In Vivo Antitumor and Antimetastatic Activity of Sunitinib in Preclinical Neuroblastoma Mouse Model
Bibliographic record
Abstract
Neuroblastoma (NB) is one of the most common pediatric solid tumors originating from the neural crest lineage. Despite intensive treatment protocols including megatherapy with hematopoietic stem cell transplantation, the prognosis of NB patients remains poor. More effective therapeutics are required. High vascularity has been described as a feature of aggressive, widely disseminated NB. Our previous work demonstrated the overexpression of vascular endothelial growth factor (VEGF) in NB, and we showed that an anti-VEGF receptor (VEGFR-2) antibody could induce sustained NB tumor suppression and regression. Sunitinib is a kinase inhibitor targeting platelet-derived growth factor receptors and VEGFRs and, therefore, a promising antiangiogenic agent. In this study, we investigated the antitumor activity of sunitinib and its synergistic cytotoxicity with conventional (cyclophosphamide) and novel (rapamycin) therapies. Both NB cell lines and tumor-initiating cells from patient tumor samples were used in our in vitro and in vivo models for these drug testing. We show that sunitinib inhibits tumor cell proliferation and phosphorylation of VEGFRs. It also inhibits tumor growth, angiogenesis, and metastasis in tumor xenograft models. Low-dose sunitinib (20 mg/kg) demonstrates synergistic cytotoxicity with an mTOR inhibitor, rapamycin, which is more effective than the traditional chemotherapeutic drug, cyclophosphamide. These preclinical studies provide the evidence of antitumor activity of sunitinib both in the early stage of tumor formation and in the progressive metastatic disease. These studies also provide the framework for clinical trial of sunitinib, alone and in combination with conventional and novel therapies to increase efficacy and improve patient outcome in NB.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".