Potent antitumor effects of ZD6474 on neuroblastoma via dual targeting of tumor cells and tumor endothelium
Bibliographic record
Abstract
Among children with relapsed or refractory neuroblastoma, the prognosis is poor and novel therapeutic strategies are needed to improve long-term survival. As with other solid tumors, high vascular density within neuroblastoma is associated with advanced disease, and therapeutic regimens directed against the tumor vasculature may provide clinical benefit. The receptor tyrosine kinase RET is widely expressed in neuroblastoma and is known to activate key signal transduction pathways involved in tumor cell survival and progression including Ras/mitogen-activated protein kinase and phosphatidylinositol 3-kinase/Akt. We investigated the effect of dual targeting of tumor cells and tumor endothelium with ZD6474, a small-molecule tyrosine kinase inhibitor of vascular endothelial growth factor (VEGF) receptor 2, epidermal growth factor receptor, and RET. ZD6474 inhibited the phosphorylation of RET in neuroblastoma cells and had a direct effect on tumor cell viability in seven neuroblastoma cell lines. In a human neuroblastoma xenograft model, ZD6474 inhibited tumor growth by 85% compared with treatment with vehicle alone. In contrast, no significant inhibition of tumor growth was observed after treatment with bevacizumab, an antihuman VEGF monoclonal antibody, or the epidermal growth factor receptor inhibitor erlotinib, either alone or in combination. Immunohistochemical analysis showed that ZD6474 treatment led to an increase in endothelial cell apoptosis along with inhibition of VEGF receptor-2 activation on tumor endothelium. In conclusion, dual targeting of tumor cells, potentially through RET inhibition, and tumor vasculature with ZD6474 leads to potent antitumor effects. This approach merits further investigation for patients with neuroblastoma.
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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) | 0.001 | 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".