Preclinical 3D model screening reveals digoxin as an effective therapy for a rare and aggressive type of endometrial cancer
Bibliographic record
Abstract
OBJECTIVE: Dedifferentiated endometrial carcinoma (DDEC) characterized by SWItch/Sucrose Non-Fermentable (SWI/SNF) complex inactivation is a highly aggressive type of endometrial cancer without effective systemic therapy options. Its uncommon nature and aggressive disease trajectory pose significant challenges for therapeutic progress. To address this obstacle, we focused on developing preclinical models tailored to this tumor type and established patient tumor-derived three-dimensional (3D) spheroid models of DDEC. METHODS: High-throughput drug repurposing screens were performed on in vitro 3D spheroid models of DDEC cell lines (SMARCA4-inactivated DDEC-1 and ARID1A/ARID1B co-inactivated DDEC-2). The dose-response relationships of the identified candidate drugs were evaluated in vitro, followed by in vivo evaluation using xenograft models of DDEC-1 and DDEC-2. RESULTS: in DDEC cells compared to non-DDEC endometrial cancer cells. In in vivo xenograft models, digoxin significantly suppressed the growth of DDEC tumors at clinically relevant serum concentrations. CONCLUSION: Using biologically precise preclinical models of DDEC derived from patient tumor samples, our study identified digoxin as an effective drug in suppressing DDEC tumor growth. These findings provide compelling preclinical evidence for the use of digoxin as systemic therapy for SWI/SNF-inactivated DDEC, which may also be applicable to other SWI/SNF-inactivated tumor types.
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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 | 0.001 | 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".