Wilms tumor characteristics in children with heterozygous germline DIS3L2 variants
Bibliographic record
Abstract
PURPOSE: Heterozygous germline DIS3L2 pathogenic variants were recently linked to Wilms tumor (WT) predisposition. Limited data on cancer penetrance and characteristics complicate surveillance/management recommendations. This study aims to describe an extended cohort of children with WTs and heterozygous germline DIS3L2 (likely) pathogenic variants ([L]PVs). METHODS: Clinical and tumor data of children with WT and heterozygous germline DIS3L2 (L)PVs were retrospectively collected. RESULTS: Thirty-four children were identified, including 4 familial cases. Germline (L)PVs included exon 9 deletions (n = 28) and other (n = 6) (L)PVs. Seventeen parents were confirmed to have the DIS3L2 (L)PV, of whom 1 had a past WT. Median age at WT diagnosis was 41 months (range: 8-101). A somatic second hit in DIS3L2 was found in 19 of 20 children with genetic tumor data. Five children had bilateral WTs and 11 had metastases (32%). Eight children had high-risk tumor histology (24%, of which 7 post-chemotherapy blastemal). Three children relapsed or developed a second primary tumor; 4 children were deceased. Recurring clinical features were lacking. CONCLUSION: Children with WTs and heterozygous germline DIS3L2 (L)PVs lack a recognizable phenotype. DIS3L2 (L)PVs are a cause for familial WT, but WT penetrance is likely low. This cohort exhibits a high percentage of metastases and high-risk blastemal tumors, which need further study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".