Hsp90 Inhibitor 17-Allylamino-17-Demethoxygeldanamycin Prevents Synovial Sarcoma Proliferation via Apoptosis in <i>In vitro</i> Models
Bibliographic record
Abstract
Synovial sarcoma is a soft tissue malignancy with a poor prognosis; many patients will die from this disease within 10 years of diagnosis, despite treatment. Gene expression profiling and immunohistochemistry studies have identified oncogenes that are highly expressed in synovial sarcoma. Included in this group are receptor tyrosine kinases such as epidermal growth factor receptor, insulin-like growth factor receptor 1, fibroblast growth factor receptor 3, KIT, and HER2. Inhibitors of these growth-promoting receptors are likely to inhibit proliferation of synovial sarcoma; however, the effect of receptor tyrosine kinase inhibitors on synovial sarcoma is largely unknown. We assessed the ability of the following receptor tyrosine kinase inhibitors to halt proliferation and induce apoptosis in synovial sarcoma monolayer and three dimensional spheroid in vitro models: gefitinib (Iressa), NVP-AEW541, imatinib mesylate (Gleevec), SU5402, PRO-001, trastuzumab (Herceptin), and 17-allylamino-17-demethoxygeldanamycin (17-AAG). Gefitinib, NVP-AEW541, and imatinib inhibited proliferation only at relatively high concentrations, which are not clinically applicable. 17-AAG, which destabilizes multiple receptor tyrosine kinases and other oncoproteins through heat shock protein 90 inhibition, prevented proliferation and induced apoptosis in synovial sarcoma monolayer models at concentrations achievable in human serum. 17-AAG treatment was also associated with receptor tyrosine kinase degradation and induction of apoptosis in synovial sarcoma spheroid models. 17-AAG was more effective than doxorubicin, particularly in the spheroid models. Here we provide in vitro evidence that 17-AAG, a clinically applicable drug with known pharmacology and limited toxicity, inhibits synovial sarcoma proliferation by inducing apoptosis, and thus has potential as a systemic therapy for this disease.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".