Abstract 4592: TERT expression predicts progression-free survival in meningiomas
Bibliographic record
Abstract
Abstract Background: TERT promoter mutation (TPM) is a rare but established biomarker in meningiomas associated with aberrant TERT expression, reduced progression-free survival (PFS), and reduced overall survival. While TERT is highly expressed in tumors with promotor mutations, its expression has also been observed even in tumors with wildtype TERT promoters (TP-WT). This study aimed to assess the prevalence of TERT expression and its association with clinical outcome in meningiomas. Methods: Bulk RNA sequencing (n=604), Sanger sequencing of the TERT promoter (n=1095), and methylation profiling (n=1218) of a multi-institutional cohort of meningiomas (total n=1241) were performed to determine TERT expression, TERT promoter mutation status, and TERT promoter methylation. A cohort of 380 meningiomas from Toronto was used for discovery, and 861 meningioma samples from external institutions were compiled as a validation cohort. Results: TERT expression was significantly higher in meningiomas with TPMs compared to TP-WT. However, TERT was still expressed in 30.4% of meningiomas that lacked TPM. TERT expression increased with higher WHO grades and was associated with shorter progression-free survival, even among TP-WT tumors. WHO grade 1 tumors that expressed TERT had PFS similar to those of WHO grade 2, while WHO grade 2 meningiomas expressing TERT had a PFS similar to those of WHO grade 3 meningiomas. Among grade 3 meningiomas, tumors expressing TERT had PFS similar to those with TPMs. Conclusions: Our findings highlight the prognostic significance of TERT expression in meningiomas, even in the absence of TPMs. Its presence may identify patients at greater risk of rapid progression. These results support the inclusion of TERT expression in risk stratification models and management strategies, including future WHO classification and cIMPACT-NOW molecular testing criteria. Citation Format: Chloe Gui, Justin Z. Wang, Vikas Patil, Andrew Ajisebutu, Jeff Liu, Zeel Patel, Rebeca Yakubov, Ramneet Kaloti, Yosef Ellenbogen, Christopher Wilson, Aaron Cohen-Gadol, Ghazaleh Tabatabai, Marcos Tatagiba, Felix Behling, Eric C. Holland, Jill S. Barnholtz-Sloan, Andrew E. Sloan, Craig Horbinski, Silky Chotai, Lola B. Chambless, Andrew Gao, Serge Makarenko, Stephen Yip, Kenneth Aldape, Farshad Nassiri, Gelareh Zadeh. TERT expression predicts progression-free survival in meningiomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4592.
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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 | 0.001 |
| 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 | 0.000 |
| 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".