Adaptive Antiviral Immunity Is a Determinant of the Therapeutic Success of Oncolytic Virotherapy
Bibliographic record
Abstract
Oncolytic virotherapy, the selective killing of tumor cells by oncolytic viruses (OVs), has emerged as a promising avenue of anticancer research. We have previously shown that KM100, a Herpes simplex virus type-1 (HSV) deficient for infected cell protein 0 (ICP0), possesses substantial oncolytic properties in vitro and has antitumor efficacy in vivo, in part by inducing antitumor immunity. Here, we illustrate through T-cell immunodepletion studies in nontolerized tumor-associated antigen models of breast cancer that KM100 treatment promotes antiviral and antitumor CD8(+) cytotoxic T-cell responses necessary for complete tumor regression. In tolerized tumor-associated antigen models of breast cancer, antiviral CD8(+) cytotoxic T-cell responses against infected tumor cells correlated with the induction of significant tumoristasis in the absence of tumor-associated antigen-specific CD8(+) cytotoxic T-cells. To enhance oncolysis, we tested a more cytopathic ICP0-null HSV and a vesicular stomatitis virus M protein mutant and found that despite improved in vitro replication, oncolysis in vivo did not improve. These studies illustrate that the in vitro cytolytic properties of OVs are poor prognostic indicators of in vivo antitumor activity, and underscore the importance of adaptive antiviral CD8(+) cytotoxic T-cells in effective cancer virotherapy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".