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Record W2165603851 · doi:10.1182/blood-2009-09-240440

Deregulation of the telomerase reverse transcriptase (TERT) gene by chromosomal translocations in B-cell malignancies

2010· article· en· W2165603851 on OpenAlexaff
Inga Nagel, Monika Szczepanowski, José I. Martín‐Subero, Lana Harder, Takashi Akasaka, Ole Ammerpohl, Evelyne Callet‐Bauchu, Randy D. Gascoyne, Stefan Gesk, Doug Horsman, Wolfgang Hiddemann, Aneela Majid, José A. Martinez‐Climent, Stephan Stilgenbauer, Holger Tönnies, Martin J.S. Dyer, Reiner Siebert

Bibliographic record

VenueBlood · 2010
Typearticle
Languageen
FieldMedicine
TopicTelomeres, Telomerase, and Senescence
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsTelomeraseBiologyChromosomal translocationLocus (genetics)Telomerase reverse transcriptaseMantle cell lymphomaChronic lymphocytic leukemiaGeneCancer researchMolecular biologyGeneticsLeukemiaLymphomaImmunology

Abstract

fetched live from OpenAlex

Sequence variants at the TERT-CLPTM1L locus in chromosome 5p have been recently associated with disposition for various cancers. Here we show that this locus including the gene encoding the telomerase reverse-transcriptase TERT at 5p13.33 is rarely but recurrently targeted by somatic chromosomal translocations to IGH and non-IG loci in B-cell neoplasms, including acute lymphoblastic leukemia, chronic lymphocytic leukemia, mantle cell lymphoma and splenic marginal zone lymphoma. In addition, cases with genomic amplification of TERT locus were identified. Tumors bearing chromosomal aberrations involving TERT showed higher TERT transcriptional expression and increased telomerase activity. These data suggest that deregulation of TERT gene by chromosomal abnormalities leading to increased telomerase activity might contribute to B-cell lymphomagenesis.

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: Observational · Consensus signal: none
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
Bibliometrics0.0010.000
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 head0.007
GPT teacher head0.208
Teacher spread0.201 · 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 designObservational
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

Citations48
Published2010
Admission routes1
Has abstractyes

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