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Record W2593185963 · doi:10.1182/blood.v110.11.16.16

Constitutional Loss-of-Function Mutations in Telomerase Are Genetic Risk Factors for Acute Myeloid Leukemia.

2007· article· en· W2593185963 on OpenAlexaff
Rodrigo T. Calado, Joshua A. Regal, William T. Yewdell, Roberto Passetto Falcão, L. L. Figueiredo, Elihu H. Estey, Marco A. Zago, Mark Hills, Stephen J. Chanock, Peter M. Lansdorp, Neal S. Young

Bibliographic record

VenueBlood · 2007
Typearticle
Languageen
FieldMedicine
TopicTelomeres, Telomerase, and Senescence
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDyskeratosis congenitaTelomeraseMyeloid leukemiaTelomerase RNA componentTelomereBiologyLeukemiaAcute leukemiaBone marrow failureAplastic anemiaImmunologyGene mutationCancer researchMutationInternal medicineGeneticsMedicineTelomerase reverse transcriptaseGeneBone marrowHaematopoiesisStem cell

Abstract

fetched live from OpenAlex

Abstract Telomeres protect chromosome ends from end-to-end fusion and recombination but are gradually eroded with proliferation. Critically short telomeres are repaired by the telomerase complex and mutations in telomerase complex genes (TERT and TERC) are associated with dyskeratosis congenita and acquired aplastic anemia. Telomere shortening eventually results in cell proliferation arrest, apoptosis, or genomic instability, and patients with dyskeratosis congenita and acquired aplastic anemia are at risk for developing leukemia and other malignancies. We therefore investigated whether TERT and TERC gene mutations were associated with acute myeloid leukemia (AML) by screening 100 consecutive Brazilian patients diagnosed with AML (excluding acute promyelocytic leukemia) at the same institution and 198 ethnic-, age-, and sex-matched healthy volunteers. Eight patients carried a non-synonymous TERT gene variant; one was homozygous and five heterozygous for A1062T, one was heterozygous for H412Y, and another was heterozygous for a novel R522K TERT mutation. None of these gene variants was present in matched controls (Fisher’s exact test, P=0.0001). We validated our results by screening an additional 89 AML patients from the MD Anderson Cancer Center, selected based on cytogenetic status and 528 healthy controls. Four of these 89 patients had a non-synonymous TERT gene variant (one homozygous, three heterozygous), an incidence higher than in controls (P=0.028). To further address the higher prevalence of the A1062T gene variant in AML, we screened a total of 1,111 controls, and found a 3.8 times higher A1062T allele frequency in AML than in controls (P=0.002). The germ-line origin of mutations was established by mutation detection in non-hematopoietic tissues and in relatives. Cytogenetics were available for four TERT-mutant patients in the Brazilian cohort: two had inv(16), one had t(5;11)(q35;q13) and del(10)(p15), and another had complex karyotype (including trisomy 8). In the MD Anderson cohort, 2 patients had inv(16) and two had trisomy 8. The incidence of TERT mutations was higher in patients with trisomy 8 (2/10) or inv (16) (2/22) than in other patients (0/57; P =0.02 for trisomy 8 and 0.08 for inv(16) vs. other). Telomere lengths of blast cells were extremely short (median length, 3.1 kb; range, 2.4-5.9 kb). Vectors containing TERT mutants were transfected into VA13 cells and telomerase activity of transfected cell lysates, measured by the fluorescent telomere repeat amplification protocol (TRAP) assay, demonstrated that AML-associated TERT gene variants resulted in significantly reduced telomerase function in comparison to wild-type TERT by haploinsufficiency. Our results indicate that constitutional TERT gene mutations are risk factors for AML. Abnormal telomerase function of hematopoietic stem cells may result in short and dysfunctional telomeres, facilitating genomic instability, aneuploidy, and probably contributing to an early stage of leukemogenesis.

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)0.002
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0030.000

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 head0.018
GPT teacher head0.277
Teacher spread0.259 · 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

Citations7
Published2007
Admission routes1
Has abstractyes

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