Predisposition Footprints in the Somatic Genome of Wilms Tumors
Bibliographic record
Abstract
Approximately 10% of children with cancer harbor a mutation in a predisposition gene. In children with the kidney cancer Wilms tumor, the prevalence is as high as 30%. Certain predispositions are associated with defined histological and clinical features, suggesting differences in tumorigenesis. To investigate this, we assembled a cohort of 137 children with Wilms tumor, of whom 71 had a pathogenic germline or mosaic variant. We examined 237 neoplasms (including two secondary leukemias), utilizing whole-genome sequencing, RNA sequencing, and genome-wide methylation, validating our findings in an independent cohort. Tumor development differed in children harboring a predisposition, depending on the variant gene and its developmental timing. Differences pervaded the repertoire of driver events, including high-risk mutations, the clonal architecture of normal kidneys, and the relatedness of neoplasms from the same individual. Our findings indicate that predisposition may preordain Wilms tumorigenesis, suggesting a variant-specific approach to managing children merits consideration. Significance: Tumors that arise in children with a cancer predisposition may develop through the same mutational pathways as sporadic tumors. We examined this question in the childhood kidney cancer, Wilms tumor. We found that certain predispositions dictate the genetic development of tumors, with clinical implications for these children. See related commentary by Brzezinski and Malkin, p. 258.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".