Oncolytic BHV-1 Is Sufficient to Induce Immunogenic Cell Death and Synergizes with Low-Dose Chemotherapy to Dampen Immunosuppressive T Regulatory Cells
Bibliographic record
Abstract
Immunogenic cell death (ICD) can switch immunologically “cold” tumors “hot”, making them sensitive to immune checkpoint inhibitor (ICI) therapy. Many therapeutic platforms combine multiple modalities such as oncolytic viruses (OVs) and low-dose chemotherapy to induce ICD and improve prognostic outcomes. We previously detailed many unique properties of oncolytic bovine herpesvirus type 1 (oBHV) that suggest widespread clinical utility. Here, we show for the first time, the ability of oBHV monotherapy to induce bona fide ICD and tumor-specific activation of circulating CD8+ T cells in a syngeneic murine model of melanoma. The addition of low-dose mitomycin C (MMC) was necessary to fully synergize with ICI through early recruitment of CD8+ T cells and reduced infiltration of highly suppressive PD-1+ Tregs. Cytokine and gene expression analyses within treated tumors suggest that the addition of MMC to oBHV therapy shifts the immune response from predominantly anti-viral, as evidenced by a high level of interferon-stimulated genes, to one that stimulates myeloid cells, antigen presentation and adaptive processes. Collectively, these data provide mechanistic insights into how oBHV-mediated therapy modalities overcome immune suppressive tumor microenvironments to enable the efficacy of ICI therapy.
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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".