Using a Prime-Boost Vaccination Strategy That Proved Effective for High Resolution Epitope Mapping to Characterize the Elusive Immunogenicity of Survivin
Bibliographic record
Abstract
Survivin is a member of the inhibitor of apoptosis family of proteins and has been reported to be highly expressed in a variety of cancer types, making it a high priority target for cancer vaccination. We previously described a heterologous prime-boost strategy using a replication-deficient adenovirus, followed by an oncolytic rhabdovirus that generates unprecedented antigen-specific T cell responses. We engineered each vector to express a mutated version of full-length murine survivin. We first sought to uncover the complete epitope map for survivin-specific T cell responses in C57BL/6 and BALB/c mice by flow cytometry. However, no T cell responses were detected by intracellular cytokine staining after re-stimulation of T cells. Survivin has been found to be expressed by activated T cells, which could theoretically cause T cell-mediated killing of activated T cells, known as fratricide. We were unable to recapitulate this phenomenon in experiments. Interestingly, the inactivated survivin construct has been previously shown to directly kill tumor cells in vitro. However, there was no evidence in our models of induction of death in antigen-presenting cells due to treatment with a survivin-expressing vector. Using the same recombinant virus-vectored prime-boost strategy targeting the poorly immunogenic enhanced green fluorescent protein proved to be a highly sensitive method for mapping T cell epitopes, particularly in the context of identifying novel epitopes recognized by CD4+ T cells. Overall, these results suggested there may be unusually robust tolerance to survivin in commonly used mouse strains that cannot be broken, even when using a particularly potent vaccination platform. However, the vaccination method shows great promise as a strategy for identifying novel and subdominant T cell epitopes.
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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 | 0.001 | 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".