Multi-tyrosine kinase inhibitors in preclinical studies for pediatric CNS AT/RT: Evidence for synergy with Topoisomerase-I inhibition
Bibliographic record
Abstract
BACKGROUND: Currently, Atypical Teratoid Rhabdoid Tumor (AT/RT) constitutes one of the most difficult to treat malignancies in pediatrics. Hence, new knowledge of potential targets for therapeutics and the development of novel treatment approaches are urgently needed. We have evaluated the presence of cytokine pathways and the effects of two clinically available multi-tyrosine kinase inhibitors for cytotoxicity, target modulation and drug combinability against AT/RT cell lines. RESULTS: AT/RT cell lines expressed measurable quantities of VEGF, FGF, PDGF and SDF-1, although the absolute amounts varied between the cell lines. The targeted receptor tyrosine kinase inhibitor sorafenib inhibited the key signaling molecule Erk, which was activated following the addition of own conditioned media, suggesting the existence of autocrine/paracrine growth stimulatory pathways. The multi-tyrosine kinase inhibitors sorafenib and sunitinib also showed significant growth inhibition of AT/RT cells and their activity was enhanced by combination with the topoisomerase inhibitor, irinotecan. The loss of cytoplasmic NF-kappa-B in response to irinotecan was diminished by sorafenib, providing evidence for a possible benefit for this drug combination. CONCLUSIONS: In addition to previously described involvement of insulin like growth factor (IGF) family of cytokines, a multitude of other growth factors may contribute to the growth and survival of AT/RT cells. However, consistent with the heterogeneous nature of this tumor, quantitative and qualitative differences may exist among different tumor samples. Multi-tyrosine kinase inhibitors appear to have effective antitumor activity against all cell lines studied. In addition, the target modulation studies and drug combinability data provide the groundwork for additional studies and support the evaluation of these agents in future treatment protocols.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.003 | 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".