The Strength of the T Cell Response Against a Surrogate Tumor Antigen Induced by Oncolytic VSV Therapy Does Not Correlate With Tumor Control
Bibliographic record
Abstract
Cancer therapy using oncolytic viruses has gained interest in the last decade. Vesicular stomatitis virus is an attractive candidate for this alternative treatment approach. The importance of the immune response against tumor antigens in virotherapy efficacy is now well recognized, however, its relative contribution versus the intrinsic oncolytic capacity of viruses has been difficult to evaluate. To start addressing this question, we compared glycoprotein and matrix mutants of vesicular stomatitis virus (VSV), showing different oncolytic potentials for B16/B16gp33 melanoma tumor cells in vitro, with the wild-type virus in their ability to induce tumor-specific CD8(+) T cell responses and control tumor progression in vivo. Despite the fact that wild-type and G mutants induced a stronger gp33-specific immune response compared to the MM51R mutant, all VSV strains showed a similar capacity to slow down tumor progression. The effectiveness of the matrix mutant treatment proved to be CD8(+) dependent and directed against tumor antigens other than gp33 since adoptive transfer of isolated CD8(+) T lymphocytes from treated B16gp33-bearing mice resulted in significant protection of naive mice against challenge with the parental tumor. Remarkably, the VSV matrix mutant induced the upregulation of major histocompatibility class-I antigen at the tumor cell surface thus favoring recognition by CD8(+) T cells. These results demonstrate that VSV mutants induce an antitumor immune response using several mechanisms. A better understanding of these mechanisms will prove useful for the rational design of viruses with improved therapeutic efficacy.
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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) | 0.001 | 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".