Combining Vascular Normalization with an Oncolytic Virus Enhances Immunotherapy in a Preclinical Model of Advanced-Stage Ovarian Cancer
Bibliographic record
Abstract
Abstract Purpose: Intravenous delivery of oncolytic viruses often leads to tumor vascular shutdown, resulting in decreased tumor perfusion and elevated tumor hypoxia. We hypothesized that using 3TSR to normalize tumor vasculature prior to administration of an oncolytic Newcastle disease virus (NDV) would enhance virus delivery and trafficking of immunologic cell subsets to the tumor core, resulting in systemically enhanced immunotherapy and regression of advanced-stage epithelial ovarian cancer (EOC). Experimental Design: Using an orthotopic, syngeneic mouse model of advanced-stage EOC, we pretreated mice with 3TSR (4 mg/kg per day) alone or followed by combination with fusogenic NDV(F3aa) (1.0 × 108 plaque-forming units). Results: Treatment with 3TSR normalized tumor vasculature, enhanced blood perfusion of primary EOC tumors, and induced disease regression. Animals treated with combination therapy had the greatest reduction in primary tumor mass, ascites accumulation, and secondary lesions (50% of mice were completely devoid of peritoneal metastases). Combining 3TSR + NDV(F3aa) led to enhanced trafficking of immunologic cells into the primary tumor core. Conclusions: We have shown, for the first time, that NDV, like other oncolytic viruses, is a potent mediator of acute vascular shutdown and that preventing this through vascular normalization can promote regression in a preclinical model of advanced-stage ovarian cancer. This challenges the current focus on induction of intravascular thrombosis as a requisite for successful oncolytic virotherapy. See related commentary by Bykov and Zamarin, p. 1446
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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".