Oncolytic vaccinia virus expression of a defined peptide-MHCI complex as a precision cancer immunotherapy platform
Bibliographic record
Abstract
Abstract Oncolytic viruses are immunotherapeutic agents that selectively replicate in and kill tumour cells with the goal of promoting anti-cancer immune responses. Vaccinia virus (VACV) is a strong oncolytic virus candidate as it infects a wide range of cancer cells, is amenable to genetic tailoring and induces potent, long-lasting immunity. Here, we examine the use of genetically engineered oncolytic VACVs (oVACV) to express a variety of MHC-I complexes and the co-stimulatory ligand CD80 to stimulate anti-tumour CD8 + T cell responses in two syngeneic cancer models. Tumour antigen specific CD8 + T cells were detected in both the tumour and spleen following oVACV treatment, demonstrating the ability to induce tumour targeted T cell responses with viral therapy. oVACV expression of peptide-MHC-I complexes enhanced the efficacy of primary tumour clearance with tumour specific antigen targeting showing the greatest efficacy. Initial tumour clearance following treatment with oVACVs led to variable anti-tumour immune memory depending on the tumour model. Depletion of CD8 + T cells prevented therapeutic efficacy of oVACV while combination with αPD-L1 immune checkpoint blockade enhanced tumour clearance. Overall, we demonstrate the ability to generate an oncolytic virus capable of inducing recognition and elimination of tumour cells through VACV-mediated expression of defined peptide-MHC-I complexes.
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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 | 0.000 |
| 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".