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Association between immune-related adverse events and microbiome composition in patients with advanced non–small cell lung cancer treated with immunotherapy.

2022· article· en· W4281632216 on OpenAlexaffabout
Marion Tonneau, Corentin Richard, Alexis Nolin-Lapalme, Édouard Auclin, Myriam Benlaïfaoui, Mayra Ponce, Afnan Al-Saleh, Normand Blais, Marie Florescu, Mustapha Tehfé, Julie Malo, Meriem Messaoudene, Yusuke Okuma, Taiki Hakozaki, Bertrand Routy

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversité de MontréalCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsMicrobiomeMedicineLung cancerInternal medicineCohortAdverse effectImmune systemCancerOncologyCohort studyGastroenterologyImmunologyBiologyBioinformatics

Abstract

fetched live from OpenAlex

9036 Background: Despite immune-checkpoint inhibitors (ICI) achieving high response rates in patients (pts) with advanced non-small cell lung cancer (NSCLC), immune-related adverse events (irAE) remain an important therapeutic hurdle. The gut microbiome represents a novel prognostic marker of ICI response and early clinical evidence suggests that the microbiome may also regulate the development of irAE. We aimed to assess the association between irAE and the gut microbiome composition in advanced NSCLC pts amenable to ICI. Methods: In this study, we enrolled 464 pts from two independent cohorts (Montreal and Tokyo) with advanced NSCLC treated with ICI monotherapy or combination with chemotherapy. Fecal samples were collected prior to ICI initiation. Metagenomics microbiome profiling and 16S rRNA microbiome sequencing were performed for 81 pts and 133 pts respectively. The irAE were classified as CTCEA grade 0-1 (G0-1 irAE) and grade > 2 (G2-5 irAE). Microbiome composition of both groups was represented using alpha diversity, beta diversity indexes and LEfSe relative abundances. Results: Median follow-up was 28.3 months (mos) and the incidence of G2-5 irAE was 25.1% in the Montreal cohort. For the Tokyo cohort, the median follow-up was 19.6 mos and 30.8% pts developed G2-5 irAE. First, in both cohorts, overall survival (OS) and progression-free survival (PFS) were significantly improved for pts with G2-5 irAE compared to G0-1 irAE (OS: HR: 1.71, p = 0.008 and PFS: HR: 1.65, p < 0.001) and (OS: HR: 2.34, p = 0.001, and PFS: HR = 2.34, p = 0.006) respectively. Second, metagenomics analysis of 81 pts demonstrated a numerical decrease of the alpha diversity in terms of observed species in the G2-5 irAE. Dorea sp CAG 31, Anaerosporobacter mobilis, Butyricicococcus, and Enterococcus faecium were overrepresented in pts with G2-5 irAE. Conversely, Gordonibacter was increased in G0-1 irAE. Next, 16S rRNA profiling from the Tokyo cohort revealed an enrichment of Dorea, Butyricicococcus, and Eubacterium ventriosum in G2-5 irAE . Finally, we observed an enrichment in Dorea and Butyricicococcus in the pts with PFS > 6 months, as observed in the G2-5 irAE group. Conclusions: IrAE correlated with clinical outcome and microbiome composition in two independent cohorts of pts with advanced NSCLC. These results prompt further research on the potential mechanism underling the role of the microbiome in the development of irAE.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.328
Teacher spread0.318 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations3
Published2022
Admission routes2
Has abstractyes

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