Sensitive Sequencing Analysis Suggests Thyrotropin Receptor and Guanine Nucleotide-Binding Protein G Subunit Alpha as Sole Driver Mutations in Hot Thyroid Nodules
Bibliographic record
Abstract
Background: Constitutively activating mutations in the thyrotropin receptor ( TSHR ) and the guanine nucleotide-binding protein G subunit alpha ( GNAS ) are the primary cause of hot thyroid nodules (HTNs). The reported prevalence of TSHR and GNAS mutations in HTNs varies. Previous studies show TSHR mutations in 8–82% of HTNs and GNAS mutations in 8–75% of HTNs. With sensitive and comprehensive targeted next-generation sequencing (tNGS), we re-evaluated the prevalence of TSHR and GNAS mutations in HTNs. Methods: Samples from three previous studies found to be TSHR and GNAS mutation negative were selected and re-evaluated using high-resolution melting (HRM) PCR. Remaining mutation negative samples were further reanalyzed by tNGS with a sequencing depth between 3000 × and 10,000 × . Our tNGS panel covered the entire TSHR coding sequence along with mutation hot spots in GNAS . Sequencing reads were aligned to reference and variants were called using Torrent Suite software v5.8. Results: In total, 154 of 182 previously mutation negative HTNs were positive for TSHR or GNAS mutations, resulting in an 85% prevalence of TSHR and GNAS mutations in HTNs, 79% and 6%, respectively. In a subset of 25 HTNs with multiple samples per nodule, and analyzed by tNGS at high sequencing depth, TSHR mutations were detected in 23 (92%) HTNs and 1 GNAS mutation was detected in 1 (4%) HTN, 96% mutation positive HTNs in this subset. Conclusions: Owing to the higher sensitivity of tNGS as compared with denaturing gradient gel electrophoresis and HRM-PCR, TSHR or GNAS mutations could be detected in 85% of HTNs. The detection of TSHR and GNAS mutations occurred in 96% of HTNs in a sample set with multiple samples per nodule analyzed by tNGS. Taken together with the fact that no other driver mutations could be identified by whole exome sequencing, our study strongly supports the hypothesis that TSHR and GNAS mutations are the main somatic mutations leading to HTNs.
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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 | 0.001 |
| 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 | 0.001 | 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".