<i><scp>TERT</scp></i> promoter mutations in solitary fibrous tumour
Bibliographic record
Abstract
AIMS: TERT promoter mutations have been reported in 22% of solitary fibrous tumours (SFT) and have been associated with poor outcomes. We performed testing for TERT hot-spot mutations in a large series of SFT in order to confirm this finding and explore clinicopathological correlates of mutation status. METHODS AND RESULTS: PCR for TERT hot-spot mutations C250T and C228T was performed on DNA extracted from 216 SFT and mutation status correlated with clinicopathological factors, including predicted risk for metastasis using a previously published model. Testing was successful in 189 tumours from 172 patients, and mutations were present in 29%. The presence of TERT promoter mutation was associated with larger primary tumour size, necrosis and older patient age. TERT promoter mutations were most common in high-risk tumours (nine of 20, 45%), and were present in 11 of 26 (42%) moderate-risk tumours and 14 of 67 (21%) low-risk tumours (P = 0.004). Overall, TERT mutations were associated with shorter time to first metastasis (P = 0.04), but had no impact on overall survival. TERT promoter mutation status was found not to provide additional prognostic information in low- and high-risk SFT, but did identify a group of patients with intermediate risk SFT who had an increased risk of metastasis. CONCLUSIONS: TERT promoter mutations were more frequent in SFT with higher risk of metastasis, but TERT promoter mutation status was not a reliable predictor of clinical outcome by itself. However, mutations in the TERT promoter may be useful in further stratifying patients with intermediate risk tumours.
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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 | 0.000 |
| 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".