Abstract CT022: Evaluation of oral microbiome profiling as a response biomarker in squamous cell carcinoma of the head and neck: Analyses from CheckMate 141
Notice bibliographique
Résumé
Abstract Introduction: Recent studies indicated that specific intestinal microbiota may modulate efficacy of anti-PD-1 and anti-CTLA-4 immunotherapy in preclinical tumor models (Sivan et al, Science. 2015;350:1084-9; Vétizou et al, Science. 2015;350:1079-84). However, little is known regarding the association of the oral microbiome with checkpoint blockade immunotherapy. In CheckMate 141 (NCT02105636), a randomized global phase 3 study comparing nivolumab with investigator’s choice (IC) therapy in patients with platinum-refractory squamous cell carcinoma of the head and neck (SCCHN), nivolumab improved median overall survival compared with IC (7.5 vs 5.1 months; P=0.01) (Ferris et al, NEJM. 2016;375:1856-67). This analysis assessed if oral microbiome profiling would yield prognostic biomarkers of response to anti-PD-1 immunotherapy in patients with SCCHN treated in CheckMate 141. Methods: Saliva samples were obtained at screening (nivolumab n=85, IC n=31) and week 7 of treatment (nivolumab n=77, IC n=28), and profiled using high-throughput 16S ribosomal RNA sequencing. Bacterial abundance was estimated using a combined differential abundance modeling approach, normalized using cumulative sum scaling to correct for sequencing depth, and analyzed using a linear model for association with response (complete response/partial response vs stable disease vs progressive disease), tumor PD-L1 expression, HPV16 status, treatment history, and patient demographics. Results: Among 221 saliva samples analyzed, bacteria from 13 phyla and 542 species were detected. At baseline, no significant associations were detected in richness of bacterial diversity with best overall response, tumor PD-L1 expression, or HPV16 status. No associations in microbial alpha and beta diversity were detected with treatment modality (nivolumab vs IC) or treatment duration (baseline vs week 7). Patients with prior radiation therapy (n=97) had lower abundance of bacteria from the families Prevotellaceae and Flavobacteriaceae than patients without prior radiation therapy (n=17). The abundance of bacteria from the families Desulfobulbaceae and Flavobacteriaceae was higher in European (n=56) than North American (n=46) patients. Conclusion: Differences in certain bacterial species were observed in patients with prior radiation therapy and between different geographical locations. However, no significant associations were detected between oral bacterial diversity and clinical response, tumor PD-L1 expression, HPV16 status, or treatment modality. This exploratory analysis is the first to evaluate the oral microbiome as a biomarker in a randomized, phase 3 clinical trial for immunotherapy in SCCHN and may serve as a basis for future assessments and experimental design. Correlation of salivary and intestinal microbiota with that from tumor specimens is warranted. Citation Format: Robert L. Ferris, George Blumenschein, Kevin Harrington, Jérôme Fayette, Joël Guigay, A. Dimitrios Colevas, Lisa Licitra, Everett Vokes, Maura Gillison, Caroline Even, Cheryl Ho, Makoto Tahara, Robert Haddad, Mark Lynch, Manish Monga, Somnath Bandyopadhyay, Omar Jabado, Henry Kao. Evaluation of oral microbiome profiling as a response biomarker in squamous cell carcinoma of the head and neck: Analyses from CheckMate 141 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr CT022. doi:10.1158/1538-7445.AM2017-CT022
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 | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 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) | 0,000 | 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 ».