Cargando…

Meta-analysis of the gut microbiota in predicting response to cancer immunotherapy in metastatic melanoma

BACKGROUND: Identifying factors conferring responses to therapy in cancer is critical to select the best treatment for patients. For immune checkpoint inhibition (ICI) therapy, mounting evidence suggests that the gut microbiome can determine patient treatment outcomes. However, the extent to which g...

Descripción completa

Detalles Bibliográficos
Autores principales: Limeta, Angelo, Ji, Boyang, Levin, Max, Gatto, Francesco, Nielsen, Jens
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Society for Clinical Investigation 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7714408/
https://www.ncbi.nlm.nih.gov/pubmed/33268597
http://dx.doi.org/10.1172/jci.insight.140940
_version_ 1783618749781245952
author Limeta, Angelo
Ji, Boyang
Levin, Max
Gatto, Francesco
Nielsen, Jens
author_facet Limeta, Angelo
Ji, Boyang
Levin, Max
Gatto, Francesco
Nielsen, Jens
author_sort Limeta, Angelo
collection PubMed
description BACKGROUND: Identifying factors conferring responses to therapy in cancer is critical to select the best treatment for patients. For immune checkpoint inhibition (ICI) therapy, mounting evidence suggests that the gut microbiome can determine patient treatment outcomes. However, the extent to which gut microbial features are applicable across different patient cohorts has not been extensively explored. METHODS: We performed a meta-analysis of 4 published shotgun metagenomic studies (N(tot) = 130 patients) investigating differential microbiome composition and imputed metabolic function between responders and nonresponders to ICI. RESULTS: Our analysis identified both known microbial features enriched in responders, such as Faecalibacterium as the prevailing taxa, as well as additional features, including overrepresentation of Barnesiella intestinihominis and the components of vitamin B metabolism. A classifier designed to predict responders based on these features identified responders in an independent cohort of 27 patients with the area under the receiver operating characteristic curve of 0.625 (95% CI: 0.348–0.899) and was predictive of prognosis (HR = 0.35, P = 0.081). CONCLUSION: These results suggest the existence of a fecal microbiome signature inherent across responders that may be exploited for diagnostic or therapeutic purposes. FUNDING: This work was funded by the Knut and Alice Wallenberg Foundation, BioGaia AB, and Cancerfonden.
format Online
Article
Text
id pubmed-7714408
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher American Society for Clinical Investigation
record_format MEDLINE/PubMed
spelling pubmed-77144082020-12-08 Meta-analysis of the gut microbiota in predicting response to cancer immunotherapy in metastatic melanoma Limeta, Angelo Ji, Boyang Levin, Max Gatto, Francesco Nielsen, Jens JCI Insight Clinical Medicine BACKGROUND: Identifying factors conferring responses to therapy in cancer is critical to select the best treatment for patients. For immune checkpoint inhibition (ICI) therapy, mounting evidence suggests that the gut microbiome can determine patient treatment outcomes. However, the extent to which gut microbial features are applicable across different patient cohorts has not been extensively explored. METHODS: We performed a meta-analysis of 4 published shotgun metagenomic studies (N(tot) = 130 patients) investigating differential microbiome composition and imputed metabolic function between responders and nonresponders to ICI. RESULTS: Our analysis identified both known microbial features enriched in responders, such as Faecalibacterium as the prevailing taxa, as well as additional features, including overrepresentation of Barnesiella intestinihominis and the components of vitamin B metabolism. A classifier designed to predict responders based on these features identified responders in an independent cohort of 27 patients with the area under the receiver operating characteristic curve of 0.625 (95% CI: 0.348–0.899) and was predictive of prognosis (HR = 0.35, P = 0.081). CONCLUSION: These results suggest the existence of a fecal microbiome signature inherent across responders that may be exploited for diagnostic or therapeutic purposes. FUNDING: This work was funded by the Knut and Alice Wallenberg Foundation, BioGaia AB, and Cancerfonden. American Society for Clinical Investigation 2020-12-03 /pmc/articles/PMC7714408/ /pubmed/33268597 http://dx.doi.org/10.1172/jci.insight.140940 Text en © 2020 Limeta et al. http://creativecommons.org/licenses/by/4.0/ This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Clinical Medicine
Limeta, Angelo
Ji, Boyang
Levin, Max
Gatto, Francesco
Nielsen, Jens
Meta-analysis of the gut microbiota in predicting response to cancer immunotherapy in metastatic melanoma
title Meta-analysis of the gut microbiota in predicting response to cancer immunotherapy in metastatic melanoma
title_full Meta-analysis of the gut microbiota in predicting response to cancer immunotherapy in metastatic melanoma
title_fullStr Meta-analysis of the gut microbiota in predicting response to cancer immunotherapy in metastatic melanoma
title_full_unstemmed Meta-analysis of the gut microbiota in predicting response to cancer immunotherapy in metastatic melanoma
title_short Meta-analysis of the gut microbiota in predicting response to cancer immunotherapy in metastatic melanoma
title_sort meta-analysis of the gut microbiota in predicting response to cancer immunotherapy in metastatic melanoma
topic Clinical Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7714408/
https://www.ncbi.nlm.nih.gov/pubmed/33268597
http://dx.doi.org/10.1172/jci.insight.140940
work_keys_str_mv AT limetaangelo metaanalysisofthegutmicrobiotainpredictingresponsetocancerimmunotherapyinmetastaticmelanoma
AT jiboyang metaanalysisofthegutmicrobiotainpredictingresponsetocancerimmunotherapyinmetastaticmelanoma
AT levinmax metaanalysisofthegutmicrobiotainpredictingresponsetocancerimmunotherapyinmetastaticmelanoma
AT gattofrancesco metaanalysisofthegutmicrobiotainpredictingresponsetocancerimmunotherapyinmetastaticmelanoma
AT nielsenjens metaanalysisofthegutmicrobiotainpredictingresponsetocancerimmunotherapyinmetastaticmelanoma