Virus replication is not required for oncolytic bovine herpesvirus-1 immunotherapy
Bibliographic record
Abstract
Oncolytic viruses are a promising approach for cancer treatment where viruses selectively target and kill cancer cells while also stimulating an immune response. Among viruses with this ability, bovine herpesvirus-1 (BoHV-1) has several advantages, including observations suggesting it may not require viral replication for its anti-cancer effects. We previously demonstrated that binding and penetration of enveloped virus particles are sufficient to trigger intrinsic and innate immune signaling in normal cells, while other groups have published the efficacy of non-replicating viruses as viable immunotherapies in different cancer models. In this work, we definitively show that live and UV-inactivated (UV) (non-replicating) BoHV-1-based regimens extend survival of tumor-bearing mice to similar degrees and induce infiltration of similar immune cell populations, with the exception of neutrophils. Transcriptomic analysis of tumors treated with either live or UV BoHV-1-based regimens revealed similar pathway enrichment and a subset of overlapping differentially regulated genes, suggesting live and UV BoHV-1 have similar mechanisms of activity. Last, we present a gene signature across our in vitro and in vivo models that could potentially be used to validate new BoHV-1 therapeutics. This work contributes to the growing body of literature showing that replication may not be necessary for therapeutic efficacy of viral immunotherapies.
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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 | 0.001 | 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".