Fecal microbiota transplantation plus anti-PD-1 immunotherapy in advanced melanoma: a phase I trial.
Journal
Nature medicine
ISSN: 1546-170X
Titre abrégé: Nat Med
Pays: United States
ID NLM: 9502015
Informations de publication
Date de publication:
08 2023
08 2023
Historique:
received:
09
02
2023
accepted:
08
06
2023
medline:
17
8
2023
pubmed:
7
7
2023
entrez:
6
7
2023
Statut:
ppublish
Résumé
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 .
Identifiants
pubmed: 37414899
doi: 10.1038/s41591-023-02453-x
pii: 10.1038/s41591-023-02453-x
doi:
Substances chimiques
Immune Checkpoint Inhibitors
0
Banques de données
ClinicalTrials.gov
['NCT03772899']
Types de publication
Clinical Trial, Phase I
Multicenter Study
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
2121-2132Subventions
Organisme : CIHR
ID : PJT-178341
Pays : Canada
Organisme : CIHR
ID : PJT – 156295
Pays : Canada
Organisme : CIHR
ID : MOP 389137
Pays : Canada
Commentaires et corrections
Type : CommentIn
Type : CommentIn
Type : ErratumIn
Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.
Références
Robert, C. et al. Five-year outcomes with nivolumab in patients with wild-type BRAF advanced melanoma. JCO 38, 3937–3946 (2020).
doi: 10.1200/JCO.20.00995
Larkin, J. et al. Five-year survival with combined nivolumab and ipilimumab in advanced melanoma. N. Engl. J. Med. 381, 1535–1546 (2019).
pubmed: 31562797
doi: 10.1056/NEJMoa1910836
Robert, C. et al. Pembrolizumab versus ipilimumab in advanced melanoma (KEYNOTE-006): post-hoc 5-year results from an open-label, multicentre, randomised, controlled, phase 3 study. Lancet Oncol. 20, 1239–1251 (2019).
pubmed: 31345627
doi: 10.1016/S1470-2045(19)30388-2
Esfahani, K. et al. Moving towards personalized treatments of immune-related adverse events. Nat. Rev. Clin. Oncol. 17, 504–515 (2020).
pubmed: 32246128
doi: 10.1038/s41571-020-0352-8
Derosa, L. et al. Microbiota-centered interventions: the next breakthrough in immuno-oncology? Cancer Discov. 11, 2396–2412 (2021).
pubmed: 34400407
doi: 10.1158/2159-8290.CD-21-0236
Sepich-Poore, G. D. et al. The microbiome and human cancer. Science 371, eabc4552 (2021).
pubmed: 33766858
pmcid: 8767999
doi: 10.1126/science.abc4552
Routy, B. et al. The gut microbiota influences anticancer immunosurveillance and general health. Nat. Rev. Clin. Oncol. 15, 382–396 (2018).
pubmed: 29636538
doi: 10.1038/s41571-018-0006-2
Aghamajidi, A. & Maleki Vareki, S. The effect of the gut microbiota on systemic and anti-tumor immunity and response to systemic therapy against cancer. Cancers 14, 3563 (2022).
pubmed: 35892821
pmcid: 9330582
doi: 10.3390/cancers14153563
Routy, B. et al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science 359, 91–97 (2018).
pubmed: 29097494
doi: 10.1126/science.aan3706
Gopalakrishnan, V. et al. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 359, 97–103 (2018).
pubmed: 29097493
doi: 10.1126/science.aan4236
Andrews, L. P., Yano, H. & Vignali, D. A. A. Inhibitory receptors and ligands beyond PD-1, PD-L1 and CTLA-4: breakthroughs or backups. Nat. Immunol. 20, 1425–1434 (2019).
pubmed: 31611702
doi: 10.1038/s41590-019-0512-0
Lee, K. A. et al. Cross-cohort gut microbiome associations with immune checkpoint inhibitor response in advanced melanoma. Nat. Med. 28, 535–544 (2022).
pubmed: 35228751
pmcid: 8938272
doi: 10.1038/s41591-022-01695-5
Matson, V. et al. The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients. Science 359, 104–108 (2018).
pubmed: 29302014
pmcid: 6707353
doi: 10.1126/science.aao3290
McCulloch, J. A. et al. Intestinal microbiota signatures of clinical response and immune-related adverse events in melanoma patients treated with anti-PD-1. Nat. Med. 28, 545–556 (2022).
pubmed: 35228752
pmcid: 10246505
doi: 10.1038/s41591-022-01698-2
Simpson, R. C. et al. Diet-driven microbial ecology underpins associations between cancer immunotherapy outcomes and the gut microbiome. Nat. Med. 38, 2344–2352 (2022).
