<i><scp>TERT</scp></i> promoter mutations in solitary fibrous tumour
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it