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Record W3015897253 · doi:10.1089/thy.2019.0648

Sensitive Sequencing Analysis Suggests Thyrotropin Receptor and Guanine Nucleotide-Binding Protein G Subunit Alpha as Sole Driver Mutations in Hot Thyroid Nodules

2020· article· en· W3015897253 on OpenAlex
Alexandra Stephenson, Markus Eszlinger, Paul Stewardson, John B. McIntyre, Eileen Boesenberg, Rıfat Bircan, Seda Sancak, Hülya Ilıksu Gözü, Sana Ghaznavi, Knut Krohn, Ralf Paschke

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThyroid · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsAlberta Health ServicesUniversity of Calgary
Fundersnot available
KeywordsThyrotropin receptorGuanineG alpha subunitAlpha (finance)Thyroid nodulesProtein subunitMolecular biologyThyroidNucleotideChemistryBiologyGeneticsGeneMedicineGraves' disease

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.864

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.249
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it