# Faecalibacterium prausnitzii Is Associated With Clinical Response to Immune Checkpoint Inhibitors in Patients With Advanced Gastric Adenocarcinoma: Results of Microbiota Analysis of PRODIGE 59-FFCD 1707-DURIGAST Trial

Marius Bredon, Karine Le Malicot, Christophe Louvet, Ludovic Evesque, Daniel Gonzalez, David Tougeron, Harry Sokol

► To cite this version:

Marius Bredon, Karine Le Malicot, Christophe Louvet, Ludovic Evesque, Daniel Gonzalez, et al. Faecalibacterium prausnitzii Is Associated With Clinical Response to Immune Checkpoint Inhibitors in Patients With Advanced Gastric Adenocarcinoma: Results of Microbiota Analysis of PRODIGE 59-FFCD 1707-DURIGAST Trial. Gastroenterology, 2025, 168 (3), pp.601-603.e2. 10.1053/j.gastro.2024.10.020. hal-05157519

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# Faecalibacterium prausnitzii Is Associated With Clinical Response to Immune Checkpoint Inhibitors in Patients With Advanced Gastric Adenocarcinoma: Results of Microbiota Analysis of PRODIGE 59-FFCD 1707-DURIGAST Trial

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The gut microbiota plays a key role in shaping the immune response and has been found to affect responses to immune checkpoint inhibitors (ICIs) in different cancer types, but mostly in melanoma.¹

Enrichment of specific commensal bacteria has been correlated with improved progression-free survival (PFS) and overall survival (OS).

Anti-programmed cell death 1 ICIs have recently been approved as a first-line treatment for advanced esophageal and gastric adenocarcinomas in combination with chemotherapy. Although some data showed that antibiotic administration is associated with worse outcomes in patients treated with programmed cell death 1 blockade,² data regarding the specific role of some microbiome taxa in this setting are scarce.

Here, we took advantage of the DURIGAST study³ to address ICI efficacy and toxicity. Patients were randomized to receive FOLFIRI (leucovorin calcium [folinic acid], fluorouracil, and irinotecan hydrochloride) plus durvalumab (anti-programmed cell death ligand 1) with or without tremelimumab (anti-cytotoxic T-lymphocyte-associated protein 4). A prospective stools collection was planned with 1 stool sample before treatment (week 0) and 1 at the first evaluation at 8 weeks. As no difference was observed in terms of PFS or OS between the 2 arms, all of the patients were considered together in the current study. Indeed, we analyzed 41 stools before treatment and 20 after 8 weeks of treatment. Patients, tumors, and stool characteristics are described in Supplementary Table 1 (the characteristics of the patients were similar to those of the overall study population). Overall, 36% of patients had 2 stools or more by day.

After treatment by chemotherapy plus ICI, we observed significant changes in gut microbiota composition (Figure 1A and B, Supplementary Figure 1A) and function (Figure 1C, Supplementary Figure 1B), but not in terms of α-diversity (Shannon's H diversity index P = .112). Notably, *Fusobacterium nucleatum*, which is involved in the progression of several cancer types,⁴ and several other bacterial species usually found in the oral microbiota were increased at 8 weeks after treatment start as compared with baseline (week 0). At the functional level, the strongest signal driving the difference between week 0 and week 8 is the increased “L-lysine fermentation to acetate and butanoate” pathway, which is directly related to *F nucleatum* (Supplementary Figure 1B). Moreover, there was no difference in the microbiota according to programmed cell death ligand 1 combined positive score (data not shown).

Patients were divided into 2 groups based on the baseline (week 0) relative abundance of each bacteria in their stools and then compared with each end point. Using multivariate Cox regression analyses, we assessed the potential predictive role of

these bacteria in treatment efficacy, taking into account the other clinical factors known to impact OS and PFS. Several bacterial species were independently associated with OS (Figure 1D). These include some bacteria that have already been associated with the occurrence of different types of cancers (Veillonella parvula and Streptococcus thermophilus) or with bad (Enterocloster⁵) or good (Faecalibacterium prausnitzii⁶) responses to ICI. Several pathways involved in metabolism and proliferation were also associated with OS (Figure 1E). Interestingly, only 1 species, *F prausnitzii*, was independently associated with a lower risk of disease progression (hazard ratio, 0.13; 95% CI, 0.04–0.40; adjusted P = .05).

