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Record W4311835442 · doi:10.1186/s13148-022-01396-3

THOR is a targetable epigenetic biomarker with clinical implications in breast cancer

2022· article· en· W4311835442 on OpenAlex
Joana Apolónio, João S. Dias, Mónica T. Fernandes, Martin Komosa, Tatiana Lipman, Cindy H. Zhang, Ricardo Leão, Dong‐Hyun Lee, Nuno M. Nunes, Ana-Teresa Maia, José L. Morera, Luís Vicioso, Uri Tabori, Pedro Castelo‐Branco

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueClinical Epigenetics · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsSickKids FoundationUniversity of TorontoHospital for Sick Children
FundersFundação para a Ciência e a TecnologiaCentro de Investigação em BiomedicinaLiga Portuguesa Contra o CancroCanadian Cancer Society Research InstituteCanadian Institutes of Health ResearchHospital for Sick Children
KeywordsBreast cancerBiomarkerEpigeneticsHuman geneticsMedicineCancerOncologyBioinformaticsInternal medicineBiologyComputational biologyGeneticsGene

Abstract

fetched live from OpenAlex

BACKGROUND: Breast cancer (BC) is the most frequently diagnosed cancer and a leading cause of death among women worldwide. Early BC is potentially curable, but the mortality rates still observed among BC patients demonstrate the urgent need of novel and more effective diagnostic and therapeutic options. Limitless self-renewal is a hallmark of cancer, governed by telomere maintenance. In around 95% of BC cases, this process is achieved by telomerase reactivation through upregulation of the human telomerase reverse transcriptase (hTERT). The hypermethylation of a specific region within the hTERT promoter, termed TERT hypermethylated oncological region (THOR) has been associated with increased hTERT expression in cancer. However, its biological role and clinical potential in BC have never been studied to the best of our knowledge. Therefore, we aimed to investigate the role of THOR as a biomarker and explore the functional impact of THOR methylation status in hTERT upregulation in BC. RESULTS: THOR methylation status in BC was assessed by pyrosequencing on discovery and validation cohorts. We found that THOR is significantly hypermethylated in malignant breast tissue when compared to benign tissue (40.23% vs. 12.81%, P < 0.0001), differentiating malignant tumor from normal tissue from the earliest stage of disease. Using a reporter assay, the addition of unmethylated THOR significantly reduced luciferase activity by an average 1.8-fold when compared to the hTERT core promoter alone (P < 0.01). To further investigate its biological impact on hTERT transcription, targeted THOR demethylation was performed using novel technology based on CRISPR-dCas9 system and significant THOR demethylation was achieved. Cells previously demethylated on THOR region did not develop a histologic cancer phenotype in in vivo assays. Additional studies are required to validate these observations and to unravel the causality between THOR hypermethylation and hTERT upregulation in BC. CONCLUSIONS: THOR hypermethylation is an important epigenetic mark in breast tumorigenesis, representing a promising biomarker and therapeutic target in BC. We revealed that THOR acts as a repressive regulatory element of hTERT and that its hypermethylation is a relevant mechanism for hTERT upregulation in BC.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.061
GPT teacher head0.400
Teacher spread0.339 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it