Deep Sequencing Reveals Spatially Distributed Distinct Hot Spot Mutations in DICER1-Related Multinodular Goiter
Bibliographic record
Abstract
CONTEXT: Nontoxic multinodular goiter (MNG) occurs frequently, but its genetic etiology is not well established. Familial MNG and MNG occurring with ovarian Sertoli-Leydig cell tumor are associated with germline DICER1 mutations. We recently identified second somatic DICER1 ribonuclease (RNase) IIIb mutations in two MNGs. OBJECTIVE: The objective of the study was to investigate the occurrence of somatic DICER1 mutations and mutational clonality in MNG. PATIENTS: MNGs from 15 patients (10 with and five without germline DICER1 mutations) were selected based on tissue availability. DESIGN: Core biopsies/scrapings (n = 70) were obtained, sampling areas of follicular hyperplasia, hyperplasia within colloid pools, unremarkable thyroid parenchyma, and areas of thyroid parenchyma, not classified. After capture with a Fluidigm access array, the coding sequence of DICER1 was deep sequenced using DNA from each core/scraping. RESULTS: All germline DICER1-mutated cases were found to harbor at least one RNase III mutation. Specifically, we identified 12 individually distinct DICER1 RNase IIIb hot spot mutations in 32 of the follicular hyperplasia or hyperplasia within colloid pools cores/scrapings. These mutations are predicted to affect the metal-ion binding residues at positions p.Glu1705, p.Asp1709, p.Gly1809, p.Asp1810, and p.Glu1813. Somatic RNase IIIb mutations were identified in the 10 DICER1 germline mutated MNGs as follows: two cases contained one somatic mutation, five cases contained two mutations, and three cases contained three distinct somatic hot spot mutations. No RNase IIIb mutations were identified in the MNGs from individuals without germline DICER1 mutations. CONCLUSIONS: This study demonstrates that nodules within MNG occurring in DICER1 syndrome are associated with spatially distributed somatic DICER1 RNase IIIb mutations.
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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".