Histone deacetylase inhibitor panobinostat induces antitumor activity in epithelioid sarcoma and rhabdoid tumor by growth factor receptor modulation
Bibliographic record
Abstract
BACKGROUND: Epithelioid sarcomas and rhabdoid tumors are rare, aggressive malignancies with poor prognosis. Both are characterized by INI1 alterations and deregulation of growth factor receptors albeit their interaction has not been elucidated. METHODS: In this study, we investigated the activity of a panel of epigenetic modulators and receptor tyrosine kinase inhibitors in vitro on respective cell lines as well as on primary patient-derived epithelioid sarcoma cells, and in vivo on xenografted mice. Focusing on histone deacetylase (HDAC) inhibitors, we studied the mechanism of action of this class of agents, its effect on growth factor receptor regulation, and changes in epithelial-to-mesenchymal transition by using cell- and RT-qPCR-based assays. RESULTS: Pan-HDAC inhibitor panobinostat exhibited potent anti-proliferative activity at low nanomolar concentrations in A204 rhabdoid tumor, and VAESBJ/GRU1 epithelioid sarcoma cell lines, strongly induced apoptosis, and resulted in significant tumor growth inhibition in VAESBJ xenografts. It differentially regulated EGFR, FGFR1 and FGFR2, leading to downregulation of EGFR in epithelioid sarcoma and to mesenchymal-to-epithelial transition whereas in rhabdoid tumor cells, EGFR was strongly upregulated and reinforced the mesenchymal phenotype. All three cell lines were rendered more susceptible towards combination with EGFF inhibitor erlotinib, further enhancing apoptosis. CONCLUSIONS: HDAC inhibitors exhibit significant anticancer activity due to their multifaceted actions on cytotoxicity, differentiation and drug sensitization. Our data suggest that the tailored, tissue-specific combination of HDAC inhibitors with therapeutics which target cellular salvage mechanisms might increase their therapeutic relevance.
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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".