Precise Scheduling of Chemotherapy Primes VEGF-producing Tumors for Successful Systemic Oncolytic Virotherapy
Bibliographic record
Abstract
We have previously reported that a burst of vascular endothelial growth factor (VEGF) signaling to tumor-associated endothelium induces a proviral state, during which systemically delivered oncolytic reovirus can replicate in endothelium, thereby inducing immune-mediated vascular collapse and significant antitumor therapy. Using chimeric receptors, we show here that induction of the proviral state proceeds through VEGFR2, but not VEGFR1, signaling in endothelial cells. In contrast, innate immune activation by reovirus-exposed endothelial cells was predominantly through VEGFR1. By screening conventional chemotherapies for their ability to induce similar effects in combination with reovirus both in vitro and in vivo, we observed that the proviral state could also be induced in endothelial cells exposed to VEGF during rebound from paclitaxel-mediated inhibition of VEGF signaling. We translated these in vitro findings in vivo by careful scheduling of paclitaxel chemotherapy with systemic virotherapy, neither of which alone had therapeutic effects against B16 tumors. Systemic availability of reovirus during endothelial cell recovery from paclitaxel treatment allowed for endothelial replication of the virus, immune-mediated therapy, and tumor cures. Therefore, careful scheduling of combination viro- and chemotherapies, which preclinical testing suggests are individually ineffective against tumor cells, can lead to rational new clinical protocols for systemic treatments with oncolytic viruses.
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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".