Recurrent <i><scp>DICER1</scp></i> hotspot mutations in endometrial tumours and their impact on <scp>microRNA</scp> biogenesis
Bibliographic record
Abstract
DICER1 plays a critical role in microRNA (miRNA) biogenesis. Recurrent somatic 'hotspot' mutations at the four metal-binding sites within the RNase IIIb domain of DICER1 were identified in ovarian sex cord-stromal tumours and have since been described in other paediatric tumours. In this study, we screened the RNase IIIb domain of DICER1 in 290 endometrial tumours and identified six cases with hotspot mutations, including two cases affected by an atypical G1809R mutation directly adjacent to a metal-binding site. Using Illumina and Sanger targeted resequencing, we observed and validated biallelic DICER1 mutations in several cases with hotspot mutations. Through in vitro DICER1 cleavage assays, small RNA deep sequencing and real-time PCR, we demonstrated that mutations adding a positively charged side chain to residue 1809 have similar detrimental effects on 5p miRNA production to mutations at the metal-binding sites. As expected, 5p miRNAs were globally reduced in tumours and cell lines with hotspot mutations. Pathway analysis of gene expression profiles indicated that genes de-repressed due to loss of 5p miRNAs are strongly associated with pathways regulating the cell cycle. Using a Dicer1-null mouse cell line model, we found that expression of DICER1 hotspot mutants promoted cell proliferation, whereas wild-type (WT) DICER1 inhibited cell proliferation. Furthermore, targets of let-7 family miRNAs are enriched among the up-regulated genes, suggesting that loss of let-7 may be impacting downstream pathways. Our results reveal that DICER1 hotspot mutations are implicated in common malignancies and may constitute a unique oncogenic pathway.
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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".