Preclinical evaluation of the antineoplastic action of 5-aza-2'-deoxycytidine and different histone deacetylase inhibitors on human Ewing's sarcoma cells
Bibliographic record
Abstract
BACKGROUND: Most patients with advanced Ewing's sarcoma (EWS) respond poorly to conventional chemotherapy, indicating the need for new treatment approaches. Epigenetic events, such as promoter hypermethylation and chromatin histone deacetylation, silence the expression of tumor suppressor genes (TSGs) and play an important role in tumorigenesis. These epigenetic changes can be reversed by using 5-aza-2'-deoxycytidine (5AZA-CdR), a potent inhibitor of DNA methylation, in combination with an inhibitor of histone deacetylase (HDAC). RESULTS: Here, we used a clonogenic assay to evaluate the in vitro antineoplastic activity of 5AZA-CdR in combination with different HDAC inhibitors on EWS cells. We observed that the HDAC inhibitors, MS-275, trichostatin-A, phenylbutyrate, LAQ824 and depsipeptide, enhanced the antineoplastic action of 5AZA-CdR on EWS cells. The combination of 5AZA-CdR and MS-275 showed marked synergy, and was correlated with significant reactivation of the expression of two TSGs, E-cadherin and tumor suppressor lung cancer-1 (TSLC1), in a EWS cell line. CONCLUSION: These results suggest the value of future clinical studies investigating the combination of 5AZA-CdR and MS-275 in patients with advanced EWS.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".