Abstract PR003: Preventing and treating HPV-related Cancer with mRNA Therapy Expressing A DC-targeting Antigen
Bibliographic record
Abstract
Abstract Persistent human papillomavirus (HPV) infection is linked to several malignancies, highlighting the need for effective therapeutic vaccines. In this study, we tested a lipid nanoparticle-encapsulated mRNA vaccine expressing tHA-mE7-mE6. By mutating the E6 and E7 proteins to reduce their tumorigenicity and fusing them with a truncated influenza hemagglutinin protein (tHA) for better antigen uptake, we improved the vaccine’s efficacy. The tHA-mE7-mE6 mRNA vaccine showed superior results compared to mE7-mE6 mRNA, achieving complete tumor regression and preventing new tumors in an E6 and E7 positive model. It elicited a strong CD8+ T-cell response, with antigen-specific CD8+ T-cells found in the spleen, blood, and tumors. Additionally, the therapy increased DC and NK cell infiltration into tumors. Overall, this study demonstrates that the tHA-mE7-mE6 mRNA vaccine induces a potent anti-tumor immune response, showing promise for treating HPV-induced cancers and preventing recurrence. Citation Format: William Jia. Preventing and treating HPV-related Cancer with mRNA Therapy Expressing A DC-targeting Antigen [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: RNAs as Drivers, Targets, and Therapeutics in Cancer; 2024 Nov 14-17; Bellevue, Washington. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(11_Suppl):Abstract nr PR003.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".