Synovial Sarcoma: Recent Discoveries as a Roadmap to New Avenues for Therapy
Bibliographic record
Abstract
UNLABELLED: Oncogenesis in synovial sarcoma is driven by the chromosomal translocation t(X,18; p11,q11), which generates an in-frame fusion of the SWI/SNF subunit SS18 to the C-terminal repression domains of SSX1 or SSX2. Proteomic studies have identified an integral role of SS18-SSX in the SWI/SNF complex, and provide new evidence for mistargeting of polycomb repression in synovial sarcoma. Two recent in vivo studies are highlighted, providing additional support for the importance of WNT signaling in synovial sarcoma: One used a conditional mouse model in which knockout of β-catenin prevents tumor formation, and the other used a small-molecule inhibitor of β-catenin in xenograft models. SIGNIFICANCE: Synovial sarcoma appears to arise from still poorly characterized immature mesenchymal progenitor cells through the action of its primary oncogenic driver, the SS18-SSX fusion gene, which encodes a multifaceted disruptor of epigenetic control. The effects of SS18-SSX on polycomb-mediated gene repression and SWI/SNF chromatin remodeling have recently come into focus and may offer new insights into the basic function of these processes. A central role for deregulation of WNT-β-catenin signaling in synovial sarcoma has also been strengthened by recent in vivo studies. These new insights into the the biology of synovial sarcoma are guiding novel preclinical and clinical studies in this aggressive 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".