Very few studies are available regarding the predictive role of the fecal microbiota in the response to ICI in patients with esophageal and gastric adenocarcinomas. One Korean study found some association between *Lactobacillus gasseri* and response to ICI,² but 16S (and not shotgun) sequencing methods were used, only 24 patients were analyzed, and confounding factors were not taken into account in the analysis. Another study in a Chinese population, including 32 patients treated with chemotherapy plus ICI, observed a link between the *Lactobacillus* genus and response to treatment (no progression at 6 months of treatment), but no confounding factors were taken into account in the analysis.⁷

The current study took advantage of a prospective trial, used state-of-the-art shotgun metagenomics sequencing and analysis, and is the first performed in a non-Asian population. Interestingly, *Faecalibacterium prausnitzii* was the only taxa significantly associated with both OS and PFS (after correction for multiple testing and taking into account other prognostic factors). Several studies pointed out the potential role of *F prausntzii* as a strong signal associated with response to ICI⁶ in multiple cancers, including melanoma, hepatocellular carcinoma, and non-small cell lung cancer. Recently, Derosa et al⁸ identified 45 bacteria associated with a positive response across different types of cancer, including *F prausnitzii*. In 3 open-label uncontrolled studies, the effect of fecal microbiota transplantation in rescuing the failure to ICI in patients with metastatic melanoma was linked to *F prausnitzii* level.⁹ Finally, in mice models, *F prausnitzii* administration has been found to promote response to ICI,¹⁰ suggesting that this

Abbreviations used in this paper: ICI, immune checkpoint inhibitor; OS, overall survival; PFS, progression-free survival.

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Most current article

© 2025 The Author(s). Published by Elsevier Inc. on behalf of the AGA Institute. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
0016-5085
https://doi.org/10.1053/j.gastro.2024.10.020
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**Figure 1.** (A) The 40 most significant bacterial species (best adjusted *P* values) differentially represented between week 0 (W0) and week 8 (W8) time points according to the multivariate analysis (MaAsLin2). Factors significantly correlated with microbiota composition in these patients (permutational multivariate analysis of variance [Permanova]: *P* < .05) were included in this analysis: sex, age, weight, and metachronous/synchronous metastases. (B) Principal coordinates analysis built from the Bray-Curtis dissimilarity matrices constructed from the normalized abundance of species of each microbiota and (C) their pathways. Ellipses were drawn around the centroids of each emerging community at 95% (inner) and 97% (outer) CIs. (D) Bacterial taxa at W0 significantly associated with OS according to Cox regression multivariate analysis (*P* < .05 and adjusted *P* < .25). Each bacterium was integrated into a Cox model, including factors significantly correlated with OS (*P* < .1; age, body mass index (BMI), number of stools per day, HER2 status, primary tumor site, combined positive score (CPS), and tumor proportion score (TPS). The *P* value was then adjusted using the Benjamini-Hochberg *P* value correction method. (E) Pathways quantified in microbiota significantly correlated with OS in W0 patients according to Cox regression multivariate analysis (adjusted *P* < .25). Each pathway was integrated into a Cox model, including factors significantly correlated with OS (*P* < .1; age, BMI, number of stools per day, HER2 status, primary tumor site, tumor subtype, CPS, and TPS). The *P* value was then adjusted using the Benjamini-Hochberg *P* value correction method.
taxon is more an actor in response to ICI than a simple
marker. Indeed, an *F prausnitzii* strain is currently being
evaluated as an adjuvant to ICI in first-line metastatic gastric
adenocarcinoma (ClinicalTrials.gov Number, NCT06253611).

