Oncolytic vesicular stomatitis virus quantitatively and qualitatively improves primary CD8<sup>+</sup>T-cell responses to anticancer vaccines
Bibliographic record
Abstract
The ability of heterologous prime-boost vaccination to elicit robust CD8+ T cell responses has been well documented. In contrast, relatively little is known about how this immunotherapeutic strategy impacts the functional qualities of expanded T cells in the course of effector and memory responses. Using vesicular stomatitis virus (VSV) as a boosting vector in mice, we demonstrate that a massive secondary expansion of CD8+ T cells can be achieved shortly after priming with recombinant adenoviral vectors. Importantly, VSV-boosted CD8+ T cells were more potent than those primed by adenoviruses only, as measured by cytokine production, granzyme B expression, and functional avidity. Upon adoptive transfer, equivalent numbers of VSV-expanded CD8+ T cells were more effective (on a per-cell basis) in mediating antitumor and antiviral immunity than T cells only primed with adenoviruses. Furthermore, VSV boosting accelerated the progression of expanded CD8+ T lymphocytes to a central memory phenotype, thereby altering the effector memory profile typically associated with adenoviral vaccination. Finally, the functional superiority of VSV-expanded T cells remained evident 100 d after boosting, suggesting that VSV-driven immunological responses are of sufficient duration for therapeutic applications. Our data strongly support the choice of VSV as a boosting vector in prime-boost vaccination strategies, enabling a rapid amplification of CD8+ T cells and improving the quality of expanded T cells during both early and late immunological responses.
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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) | 0.000 | 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) | 0.002 | 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".