Cytotoxicity and Target Modulation in Pediatric Solid Tumors by the Proteasome Inhibitor Carfilzomib
Bibliographic record
Abstract
BACKGROUND: Most children with recurrent metastatic solid tumors have high mortality rates. Recent studies have shown that proteasome inhibition leads to effective tumor killing in cells that have acquired treatment resistance and metastatic properties. OBJECTIVE: The purpose of this study was to test the potential of Carfilzomib (CFZ), a proteasome inhibitor, in refractory pediatric solid tumors which is currently unknown. METHODS: A panel of pediatric solid tumor cell lines, including neuroblastoma, Ewing's sarcoma, osteosarcoma, rhabdomyosarcoma and atypical teratoid rhabdoid tumor (ATRT), was used to evaluate the cytotoxic and proteasomal inhibitory effects of CFZ. A drug scheduling experiment was performed to determine the optimal dose and time to obtain effective cell killing. Combination studies of CFZ with chemotherapeutic drugs of different classes were performed to determine the extent of synergy. RESULTS: CFZ showed effective cytotoxicity against all cell lines tested (mean IC50 = 7nM, range = 1-20nM) and activity in a fluorophore-tagged cell-based proteasome assay. Drug scheduling experiments showed that the minimum exposure of 4-8 hours/day is needed for effective cumulative killing. CFZ, when combined with chemotherapeutic drugs of different classes, synergistically enhanced the extent of cell death. CONCLUSION: CFZ showed cytotoxic activity against all the solid pediatric cancer cell lines tested. This study provides initial in vitro data on the potential of CFZ to treat pediatric solid tumors and supports further investigations into the components of drug scheduling, biological correlates and drug combinations for future early phase clinical trials in children.
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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) | 0.000 | 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) | 0.001 | 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".