Predicting Relapse in Favorable Histology Wilms Tumor Using Gene Expression Analysis: A Report from the Renal Tumor Committee of the Children's Oncology Group
Bibliographic record
Abstract
PURPOSE: The past two decades has seen significant improvement in the overall survival of patients with favorable histology Wilms tumor (FHWT); however, this progress has reached a plateau. Further improvements may rely on the ability to better stratify patients by risk of relapse. This study determines the feasibility and potential clinical utility of classifiers of relapse based on global gene expression analysis. EXPERIMENTAL DESIGN: Two hundred fifty FHWT of all stages enriched for relapses treated on National Wilms Tumor Study-5 passed quality variables and were suitable for analysis using oligonucleotide arrays. Relapse risk stratification used support vector machine; 2- and 10-fold cross-validations were applied. RESULTS: The number of genes associated with relapse was less than that predicted by chance alone for 106 patients (32 relapses) with stages I and II FHWT treated with chemotherapy, and no further analyses were done. This number was greater than expected by chance for 76 local stage III patients. Cross-validation including an additional 68 local stage III patients (total 144 patients, 53 relapses) showed that classifiers for relapse composed of 50 genes were associated with a median sensitivity of 47% and specificity of 70%. CONCLUSIONS: This study shows the feasibility and modest accuracy of stratifying local stage III FHWT using a classifier of <50 genes. Validation using an independent patient population is needed. Analysis of genes differentially expressed in relapse patients revealed apoptosis, Wnt signaling, insulin-like growth factor pathway, and epigenetic modification to be mechanisms important in relapse. Potential therapeutic targets include FRAP/MTOR and CD40.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".