PRE-CLINICAL STUDIES FOR THE IDENTIFICATION OF NOVEL THERAPEUTIC AGENTS FOR THE TREATMENT OF CNS ATYPICAL TERATOID RHABDOID TUMOR IN CHILDREN
Bibliographic record
Abstract
Objective Atypical teratoid rhabdoid tumor (AT/RT) of the central nervous system is a highly malignant and difficult to treat tumor affecting mainly infants and young children. Because of the high treatment failure rate, novel treatment protocols are urgently needed for its treatment. Using an experimental model we are investigating the effects of novel targeted therapeutic agents against AT/RT tumor derived cell lines. Methods ATRT cell lines, BT12, BT16 and KCCF1, were cultured with increasing concentrations of conventional and new chemotherapeutic agents. Cell death was determined by Alamar blue assay. Target modulation was carried out by Western blots against growth regulators and apoptosis related proteins. Drug combination studies were done by the addition of individual drugs at low concentrations with increasing concentrations of a second agent. From these data, inhibitory concentrations at 50% (IC50) and drug combination indices were calculated. Results Our results show the induction of apoptosis and changes in signaling molecules by the multi-kinase inhibitors Sorafenib and Sunitinib at physiologically attainable concentrations. In addition, effective cell killing was also observed with the histone-deacetylase inhibitor, Apicidin and the new generation topoisomerase inhibitor, Irinotecan. Importantly, our studies show that the combination of a multi-kinase inhibitor with other anti-neoplastic agents may potentiate their anti-tumor activity. Conclusions We present data that identify potential targeted therapeutic agents for the treatment of AT/RT in children. It is hoped that the target modulation assays presented here will provide an effective way to identify the group of children who may show responsiveness to regimens containing these agents.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".