doi: 10.1038/s41591-022-01965-2
Derosa, L. et al. Gut bacteria composition drives primary resistance to cancer immunotherapy in renal cell carcinoma patients. Eur. Urol. 78, 195–206 (2020).
pubmed: 32376136
doi: 10.1016/j.eururo.2020.04.044
Derosa, L. et al. Intestinal Akkermansia muciniphila predicts clinical response to PD-1 blockade in patients with advanced non-small-cell lung cancer. Nat. Med. 28, 315–324 (2022).
pubmed: 35115705
pmcid: 9330544
doi: 10.1038/s41591-021-01655-5
Messaoudene, M. et al. A natural polyphenol exerts antitumor activity and circumvents anti-PD-1 resistance through effects on the gut microbiota. Cancer Discov. https://doi.org/10.1158/2159-8290.CD-21-0808 (2022).
Baruch, E. N. et al. Fecal microbiota transplant promotes response in immunotherapy-refractory melanoma patients. Science 371, 602–609 (2021).
pubmed: 33303685
doi: 10.1126/science.abb5920
Davar, D. et al. Fecal microbiota transplant overcomes resistance to anti-PD-1 therapy in melanoma patients. Science 371, 595–602 (2021).
pubmed: 33542131
pmcid: 8097968
doi: 10.1126/science.abf3363
Craven, L. J., Nair Parvathy, S., Tat-Ko, J., Burton, J. P. & Silverman, M. S. Extended screening costs associated with selecting donors for fecal microbiota transplantation for treatment of metabolic syndrome-associated diseases. Open Forum Infect. Dis. 4, ofx243 (2017).
pubmed: 29255739
pmcid: 5730934
doi: 10.1093/ofid/ofx243
Parvathy, S. N. et al. Enhanced donor screening for faecal microbial transplantation during COVID-19. Gut 70, 2219–2220 (2021).
pubmed: 33789964
doi: 10.1136/gutjnl-2021-324593
Ianiro, G. et al. Variability of strain engraftment and predictability of microbiome composition after fecal microbiota transplantation across different diseases. Nat. Med. 28, 1913–1923 (2022).
pubmed: 36109637
pmcid: 9499858
doi: 10.1038/s41591-022-01964-3
Blanco-Míguez, A. et al. Extending and improving metagenomic taxonomic profiling with uncharacterized species using MetaPhlAn 4. Nat. Biotechnol. https://doi.org/10.1038/s41587-023-01688-w (2023).
Moldoveanu, D. et al. Spatially mapping the immune landscape of melanoma using imaging mass cytometry. Sci. Immunol. 7, eabi5072 (2022).
pubmed: 35363543
doi: 10.1126/sciimmunol.abi5072
Kamphorst, A. O. et al. Proliferation of PD-1
pubmed: 28446615
pmcid: 5441721
doi: 10.1073/pnas.1705327114
Kunert, A. et al. CD45RA
pubmed: 31176366
pmcid: 6555948
doi: 10.1186/s40425-019-0608-y
Ninkov, M. et al. Improved MAIT cell functions following fecal microbiota transplantation for metastatic renal cell carcinoma. Cancer Immunol. Immunother. https://doi.org/10.1007/s00262-022-03329-8 (2022).
doi: 10.1007/s00262-022-03329-8
pubmed: 36396738
pmcid: 9672546
Yonekura, S. et al. Cancer induces a stress ileopathy depending on β-adrenergic receptors and promoting dysbiosis that contributes to carcinogenesis. Cancer Discov. 12, 1128–1151 (2022).
pubmed: 34930787
doi: 10.1158/2159-8290.CD-21-0999
Kao, D. et al. Effect of oral capsule- vs colonoscopy-delivered fecal microbiota transplantation on recurrent clostridium difficile infection: a randomized clinical trial. JAMA 318, 1985–1993 (2017).
pubmed: 29183074
pmcid: 5820695
doi: 10.1001/jama.2017.17077
Saha, S., Mara, K., Pardi, D. S. & Khanna, S. Long-term safety of fecal microbiota transplantation for recurrent clostridioides difficile infection. Gastroenterology 160, 1961–1969 (2021).
pubmed: 33444573
doi: 10.1053/j.gastro.2021.01.010
Robert, C. et al. Nivolumab in previously untreated melanoma without BRAF mutation. N. Engl. J. Med. 372, 320–330 (2015).
pubmed: 25399552
doi: 10.1056/NEJMoa1412082
Ribas, A. et al. Association of pembrolizumab with tumor response and survival among patients with advanced melanoma. JAMA 315, 1600–1609 (2016).
pubmed: 27092830
doi: 10.1001/jama.2016.4059
Wolchok, J. D. et al. Long-term outcomes with nivolumab plus ipilimumab or nivolumab alone versus ipilimumab in patients with advanced melanoma. JCO 40, 127–137 (2022).
