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Record W2530614832 · doi:10.1038/leu.2016.275

Isocitrate dehydrogenase mutations in myeloid malignancies

2016· review· en· W2530614832 on OpenAlexaff
Bruno C. Medeiros, Amir T. Fathi, Courtney D. DiNardo, Daniel A. Pollyea, Steven M. Chan, Ronan Swords

Bibliographic record

VenueLeukemia · 2016
Typereview
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsPrincess Margaret Cancer Centre
FundersNational Center for Advancing Translational SciencesCelgene
KeywordsIDH2Isocitrate dehydrogenaseBiologyMyeloid leukemiaIDH1MyeloidCancer researchMyelodysplastic syndromesEpigeneticsHistoneMutationGeneticsGeneBiochemistryEnzymeImmunology

Abstract

fetched live from OpenAlex

Alterations to genes involved in cellular metabolism and epigenetic regulation are implicated in the pathogenesis of myeloid malignancies. Recurring mutations in isocitrate dehydrogenase (IDH) genes are detected in approximately 20% of adult patients with acute myeloid leukemia (AML) and 5% of adults with myelodysplastic syndromes (MDS). IDH proteins are homodimeric enzymes involved in diverse cellular processes, including adaptation to hypoxia, histone demethylation and DNA modification. The IDH2 protein is localized in the mitochondria and is a critical component of the tricarboxylic acid (also called the 'citric acid' or Krebs) cycle. Both IDH2 and IDH1 (localized in the cytoplasm) proteins catalyze the oxidative decarboxylation of isocitrate to α-ketoglutarate (α-KG). Mutant IDH enzymes have neomorphic activity and catalyze reduction of α-KG to the (R) enantiomer of 2-hydroxyglutarate, which is associated with DNA and histone hypermethylation, altered gene expression and blocked differentiation of hematopoietic progenitor cells. The prognostic significance of mutant IDH (mIDH) is controversial but appears to be influenced by co-mutational status and the specific location of the mutation (IDH1-R132, IDH2-R140, IDH2-R172). Treatments specifically or indirectly targeted to mIDH are currently under clinical investigation; these therapies have been generally well tolerated and, when used as single agents, have shown promise for inducing responses in some mIDH patients when used as first-line treatment or in relapsed or refractory AML or MDS. Use of mIDH inhibitors in combination with drugs with non-overlapping mechanisms of action is especially promising, as such regimens may address the clonal heterogeneity and the multifactorial pathogenic processes involved in mIDH myeloid malignancies. Advances in mutational analysis have made testing more rapid and convenient, and less expensive; such testing should become part of routine diagnostic workup and repeated at relapse to identify patients who may benefit from treatments that target mIDH.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.046
GPT teacher head0.352
Teacher spread0.306 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations343
Published2016
Admission routes1
Has abstractno

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