Prognostic implications of gain of 1q in favorable histology Wilms tumor: A report from the Children’s Oncology Group.
Bibliographic record
Abstract
10014 Background: Wilms tumor is the most common childhood renal tumor. While the majority of patients with favorable histology Wilms Tumor (FHWT) have good outcomes, many patients still experience recurrence and death from disease. This study’s goal was to determine if chromosome 1q gain is associated with event-free (EFS) and overall survival (OS) in FHWT. Methods: Unilateral FHWT samples were obtained from patients enrolled on National Wilms Tumor Study-4 and Pediatric Oncology Group 9046, “A Molecular Genetic analysis of Wilms Tumor.” 1q gain, 1p loss, and 16q loss were determined using multiplex ligation-dependent probe amplification (MLPA). Results: The eight-year EFS was 87% (95% CI 82%, 91%) for the entire cohort of 212 patients. Tumors of 58/212 patients (27%) displayed 1q gain. A strong relationship between 1q gain and 1p/16q loss was observed. The eight-year EFS was 76% (95% CI 63%, 85%) for those with 1q gain and 93% (95% CI 87%, 96%) for those lacking 1q gain (p=0.0024). The eight-year OS was 89% (95% CI 78%, 95%) for those with 1q gain, and 98% (95% CI 94%, 99%) for those lacking 1q gain (p=0.0075). Gain of 1q did not correlate with disease stage (p=0.16). After stratification for stage, 1q gain was associated with a significant increase in the risk of failure (risk ratio estimate: 2.72, p=0.0089). Conclusions: Gain of 1q is associated with inferior EFS and OS in FHWT and may provide a new and valuable prognostic marker to stratify therapy for patients with FHWT. A confirmatory study is necessary before this biomarker is incorporated into risk stratification schema of future therapeutic studies.
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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".