MARIUS BREDON

Sorbonne Université
Institut National de la Santé et de la Recherche Médical
Gastroenterology Department
Centre de Recherche Saint-Antoine
Assistance Publique-Hôpitaux de Paris
Saint-Antoine Hospital
Paris, France, and
Paris Center for Microbiome Medicine
Fédération Hospitalo-Universitaire
Paris, France

KARINE LE MALICOT

Fédération Francophone de Cancérologie Digestive
EPICAD
Institut National de la Santé et de la Recherche Médical
LNC-UMR 1231
Bourgogne Franche-Comté University
Dijon, France

CHRISTOPHE LOUVET

Department of Medical Oncology
Institute Mutualiste Montsouris
Paris, France

LUDOVIC EVESQUE

Department of Digestive Oncology
Antoine Lacassagne Centre
Nice, France

DANIEL GONZALEZ

Paris Center for Microbiome Medicine
Fédération Hospitalo-Universitaire
Paris, France

DAVID TOUGERON

Department of Gastroenterology and Hepatology
Poitiers University Hospital
Poitiers, France

HARRY SOKOL

Sorbonne Université
Institut National de la Santé et de la Recherche Médical
Gastroenterology Department
Centre de Recherche Saint-Antoine
Assistance Publique-Hôpitaux de Paris
Saint-Antoine Hospital
Paris, France, and
Paris Center for Microbiome Medicine
Fédération Hospitalo-Universitaire
Paris, France, and

Université Paris-Saclay
INRAE
AgroParisTech
Micalis Institute
Jouy-en-Josas, France

**Supplementary Material**

Note: To access the supplementary material accompanying this article, visit the online version of *Gastroenterology* at www.gastrojournal.org, and at https://doi.org/10.1053/j.gastro.2024.10.020.

**References**

1. Routy B, et al. Science 2018;359:91-97.

2. Kim CG, et al. Cell Rep Med 2023;4:101251.

3. Tougeron D, et al. JAMA Oncol 2024;10:709-717.

4. Alon-Maimon T, et al. Periodontol 2000 2022;89:166-180.

5. Fidelle M, et al. Science 2023;380:eabo2296.

6. Gopalakrishnan V, et al. Science 2018;359:97-103.

7. Han Z, et al. Clin Transl Med 2023;13:e1312.

8. Derosa L, et al. Cell 2024;187:3373-3389.e16.

9. Routy B, et al. Nat Med 2023;29:2121-2132.

10. Bredon M, et al. Oncoimmunology 2024;13:2374954.

Received June 20, 2024. Accepted October 10, 2024.

**Correspondence**

Address correspondence to: Harry Sokol, MD, PhD, Service de Gastroentérologie, Hôpital Saint-Antoine, 184 rue du Faubourg Saint-Antoine, 75571 Paris Cedex 12, France. e-mail: harry.sokol@gmail.com; or David Tougeron, MD, PhD, Service d'hépato-gastro-entérologie, Centre Hospitalier Universitaire de Poitiers, 1 rue de la Milétrie, 86000 Poitiers, France. e-mail: david.tougeron@chu-poitiers.fr.

**CrediT Authorship Contributions**

Marius Bredon, PhD (Formal analysis: Lead; Writing – original draft: Lead)
Karine le Malicot, MD (Data curation: Lead)
Christophe Louvet, MD (Investigation: Equal; Writing – review & editing: Supporting)
Ludovic Evesque, MD (Investigation: Equal; Writing – review & editing: Supporting)
Daniel Gonzalez, MD (Investigation: Equal; Writing – review & editing: Supporting)
David Tougeron, MD (Formal analysis: Supporting; Investigation: Lead; Supervision: Equal; Writing – review & editing: Equal)
Harry Sokol, PhD, MD (Conceptualization: Lead; Methodology: Lead; Supervision: Lead; Writing – review & editing: Lead)

