The role of the gut microbiome on the efficacy of immune checkpoint inhibitors in Japanese responder patients with advanced non-small cell lung cancer

Study information

Reviewed Marked as Reviewed by Fatima on 2022/09/7

study design

prospective cohort

Citation

PMID PubMed identifier for scientific articles.

32010563

DOI Digital object identifier for electronic documents.

10.21037/tlcr.2019.10.23

URI

Authors

Katayama Y, Yamada T, Shimamoto T, Iwasaku M, Kaneko Y, Uchino J, Takayama K

Journal

Translational lung cancer research

Year

2019

Keywords:

Immunotherapy, gut microbiome, non-small cell lung cancer (NSCLC), retrospective analysis

BACKGROUND: Cancer immunotherapy is being developed as a promising alternative for advanced non-small cell lung cancer (NSCLC). However, novel biomarkers are required to select patients that will benefit from treatment with immune checkpoint inhibitors (ICIs) for a long period of time. The gut microbiome is expected to be a promising biomarker of ICI response owing to the regulation of the immune status within the host. METHODS: In this retrospective study, we included 17 Japanese patients with advanced NSCLC who were treated with ICIs for >3 months in our hospital. Fecal samples obtained from the patients during ICI treatment were analyzed by 16S ribosomal RNA gene sequencing. We examined the correlation between the diversity of the gut microbiome and treatment with ICIs. RESULTS: Several bacterial species were more abundant in ICI responders than in non-responders. Patients with abundant Lactobacillus and Clostridium tended to have a longer time to treatment failure (TTF) after receiving ICI than those with a lower abundance. CONCLUSIONS: In conclusion, the composition of the gut microbiome is associated with better clinical benefits from ICI treatment in Japanese patients with NSCLC. A further large-scale study is warranted to validate the composition of the gut microbiome as a novel clinical factor influencing the response to ICIs for an extended time in NSCLC.

Experiment 1

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Reviewed Marked as Reviewed by Fatima on 2022/09/7

Curated date: 2022/09/02

Curator: Sharmilac

Revision editor(s): Sharmilac, Fatima, Peace Sandy

Subjects

Location of subjects: Japan

Host species Species from which microbiome was sampled. Contact us to have more species added.: Homo sapiens

Body site Anatomical site where microbial samples were extracted from according to the Uber Anatomy Ontology: Feces Cow dung,Cow pat,Droppings,Dung,Excrement,Excreta,Faeces,Fecal material,Fecal matter,Fewmet,Frass,Guano,Matières fécales@fr,Merde@fr,Ordure,Partie de la merde@fr,Piece of shit,Porción de mierda@es,Portion of dung,Portion of excrement,Portion of faeces,Portion of fecal material,Portion of fecal matter,Portion of feces,Portion of guano,Portion of scat,Portionem cacas,Scat,Spoor,Spraint,Stool,Teil der fäkalien@de,Feces,feces

Condition The experimental condition / phenotype studied according to the Experimental Factor Ontology: Response to immunochemotherapy Response to immunochemotherapy,response to immunochemotherapy

Group 0 name Corresponds to the control (unexposed) group for case-control studies: Non-responders

Group 1 name Corresponds to the case (exposed) group for case-control studies: Responders

Group 1 definition Diagnostic criteria applied to define the specific condition / phenotype represented in the case (exposed) group: We defined responder (R) (partial response to ICI treatment) or non-responder (NR) (stable or progressive disease after ICI treatment at the time of first clinical evaluation) according to the RECIST 1.1 evaluation.

Group 0 sample size Number of subjects in the control (unexposed) group: 11

Group 1 sample size Number of subjects in the case (exposed) group: 6

Lab analysis

Sequencing type: 16S

16S variable region One or more hypervariable region(s) of the bacterial 16S gene: V1-V2

Sequencing platform Manufacturer and experimental platform used for quantifying microbial abundance: Illumina

Statistical Analysis

Data transformation Data transformation applied to microbial abundance measurements prior to differential abundance testing (if any).: relative abundances

Statistical test: LEfSe

Significance threshold p-value or FDR threshold used for differential abundance testing (if any): 0.05

LDA Score above Threshold for the linear discriminant analysis (LDA) score for studies using the popular LEfSe tool: 3

Alpha Diversity

Shannon Estimator of species richness and species evenness: more weight on species richness: unchanged

Signature 1

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Reviewed Marked as Reviewed by Fatima on 2022/09/7

Curated date: 2022/09/02

Curator: Sharmilac

Revision editor(s): Sharmilac, Fatima

Source: Figure 2

Description: The differential abundant taxa in the gut microbiomes of R (green) and NR (red) was analyzed by linear discriminate analysis coupled with effect size measurements (LEfSe)

Abundance in Group 1: increased abundance in Responders

NCBI	Quality Control	Links
Clostridium
Lactobacillus
Syntrophococcus
Lactobacillaceae

Revision editor(s): Sharmilac, Fatima

Signature 2

Edit History Delete

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Your review change was submitted

Reviewed Marked as Reviewed by Fatima on 2022/09/7

Curated date: 2022/09/02

Curator: Sharmilac

Revision editor(s): Sharmilac, Fatima

Source: Figure 2

Description: The differential abundant taxa in the gut microbiomes of R (green) and NR (red) was analyzed by linear discriminate analysis coupled with effect size measurements (LEfSe)

Abundance in Group 1: decreased abundance in Responders

NCBI	Quality Control	Links
Bilophila
Parabacteroides
Sutterella
Alphaproteobacteria
Alcaligenaceae
Betaproteobacteria
Burkholderiales
Porphyromonadaceae

Revision editor(s): Sharmilac, Fatima