Efficacy of Minnelide in a Next-Generation Dual-Recombinase Regulated Genetically Engineered Mouse Model of CIC::DUX4 Sarcoma
Bibliographic record
Abstract
CIC::DUX4 sarcoma (CDS) is a lethal cancer driven by a fusion between tumor suppressor Capicua (CIC) and pioneer transcription factor double homeobox 4 (DUX4). To develop an immunocompetent pre-clinical model of CDS, we previously generated three genetically engineered mouse models (GEMMs) of CDS with CIC::DUX4 regulated by loxP-STOP-loxP cassettes. However, all three models developed spontaneous tumors without Cre recombinase. Here, we established an innovative GEMM of CDS (dFLEx CDS) that employs a dual recombinase (Cre + FLPE) FLEx-switch design to activate CIC::DUX4 expression and initiate sarcomagenesis in a spatially and temporally-controlled manner. Because CIC::DUX4 drives sarcoma development by activating a distinct oncogenic transcriptional program, we performed a drug screen on human-derived CDS cell lines using a library of compounds that modulate transcriptional regulation. This screen identified Minnelide, an inhibitor of RNA polymerase II-mediated transcription, as a selective inhibitor of CDS. Mechanistically, Minnelide acted through xeroderma pigmentosum type B to alter phosphorylation of RPB1, the largest subunit of RNA polymerase II. Subsequently, RPB1 underwent degradation leading to apoptosis of CDS cells. Minnelide demonstrated in vivo efficacy in autochthonous dFLEx CDS GEMMs and in human CDS xenografts. As Minnelide has already been demonstrated to be safe in clinical trials with activity for adult cancers, these findings nominate Minnelide as a novel therapeutic option to test in CDS patients.
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 | 0.001 |
| 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".