**Conflicts of interest**

These authors disclose the following: David Tougeron reports consultancy, advisory fees, and honoraria from Servier, Pierre Fabre, Merck Serono, MSD, BMS, AZ, Roche, and Sanofi; research funding from Sandoz, AstraZeneca, Servier, and MSD; and travel grants from Pierre Fabre, MSD, Roche. Ludovic Evesque reports consultancy, advisory fees, honoraria from MSD, BMS, Astra Zeneca, Servier, Astellas. Harry Sokol reports lecture fee, board membership, or consultancy from Amgen, Fresenius, IPSEN, Actial, Astellas, Danone, THAC, Biose, BiomX, Eligo, Immusmol, Adare, Nestle, Ferring, MSD, Bledina, Pfizer, Biocodex, BMS, Bromatech, Gilead, Janssen, Mayoli, Roche, Sanofi, Servier, Takeda, and Abbvie, has stocks from Enterome Bioscience and is co-founder of Exeliom Biosciences. The remaining authors disclose no conflicts.

**Funding**

This study was funded by Fédération Francophone de Cancérologie Digestive and Sorbonne Université.

**Data Availability**

Data will be supplied on reasonable request to the corresponding authors.
# Supplementary Materials and Methods

## Patients, Study Design, and Stool Samples

Stool samples were obtained from patients with advanced gastric/gastroesophageal junction adenocarcinoma who had disease progression after platinum-based first-line chemotherapy in the PRODIGE 59-FFCD 1707-DURIGAST study (ClinicalTrials.gov, Number NCT03959293).³ The PRODIGE 59-FFCD 1707-DURIGAST study is a randomized, open-label, multicenter, non-comparative, phase 2 study designed to evaluate the safety and efficacy of FOLFIRI plus durvalumab (FD arm) and FOLFIRI plus durvalumab and tremelimumab (FDT arm) in second-line setting of patients with advanced gastric/gastroesophageal junction adenocarcinoma. The main inclusion criteria were patients 18 years or older, histologically proven advanced unresectable gastric/gastroesophageal junction (Siewert 2 or 3) adenocarcinoma with progression or intolerance after first-line chemotherapy with fluoropyrimidine plus platinum salt ± taxane ± anti-HER2 therapies in HER2-positive tumors, with an Eastern Cooperative Oncology Group Performance Status of 0 or 1, and adequate organ function.

The primary end point was the percentage of patients alive and without progression at 4 months (PFS at 4 months) with FD or FDT based on RECIST 1.1 criteria and evaluated by the investigator. Secondary end points included OS and objective response rate.

This study was sponsored by the Fédération Francophone de Cancérologie Digestive and supported by the French PRODIGE intergroup. The PRODIGE 59-FFCD 1707-DURIGAST trial was approved by the French health authorities (Agence Nationale de Sécurité du Médicament et des Produits de Santé) and an independent ethics committee (Comité de Protection des Personnes Nord-Ouest II, number 2018-002014-13) on April 16, 2019. The study was conducted in accordance with the Declaration of Helsinki and International Conference on Harmonization Good Clinical Practice Guidelines. Written informed consent was obtained from all patients before treatment.

Patients were evaluated every 8 weeks using clinical examinations and laboratory and morphologic assessments until progression. Morphologic assessments were based on thoracic-abdominal-pelvic computed tomography scan according to RECIST 1.1 criteria. The treatment was repeated until documented disease progression, unacceptable toxicity, and withdraw of consent or patient refusal.

Analyses of tumor biomarkers included expression of DNA mismatch repair protein by immunohistochemistry, microsatellite instability, and programmed cell death ligand 1 (PD-L1) expression. PD-L1 immunohistochemistry was done at a central laboratory using PD-L1 primary antibody (QR-1; Diagomics, Blagnac, France), according to the manufacturer's instructions to calculate the PD-L1 tumor proportion score and combined positive score. Tumor cell PD-L1 expression (tumor proportion score) was defined as the percentage of viable tumor cells with partial or complete membrane staining in at least 100 viable tumor cells.

Combined positive score was defined as the number of PD-L1-positive tumor cells, lymphocytes, and macrophages divided by the total number of viable tumor cells multiplied by 100.

Stool samples were prospectively collected before the first cycle at baseline (week 0) and at 8 weeks before the first evaluation of treatment efficacy to analyze microbiota. Among the 96 patients randomized, 41 participated in a microbiota ancillary study and 41 samples were available at baseline and 20 at 8 weeks.

