Evidence map›Paper›PMID 32138779›Full record

ArticleMicrobiome2020

Predictable modulation of cancer treatment outcomes by the gut microbiota.

Yoshitaro Heshiki, Ruben Vazquez-Uribe, Jin Li, Yueqiong Ni, Scott Quainoo, Lejla Imamovic, Jun Li, Maria Sørensen, Billy K C Chow, Glen J Weiss and 3 more

Open access · goldAbstract read
In one paragraph

Article in Microbiome, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 119 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
119citing papers in PubMed, 2 pooled it
9.2field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

119 citing papers in PubMed, 2 syntheses or guidelines pooled it, 168 citations in OpenAlex.

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59 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 6 institutions in 5 countries.

Yoshitaro HeshikiSystems Biology & Bioinformatics Unit, Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute, Jena, Germany.
Ruben Vazquez-UribeNovo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs., Lyngby, Denmark.
Jin LiState Key Laboratory of Pharmaceutical Biotechnology, University of Hong Kong, Hong Kong, China.
Yueqiong NiSystems Biology and Bioinformatics Group, School of Biological Sciences, Faculty of Sciences, The University of Hong Kong, Hong Kong, China.
Scott QuainooNovo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs., Lyngby, Denmark.
Lejla ImamovicNovo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs., Lyngby, Denmark.
Jun LiDepartment of Infectious Diseases and Public Health, The Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong, China.
Maria SørensenNovo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs., Lyngby, Denmark.
Billy K C ChowSchool of Biological Sciences, Faculty of Sciences, The University of Hong Kong, Hong Kong, China.
Glen J WeissUniversity of Arizona College of Medicine-Phoenix, Phoenix, AZ, USA. drglenweiss@outlook.com.
Aimin XuState Key Laboratory of Pharmaceutical Biotechnology, University of Hong Kong, Hong Kong, China. amxu@hku.hk.
Morten O A SommerNovo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs., Lyngby, Denmark. msom@bio.dtu.dk.
Gianni PanagiotouSystems Biology & Bioinformatics Unit, Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute, Jena, Germany. Gianni.Panagiotou@hki-jena.de.ORCID 0000-0001-9393-124X
Novo Nordisk Foundation · DKCity University of Hong Kong · HKLeibniz-Institut für Naturstoff-Forschung und Infektionsbiologie e. V. - Hans-Knöll-Institut (HKI) · DEUniversity of Hong Kong · HKHKU-Pasteur Research Pole · HKUniversity of Arizona · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The 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.

Indexed as

Gastrointestinal MicrobiomeMetagenomicsAdultAgedAnimalsBacteriaDisease Models, AnimalFecesFemaleGenetic VariationHumansLongitudinal StudiesLung NeoplasmsMaleMiceMice, Inbred C57BLCancerGut microbiotaMachine learningTreatment outcome

Identifiers

PMID32138779
PMCPMC7059390
OpenAlexW3011129791

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.