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Record W2120566462 · doi:10.1182/blood-2011-11-391748

IDH2 mutations are frequent in angioimmunoblastic T-cell lymphoma

2012· article· en· W2120566462 on OpenAlexafffund
Rob A. Cairns, Javeed Iqbal, François Lemonnier, Can Küçük, Laurence de Leval, Jean‐Philippe Jais, Marie Parrens, Antoine Martin, Luc Xerri, Pierre Brousset, Li Chong Chan, Wing C. Chan, Philippe Gaulard, Tak W. Mak

Bibliographic record

VenueBlood · 2012
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
FundersNational Cancer InstituteInstitut National Du CancerNational Institutes of HealthInstitut Universitaire de FranceTerry Fox FoundationCanadian Institutes of Health ResearchLeukemia and Lymphoma Society
KeywordsIDH2Isocitrate dehydrogenaseIDH1Cancer researchLymphomaBiologyMutationAngioimmunoblastic T-cell lymphomaPathologyMedicineT cellImmunologyGeneGeneticsEnzyme

Abstract

fetched live from OpenAlex

Mutations in isocitrate dehydrogenase 1 (IDH1) and isocitrate dehydrogenase 2 (IDH2) occur in most grade 2 and 3 gliomas, secondary glioblastomas, and a subset of acute myelogenous leukemias but have not been detected in other tumor types. The mutations occur at specific arginine residues and result in the acquisition of a novel enzymatic activity that converts 2-oxoglutarate to D-2-hydroxyglutarate. This study reports IDH1 and IDH2 genotyping results from a set of lymphomas, which included a large set of peripheral T-cell lymphomas. IDH2 mutations were identified in approximately 20% of angioimmunoblastic T-cell lymphomas (AITLs), but not in other peripheral T-cell lymphoma entities. These results were confirmed in an independent set of AITL patients, where the IDH2 mutation rate was approximately 45%. This is the second common genetic lesion identified in AITL after TET2 and extends the number of neoplastic diseases where IDH1 and IDH2 mutations may play a role.

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)0.001
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.018
GPT teacher head0.272
Teacher spread0.254 · 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

Citations500
Published2012
Admission routes2
Has abstractyes

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