Influence of microbiota-associated metabolic reprogramming on clinical outcome in patients with melanoma from the randomized adjuvant dendritic cell-based MIND-DC trial
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Abstract
Tumor immunosurveillance plays a major role in melanoma, prompting the development of immunotherapy strategies. The gut microbiota composition, influencing peripheral and tumoral immune tonus, earned its credentials among predictors of survival in melanoma. The MIND-DC phase III trial (NCT02993315) randomized (2:1 ratio) 148 patients with stage IIIB/C melanoma to adjuvant treatment with autologous natural dendritic cell (nDC) or placebo (PL). Overall, 144 patients collected serum and stool samples before and after 2 bimonthly injections to perform metabolomics (MB) and metagenomics (MG) as prespecified exploratory analysis. Clinical outcomes are reported separately. Here we show that different microbes were associated with prognosis, with the health-related Faecalibacterium prausnitzii standing out as the main beneficial taxon for no recurrence at 2 years (p = 0.008 at baseline, nDC arm). Therapy coincided with major MB perturbations (acylcarnitines, carboxylic and fatty acids). Despite randomization, nDC arm exhibited MG and MB bias at baseline: relative under-representation of F. prausnitzii, and perturbations of primary biliary acids (BA). F. prausnitzii anticorrelated with BA, medium- and long-chain acylcarnitines. Combined, these MG and MB biomarkers markedly determined prognosis. Altogether, the host-microbial interaction may play a role in localized melanoma. We value systematic MG and MB profiling in randomized trials to avoid baseline differences attributed to host-microbe interactions.
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In one metastatic melanoma study, gut microbiota differed between treatment arms despite randomization, indicating host-microbiota interactions may act as a confounding factor.
“one study on metastatic melanoma was affected by differences in the gut microbiota between the two treatment arms, despite randomization, underscoring the importance of host-microbiota interactions as a potential confounding factor 17”
From Impact of the ONCOBIOME network in cancer microbiome research (2025) · cites this paper as [17]
Other F. prausnitzii SGBs are not linked to favorable prognosis.
“while others were not 17”
From Impact of the ONCOBIOME network in cancer microbiome research (2025) · cites this paper as [17]
Machine learning on clinical, metabolomics, and MGS data identified longitudinal changes in fatty and carboxylic acids, vitamins, tryptophan metabolites, biliary salts, and polyamines associated with melanoma relapse.
“Machine learning models fed clinical, metabolomics and MGS data from each patient allowed for the identification of various longitudinal alterations of fatty and carboxylic acids, vitamins, tryptophan metabolites, biliary salts and polyamines associated with melanoma relapse 17”
From Impact of the ONCOBIOME network in cancer microbiome research (2025) · cites this paper as [17]
Cancer immunotherapy both elicits immunostimulatory metabolites and may increase immunosuppressive factors, possibly reflecting regulatory pathways.
“We obtained evidence that cancer immunotherapy not only elicits immunostimulatory metabolites but may cause an increase in immunosuppressive factors, perhaps reflecting regulatory pathways 17”
From Impact of the ONCOBIOME network in cancer microbiome research (2025) · cites this paper as [17]
In melanoma patients treated with dendritic cell immunotherapy, F. prausnitzii was main beneficial taxon for no recurrence at 2 years.
