Continuous low-dose therapy with vinblastine and VEGF receptor-2 antibody induces sustained tumor regression without overt toxicity
Bibliographic record
Abstract
Various conventional chemotherapeutic drugs can block angiogenesis or even kill activated, dividing endothelial cells. Such effects may contribute to the antitumor efficacy of chemotherapy in vivo and may delay or prevent the acquisition of drug-resistance by cancer cells. We have implemented a treatment regimen that augments the potential antivascular effects of chemotherapy, that is devoid of obvious toxic side effects, and that obstructs the development of drug resistance by tumor cells. Xenografts of 2 independent neuroblastoma cell lines were subjected to either continuous treatment with low doses of vinblastine, a monoclonal neutralizing antibody (DC101) targeting the flk-1/KDR (type 2) receptor for VEGF, or both agents together. The rationale for this combination was that any antivascular effects of the low-dose chemotherapy would be selectively enhanced in cells of newly formed vessels when survival signals mediated by VEGF are blocked. Both DC101 and low-dose vinblastine treatment individually resulted in significant but transient xenograft regression, diminished tumor vascularity, and direct inhibition of angiogenesis. Remarkably, the combination therapy resulted in full and sustained regressions of large established tumors, without an ensuing increase in host toxicity or any signs of acquired drug resistance during the course of treatment, which lasted for >6 months. This article may have been published online in advance of the print edition. The date of publication is available from the JCI website, http://www.jci.org.
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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".