Abstract A20: hTERT promoter hypermethylation is a cancer signature which predicts survival and response to targeted therapy in pediatric nervous system tumors
Bibliographic record
Abstract
Abstract Purpose: Defining grade of malignancy and disease progression is a major goal in pediatric neurooncology. Since telomerase is a hallmark of cancer, we examined whether promoter methylation of hTERT, the catalytic subunit of telomerase, can be a biomarker for malignancy, response to therapy and patient outcome. Patients and methods: Whole genome methylation arrays (discovery cohort n=280), Sequenom and pyrosequencing of hTERT promoter (validation cohort, n=219) were performed on patient samples and normal tissues. Correlation between hTERT promoter methylation, telomere maintenance and patient outcome was performed. Results: Using the discovery cohort, we uncovered a specific region upstream-of-the transcription-start-site (UTSS) of hTERT that is hypermethylated in 100% of malignant neoplasms that express hTERT and unmethylated in normal tissues and in low grade tumors lacking hTERT expression. In the validation cohort, this biomarker had positive and negative predictive values of 1.0 and 0.93, respectively. UTSS methylation increased in tumors as they evolved from low to high grade and from primary to metastatic. Furthermore, UTSS methylation was able to identify which low grade neoplasms would progress to malignant cancers. Ependymomas with and without UTSS methylation had 5-year overall survival of 51+/-10% and 95+/-5% respectively (p=0.0008). Finally, UTSS methylation could predict which tumors would respond to targeted therapy with telomerase inhibitor. Conclusion: Hypermethylation of a specific region in the hTERT promoter is a cancer signature. It positively correlates with higher hTERT expression, tumor progression and poor prognosis. hTERT UTSS methylation may also represent a diagnostic tool and a therapeutic target for pediatric nervous system tumors. Citation Format: Pedro castelo-branco, Sanaa Choufani, Stephen Mack, Denis Gallagher, Cindy Zhang, Tatiana Lipman, Nataliya Zhukova, Erin J. Walker, Diana Merino, Jonathan D. Jonathan, Cynthia Elizabeth, Noa Alon, Libo Zhang, Volker Hovestadt, Marcel Kool, David TW Jones, Sidney Croul, Cynthia Hawkins, Johann Hitzler, Jean C.Y. Wang, David Malkin, Sylvain Baruchel, Peter B. Dirks, Stefan Pfister, Michael Taylor, Rosanna Weksberg, Uri Tabori. hTERT promoter hypermethylation is a cancer signature which predicts survival and response to targeted therapy in pediatric nervous system tumors. [abstract]. In: Proceedings of the AACR Special Conference on Pediatric Cancer at the Crossroads: Translating Discovery into Improved Outcomes; Nov 3-6, 2013; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2013;74(20 Suppl):Abstract nr A20.
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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".