doi: 10.1200/JCO.21.02229
Kuzmanovszki, D. et al. Anti-PD-1 monotherapy in advanced melanoma-real-world data from a 77-month-long retrospective observational study. Biomedicines 10, 1737 (2022).
pubmed: 35885042
pmcid: 9313334
doi: 10.3390/biomedicines10071737
Ibrahim, T., Mateus, C., Baz, M. & Robert, C. Older melanoma patients aged 75 and above retain responsiveness to anti-PD1 therapy: results of a retrospective single-institution cohort study. Cancer Immunol. Immunother. 67, 1571–1578 (2018).
pubmed: 30056599
doi: 10.1007/s00262-018-2219-8
Oliva, I. G. et al. 607 MCGRAW trial: evaluation of the safety and efficacy of an oral microbiome intervention (SER-401) in combination with nivolumab in first line metastatic melanoma patients. In Regular and Young Investigator Award Abstracts A637–A637 (BMJ Publishing Group, 2022).
Spencer, C. N. et al. Dietary fiber and probiotics influence the gut microbiome and melanoma immunotherapy response. Science 374, 1632–1640 (2021).
pubmed: 34941392
pmcid: 8970537
doi: 10.1126/science.aaz7015
Al-Habsi, M. et al. Spermidine activates mitochondrial trifunctional protein and improves antitumor immunity in mice. Science 378, eabj3510 (2022).
pubmed: 36302005
doi: 10.1126/science.abj3510
Vorwald, V. M. et al. Circulating CD8
doi: 10.1002/cti2.1367
Fan, X., Quezada, S. A., Sepulveda, M. A., Sharma, P. & Allison, J. P. Engagement of the ICOS pathway markedly enhances efficacy of CTLA-4 blockade in cancer immunotherapy. J. Exp. Med. 211, 715–725 (2014).
pubmed: 24687957
pmcid: 3978270
doi: 10.1084/jem.20130590
Xiao, Z., Mayer, A. T., Nobashi, T. W. & Gambhir, S. S. ICOS is an indicator of T-cell-mediated response to cancer immunotherapy. Cancer Res. 80, 3023–3032 (2020).
pubmed: 32156777
doi: 10.1158/0008-5472.CAN-19-3265
Filipazzi, P., Huber, V. & Rivoltini, L. Phenotype, function and clinical implications of myeloid-derived suppressor cells in cancer patients. Cancer Immunol. Immunother. 61, 255–263 (2012).
pubmed: 22120756
doi: 10.1007/s00262-011-1161-9
Azuma, K. et al. Clinical significance of plasma-free amino acids and tryptophan metabolites in patients with non-small cell lung cancer receiving PD-1 inhibitor: a pilot cohort study for developing a prognostic multivariate model. J. Immunother. Cancer 10, e004420 (2022).
pubmed: 35569917
pmcid: 9109096
doi: 10.1136/jitc-2021-004420
Mullish, B. H. et al. Microbial bile salt hydrolases mediate the efficacy of faecal microbiota transplant in the treatment of recurrent Clostridioides difficile infection. Gut 68, 1791–1800 (2019).
pubmed: 30816855
doi: 10.1136/gutjnl-2018-317842
Walter, J., Armet, A. M., Finlay, B. B. & Shanahan, F. Establishing or exaggerating causality for the gut microbiome: lessons from human microbiota-associated rodents. Cell 180, 221–232 (2020).
pubmed: 31978342
doi: 10.1016/j.cell.2019.12.025
Freites-Martinez, A., Santana, N., Arias-Santiago, S. & Viera, A. CTCAE versión 5.0. Evaluación de la gravedad de los eventos adversos dermatológicos de las terapias antineoplásicas. Actas Dermosifiliogr. 112, 90–92 (2021).
pubmed: 32891586
doi: 10.1016/j.ad.2019.05.009
Al, K. F., Bisanz, J. E., Gloor, G. B., Reid, G. & Burton, J. P. Evaluation of sampling and storage procedures on preserving the community structure of stool microbiota: a simple at-home toilet-paper collection method. J. Microbiol. Methods 144, 117–121 (2018).
pubmed: 29155236
doi: 10.1016/j.mimet.2017.11.014
Al, K. F. et al. Fecal microbiota transplantation is safe and tolerable in patients with multiple sclerosis: a pilot randomized controlled trial. Mult. Scler. J. Exp. Transl. Clin. https://doi.org/10.1177/20552173221086662 (2022).
Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).
pubmed: 27207943
doi: 10.1093/bioinformatics/btw313
Ndiaye, M. & Mattei, X. Endosymbiotic relationship between a rickettsia-like microorganism and the male germ-cells of Culex tigripes. J. Submicrosc. Cytol. Pathol. 25, 71–77 (1993).
pubmed: 8096432
Egermark-Eriksson, I., Carlsson, G. E. & Ingervall, B. Prevalence of mandibular dysfunction and orofacial parafunction in 7-, 11- and 15-year-old Swedish children. Eur. J. Orthod. 3, 163–172 (1981).
pubmed: 6943030
doi: 10.1093/ejo/3.3.163
Damond, N. et al. A Map of Human Type 1 diabetes progression by imaging mass cytometry. Cell Metab. 29, 755–768 (2019).
pubmed: 30713109
pmcid: 6821395
doi: 10.1016/j.cmet.2018.11.014
Levine, J. H. et al. Data-driven phenotypic dissection of aml reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).
pubmed: 26095251
pmcid: 4508757
doi: 10.1016/j.cell.2015.05.047
Berg, S. et al. ilastik: interactive machine learning for (bio)image analysis. Nat. Methods 16, 1226–1232 (2019).
pubmed: 31570887
doi: 10.1038/s41592-019-0582-9
Kjer-Nielsen, L. et al. MR1 presents microbial vitamin B metabolites to MAIT cells. Nature 491, 717–723 (2012).
pubmed: 23051753
doi: 10.1038/nature11605
Corbett, A. J. et al. T-cell activation by transitory neo-antigens derived from distinct microbial pathways. Nature 509, 361–365 (2014).
pubmed: 24695216
doi: 10.1038/nature13160
Dona, A. C. et al. Precision high-throughput proton NMR spectroscopy of human urine, serum, and plasma for large-scale metabolic phenotyping. Anal. Chem. 86, 9887–9894 (2014).
pubmed: 25180432
doi: 10.1021/ac5025039
Sands, C. J. et al. The nPYc-Toolbox, a Python module for the pre-processing, quality-control and analysis of metabolic profiling datasets. Bioinformatics 35, 5359–5360 (2019).
pubmed: 31350543
pmcid: 6954639
doi: 10.1093/bioinformatics/btz566
Takis, P. G. et al. A computationally lightweight algorithm for deriving reliable metabolite panel measurements from 1D 1H NMR. Anal. Chem. 93, 4995–5000 (2021).
pubmed: 33733737
pmcid: 8041249
doi: 10.1021/acs.analchem.1c00113
Akoka, S., Barantin, L. & Trierweiler, M. Concentration measurement by proton NMR using the ERETIC method. Anal. Chem. 71, 2554–2557 (1999).
pubmed: 21662801
doi: 10.1021/ac981422i
Sarafian, M. H. et al. Bile acid profiling and quantification in biofluids using ultra-performance liquid chromatography tandem mass spectrometry. Anal. Chem. 87, 9662–9670 (2015).
pubmed: 26327313
doi: 10.1021/acs.analchem.5b01556
Chambers, M. C. et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat. Biotechnol. 30, 918–920 (2012).
pubmed: 23051804
pmcid: 3471674
doi: 10.1038/nbt.2377
Smith, C. A., Want, E. J., O’Maille, G., Abagyan, R. & Siuzdak, G. XCMS: Processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 78, 779–787 (2006).
pubmed: 16448051
doi: 10.1021/ac051437y
Wolfer, A. M. et al. peakPantheR, an R package for large-scale targeted extraction and integration of annotated metabolic features in LC–MS profiling datasets. Bioinformatics 37, 4886–4888 (2021).
pubmed: 34125879
pmcid: 8665750
doi: 10.1093/bioinformatics/btab433
Tautenhahn, R., Böttcher, C. & Neumann, S. Highly sensitive feature detection for high resolution LC/MS. BMC Bioinform. 9, 504 (2008).
doi: 10.1186/1471-2105-9-504
Whiley, L. et al. Ultrahigh-performance liquid chromatography tandem mass spectrometry with electrospray ionization quantification of tryptophan metabolites and markers of gut health in serum and plasma—application to clinical and epidemiology cohorts. Anal. Chem. 91, 5207–5216 (2019).
pubmed: 30848589
pmcid: 6503468
doi: 10.1021/acs.analchem.8b05884
Barr, D. J., Levy, R., Scheepers, C. & Tily, H. J. Random effects structure for confirmatory hypothesis testing: keep it maximal. J. Mem. Lang. 68, 255–278 (2013).
doi: 10.1016/j.jml.2012.11.001
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).
pubmed: 27214047
pmcid: 4927377
doi: 10.1038/nmeth.3869
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2012).
pubmed: 23193283
pmcid: 3531112
doi: 10.1093/nar/gks1219
McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).
pubmed: 23630581
pmcid: 3632530
doi: 10.1371/journal.pone.0061217