Single immunizations of self-amplifying or non-replicating mRNA-LNP vaccines control HPV-associated tumors in mice
Bibliographic record
Abstract
As mRNA vaccines have proved to be very successful in battling the coronavirus disease 2019 (COVID-19) pandemic, this new modality has attracted widespread interest for the development of potent vaccines against other infectious diseases and cancer. Cervical cancer caused by persistent human papillomavirus (HPV) infection is a major cause of cancer-related deaths in women, and the development of safe and effective therapeutic strategies is urgently needed. In the present study, we compared the performance of three different mRNA vaccine modalities to target tumors associated with HPV-16 infection in mice. We generated lipid nanoparticle (LNP)–encapsulated self-amplifying mRNA as well as unmodified and nucleoside-modified non-replicating mRNA vaccines encoding a chimeric protein derived from the fusion of the HPV-16 E7 oncoprotein and the herpes simplex virus type 1 glycoprotein D (gDE7). We demonstrated that single low-dose immunizations with any of the three gDE7 mRNA vaccines induced activation of E7-specific CD8 + T cells, generated memory T cell responses capable of preventing tumor relapses, and eradicated subcutaneous tumors at different growth stages. In addition, the gDE7 mRNA-LNP vaccines induced potent tumor protection in two different orthotopic mouse tumor models after administration of a single vaccine dose. Last, comparative studies demonstrated that all three gDE7 mRNA-LNP vaccines proved to be superior to gDE7 DNA and gDE7 recombinant protein vaccines. Collectively, we demonstrated the immunogenicity and therapeutic efficacy of three different mRNA vaccines in extensive comparative experiments. Our data support further evaluation of these mRNA vaccines in clinical trials.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".