mRNA-LNP vaccination orchestrates systemic immunity to control human papillomavirus-positive head and neck squamous cell carcinoma
Bibliographic record
Abstract
Messenger RNA (mRNA) vaccines have demonstrated significant potential in cancer immunotherapy by activating both innate and adaptive immunity. However, the detailed cellular and molecular dynamics underpinning these systemic immune responses remain incompletely understood. In this study, we characterized the systemic immune landscape following human papillomavirus (HPV)-targeted mRNA-lipid nanoparticle (LNP) vaccination using single-cell RNA sequencing (scRNA-seq) in a murine model of HPV-positive head and neck squamous cell carcinoma (HNSCC). Our study revealed a coordinated remodeling of the systemic immune landscape, involving the tumor microenvironment (TME), tumor-draining lymph nodes (TDLNs), spleen, and blood. Notably, we pioneered a distinct interferon-stimulated gene (ISG) signature across multiple lymphoid subsets in TDLNs, driven by the LNP component, which contributed to rapid, non-antigen-specific immune activation. Additionally, HPV mRNA-LNP vaccination induced an antigen-specific cycling burst of immune cells that mediated tumor control through a systemic coordination of multi-directional differentiation into anti-tumor cell compositions. These findings enhance our understanding of how mRNA-LNP vaccination orchestrates systemic anti-tumor responses and highlight the therapeutic potential of targeting ISG-expressing and cycling immune cells to improve vaccine efficacy, paving the way for future clinical applications in HPV-related cancers.
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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) | 0.001 | 0.000 |
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".