Catalytic inhibition of KDM1A in Ewing sarcoma is insufficient as a therapeutic strategy
Bibliographic record
Abstract
BACKGROUND: Ewing sarcoma and desmoplastic small round cell tumors (DSRCT) are rare and clinically aggressive sarcomas usually characterized by oncogenic fusion proteins involving EWS. Emerging studies of Ewing sarcoma have demonstrated EWS-FLI1-driven chromatin remodeling as a key aspect of tumorigenicity. In particular, the lysine-specific demethylase KDM1A/LSD1 is linked to transcriptional regulation of target genes orchestrated by the EWS portion of the fusion protein interacting with repressive chromatin-remodeling complexes. Consistent with this model, depletion of KDM1A supports it is a molecular therapeutic target in Ewing sarcoma cells, but effective drugs need to be identified. PROCEDURE: A comprehensive phenotypic analysis of the effects of catalytic KDM1A inhibitors ORY-1001 and GSK2879552, including clinically relevant doses, was carried out in 2D and 3D spheroid models of Ewing sarcoma and DSRCT. RESULTS: Catalytic inhibition of KDM1A did not affect cell viability in 2D and 3D assays and had no impact on invasion in a 3D assay. CONCLUSIONS: Overall, evidence presented here does not support inhibition of KDM1A catalytic demethylase activity as an effective therapeutic strategy for Ewing sarcoma or DSRCT. However, roles of KDM1A beyond its demethylase activity should be considered for these sarcomas.
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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) | 0.002 | 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".