Modeling high-risk Wilms tumors enables the discovery of therapeutic vulnerability
Bibliographic record
Abstract
Wilms tumor (WT) is the most common pediatric kidney cancer treated with standard chemotherapy. However, less-differentiated blastemal type of WT often relapses. To model the high-risk WT for therapeutic intervention, we introduce pluripotency factors into WiT49, a mixed-type WT cell line, to generate partially reprogrammed cells, namely WiT49-PRCs. When implanted into the kidney capsule in mice, WiT49-PRCs form kidney tumors and develop both liver and lung metastases, whereas WiT49 tumors do not metastasize. Histological characterization and gene expression signatures demonstrate that WiT49-PRCs recapitulate blastemal-predominant WTs. Moreover, drug screening in isogeneic WiT49 and WiT49-PRCs leads to the identification of epithelial- or blastemal-predominant WT-sensitive drugs, whose selectivity is validated in patient-derived xenografts (PDXs). Histone deacetylase (HDAC) inhibitors (e.g., panobinostat and romidepsin) are found universally effective across different WT and more potent than doxorubicin in PDXs. Taken together, WiT49-PRCs serve as a blastemal-predominant WT model for therapeutic intervention to treat patients with high-risk WT. • Partially reprogrammed WiT49 cells develop both liver and lung metastases • Partially reprogrammed WiT49 cells recapitulate blastema-predominant Wilms tumors • WiT49 and partially reprogrammed WiT49 cells are suitable for drug screening • HDAC inhibitors are effective against epithelial and blastemal Wilms tumors Ma et al. generate a cell model that recapitulates blastemal Wilms tumors (WTs), enabling the discovery of therapeutic vulnerability for high-risk WTs. Screening of FDA-approved drugs in WiT49 and partially reprogrammed WiT49 cells identifies epithelial- or blastemal-dominant WT-sensitive drugs, among which HDAC inhibitors are found universally effective across different WTs.
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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 | 0.000 | 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".