Oncolytic and immunostimulatory efficacy of a targeted oncolytic poxvirus expressing human GM-CSF following intravenous administration in a rabbit tumor model
Bibliographic record
Abstract
Targeted oncolytic poxviruses hold promise for the treatment of cancer. Arming these agents with immunostimulatory cytokines (for example, granulocyte-monocyte colony-stimulating factor; GM-CSF) can potentially increase their efficacy and/or alter their safety. However, due to species-specific differences in both human GM-CSF (hGM-CSF) activity and poxviruses immune avoidance proteins, the impact of hGM-CSF expression from an oncolytic poxvirus cannot be adequately assessed in murine or rat tumor models. We developed a rabbit tumor model to assess toxicology, pharmacodynamics, oncolytic efficacy and tumor-specific immunity of hGM-CSF expressed from a targeted oncolytic poxvirus JX-963. Recombinant purified hGM-CSF protein stimulated a leukocyte response in this model that paralleled effects of the protein in humans. JX-963 replication and targeting was highly tumor-selective after i.v. administration, and intratumoral replication led to recurrent, delayed systemic viremia. Likewise, hGM-CSF was expressed and released into the blood during JX-963 replication in tumors, but not in tumor-free animals. hGM-CSF expression from JX-963 was associated with significant increases in neutrophil, monocyte and basophil concentrations in the peripheral blood. Finally, tumor-specific cytotoxic T lymphocytes (CTL) were induced by the oncolytic poxvirus, and expression of hGM-CSF from the virus enhanced both tumor-specific CTL and antitumoral efficacy. JX-963 had significant efficacy against both the primary liver tumor as well as metastases; no significant organ toxicity was noted. This model holds promise for the evaluation of immunostimulatory transgene-armed oncolytic poxviruses, and potentially other viral species.
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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) | 0.000 | 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".