Abstract B018: Gut microbiome influences the antitumor response to BCG immunotherapy in bladder cancer
Notice bibliographique
Résumé
Abstract Introduction: Bacillus Calmette-Guérin (BCG) intravesical immunotherapy remains the standard treatment for non-muscle-invasive bladder cancer (NMIBC). Unfortunately, up to 40% of patients experience disease recurrence. Emerging evidence suggests that gut microbiome composition and function influence responses to systemic immunotherapies. Here, we investigated the impact of the gut microbiota on the antitumor efficacy of BCG in NMIBC patients and avatar mouse models. Methods: We prospectively collected fecal samples from 58 NMIBC patients prior to BCG therapy initiation and profiled their gut microbiome composition using 16S rRNA sequencing and whole shotgun metagenomics. Targeted metabolites from feces and patient serum were analyzed using GC-FID. To assess causality, fecal microbiota transplantation (FMT) from responders (R; n=42) and non-responders (NR; n=16) was performed into gut microbiota-depleted MBT-2 tumor-bearing mice, followed by weekly intratumoral BCG treatment. Bulk RNA sequencing of tumors was performed to elucidate the molecular mechanisms. To test the biological activity of specific bacterial strains identified from R patients, NR avatar mice were supplemented with the bacterial consortium. The immune-potentiating effect of this consortium on BCG activity was evaluated using flow cytometry. The immunogenic activity of bacterial strains was further assessed by IFN-γ ELISpot assays on splenocytes. Results: Differential abundance analysis of gut microbiome between R and NR patients identified nine taxa enriched in R. Stratifying patients by median abundance of each taxon revealed that a higher abundance of three bacteria was significantly associated with prolonged recurrence-free survival (RFS). FMT from NR patients reduced BCG efficacy compared to R patients. Metabolomic analysis identified one specific SCFA enriched in R patients, which was also elevated in the cecum of R-avatar mice. Flow cytometry analysis from R- vs. NR-avatar mice showed that FMT from R patients increased IFN-γ+CD8+ T cells and reduced TAMs and MDSCs in the tumor microenvironment, supported by RNA sequencing data showing an enrichment of IFN-γ and TNF-α signaling pathways in tumors. Oral supplementation of NR-recipient mice with the R-associated bacterial consortium restored the BCG efficacy. This was accompanied by increased CD3+ T cells in mLNs, elevated IFN-γ+ CD4+ and CD8+ T cells, and a reduction in PD-L1+ myeloid cells in spleen and tumor. ELISpot showed increased IFN-γ production in response to the bacterial strains, supporting an immunogenic potential and synergistic activity with BCG immunotherapy. Conclusions: Our findings demonstrate that gut microbiome influences the response to BCG immunotherapy in NMIBC patients. This study paves the way for microbiome-targeted strategies such as bacterial supplementation or metabolite-based interventions to enhance the efficacy of BCG treatment in bladder cancer. Citation Format: Jalal Laaraj, Gabriel Lachance, Prisca Nadège. Koné, Roxane Tourigny, Typhaine Gris, Paul Toren, Alain Bergeron, Sandra Isabel, Yves Fradet, Karine Robitaille, Vincent Fradet. Gut microbiome influences the antitumor response to BCG immunotherapy in bladder cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Mechanisms of Cancer Immunity and Cancer-related Autoimmunity; 2025 Sep 24-27; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2025;13(9 Suppl):Abstract nr B018.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».