Codelivery of mRNA with α-Galactosylceramide Using a New Lipopolyplex Formulation Induces a Strong Antitumor Response upon Intravenous Administration
Bibliographic record
Abstract
Recently, the use of mRNA-based vaccines for cancer immunotherapy has gained growing attention. Several studies have shown that mRNA delivered in a vectorized format can generate a robust and efficient immune response. In this work, a new lipopolyplex vector (multi-LP), incorporating the immune adjuvant α-galactosylceramide (α-GalCer) and a multivalent cationic lipid, was proposed for the in vivo delivery of mRNA into antigen-presenting cells. We demonstrate that dendritic cells (DCs) can be targeted in vivo by intravenous administration of a α-GalCer-/mRNA-loaded multi-LP vector, without the need for its functionalization with cell-specific antibodies or ligands. The multi-LP nanoparticles loaded with a reporter mRNA efficiently led to high expression of the enhanced green fluorescence protein in DCs both in vitro and in vivo, exhibiting an intrinsic selectivity for DCs. Finally, the TRP2-mRNA/α-GalCer-based multi-LP vaccine induced a significant therapeutic effect against a highly malignant B16-F10 melanoma tumor. This study provides the first evidence that a combination of antigen-mRNA and α-GalCer can be used as an effective antitumor vaccine, inducing strong innate and adaptive immune responses.
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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 | 0.001 |
| 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".