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Record W2316854871 · doi:10.1093/neuonc/nou208.29

THOR METHYLATION PROVIDES INSIGHT INTO THE TELOMERE MAINTENANCE LANDSCAPE OF MALIGNANT GLIOMAS

2014· article· en· W2316854871 on OpenAlexaff
Uri Tabori, Pedro Castelo‐Branco, Dean A. Lee, Marco Gallo, T. Limpan, Joshua Mangerel, A. Price, Marc Remke, Chi Zhang, Asieh Heidari, Khalida Wani, Robert J. Vanner, Gelareh Zadeh, Jason Karamchandani, Sunit Das, Michael D. Taylor, Cynthia Hawkins, Hai Yan, Kenneth Aldape, P. B. Dirks

Bibliographic record

VenueNeuro-Oncology · 2014
Typearticle
Languageen
FieldMedicine
TopicRadiation Therapy and Dosimetry
Canadian institutionsOccupational Cancer Research Centre
Fundersnot available
KeywordsTelomereTelomeraseMethylationCancer researchDNA methylationGliomaBiologyGeneMolecular biologyGene expressionGenetics

Abstract

fetched live from OpenAlex

BACKGROUND: Gliomas are a deadly group of childhood and adult cancers associated with high relapse rate following current therapies. Limitless self-renewal is a hallmark of cancer recurrence and is controlled by telomerase activation and telomere maintenance. We have recently uncovered THOR (TERT Hypermethylated Oncological Region) which is paradoxically hypermethylated in gliomas with telomerase activation. In order to further explore the biological impact of THOR hypermethylation on self renewal and telomere maintenance of gliomas we undertook a stepwise approach. METHODS: RESULTS: Mapping of the human TERT promoter reveals that THOR spans 432 BP and comprises 52 CG sites. In contrast, the area where mutations in TERT promoter were uncovered is permanently hypomethylated. Promoter driven expression was analysed through luciferase assays and unveiled a repressive effect of THOR on the TERT promoter. Moreover, TERT mutations promoted hyperactivation of the reporter gene providing explanations for the methylation pattern observed in malignant gliomas. THOR methylation increases in gliomas as they transform from low to high grade and from primary tumor to established cell lines (p < 0.001). Analysis of allelic Tert expression reveals that THOR is initally methylated in the mutant allele and throughout tumor progression, the other allele eventually becomes methylated. This correlates with higher TERT expression. In contrast, tumors utilizing alternative lengthening of telomeres (ALT) lack THOR methylation and TERT mutations. Glioma stem cells (n = 32) are addicted to telomerase and have higher THOR methylation than the bulk tumor. Strikingly, glioblastomas which activate ALT lack this phenotype in their stem cells compartment. Whole exome sequencing reveals multiple ALT related mutations (TP53 and ATRX) which are present in the mature tumor cells subpopulation and absent in the TERT expressing stem cell subpopulation. THOR demethylation with Decitabine results in loss of telomerase activation only in tumor cells and not in normal and embryonic stem cells which lack THOR methylation. Combining telomerase inhibition with decitabine result in permanent loss of self renewal capacity of patient derived cell lines in vitro and lack of tumor formation in vivo. CONCLUSIONS: We offer a model and a novel classification of gliomas based on THOR hypermethylation and alterations in the telomere maintenance pathway. We also suggest that intratumoral heterogeneity in telomere maintenance might be the result of secondary hits in the non-stem cell compartment Finally, we propose that THOR demethylation can be a safe and viable option for exhausting the self renewal capacity of gliomas. SECONDARY CATEGORY: Pediatrics.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.288
Teacher spread0.273 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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