IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-45357-1.html
   My bibliography  Save this article

Influence of microbiota-associated metabolic reprogramming on clinical outcome in patients with melanoma from the randomized adjuvant dendritic cell-based MIND-DC trial

Author

Listed:
  • Carolina Alves Costa Silva

    (ClinicObiome
    Université Paris-Saclay
    Équipe Labellisée – Ligue Nationale contre le Cancer)

  • Gianmarco Piccinno

    (University of Trento)

  • Déborah Suissa

    (ClinicObiome
    Université Paris-Saclay
    Équipe Labellisée – Ligue Nationale contre le Cancer)

  • Mélanie Bourgin

    (Gustave Roussy Cancer Campus
    Université Paris Cité, Sorbonne Université)

  • Gerty Schreibelt

    (Radboud university medical center)

  • Sylvère Durand

    (Gustave Roussy Cancer Campus
    Université Paris Cité, Sorbonne Université)

  • Roxanne Birebent

    (ClinicObiome
    Université Paris-Saclay
    Équipe Labellisée – Ligue Nationale contre le Cancer)

  • Marine Fidelle

    (ClinicObiome
    Équipe Labellisée – Ligue Nationale contre le Cancer)

  • Cissé Sow

    (ClinicObiome
    Équipe Labellisée – Ligue Nationale contre le Cancer)

  • Fanny Aprahamian

    (Gustave Roussy Cancer Campus
    Université Paris Cité, Sorbonne Université)

  • Paolo Manghi

    (University of Trento)

  • Michal Punčochář

    (University of Trento)

  • Francesco Asnicar

    (University of Trento)

  • Federica Pinto

    (University of Trento)

  • Federica Armanini

    (University of Trento)

  • Safae Terrisse

    (Assistance Publique Hôpitaux de Paris (AP-HP))

  • Bertrand Routy

    (Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM)
    Centre Hospitalier de l’Université de Montréal (CHUM))

  • Damien Drubay

    (ClinicObiome
    Université Paris-Saclay
    Inserm, Université Paris-Saclay, CESP U1018, Oncostat, labeled Ligue Contre le Cancer)

  • Alexander M. M. Eggermont

    (Princess Máxima Center and University Medical Center Utrecht
    Technical University Munich & Ludwig Maximiliaan University)

  • Guido Kroemer

    (Gustave Roussy Cancer Campus
    Université Paris Cité, Sorbonne Université
    Hôpital Européen Georges Pompidou, AP-HP)

  • Nicola Segata

    (University of Trento
    IEO European Institute of Oncology IRCCS)

  • Laurence Zitvogel

    (ClinicObiome
    Université Paris-Saclay
    Équipe Labellisée – Ligue Nationale contre le Cancer
    Center of Clinical Investigations BIOTHERIS)

  • Lisa Derosa

    (ClinicObiome
    Université Paris-Saclay
    Équipe Labellisée – Ligue Nationale contre le Cancer)

  • Kalijn F. Bol

    (Radboud university medical center
    Radboud university medical center)

  • I. Jolanda M. Vries

    (Radboud university medical center)

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.

