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An Adverse Prognostic Effect of Homozygous TET2 Mutational Status on the Relapse Risk of Acute Myeloid Leukemia Patients of Normal Karyotype

2014· article· en· W2564033819 on OpenAlexaff
Jae‐Sook Ahn, Hyeoung‐Joon Kim, Yeo‐Kyeoung Kim, Il-Kwon Lee, Nan Young Kim, Mark D. Minden, Chul Won Jung, Jun‐Ho Jang, Hee‐Je Kim, Joon Ho Moon, Sang Kyun Sohn, Jong-Ho Won, Sung‐Hyun Kim, Namshin Kim, Kenichi Yoshida, Seishi Ogawa, Dennis Dong Hwan Kim

Bibliographic record

VenueBlood · 2014
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsCEBPANPM1Internal medicineMyeloid leukemiaFrameshift mutationMedicineMissense mutationOncologyGastroenterologyMutationLeukemiaNonsense mutationKaryotypeCancer researchImmunologyBiologyGeneticsGeneChromosome

Abstract

fetched live from OpenAlex

Abstract Purpose Ten-eleven-translocation oncogene family member 2 (TET2) mutations play leukemogenic roles in patients with acute myeloid leukemia (AML). However, the prognostic significance of such mutations in normal-karyotype (NK) AML patients remains controversial, especially that of homozygous TET2 mutations. n the present study, we attempted 1) to evaluate the prevalence of TET2 mutations in NK-AML patients; 2) to clarify the prognostic role played by TET2 mutation, especially homozygous mutation, in NK-AML patients; and, 3) to analyze associations among TET2 mutations and other mutations frequently observed in NK-AML patients, including those in FLT3-ITD, NPM1, and CEBPA. Patients and Methods We included 407 patients with NK-AML in the present study. NK-AML patients were diagnosed from October 1998 to September 2012 in seven participating institutes, and the median patient age was 52 years (range, 15–84 years). Sixty-five different TET2 mutations were detected in 54 patients (13.3%), of which 13 nonsense, 30 frameshift, and 22 missense and, homozygous mutations in 14 patients (25.9%) among TET2 mutated patients. Results A TET2 mutation was associated with poor prognostic features such as older age (p<0.001) or a high WBC count (p=0.013). Upon multivariate analysis, older patients (p=0.012, OR: 0.442, 95% CI 0.233-0.839), an NPM1 mutation (p=0.004, OR: 2.256, 95% CI 1.301-3.912), and a CEBPA mutation (p=0.001, OR: 5.031, 95% CI 1.921-13.173) were confirmed to be independent risk factors for complete remission (CR), but no TET2 mutation influenced CR (p=0.441, OR: 0.744, 95% CI 0.351-1.578). Upon multivariate analysis of factors affecting relapse incidence (RI), event-free survival (EFS), and overall survival (OS); performance of allogeneic stem cell transplantation (allo-SCT), and mutations in NPM1, CEBPA, or FLT3-ITD mutations, were independent risk factors for RI, EFS, and OS, but neither a TET2 mutation alone nor older age had any prognostic impact on RI, EFS, or OS. However, patients with homozygous TET2 mutations experienced a shorter EFS (p=0.046) and a higher relapse rate (p=0.010) than those with non-homozygous TET2 mutations or who were of TET2 wild-type status. Homozygous TET2 mutational status was an independent adverse prognostic factor for relapse upon multivariate analysis (p<0.001; HR 1.519; 95% CI 1.105-2.086), suggesting that the TET2 mutation exerted a threshold effect on relapse risk. Conclusion In summary, the TET2 mutation did not impact treatment outcomes, but homozygous TET2 mutational status did affect (elevate) the relapse rate, in particular. Our data suggest that homozygous TET2 mutational status increases the relapse risk in NK-AML patients. Disclosures Off Label Use: Rituximab has been used as an off-label drug for adult ALL, and has been provided by Roche Inc. for scientific purpose. .

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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.005
GPT teacher head0.246
Teacher spread0.241 · 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".

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Citations0
Published2014
Admission routes1
Has abstractyes

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