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Article: Predictable modulation of cancer treatment outcomes by the gut microbiota

TitlePredictable modulation of cancer treatment outcomes by the gut microbiota
Authors
KeywordsGut microbiota
Cancer
Treatment outcome
Machine learning
Issue Date2020
PublisherBioMed Central Ltd. The Journal's web site is located at http://www.microbiomejournal.com/
Citation
Microbiome, 2020, v. 8 n. 1, p. article no. 28 How to Cite?
AbstractThe gut microbiota has the potential to influence the efficacy of cancer therapy. Here, we investigated the contribution of the intestinal microbiome on treatment outcomes in a heterogeneous cohort that included multiple cancer types to identify microbes with a global impact on immune response. Human gut metagenomic analysis revealed that responder patients had significantly higher microbial diversity and different microbiota compositions compared to non-responders. A machine-learning model was developed and validated in an independent cohort to predict treatment outcomes based on gut microbiota composition and functional repertoires of responders and non-responders. Specific species, Bacteroides ovatus and Bacteroides xylanisolvens, were positively correlated with treatment outcomes. Oral gavage of these responder bacteria significantly increased the efficacy of erlotinib and induced the expression of CXCL9 and IFN-γ in a murine lung cancer model. These data suggest a predictable impact of specific constituents of the microbiota on tumor growth and cancer treatment outcomes with implications for both prognosis and therapy.
Persistent Identifierhttp://hdl.handle.net/10722/304992
ISSN
2023 Impact Factor: 13.8
2023 SCImago Journal Rankings: 3.802
PubMed Central ID
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorHESHIKI, Y-
dc.contributor.authorVazquez-Uribe, R-
dc.contributor.authorLI, J-
dc.contributor.authorNI, Y-
dc.contributor.authorQuainoo, S-
dc.contributor.authorImamovic, L-
dc.contributor.authorLi, J-
dc.contributor.authorSørensen, M-
dc.contributor.authorChow, BKC-
dc.contributor.authorWeiss, GJ-
dc.contributor.authorXu, A-
dc.contributor.authorSommer, MOA-
dc.contributor.authorPanagiotou, G-
dc.date.accessioned2021-10-05T02:38:11Z-
dc.date.available2021-10-05T02:38:11Z-
dc.date.issued2020-
dc.identifier.citationMicrobiome, 2020, v. 8 n. 1, p. article no. 28-
dc.identifier.issn2049-2618-
dc.identifier.urihttp://hdl.handle.net/10722/304992-
dc.description.abstractThe gut microbiota has the potential to influence the efficacy of cancer therapy. Here, we investigated the contribution of the intestinal microbiome on treatment outcomes in a heterogeneous cohort that included multiple cancer types to identify microbes with a global impact on immune response. Human gut metagenomic analysis revealed that responder patients had significantly higher microbial diversity and different microbiota compositions compared to non-responders. A machine-learning model was developed and validated in an independent cohort to predict treatment outcomes based on gut microbiota composition and functional repertoires of responders and non-responders. Specific species, Bacteroides ovatus and Bacteroides xylanisolvens, were positively correlated with treatment outcomes. Oral gavage of these responder bacteria significantly increased the efficacy of erlotinib and induced the expression of CXCL9 and IFN-γ in a murine lung cancer model. These data suggest a predictable impact of specific constituents of the microbiota on tumor growth and cancer treatment outcomes with implications for both prognosis and therapy.-
dc.languageeng-
dc.publisherBioMed Central Ltd. The Journal's web site is located at http://www.microbiomejournal.com/-
dc.relation.ispartofMicrobiome-
dc.rightsMicrobiome. Copyright © BioMed Central Ltd.-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectGut microbiota-
dc.subjectCancer-
dc.subjectTreatment outcome-
dc.subjectMachine learning-
dc.titlePredictable modulation of cancer treatment outcomes by the gut microbiota-
dc.typeArticle-
dc.identifier.emailChow, BKC: bkcc@hku.hk-
dc.identifier.emailXu, A: amxu@hkucc.hku.hk-
dc.identifier.authorityChow, BKC=rp00681-
dc.identifier.authorityXu, A=rp00485-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1186/s40168-020-00811-2-
dc.identifier.pmid32138779-
dc.identifier.pmcidPMC7059390-
dc.identifier.scopuseid_2-s2.0-85081217719-
dc.identifier.hkuros326284-
dc.identifier.volume8-
dc.identifier.issue1-
dc.identifier.spagearticle no. 28-
dc.identifier.epagearticle no. 28-
dc.identifier.isiWOS:000519018800001-
dc.publisher.placeUnited Kingdom-

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