A cancer specific hypermethylation signature of the TERT promoter predicts biochemical relapse in prostate cancer: a retrospective cohort study
Bibliographic record
Abstract
// Pedro Castelo-Branco 1, 2, 3, * , Ricardo Leão 1, 4, 5, * , Tatiana Lipman 1 , Brittany Campbell 1 , Donghyun Lee 1 , Aryeh Price 1 , Cindy Zhang 1 , Abolfazl Heidari 1 , Derek Stephens 1 , Stefan Boerno 6 , Hugo Coelho 5 , Ana Gomes 5 , Celia Domingos 2, 3 , Joana D. Apolonio 2, 3 , Georg Schäfer 11 , Robert G. Bristow 7 , Michal R. Schweiger 8, 9 , Robert Hamilton 4 , Alexandre Zlotta 4, 10 , Arnaldo Figueiredo 5 , Helmut Klocker 11 , Holger Sültmann 12 , Uri Tabori 1 1 Arthur and Sonia Labatt Brain Tumor Research Center, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada 2 Regenerative Medicine Program, Department of Biomedical Sciences and Medicine, University of Algarve, Faro, Portugal 3 Centre for Biomedical Research (CBMR), University of Algarve, Faro, Portugal 4 Division of Urology, Department of Surgical Oncology Princess Margaret Cancer Center, University of Toronto, Toronto, Ontario, Canada 5 Serviço de Urologia e Transplantação Renal, Centro Hospitalar Universitário Coimbra EPE, Faculty of Medicine, University of Coimbra, Coimbra, Portugal 6 Sequencing Core Facility, Max Planck Institute for Molecular Genetics, Berlin, Germany 7 Department of Radiation Oncology, Princess Margaret Cancer Center, Toronto, Ontario, Canada 8 Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Berlin, Germany 9 Cologne Center for Genomics, Cologne University, Cologne, Germany 10 Division of Urology, Department of Surgery, Mount Sinai Hospital, Toronto, Ontario, Canada 11 Department of Urology, Medical University of Innsbruck, Innsbruck, Austria 12 Cancer Genome Research, German Cancer Research Center (DKFZ) and German Consortium for Translational Cancer Research (DKTK), Heidelberg, Germany * These authors contributed equally to this work Correspondence to: Uri Tabori, email: uri.tabori@sickkids.ca Pedro Castelo-Branco, email: pjbranco@ualg.pt Keywords: TERT, prostate cancer, biomarker, diagnostic, Gleason score Received: March 31, 2016 Accepted: June 30, 2016 Published: July 16, 2016 ABSTRACT The identification of new biomarkers to differentiate between indolent and aggressive prostate tumors is an important unmet need. We examined the role of THOR ( TERT Hypermethylated Oncological Region) as a diagnostic and prognostic biomarker in prostate cancer (PCa). We analyzed THOR in common cancers using genome-wide methylation arrays. Methylation status of the whole TERT gene in benign and malignant prostate samples was determined by MeDIP-Seq. The prognostic role of THOR in PCa was assessed by pyrosequencing on discovery and validation cohorts from patients who underwent radical prostatectomy with long-term follow-up data. Most cancers ( n = 3056) including PCa ( n = 300) exhibited hypermethylation of THOR. THOR was the only region within the TERT gene that is differentially methylated between normal and malignant prostate tissue ( p < 0.0001). Also, THOR was significantly hypermethylated in PCa when compared to paired benign tissues ( n = 164, p < 0.0001). THOR hypermethylation correlated with Gleason scores and was associated with tumor invasiveness ( p = 0.0147). Five years biochemical progression free survival (BPFS) for PCa patients in the discovery cohort was 87% (95% CI 73–100) and 65% (95% CI 52–78) for THOR non-hypermethylated and hypermethylated cancers respectively ( p = 0.01). Similar differences in BPFS were noted in the validation cohort ( p = 0.03). Importantly, THOR was able to predict outcome in the challenging (Gleason 6 and 7 (3 + 4)) PCa ( p = 0.007). For this group, THOR was an independent risk factor for BPFS with a hazard-ratio of 3.685 ( p = 0.0247). Finally, THOR hypermethylation more than doubled the risk of recurrence across all PSA levels (OR 2.5, p = 0.02).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".