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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".