Clinically and biologically relevant subgroups of Wilms tumour defined by genomic and epigenomic analyses
Bibliographic record
Abstract
BACKGROUND: Although cure rates for Wilms tumours (WT) are high, many patients receive therapy with attendant long-term complications. Our goal was to stratify WT using genome-wide analyses to identify candidate molecular features for patients who would benefit from a reduction in therapy. METHODS: We generated DNA methylation and exome sequencing data on WT-kidney pairs (n = 57) and unpaired tumours (n = 27) collected either at our centre or by the Children's Oncology Group. Samples were divided into a discovery set (n = 32) and validation set (n = 52). RESULTS: Analysis of DNA methylation revealed two subgroups of WT with distinct features. Subgroup A has a similar DNA methylation profile to mature kidney, while Subgroup B has genome-wide dysregulation of DNA methylation. The rate of non-synonymous missense mutations and segmental chromosomal aberrations was higher in Subgroup B tumours, suggesting that this group has genome instability related to its epigenetic state. Subgroup A had a higher proportion of cases of bilateral disease. Tumours with high-risk histology or from patients who relapsed were only found in Subgroup B. CONCLUSION: We have identified subgroup-specific molecular events that could inform future work supporting more targeted therapeutic approaches and patient stratification. We propose a novel developmental tumour model based on these findings.
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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 | 0.001 |
| 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".