## Shotgun Metagenomics Sequencing

Stool samples were resuspended in MGIEasy Stool Sample Collection Kit (1000005265; MGI Tech, Shenzhen, China) and DNA extraction was performed using MagPure Stool DNA LQ Kit (384 RXN; Magen Biotech, Shenzhen, China). Seven hundred microliters stool sample suspension was transferred to 2.0-mL, deep-well plates containing MagPure grinding beads using MGI-STP7000, and extraction was performed using MGI-SP960 automation robot. Cell lysis was performed by beat-beating plate for 1 minute at 1600 rpm and thermal lysis at 65°C for 20 minutes. For DNA purification, 340 µL of sample were used. DNA libraries were prepared using MGIEasy FS DNA Library Prep Set (1000006988; MGI Tech), circularized using MGleasy circularization module V2.0 (1000005260; MGI Tech) and sequenced on DNBSEQ-T7 using High-throughput Sequencing Set (FCL PE150) (1000016106; MGI Tech).

## Read Processing and Quality Control

Reads quality was checked with FastQC (version 0.11.9), and low-quality reads and sequencing adapters were removed using Trimmomatic<sup>e1</sup> (version 0.39). Reads shorter than 75 base pairs were discarded. Host-reads were removed using KneadData with default parameters (version 0.10.0; http://huttenhower.sph.harvard.edu/kneaddata) by mapping reads to the *Homo sapiens* reference database<sup>e2</sup> (build hg37dec_v0.1). Raw sequence data are accessible in the European Nucleotide Archive (accession number PRJNA951620).

## Microbiota Analysis

The samples were taxonomically profiled using MetaPhlAn<sup>e3</sup> For the subsequent analyses (except for α-diversity calculations), the abundance of each taxon present in a sample was normalized using the relative method to allow sample-to-sample comparison. Taxa whose average abundance and prevalence were <0.1% and 3%, respectively, were discarded.

Functional potential analysis of the metagenomic samples (pathway profiles and gene-family abundances) was performed using HUMAnN3<sup>e4</sup> (version v3.0.0, UniRef database release July 2021).

Principal coordinates analyses were carried out on the Bray-Curtis dissimilarity matrices constructed from the abundance of species and pathways. Communities that emerged were verified using a permutational multivariate analysis of variance test with *Vegan* package<sup>e5</sup> (version 2.5-7).
Figures were plotted with the ggplot2<sup>e6</sup> and Complex-Heatmap<sup>e7</sup> packages.

Multivariable association between microbial community abundance and time point was examined with MaAsLin2.<sup>e8</sup> Clinical factors associated with the composition of the microbiota (permutational multivariate analysis of variance adjusted *P* < .05) were included in each analysis: age, weight, height, and hepatic metastases.

## Cox Regression Models

Patients were divided into 2 groups based on the baseline (week 0) relative abundance of each bacterium in their stools. To assess the potential prognostic role of these bacteria in the treatment efficacy, multivariate survival analyses were performed using Cox regression models (for PFS and OS) to determine hazard ratios and 95% CIs using the R Survival package (version 3.2-10). The other clinical factors significantly correlated with OS or PFS (*P* < .1) were considered in each analysis: age, body mass index, number

of stools, HER2 status, primary tumor site, tumor subtype, combined positive score PD-L1, and tumor proportion score PD-L1. *P* values were then adjusted using the Benjamini-Hochberg *P* value correction method.

## Supplementary References

e1. Bolger AM, et al. Bioinformatics 2014;30:2114-2120.

e2. Rosenbloom KR, et al. Nucl Acids. Res 2015; 43:D670-D681.

e3. Blanco-Miguez A, et al. Available at: http://biorxiv.org/lookup/doi/10.1101/2022.08.22.504593.

e4. Beghini F, et al. eLife 2021;10:e65088.

e5. Dixon P. J Veg Sci 2003;14:927-930.

e6. Wickham H. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag, 2016.

e7. Gu Z. iMeta 2022;1.

e8. Mallick H, et al. PLoS Comput Biol 2021;17:e1009442.