TERT promoter mutations in gliomas, genetic associations and clinico-pathological correlations
Bibliographic record
Abstract
BACKGROUND: The role of telomerase reverse transcriptase (TERT) in gliomagenesis has been recently further strengthened by the frequent occurrence of TERT promoter mutations (TERTp-mut) in gliomas and evidence that the TERT SNP genetic rs2736100 influences glioma risk. TERTp-mut creates a binding site for Ets/TCF transcription factors, whereas the common rs2853669 polymorphism disrupts another Ets/TCF site on TERT promoter. METHODS: We sequenced for TERTp-mut in 807 glioma DNAs and in 235 blood DNAs and analysed TERT expression by RT-PCR in 151 samples. TERTp-mut status and TERTp polymorphism rs2853669 were correlated with histology, genomic profile, TERT mRNA expression, clinical outcome and rs2736100 genotype. RESULTS: TERTp-mut identified in 60.8% of gliomas (491 out of 807) was globally associated with poorer outcome (Hazard ratio (HR)=1.50). We defined, based on TERTp-mut and IDH mutation status, four prognostic groups: (1) TERTp-mut and IDH-mut associated with 1p19q codeletion, overall survival (OS)>17 years; (2) TERTp-wt and IDH-mut, associated with TP53 mutation, OS=97.5 months; (3) TERTp-wt and IDH-wt, with no specific association, OS=31.6 months; (4) TERTp-mut and IDH-wt, associated with EGFR amplification, OS=15.4 months. TERTp-mut was associated with higher TERT mRNA expression, whereas the rs2853669 variant was associated with lower TERT mRNA expression. The mutation of CIC (a repressor of ETV1-5 belonging to the Ets/TCF family) was also associated with TERT mRNA upregulation. CONCLUSIONS: In addition to IDH mutation status, defining the TERTp-mut status of glial tumours should afford enhanced prognostic stratification of patients with glioma. We also show that TERTp-mut, rs2853669 variant and CIC mutation influence Tert expression. This effect could be mediated by Ets/TCF transcription factors.
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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) | 0.001 | 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".