High RAS Allele Frequency Signals Increased Risk of TERT Promoter Mutations in Thyroid Tumors
Bibliographic record
Abstract
Background/Objectives: RAS mutations are among the most common genetic alterations in thyroid cancer and are generally associated with less aggressive behavior. However, when co-occurring with TERT (telomerase reverse transcriptase) promoter mutations, known markers of poor prognosis, tumors exhibit markedly more aggressive features. The allele frequency (AF) of RAS may serve as a potential indicator of clonal dominance and the likelihood of additional high-risk mutations, such as TERT mutation. This study aims to assess whether a high RAS AF correlates with the presence of coexisting TERT promoter mutations and other molecular alterations. Methods: A retrospective chart review was performed on 111 patients with thyroid nodules harboring RAS mutations, either alone or in combination with TERT promoter mutations. All patients underwent molecular testing with ThyroSeq v3 and subsequent thyroidectomy at McGill University teaching hospitals. RAS AF was analyzed in relation to TERT mutation status, nodule size, and other molecular alterations including copy number alterations (CNA) and gene expression profiles (GEP). Results: The mean RAS AF was significantly higher in nodules with both RAS and TERT mutations (38.1%) compared to those with RAS mutations alone (22.1%) (p = 0.002). Nodules with coexisting TERT mutations were also significantly larger (mean size: 3.7 cm vs. 2.4 cm; p = 0.005). Malignant nodules, regardless of TERT status, showed a trend toward higher RAS AF than benign nodules (23.0% vs. 16.3%; p = 0.052). Higher RAS AF was also associated with the presence of CNA and/or GEP positivity. Notably, GEP was positive in 100% of nodules with both RAS and TERT mutations, compared to 37.5% in RAS-only nodules (p = 0.002). Conclusions: A high RAS AF increases the likelihood of a TERT promoter mutation and other genetic alterations, highlighting the importance of RAS AF in optimizing patient care and management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".