Abstract PR08: Personalized therapeutic mRNA nano-vaccines are effective across multiple preclinical and translational models of head and neck cancer
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".