Combination of a DepoVax™ peptide vaccine with a lentivector vaccine induces strong antigen-specific immune responses and provides effective tumor control in murine models
Bibliographic record
Abstract
Abstract Many strategies are being investigated to induce and sustain anti-tumor immune responses through vaccination. Viral vectors have been used to induce robust cellular immune responses, however due to immune responses against the vector they loose potency with boosting vaccinations. In this study, we investigated a heterologous prime boost strategy that combines lentivirus and peptide vaccination to induce robust anti-tumour immune responses in a preclinical model. DepoVax™ is a water-free vaccine formulation that facilities strong and sustained immune responses to peptide antigens. We showed that while homologous immunizations with DepoVax peptide vaccine containing HPV16 E749–57 T cell peptide antigen (DPX-FP) elicit potent antigen-specific responses, heterologous prime-boost immunizations with DPX-FP and lentivector vaccine expressing HPV16 E7 protein could further enhance systemic immune responses detected by IFN-γ ELISPOT. The synergy between two vaccines is influenced by the order of vaccine administration, dose, and delivery route. Using an HPV-expressing murine tumor model (C3), we found that heterologous priming with the DPX-FP vaccine and boosting with E7 lentivector vaccine enhanced tumor control and increased survival rates as compared to the treatments with either vaccine alone. RT-qPCR profiling demonstrated that the vaccine-draining lymph node cells of mice primed with DPX-FP and boosted with E7 lentivector vaccine had an altered immune gene signature with an increased expression of cytotoxic T lymphocyte markers (Gzmb and Tbx21). Taken together our results support a rationale for combining DepoVax based therapy with lentivector vaccines in future 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) | 0.000 | 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".