Abstract PR08: Personalized therapeutic mRNA nano-vaccines are effective across multiple preclinical and translational models of head and neck cancer
Notice bibliographique
Résumé
Abstract Objectives: The translational pipeline for novel immunotherapeutics is often stymied due to the lack of relevant immune-competent pre-clinical animal models which can facilitate the path to human clinical trials. The objective of this study is to demonstrate anti-tumor activity and feasibility of our novel formulation of patented therapeutic personalized mRNA lipid nanoparticle vaccines in head and neck pre-clinical models including murine models of oral cancer (metastatic and primary), metastatic thyroid cancer, and a client-owned felines with spontaneously occurring oral squamous cell carcinoma. Methods: (1) C57B/6 mice were implanted with syngeneic murine cells lines to generate; oral tongue cancer models (MOC1/MOC2 cells), flank models of oral cancer (MOC1 cells), oral cancer lung metastasis models (MOC1 cells), and thyroid cancer lung metastasis models (hRAS cells). Mice were treated with tumor derived mRNA nano-vaccines weekly, x5 weeks. Tumor volumes and survival curves were recorded. MOC1 tumors and blood were analyzed for immune cell infiltration using flow cytometry. (2) Client-owned felines (N=5) with spontaneously occurring oral squamous cell carcinoma, were enrolled into our clinical trial (conducted at the University Veterinary School) after owner consent was obtained. Tumor biopsy was used to make tumor-specific mRNA-nano-vaccines from the surgical specimen. Feline patients were administered weekly vaccines intravenously x3 and monitored for adverse events. Results: 1) Mice treated with tumor-derived mRNA nano-vaccines demonstrated significantly decreased tumor volumes and increased overall survival when compared with untreated controls in both MOC1 and the more aggressive MOC2 cell lines (p<0.001). Vaccine treated mice also had significantly decreased metastatic MOC1 and hRAS tumor burden in the lungs. Orthotopic models for MOC1 tumors were more responsive to mRNA nano-vaccine therapy than subcutaneous (flank) models (p<0.05). Immune responses were characterized by significantly increased intratumoral and systemic CD4 and CD8 T-cell infiltration and decreased tumor myeloid derived suppressor cells (MDSCs). 2) Feline patients tolerated initial dosing of mRNA nano-vaccine administration with no significant toxicities or adverse events (stable vital signs and CBC). Overall survival of feline patients was significantly improved when compared to untreated patients (mean of 690 days vs 100 days respectively, p<0.03). Conclusion: Personalized mRNA nano-vaccines are effective and safe in preclinical murine head and neck and feline patient models. Our vaccine formulation has FDA IND (Investigation New Drug) approval for human use in solid tumors. Utilizing murine models in addition to large animals of head and neck cancer has facilitated the translational research pipeline in development of novel immunotherapeutics and will guide us in designing our first-in-human clinical trial for head and neck cancer patients which will open in 2023. Citation Format: Natalie L. Silver, Johnathan Chardon-Robles, Jin Dai, Rekha Garg, John Ligon, Carlos Souza, Paul Castillo, Rowan Milner, Bikash Sahay, Timothy Chan, Duane Mitchell, Elias Sayour. Personalized therapeutic mRNA nano-vaccines are effective across multiple preclinical and translational models of head and neck cancer [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Innovating through Basic, Clinical, and Translational Research; 2023 Jul 7-8; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2023;29(18_Suppl):Abstract nr PR08.
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) | 0,000 | 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 | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».