Murine Responses to Recombinant MVA Versus ALVAC Vaccines Against Tumor-associated Antigens, gp100 and 5T4
Bibliographic record
Abstract
Virally vectored cancer vaccines comprise a new form of immunotherapy that aim to generate anti-tumor immune responses with potential for tumor clearance and enhanced patient survival. Here, we compared 2 replication-deficient poxviruses modified vaccinia Ankara (MVA) and ALVAC(2) in their ability to induce antigen expression and immunogenicity of the tumor-associated antigens (TAAs) 5T4 and gp100. To facilitate the comparison, recombinant MVA-gp100M and ALVAC(2)-5T4 were constructed to complement existing ALVAC(2)-gp100M and MVA-5T4 vectors. Recombinant TAA expression in chicken embryo fibroblast cells was confirmed by Western blot analysis. 5T4 expression was approximately equal for both viruses, whereas ALVAC-derived gp100 was quickly degraded, at a time point when MVA-derived gp100 was still stable and expressed at high levels. Human leukocyte antigen-A2 transgenic mice were vaccinated with recombinant viruses and the CD8 T-cell responses elicited against each TAA were monitored by interferon-γ enzyme-linked immunospot. No 5T4 peptide responses were detected using splenocytes from mice vaccinated with either vector, whereas vaccination with MVA elicited a significantly higher gp100-specific response than ALVAC(2) at 10 PFU (P<0.001). In CD-1 mice, each vector elicited similar 5T4 antibody responses, whereas MVA was more potent and induced gp100 antibody responses at a lower immunization dose than ALVAC (P<0.001). In this study, immunogenicity varied depending on the viral vector used and reflected vector-associated differences in in vitro TAA expression and stability. These findings suggest that novel vector-transgene combinations must be assessed individually when designing vaccines, and that stability of vector-encoded proteins produced in vitro may be useful as a predictor for in vitro immunogenicity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".