Combined genetic and epigenetic alterations of the <i>TERT</i> promoter affect clinical and biological behavior of bladder cancer
Bibliographic record
Abstract
In urothelial bladder cancer (UBC), risk stratification remains an important unmet need. Limitless self‐renewal, governed by TERT expression and telomerase activation, is crucial for cancer progression. Thus, telomerase activation through the interplay of mutations (TERTpMut) and epigenetic alterations in the TERT promoter may provide further insight into UBC behavior. Here, we investigated the combined effect of TERTpMut and the TERT Hypermethylated Oncological Region (THOR) status on telomerase activation and patient outcome in a UBC international cohort (n = 237). We verified that TERTpMut were frequent (76.8%) and present in all stages and grades of UBC. Hypermethylation of THOR was associated with higher TERT expression and higher‐risk disease in nonmuscle invasive bladder cancers (NMIBC). TERTpMut alone predicted disease recurrence (HR: 3.18, 95%CI 1.84 to 5.51, p < 0.0001) but not progression in NMIBC. Combined THORhigh/TERTpMut increased the risk of disease recurrence (HR 5.12, p < 0.0001) and progression (HR 3.92, p = 0.025). Increased THOR hypermethylation doubled the risk of stage progression of both TERTpwt and TERTpMut NMIBC. These results highlight that both mechanisms are common and coexist in bladder cancer and while TERTpMut is an early event in bladder carcinogenesis THOR hypermethylation is a dynamic process that contributes to disease progression. While the absence of alterations comprises an extremely indolent phenotype, the combined genetic and epigenetic alterations of TERT bring additional prognostic value in NMIBC and provide a novel insight into telomere biology in cancer.
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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".