Recurrent somatic mutation in DROSHA induces microRNA profile changes in Wilms tumour
Bibliographic record
Abstract
Wilms tumour (WT) is an embryonal kidney neoplasia for which very few driver genes have been identified. Here we identify DROSHA mutations in 12% of WT samples (26/222) using whole-exome sequencing and targeted sequencing of 10 microRNA (miRNA)-processing genes. A recurrent mutation (E1147K) affecting a metal-binding residue of the RNase IIIb domain is detected in 81% of the DROSHA-mutated tumours. In addition, we identify non-recurrent mutations in other genes of this pathway (DGCR8, DICER1, XPO5 and TARBP2). By assessing the miRNA expression pattern of the DROSHA-E1147K-mutated tumours and cell lines expressing this mutation, we determine that this variant leads to a predominant downregulation of a subset of miRNAs. We confirm that the downregulation occurs exclusively in mature miRNAs and not in primary miRNA transcripts, suggesting that the DROSHA E1147K mutation affects processing of primary miRNAs. Our data underscore the pivotal role of the miRNA biogenesis pathway in WT tumorigenesis, particularly the major miRNA-processing gene DROSHA. Wilms tumour (WT) is the most common paediatric kidney cancer and few driver genes related to its development have been identified. Here, the authors identify DROSHAmutations that may contribute to WT tumorigenesis through their effect on primary microRNA processing.
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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 | 0.000 | 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".