Subsets of Very Low Risk Wilms Tumor Show Distinctive Gene Expression, Histologic, and Clinical Features
Bibliographic record
Abstract
PURPOSE: Recent studies suggest that children <24 months with stage I favorable histology Wilms tumors <550 g [very low risk Wilms tumors (VLRWT)] have an excellent prognosis when treated with nephrectomy only, without adjuvant chemotherapy. The identification of risk categories within VLRWT may enable refinement of their definition and optimization of their therapy. EXPERIMENTAL DESIGN: To define biologically distinct subsets, global gene expression analysis was done on 39 VLRWT that passed all quality-control parameters and the clusters identified were validated in an independent set of 11 VLRWT. Validation of select differentially expressed genes was done with immunohistochemistry on a tissue microarray from 20 of 39 tumors. Loss of heterozygosity (LOH) for 11p15, 1p, and 16q was analyzed in 52 tumors using PCR. RESULTS: Two distinctive clusters were identified. One cluster included 9 tumors with epithelial differentiated tubular histology, paucity of nephrogenic rests, lack of LOH for 1p, 16q, and 11p, absence of relapse, and a unique gene expression profile consistent with arrest following mesenchymal-to-epithelial transition. The second cluster included 13 tumors with mixed histology, intralobar nephrogenic rests, and decreased expression of WT1. Three of 6 relapses occurred in this cluster. Of 43 informative tumors, 11p LOH was present in 5 of 5 relapses and 11 of 38 nonrelapses. CONCLUSIONS: Two subsets comprising a total of 56% of VLRWT are identified that have pathogenetic and molecular differences and apparent differences in risk for relapse. If these predictors can be prospectively validated, this would enable the refinement of clinical stratification and less arbitrary definition of VLRWT.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".