Investigation of the TERT promoter DNA methylation status in human cancer
Bibliographic record
Abstract
Telomere maintenance is a hallmark of human cancer. In the majority of human cancer, telomere maintenance is achieved by reactivation of telomerase. TERT – a reverse transcriptase component of telomerase – is a proto-oncogene that governs the telomerase activity and is transcriptionally dysregulated in cancer. While genetic alterations including mutations affecting the TERT promoter has been described, the exact mechanism of TERT reactivation in human cancer cells remains poorly understood. The focus of this Ph.D. thesis is to investigate biological and clinical implications of aberrant DNA hypermethylation within the TERT promoter observed uniquely in human cancer. I initially uncovered a cancer-specific DNA hypermethylated region within the TERT promoter – which I defined as the TERT Hypermethylated Oncological Region (THOR) – and show high prevalence of this epigenetic alteration in the context of human cancer. I then determine the biological and clinical implications of THOR hypermethylation, leading to its potency as a therapeutic target and a diagnostic biomarker. Lastly, I elucidate a new model of differential allelic THOR hypermethylation in human cancer and how it can explain differential allelic expression of TERT. Together, these studies provide an insight into the DNA methylation landscape of the TERT promoter, introduce a THOR-dependent TERT upregulatory mechanism, and propose therapeutic and diagnostic potential of THOR hypermethylation signature.
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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 | 0.000 |
| 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) | 0.003 | 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 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".