Oncolytic Vaccinia Virus Disrupts Tumor-Associated Vasculature in Humans
Bibliographic record
Abstract
Efforts to selectively target and disrupt established tumor vasculature have largely failed to date. We hypothesized that a vaccinia virus engineered to target cells with activation of the ras/MAPK signaling pathway (JX-594) could specifically infect and express transgenes (hGM-CSF, β-galactosidase) in tumor-associated vascular endothelial cells in humans. Efficient replication and transgene expression in normal human endothelial cells in vitro required either VEGF or FGF-2 stimulation. Intravenous infusion in mice resulted in virus replication in tumor-associated endothelial cells, disruption of tumor blood flow, and hypoxia within 48 hours; massive tumor necrosis ensued within 5 days. Normal vessels were not affected. In patients treated with intravenous JX-594 in a phase I clinical trial, we showed dose-dependent endothelial cell infection and transgene expression in tumor biopsies of diverse histologies. Finally, patients with advanced hepatocellular carcinoma, a hypervascular and VEGF-rich tumor type, were treated with JX-594 on phase II clinical trials. JX-594 treatment caused disruption of tumor perfusion as early as 5 days in both VEGF receptor inhibitor-naïve and -refractory patients. Toxicities to normal blood vessels or to wound healing were not evident clinically or on MRI scans. This platform technology opens up the possibility of multifunctional engineered vaccinia products that selectively target and infect tumor-associated endothelial cells, as well as cancer cells, resulting in transgene expression, vasculature disruption, and tumor destruction in humans systemically.
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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".