DNA demethylating agents suppress preclinical models of synovial sarcoma
Bibliographic record
Abstract
Synovial sarcoma is an aggressive soft-tissue cancer driven by the chimeric SS18::SSX fusion oncoprotein, which disrupts chromatin remodeling by combining two antagonistic transcriptional regulators. SS18 participates in BAF complexes that open chromatin, while the SSX genes are cancer-testis antigens that interface with chromatin decorated with monoubiquitinated histone H2A placed by polycomb repressive complex activity. Because KDM2B brings polycomb repressive complex to unmethylated CpG islands, it is plausible that methylation directly determines the distribution of SS18::SSX to target loci. Given that synovial sarcoma is also characterized by a peculiarly low DNA hypomethylation profile, we hypothesized that further disturbance of DNA methylation would have a negative impact on synovial sarcoma growth. DNMT1 disruption by CRISPR/Cas9 targeting or pharmacological inhibition with cytidine analogs 5-aza-2'-deoxycytidine (decitabine) and 5-azacytidine led to decreased genome-wide methylation, redistribution of SS18::SSX, and altered gene expression profiles, most prominently including upregulation of tumor suppressor genes, immune-related genes, and mesenchymal differentiation-related genes. These drugs suppressed growth of synovial sarcoma cell lines and drove cytoreduction in mouse genetic models. DNMT1 inhibitors, already approved for treating myelodysplastic syndromes, warrant further clinical investigation for synovial sarcoma as repurposed, targeted treatments exploiting a vulnerability in the intrinsic biology of this cancer.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".