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High Prevalence of TERT Mutations in Chronic Lymphocytic Leukemia

2008· article· en· W2987642284 on OpenAlexaff
Mark Hills, Alexander Roeth, Holger Nückel, Doug Horsman, Jan Duerig, Randy D. Gascoyne, Peter M. Lansdorp

Bibliographic record

VenueBlood · 2008
Typearticle
Languageen
FieldMedicine
TopicTelomeres, Telomerase, and Senescence
Canadian institutionsTerry Fox Research InstituteBC Cancer Agency
Fundersnot available
KeywordsDyskeratosis congenitaTelomereTelomeraseBone marrow failureCancer researchMyeloid leukemiaTelomerase reverse transcriptaseBiologyChronic lymphocytic leukemiaFanconi anemiaLeukemiaImmunologyDNA repairGeneticsGeneHaematopoiesisStem cell

Abstract

fetched live from OpenAlex

Abstract Heritable mutations in the genes that encode the three minimal components of the human telomerase complex, hTERT, hTERC and DKC are known to give rise to Dyskeratosis Congenita (DC), a rare disorder characterized by skin pigmentation abnormalities, nail dystrophy and leukoplakia. Telomeres in leukocytes of patients with DC are invariably very short and patients typically succumb to consequences of bone marrow failure, pulmonary fibrosis or malignancies. Other genetic defects including mutations in the telomeric protein TINF2 are also known to give rise to DC. However, not all individuals with mutations in “telomere maintenance” genes such as TERT, TERC, DKC and TINF2 will develop clinical symptoms during their lifetime and some patients, without clinical signs of DC, present with aplastic anemia (AA) and idiopathic pulmonary fibrosis (IPF). It was previously shown that hypomorphic mutations in hTERT are 3-fold more common in patients with acute myeloid leukemia (AML) than in controls (Calado et al., ASH abstracts 2007 110: 16). Together with the increased incidence of malignancies in DC these observations suggest that telomere dysfunction can trigger dysplastic as well as neoplastic disorders, most likely because progressive telomere loss results in loss of normal cells and thereby selects for cells with defects in the DNA damage checkpoint(s) that are normally triggered when chromosome ends have insufficient telomere repeats. Such cells are expected to have DNA repair defects and their malignant evolution could be facilitated further by telomere dysfunction triggering cycles of chromosome fusions/bridge/breakage before telomerase is eventually upregulated. In view of these considerations and the important role of telomeres in B cell biology (with telomeres being elongated in the germinal centre and memory B cells having longer telomeres than naïve B cells) we postulated that heritable genetic defects in telomere maintenance could predispose to B cell malignancies as well as AML. To test this hypothesis we sequenced TERC and TERT genes in 80 consecutive CLL patients. No mutations in TERC were found. Sequence variants in TERT were identified in 14 patients with one patient, a compound heterozygous, carrying 2 separate mutations. 5 of the 80 CLL patients carried the A279T TERT variant but this allele was also present in ~ 3% of control individuals. This TERT allele did not significantly reduce telomerase activity in telomerase reconstitution experiments measured by TRAP assay. All other TERT sequence variants that we found in CLL appear to be hypomorhic mutations (that reduce but not completely disable telomerase reverse transcriptase activity) and all were previously described in DC, AA, AML and IPF. Two common variants, D441E and A1062T were screened by high throughput dotblotting of DNA from CLL patients and frequencies of 3/142 (2.1 %) and 12/195 (6.2 %) were found respectively. The presumed germline origin of the TERT mutations in CLL needs to be confirmed. Our results indicate that hypomorphic TERT mutations are common in CLL and contribute to disease in over 10% of patients.

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.016
GPT teacher head0.247
Teacher spread0.231 · 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

Citations0
Published2008
Admission routes1
Has abstractyes

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