In Vivo Antitumor and Antimetastatic Activity of Sunitinib in Preclinical Neuroblastoma Mouse Model
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it