Abstract 5915: Exploring the impact of microbiome in the response of combined radiation with immune checkpoint blockade in muscle invasive bladder cancer (MIBC)
Notice bibliographique
Résumé
Abstract BACKGROUND: Radiation therapy is a promising bladder-sparing option for MIBC treatment. Yet, 30% of patients do not respond and of those, half will later die of metastasis. Combining radiation therapy with immunotherapy (CT) thus presents a worthy candidate to fulfill this unmet need. In preclinical models, improved antitumor responses when RT is combined with PD-1/PD-L1 blockade have been described. Yet, there remains a flagrant lack of knowledge on the determinants of combination therapy success. In this context, the composition of the gut microbiome is critical in conditioning local and peripheral immunity. Recent metagenomic studies have shown the gut microbiome influences the efficacy of PD-1-based IT in epithelial tumors, melanoma, and lung cancer, to name a few. In addition, modification of the gut microbiome with oral gavage of immunogenic bacteria potentiates the activity of combined RT and anti-PD-L1 therapy. Furthermore, immune profiling shows enhanced systemic and antitumor immunity in responding patients with a favorable gut microbiome (such as enrichment in Akkermansia muciniphila, Bifidobacterium and Faecalibacterium). Therefore, we aim to document the role of patients’ microbiome in polarizing anti-tumor immune responses to CT in order to eventually use its composition as a predicting factor of CT success. METHODS: Fecal material from responder (R, n=3) and non-responder (NR, n=3) MIBC patients was gavaged into 20 germ-free (GF) mice in two administrations one week apart. 3 weeks after the last gavage, tumor cells (MB49) were delivered subcutaneaously. Once tumors reached 0.1-0.15cm3, mice were randomized into 4 groups: control; anti-PD-L1 alone; RT alone; RT + anti-PD-L1. 7 days after the start of treatment, tumors were harvested, dissociated and frozen as cell suspensions for single cell immune profiling and TCR sequencing (10X Genomics). Stool were collected weekly for 16S sequencing. Correlation networks from sequencing data were built (TransNet and Microbiome R packages) and visualized using Cytoscape to show interactions between the tumor immune microenvironment and the gut microbiome in R and NR-receiving mice. RESULTS: We show feasibility of the experimental design as well as robust engraftment of human FMT to germ-free mice in a bladder cancer tumor model. FMT from NR lessened the known beneficial effects of RT in the MB49 model compared to FMT from R. Transkingdom analysis of sc-RNA-seq and 16S show robust statistical interactions between immunosuppression and enrichment in microbes associated to poor outcome in immunotherapy. SIGNIFICANCE: To our knowledge, this is the first study to use FMT as a modulator of response in the context of radiation therapy combinations in MIBC. Findings from this study have a strong predictive value that could be used to select patients who will benefit most from a personalized therapeutic approach. Citation Format: Eva Michaud, Cynthia Faubert, Jose Joao Mansure, Irah King, Wassim Kassouf. Exploring the impact of microbiome in the response of combined radiation with immune checkpoint blockade in muscle invasive bladder cancer (MIBC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5915.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 source (Gemma direct ou Codex distillé), 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 ».