Suggested Citation

  • Carolina Alves Costa Silva & Gianmarco Piccinno & Déborah Suissa & Mélanie Bourgin & Gerty Schreibelt & Sylvère Durand & Roxanne Birebent & Marine Fidelle & Cissé Sow & Fanny Aprahamian & Paolo Manghi, 2024. "Influence of microbiota-associated metabolic reprogramming on clinical outcome in patients with melanoma from the randomized adjuvant dendritic cell-based MIND-DC trial," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-45357-1
    DOI: 10.1038/s41467-024-45357-1
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-45357-1
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-024-45357-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Himel Mallick & Ali Rahnavard & Lauren J McIver & Siyuan Ma & Yancong Zhang & Long H Nguyen & Timothy L Tickle & George Weingart & Boyu Ren & Emma H Schwager & Suvo Chatterjee & Kelsey N Thompson & Je, 2021. "Multivariable association discovery in population-scale meta-omics studies," PLOS Computational Biology, Public Library of Science, vol. 17(11), pages 1-27, November.
    2. Andreas Draube & Nela Klein-González & Stefanie Mattheus & Corinne Brillant & Martin Hellmich & Andreas Engert & Michael von Bergwelt-Baildon, 2011. "Dendritic Cell Based Tumor Vaccination in Prostate and Renal Cell Cancer: A Systematic Review and Meta-Analysis," PLOS ONE, Public Library of Science, vol. 6(4), pages 1-11, April.
    3. Kursa, Miron B. & Rudnicki, Witold R., 2010. "Feature Selection with the Boruta Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i11).
    4. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    5. Kalijn F. Bol & Gerty Schreibelt & Martine Bloemendal & Wouter W. Willigen & Simone Hins-de Bree & Anna L. Goede & Annemiek J. Boer & Kevin J. H. Bos & Tjitske Duiveman-de Boer & Michel A. M. Olde Nor, 2024. "Adjuvant dendritic cell therapy in stage IIIB/C melanoma: the MIND-DC randomized phase III trial," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sara Saadatmand & Khodakaram Salimifard & Reza Mohammadi & Alex Kuiper & Maryam Marzban & Akram Farhadi, 2023. "Using machine learning in prediction of ICU admission, mortality, and length of stay in the early stage of admission of COVID-19 patients," Annals of Operations Research, Springer, vol. 328(1), pages 1043-1071, September.
    2. Reza Arabi Belaghi & Joseph Beyene & Sarah D McDonald, 2021. "Prediction of preterm birth in nulliparous women using logistic regression and machine learning," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-22, June.
    3. Noémi Kreif & Richard Grieve & Iván Díaz & David Harrison, 2015. "Evaluation of the Effect of a Continuous Treatment: A Machine Learning Approach with an Application to Treatment for Traumatic Brain Injury," Health Economics, John Wiley & Sons, Ltd., vol. 24(9), pages 1213-1228, September.
    4. Abhilash Bandam & Eedris Busari & Chloi Syranidou & Jochen Linssen & Detlef Stolten, 2022. "Classification of Building Types in Germany: A Data-Driven Modeling Approach," Data, MDPI, vol. 7(4), pages 1-23, April.
    5. Boonstra Philip S. & Little Roderick J.A. & West Brady T. & Andridge Rebecca R. & Alvarado-Leiton Fernanda, 2021. "A Simulation Study of Diagnostics for Selection Bias," Journal of Official Statistics, Sciendo, vol. 37(3), pages 751-769, September.
    6. Tong, Jianfeng & Liu, Zhenxing & Zhang, Yong & Zheng, Xiujuan & Jin, Junyang, 2023. "Improved multi-gate mixture-of-experts framework for multi-step prediction of gas load," Energy, Elsevier, vol. 282(C).
    7. Ruairi C. Robertson & Thaddeus J. Edens & Lynnea Carr & Kuda Mutasa & Ethan K. Gough & Ceri Evans & Hyun Min Geum & Iman Baharmand & Sandeep K. Gill & Robert Ntozini & Laura E. Smith & Bernard Chasekw, 2023. "The gut microbiome and early-life growth in a population with high prevalence of stunting," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    8. Asma Shaheen & Javed Iqbal, 2018. "Spatial Distribution and Mobility Assessment of Carcinogenic Heavy Metals in Soil Profiles Using Geostatistics and Random Forest, Boruta Algorithm," Sustainability, MDPI, vol. 10(3), pages 1-20, March.
    9. Ramón Ferri-García & María del Mar Rueda, 2022. "Variable selection in Propensity Score Adjustment to mitigate selection bias in online surveys," Statistical Papers, Springer, vol. 63(6), pages 1829-1881, December.
    10. Christopher J Greenwood & George J Youssef & Primrose Letcher & Jacqui A Macdonald & Lauryn J Hagg & Ann Sanson & Jenn Mcintosh & Delyse M Hutchinson & John W Toumbourou & Matthew Fuller-Tyszkiewicz &, 2020. "A comparison of penalised regression methods for informing the selection of predictive markers," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    11. Liangyuan Hu & Lihua Li, 2022. "Using Tree-Based Machine Learning for Health Studies: Literature Review and Case Series," IJERPH, MDPI, vol. 19(23), pages 1-13, December.
    12. Norah Alyabs & Sy Han Chiou, 2022. "The Missing Indicator Approach for Accelerated Failure Time Model with Covariates Subject to Limits of Detection," Stats, MDPI, vol. 5(2), pages 1-13, May.
    13. Feldkircher, Martin, 2014. "The determinants of vulnerability to the global financial crisis 2008 to 2009: Credit growth and other sources of risk," Journal of International Money and Finance, Elsevier, vol. 43(C), pages 19-49.
    14. Yvan Devaux & Lu Zhang & Andrew I. Lumley & Kanita Karaduzovic-Hadziabdic & Vincent Mooser & Simon Rousseau & Muhammad Shoaib & Venkata Satagopam & Muhamed Adilovic & Prashant Kumar Srivastava & Costa, 2024. "Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    15. Ghosh, Indranil & Chaudhuri, Tamal Datta & Alfaro-Cortés, Esteban & Gámez, Matías & García, Noelia, 2022. "A hybrid approach to forecasting futures prices with simultaneous consideration of optimality in ensemble feature selection and advanced artificial intelligence," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    16. Eunsil Seok & Akhgar Ghassabian & Yuyan Wang & Mengling Liu, 2024. "Statistical Methods for Modeling Exposure Variables Subject to Limit of Detection," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 16(2), pages 435-458, July.
    17. Ida Kubiszewski & Kenneth Mulder & Diane Jarvis & Robert Costanza, 2022. "Toward better measurement of sustainable development and wellbeing: A small number of SDG indicators reliably predict life satisfaction," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(1), pages 139-148, February.
    18. Georges Steffgen & Philipp E. Sischka & Martha Fernandez de Henestrosa, 2020. "The Quality of Work Index and the Quality of Employment Index: A Multidimensional Approach of Job Quality and Its Links to Well-Being at Work," IJERPH, MDPI, vol. 17(21), pages 1-31, October.
    19. Christopher Kath & Florian Ziel, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Papers 1811.08604, arXiv.org.
    20. Conor Waldock & Bernhard Wegscheider & Dario Josi & Bárbara Borges Calegari & Jakob Brodersen & Luiz Jardim de Queiroz & Ole Seehausen, 2024. "Deconstructing the geography of human impacts on species’ natural distribution," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-45357-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.