Analysis by array CGH of genomic changes associated with the progression or relapse of Wilms' tumour
Bibliographic record
Abstract
Despite aggressive salvage regimens, approximately half of all children who suffer a Wilms' tumour recurrence will die of their disease. Although there are increasing data on molecular genetic prognostic factors present in the tumour at diagnosis, there is little information regarding the molecular events that occur with Wilms' tumour progression and relapse. In the present study, microarray-based comparative genomic hybridization (aCGH) analysis has been carried out on 58 Wilms' tumour samples, which included 38 untreated primary and 20 recurrent tumours. A higher degree of copy number changes was observed in the recurrent tumours (33.0% genomic clones) than in the primary tumour (21.2%). Paired analysis highlighted the acquisition of 15q gain with high levels of IGF1R expression in the tumour recurrence in two cases. The most statistically significant abnormality acquired between diagnosis and relapse was loss of 17p. One case that experienced 17p loss was classified as favourable histology at diagnosis, but exhibited diffuse anaplasia at recurrence and had a homozygous TP53 deletion. Another instructive case with a constitutional 11p13 deletion presented with bilateral tumours and suffered two subsequent recurrences in the left kidney. A somatic WT1 mutation was found only in the right kidney tumour, while the constitutional 11p13 deletion was the only abnormality detected in the initial left kidney tumour by aCGH. The two subsequent relapses in the left kidney contained an accumulation of additional genetic alterations, including an independent WT1 mutation.
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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 | 0.000 |
| 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".