TP53 Mutational Status Is a Potential Marker for Risk Stratification in Wilms Tumour with Diffuse Anaplasia
Bibliographic record
Abstract
PURPOSE: The presence of diffuse anaplasia in Wilms tumours (DAWT) is associated with TP53 mutations and poor outcome. As patients receive intensified treatment, we sought to identify whether TP53 mutational status confers additional prognostic information. PATIENTS AND METHODS: We studied 40 patients with DAWT with anaplasia in the tissue from which DNA was extracted and analysed for TP53 mutations and 17p loss. The majority of cases were profiled by copy number (n = 32) and gene expression (n = 36) arrays. TP53 mutational status was correlated with patient event-free and overall survival, genomic copy number instability and gene expression profiling. RESULTS: From the 40 cases, 22 (55%) had TP53 mutations (2 detected only after deep-sequencing), 20 of which also had 17p loss (91%); 18 (45%) cases had no detectable mutation but three had 17p loss. Tumours with TP53 mutations and/or 17p loss (n = 25) had an increased risk of recurrence as a first event (p = 0.03, hazard ratio (HR), 3.89; 95% confidence interval (CI), 1.26-16.0) and death (p = 0.04, HR, 4.95; 95% CI, 1.36-31.7) compared to tumours lacking TP53 abnormalities. DAWT carrying TP53 mutations showed increased copy number alterations compared to those with wild-type, suggesting a more unstable genome (p = 0.03). These tumours showed deregulation of genes associated with cell cycle and DNA repair biological processes. CONCLUSION: This study provides evidence that TP53 mutational analysis improves risk stratification in DAWT. This requires validation in an independent cohort before clinical use as a biomarker.
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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 | 0.000 | 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 | 0.000 |
| 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".