Biallelic <i><scp>DICER1</scp></i> mutations occur in Wilms tumours
Bibliographic record
Abstract
DICER1 is an endoribonuclease central to the generation of microRNAs (miRNAs) and short interfering RNAs (siRNAs). Germline mutations in DICER1 have been associated with a pleiotropic tumour predisposition syndrome and Wilms tumour (WT) is a rare manifestation of this syndrome. Three WTs, each in a child with a deleterious germline DICER1 mutation, were screened for somatic DICER1 mutations and were found to bear specific mutations in either the RNase IIIa (n = 1) or the RNase IIIb domain (n = 2). In the two latter cases, we demonstrate that the germline and somatic DICER1 mutations were in trans, suggesting that the two-hit hypothesis of tumour formation applies for these examples of WT. Among 191 apparently sporadic WTs, we identified five different missense or deletion somatic DICER1 mutations (2.6%) in four individual WTs; one tumour had two very likely deleterious somatic mutations in trans in the RNase IIIb domain (c.5438A>G and c.5452G>A). In vitro studies of two somatic single-base substitutions (c.5429A>G and c.5438A>G) demonstrated exon 25 skipping from the transcript, a phenomenon not previously reported in DICER1. Further we show that DICER1 transcripts lacking exon 25 can be translated in vitro. This study has demonstrated that a subset of WTs exhibits two 'hits' in DICER1, suggesting that these mutations could be key events in the pathogenesis of these tumours.
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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 | 0.001 |
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".