifice. Left panel FMT from R S1 or NR S1 + NR S3 samples and right panel from R S3 The color code represents one patient, orange: 802-1, green: 802-2 and purple: 802-4 and pink: 802-05. d. Tumor size at sacrifice in B16 model from Fig. 4c, where one square represents the mean value of one FMT performed on 5 mice at the time of sacrifice. Left panel FMT from R S1 or NR S1 + NR S3 samples and right panel from R S3. The color code represents one patient, orange: 802-1, green: 802-2 and purple: 802-4 and pink: 802-05 e. Flow cytometry analysis of the frequency of TIM3+CD8+ T cells in B16-OVA tumor infiltrating cells from mice that received FMT from 1R patients at S1 or S3 (Fig. 4c). f. 16S rRNA gene analysis of fecal samples from pooled mice associated with anti-PD-1 resistance following FMT from R S1 + NR S1 + NR S3 (anti-PD-1-resistant) n = 74 compared to anti-PD-1 sensitive mice following FMT from R S3 n = 45. Representation of alpha-diversity between both groups and, g. Beta-diversity between both groups. All groups reared in separated cages then received isotype control (IsoPD-1) or anti-PD-1 treatment. Means ± SEM are represented. * p < 0.05; ** p < 0.01; *** p < 0.001. S1: Baseline, S3: one month after FMT; R: responder, NR: nonresponder.

Extended Data Fig. 10

a. Representation of tumor size at sacrifice of germ-free mice recolonized with 2 donors in both isotype control (IsoPD-1) or anti-PD-1. b. Kinetic representation of three different conditions in antibiotic-treated mice. Mice were recolonized at D-15 with R S1 (blue), D2 marching donor (green) and, R S1 followed by two FMT from the matching donor (D2) at day +3 and day +7 (green). Means ± SEM are represented. * p < 0.05; ** p < 0.01. Each circle represents one mouse. S1: Baseline, S3: one month after FMT; R: responder, NR: non-responder; D: donor.

Supplementary information

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Supplementary Figs.1–3 and Supplementary Tables 1–4.

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Routy, B., Lenehan, J.G., Miller, W.H. et al. Fecal microbiota transplantation plus anti-PD-1 immunotherapy in advanced melanoma: a phase I trial. Nat Med 29, 2121–2132 (2023). https://doi.org/10.1038/s41591-023-02453-x

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