“They show that different microbes were associated with prognosis, with F. prausnitzii standing out as the main beneficial taxon for no recurrence at 2 year in the patients receiving the DC treatment.17”
From <i>Faecalibacterium prausnitzii</i> strain EXL01 boosts efficacy of immune checkpoint inhibitors (2024) · cites this paper as [17]
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Hu, W., Gao, W., Liu, Z., Fang, Z., Wang, H., Zhao, J., Zhang, H., Lu, W., & Chen, W. (2022) · Nutrients · doi:10.3390/nu14142945 · Cited by 98 · see citing works
Zhang, M., Zhou, L., Wang, Y., Dorfman, R. G., Tang, D., Xu, L., Pan, Y., Zhou, Q., Li, Y., Yin, Y., et al. (2019) · International Immunology · doi:10.1093/intimm/dxz022 · Cited by 94 · see citing works
Twelve-year survival and immune correlates in dendritic cell–vaccinated melanoma patients
Groß, S., Erdmann, M., Haendle, I., Voland, S., Berger, T. G., Schultz, E. S., Strasser, E. F., Dankerl, P., Janka, R., Schliep, S., et al. (2017) · JCI Insight · doi:10.1172/jci.insight.91438 · Cited by 87 · see citing works
Lipid metabolic Reprogramming: Role in Melanoma Progression and Therapeutic Perspectives
Pellerin, L., Carrié, L., Dufau, C., Nieto, L., Ségui, B., Levade, T., Riond, J., & Andrieu‐Abadie, N. (2020) · Cancers · doi:10.3390/cancers12113147 · Cited by 82 · see citing works
Noma, H., Shinozaki, T., Iba, K., Teramukai, S., & Furukawa, T. A. (2021) · Statistics in Medicine · doi:10.1002/sim.9148 · Cited by 81 · see citing works
Touch, S., Godefroy, E., Rolhion, N., Danne, C., Oeuvray, C., Straube, M., Galbert, C., Brot, L., Alonso Salgueiro, I., Chadi, S., et al. (2022) · JCI Insight · doi:10.1172/jci.insight.154722 · Cited by 80 · see citing works
Lesterhuis, W. J., Schreibelt, G., Scharenborg, N. M., Brouwer, H., Gerritsen, M. P., Croockewit, S., Coulie, P. G., Torensma, R., Adema, G. J., Figdor, C. G., et al. (2010) · Cancer Immunology Immunotherapy · doi:10.1007/s00262-010-0942-x · Cited by 78 · see citing works
Self-citationLiu, X., Zhang, M., Cheng, X., et al. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.00717 · Cited by 62 · see citing works
Aarntzen, E. H. J. G., Bol, K. F., Schreibelt, G., Jacobs, J. F. M., Lesterhuis, W. J., van Rossum, M. M., Adema, G. J., Figdor, C. G., Punt, C. J. A., & de Vries, I. J. M. (2012) · Cancer Research · doi:10.1158/0008-5472.can-12-2479 · Cited by 61 · see citing works
Self-citationAdjuvant dendritic cell therapy in stage IIIB/C melanoma: the MIND-DC randomized phase III trial
Bol, K. F., Schreibelt, G., Bloemendal, M., van Willigen, W. W., Bree, S. H.-D., de Goede, A. L., de Boer, A. J., Bos, K., Boer, T. D., Nordkamp, M. A. M. O., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-45358-0 · Cited by 57 · see citing works
Self-citationPalucka, K., Ueno, H., Roberts, L. K., Fay, J. W., & Banchereau, J. (2010) · The Cancer Journal · doi:10.1097/ppo.0b013e3181eaca83 · Cited by 53 · see citing works
Liu, N., Zhang, J., Yan, M., Chen, L., Wu, J., Tao, Q., Yan, B., Chen, X., & Peng, C. (2023) · Cell Death and Disease · doi:10.1038/s41419-023-05692-5 · Cited by 47 · see citing works
Neoadjuvant ipilimumab plus nivolumab in synchronous clinical stage III melanoma
Versluis, J. M., Reijers, I. L. M., Rozeman, E. A., Menzies, A. M., van Akkooi, A. C. J., Wouters, M. W. J. M., Ch’ng, S., Saw, R. P. M., Scolyer, R. A., van de Wiel, B. A., et al. (2021) · European Journal of Cancer · doi:10.1016/j.ejca.2021.02.012 · Cited by 31 · see citing works
Macrophage-Mediated Immune Responses: From Fatty Acids to Oxylipins
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The intracellular metabolome of starving cells
Durand, S., Grajeda‐Iglesias, C., Aprahamian, F., Nirmalathasan, N., Kepp, O., & Kroemer, G. (2021) · Methods in cell biology · doi:10.1016/bs.mcb.2021.04.001 · Cited by 14 · see citing works
Self-citation
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<i>Faecalibacterium prausnitzii</i> strain EXL01 boosts efficacy of immune checkpoint inhibitors
Bredon, M., Danne, C., Pham, H. P., Ruffié, P., Bessede, A., Rolhion, N., Creusot, L., Brot, L., Alonso, I., Langella, P., et al. (2024) · OncoImmunology · doi:10.1080/2162402x.2024.2374954 · Cited by 74 · see citing works
Self-citationGut Microbiota–Related Biomarkers in Immuno-Oncology
Silva, C. A. C., Fidelle, M., Almonte, A. A., Derosa, L., & Zitvogel, L. (2025) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-061124-102218 · Cited by 22 · see citing works
Self-citationImpact of the ONCOBIOME network in cancer microbiome research
Zitvogel, L., Derosa, L., Routy, B., Loibl, S., Heinzerling, L., de Vries, I. J. M., Engstrand, L., ONCOBIOME Network, Silva, C. A. C., Barbieri, D., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03608-8 · Cited by 23 · see citing works
Self-citation
- Authors
- Alves Costa Silva, C., Piccinno, G., Suissa, D., Bourgin, M., Schreibelt, G., Durand, S., Birebent, R., Fidelle, M., Sow, C., Aprahamian, F., Manghi, P., Punčochář, M., Asnicar, F., Pinto, F., Armanini, F., Terrisse, S., Routy, B., Drubay, D., Eggermont, A. M. M., Kroemer, G., Segata, N., Zitvogel, L., Derosa, L., Bol, K. F., & de Vries, I. J. M.
