Enhancing Immune Responses to Cancer Vaccines Using Multi-Site Injections
Bibliographic record
Abstract
Abstract For a vaccine to be effective it must induce a sufficiently robust and specific immune response. Multi-site injection protocols can increase the titers of rabies virus-neutralizing antibodies. Hypothetically, spreading a vaccine dose across multiple lymphatic drainage regions could also potentiate T cell responses. We used a replication-deficient adenovirus serotype 5-vectored cancer vaccine targeting the melanoma-associated antigen dopachrome tautomerase. Clinically, high numbers of tumor-infiltrating CD8+ T cells are a positive prognostic indicator. As such, there is interest in maximizing tumor-specific T cell responses. Our findings confirm a positive correlation between the number of tumor-specific T cells and survival. More importantly, we show for the first time that using multiple injection sites could increase the number of vaccine-induced CD8+ T cells specific for a self-tumor antigen. Further, the number of tumor antigen-specific antibodies, as well CD8+ T cells specific for a foreign antigen could also be enhanced. Our results show that multi-site vaccination induces higher magnitude immune responses than a single-bolus injection. This provides a very simple and almost cost-free strategy to potentially improve the efficacy of any current and future vaccine. Broader clinical adoption of multi-site vaccination protocols for the treatment of cancers and infectious diseases should be given serious consideration.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".