Familial multinodular goiter and Sertoli-Leydig cell tumors associated with a large intragenic in-frame DICER1 deletion
Bibliographic record
Abstract
Objective Familial multinodular goiter (MNG), with or without ovarian Sertoli-Leydig cell tumor (SLCT), has been linked to DICER1 syndrome. We aimed to search for the presence of a germlineDICER1mutation in a large family with a remarkable history of MNG and SLCT, and to further explore the relevance of the identified mutation. Design and methods Sanger sequencing, Fluidigm Access Array and multiplex ligation-dependent probe amplification (MLPA) techniques were used to screen forDICER1mutations in germline DNA from 16 family members. Where available, tumor DNA was also studied. mRNA and protein extracted from carriers’ lymphocytes were used to characterize the expression of the mutant DICER1. Results Nine of 16 tested individuals carried a germline, in-frameDICER1deletion (c.4207-41_5364+1034del), which resulted in the loss of exons 23 and 24 from the cDNA. The mutant transcript does not undergo nonsense-mediated decay and the protein is devoid of specific metal ion-binding amino acids (p.E1705 and p.D1709) in the RNase IIIb domain. In addition, characteristic somatic ‘second hit’ mutations in this region were found on the other allele in tumors. Conclusions Patients with DICER1 syndrome usually present a combination of a typically truncating germlineDICER1mutation and a tumor-specific hotspot missense mutation within the sequence encoding the RNase IIIb domain. The in-frame deletion found in this family suggests that the germline absence of p.E1705 and p.D1709, which are crucial for RNase IIIb activity, may be enough to permit DICER1 syndrome to occur.
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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) | 0.002 | 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".