- Affiliations
- University of Trento (IT)
- Inserm (FR)
- Radboud University Nijmegen (NL)
- Assistance Publique – Hôpitaux de Paris (FR)
- Centre Hospitalier de l’Université de Montréal (CA)
- University Medical Center Utrecht (NL)
- Corresponding author
- Zitvogel, Laurence
- ORCID
- Alves Costa Silva, Carolina
- Piccinno, Gianmarco
- Suissa, Déborah
- Bourgin, Mélanie
- Schreibelt, Gerty
- Durand, Sylvère
- Birebent, Roxanne
- Fidelle, Marine
- Sow, Cissé
- Aprahamian, Fanny
- Manghi, Paolo
- Punčochář, Michal
- Asnicar, Francesco
- Pinto, Federica
- Armanini, Federica
- Terrisse, Safae
- Routy, Bertrand
- Drubay, Damien
- Eggermont, Alexander M. M.
- Kroemer, Guido
- Segata, Nicola
- Zitvogel, Laurence
- Derosa, Lisa
- Bol, Kalijn F.
- de Vries, I. Jolanda M.
- Venue
- Nature Communications
- Published
- 2024-02-23
- Volume
- 15
- Issue
- 1
- Pages
- 1633-1633
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC10891084
- OpenAlex
- W4392110421
- Cited by
- 33
- References
- 87
- Citations by year
- 2026: 10 · 2025: 13 · 2024: 10
- FWCI
- 4.49
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion, other-oa)
- Iris (University of Trento) (submittedVersion, cc-by)
- Radboud Repository (Radboud University) (submittedVersion)
- HAL (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
- Funders
- Association pour la Recherche sur le Cancer
- Seerave Foundation
- Equipex
- European Commission (101095604, 825410, HORIZON2020, ERC-2021-ADG)
- Agence Nationale de la Recherche (ANR-21-ESRE-0028, ANR-18-IBHU-0002, ANR-19-CE15-0029, ANR18, ANR-21-RHUS-0017, ANR-18-IDEX-0001)
- Institut National de la Santé et de la Recherche Médicale (ANR-18-IDEX-0001)
- ZonMw
- Radboud Universiteit
- Fondation pour la Recherche Médicale
- Ligue Contre le Cancer
- Centre National de la Recherche Scientifique (UMS3655)
- Institut Universitaire de France (ANR-18-IDEX-0001)
- Radboud Universitair Medisch Centrum
- Institut National Du Cancer (INCa-DGOS-INSERM-6043)
- Institut Gustave-Roussy
- Direction Générale de l’offre de Soins
- Fondation Gustave Roussy
- Université de Paris (ANR-18- IDEX-0001, 18-IDEX-0001)
- Labex Immuno-Oncology ((ANR-18-IDEX-0001))
- HORIZON EUROPE European Research Council (101052444)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- Keywords
- melanoma, gut microbiota, metabolic reprogramming, Faecalibacterium prausnitzii, adjuvant immunotherapy, tumor recurrence, prognostic factors, bile acids, acylcarnitines, host-microbe interactions, metabolomics, metagenomics
- MeSH terms
- Metabolic Reprogramming, Dendritic Cells, Humans, Melanoma, Microbiota
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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