Super-enhancers and efficacy of triptolide in small cell carcinoma of the ovary hypercalcemic type
Bibliographic record
Abstract
Small cell carcinoma of the ovary-hypercalcemic type (SCCOHT) is a rare ovarian cancer affecting young females and is driven by the loss of both SWI/SNF ATPases SMARCA4 and SMARCA2. As loss of SWI/SNF alters enhancers, we hypothesized that super-enhancers, which regulate oncogene expression in cancer, are disparately impacted by SWI/SNF loss. We discovered differences between SWI/SNF occupancy at enhancers vs. super-enhancers. SCCOHT super-enhancer target genes were enriched in developmental processes, most notably nervous system development. This may further support neuronal cell-of-origin previously proposed. We found high sensitivity of SCCOHT cell lines to triptolide. Triptolide inhibits expression of many super-enhancer-associated genes, including oncogenes. SALL4 expression is decreased by triptolide and is highly expressed in SCCOHT tumors. In patient-derived xenograft models, triptolide and prodrug minnelide effectively inhibit tumor growth. These results reveal unique features of super-enhancers in SCCOHT, which may be one mechanism through which triptolide has high activity in these tumors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".