Abstract 2210: Microbiome profiling reveals that fecal microbiota transplantation (FMT) modulates response and toxicity when combined with immunotherapy in patients with lung cancer and melanoma (FMT-LUMINate NCT04951583)
Notice bibliographique
Résumé
Background: Trials have shown that FMT plus immune checkpoint inhibitors (ICI) could improve ⍺PD-1 response in melanoma. The microbiome and immune shift, along with the clinical impact of FMT with ⍺PD-1 in non-small cell lung cancer (NSCLC) and in combination with ⍺PD-1+⍺CTLA-4 (dual ICI) in melanoma is unknown. Methods: 20 patients (pts) with advanced NSCLC treated with first-line (1L) ⍺PD-1 and 20 with advanced melanoma treated with 1L dual ICI were recruited. FMT from 1 healthy donor (HD) was administered orally to each pt post bowel prep. 1 week later ICI was initiated. A total of 11 different HD were used. The primary endpoint was objective response rate (ORR). The gut microbiome was profiled using shotgun metagenomics. Metabolomic profiling was performed using HPLC. Olink proteomics (96 markers) on plasma and flow cytometry (40 markers) on PBMCs were assessed. FMT was performed in germ-free (GF) mice to recapitulate engraftment. Results: In NSCLC, ORR was 80% (16 PR) with no grade >3 immune-related adverse events (irAE). In melanoma, ORR was 75% (5 CR), with 65% (13 pts) developing grade >3 irAE, including 3 (15%) myocarditis. The irAE occurred earlier than previously reported (median onset of 42 days). Pre-FMT patient microbiome composition was distinct from HD. Post-FMT, the responders (R) and non-responders (NR) clustered separately (p=0.03). Post-FMT, we observed increased polyamines and bile salts in R. Elevation of exhausted T central and T effector memory cells were noted in R. Conversely, proteomics analyses revealed elevated markers of immune exhaustion including CD28 and IL10 in NR. Addressing the link between microbiome and irAE, in melanoma, 6 out of the 13 pts (46%) that developed grade >3 irAE, including 2/3 of the myocarditis received FMT from the same donor (D5). D5 was not associated with irAE in NSCLC treated with ⍺PD-1 alone. Microbiome analysis revealed that D5 clustered separately (p<0.01) from other HD with a high relative abundance of Prevotella copri. We observed a signal for the development of irAE in pts that engrafted P. copri. Pts with grade>3 irAE exhibited higher levels of leaky gut markers such as sST2 post-FMT (p=0.002). In GF mice, oral gavage of baseline stool of R, only when followed by the stool of the respective donor, led to a significant increase in ⍺ diversity and number of species engrafted over time (p<0.01), while those that received baseline stool from NR with or without donor had no change in ⍺ diversity or species engrafted. Conclusions: This study validates the role of FMT to improve ICI activity in NSCLC and melanoma, while differentially modulating irAE according to ICI backbone. We demonstrated that the microbiome shift is distinct among R while engraftment of P. copri was associated with dual ICI irAE. Our results directly inform future microbiome-centered trials. Citation Format: Sreya Duttagupta, Meriem Messaoudene, Rahima Jamal, Catalin Mihalcioiu, Nicolas Marcoux, Sebastian Hunter, Gianmarco Piccinno, Michal Puncochár, Karl Bélanger, Mustapha Tehfe, Normand Blais, Scott Owen, Jacques Raphael, Jade Maillou, Yongjia Hu, Myriam Benlaifaoui, Somayeh Nilli, John Lenehan, Wiam Belkaid, Seema Nair Parvathy, Diogjena Katerina Prifti, Marine Fidelle, Seunghee Kim-schulze, Thomas Marron, Sylvere Durand, Guido Kroemer, Nicola Segata, Lisa Derosa, Laurence Zitvogel, Antoine Desilets, Michael Silverman, Saman MalekiVareki, Bertrand Routy, Arielle Elkrief. Microbiome profiling reveals that fecal microbiota transplantation (FMT) modulates response and toxicity when combined with immunotherapy in patients with lung cancer and melanoma (FMT-LUMINate NCT04951583) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2210.
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 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».