Different immunogenicity but similar antitumor efficacy of two DNA vaccines coding for an antigen secreted in different membrane vesicle‐associated forms
Bibliographic record
Abstract
The induction of an active immune response to control or eliminate tumours is still an unfulfilled challenge. We focused on plasmid DNA vaccines using an innovative approach whereby the antigen is expressed in association with extracellular vesicles (EVs) to facilitate antigen cross-presentation and improve induced immunity. Our two groups had independently shown previously that DNA vaccines encoding EV-associated antigens are more efficient at inducing cytotoxic T-cell responses than vaccines encoding the non-EV-associated antigen. Here, we compared our two approaches to associate the ovalbumin (OVA) antigen to EVs: (a) by fusion to the lipid-binding domain C1C2 of MFGE8(=lactadherin), which is exposed on the surface of secreted membrane vesicles; and (b) by fusion to retroviral Gag capsid protein, which is incorporated inside membrane-enclosed virus-like particles. Plasmids encoding either form of modified OVA were used as DNA-based vaccines (i.e. injected into mice to allow in vivo expression of the antigen associated to EVs). We show that both DNA vaccines induced, with similar efficiency, OVA-specific CD8(+) T cells and total IgG antibodies. By contrast, each vaccine preferentially stimulated different isotypes of immunoglobulins, and the OVA-C1C2-encoding vaccine favoured antigen-specific CD4(+) T lymphocyte induction as compared to the Gag-OVA vaccine. Nevertheless, both OVA-C1C2 and Gag-OVA vaccines efficiently prevented in vivo outgrowth of OVA-expressing tumours and reduced tumour progression when administered to tumour-bearing mice, although with variable efficacies depending on the tumour models. DNA vaccines encoding EV-associated antigens are thus promising immunotherapy tools in cancer but also potentially other diseases.
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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 | 0.000 |
| 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) | 0.001 | 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".