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Record W4395005490 · doi:10.1002/ajh.27321

Chronic neutrophilic leukemia and atypical chronic myeloid leukemia: 2024 update on diagnosis, genetics, risk stratification, and management

2024· review· en· W4395005490 on OpenAlexaff
Natasha Szuber, Attilio Orazi, Ayalew Tefferi

Bibliographic record

VenueAmerican Journal of Hematology · 2024
Typereview
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsLeukocytosisNeutrophiliaMonocytosisMyeloidBasophiliaMedicineChronic myelomonocytic leukemiaImmunologyMyeloproliferative neoplasmMyeloid leukemiaBone marrowLeukemiaPathologyMyelodysplastic syndromesMyelofibrosis

Abstract

fetched live from OpenAlex

Abstract Chronic neutrophilic leukemia (CNL) is a rare BCR :: ABL1 ‐negative myeloproliferative neoplasm (MPN) defined by persistent mature neutrophilic leukocytosis and bone marrow granulocyte hyperplasia. Atypical chronic myeloid leukemia (aCML) (myelodysplastic “[ MDS ]/ MPN with neutrophilia ” per World Health Organization [WHO]) is a MDS/MPN overlap disorder featuring dysplastic neutrophilia and circulating myeloid precursors. Both manifest with frequent hepatosplenomegaly and less commonly, bleeding, with high rates of leukemic transformation and death. The 2022 revised WHO classification conserved CNL diagnostic criteria of leukocytosis ≥25 × 10 9 /L, neutrophils ≥80% with <10% circulating precursors, absence of dysplasia, and presence of an activating CSF3R mutation. ICC criteria are harmonized with those of other myeloid entities, with a key distinction being lower leukocytosis threshold (≥13 × 10 9 /L) for cases CSF3R‐ mutated. Criteria for aCML include leukocytosis ≥13 × 10 9 /L, dysgranulopoiesis, circulating myeloid precursors ≥10%, and at least one cytopenia for MDS‐thresholds (ICC). In both classifications ASXL1 and SETBP1 (ICC), or SETBP1 ± ETNK1 (WHO) mutations can be used to support the diagnosis. Both diseases show hypercellular bone marrow due to a granulocytic proliferation, aCML distinguished by dysplasia in granulocytes ± other lineages. Absence of monocytosis, rare/no basophilia, or eosinophilia, <20% blasts, and exclusion of other MPN, MDS/MPN, and tyrosine kinase fusions, are mandated. Cytogenetic abnormalities are identified in ~1/3 of CNL and ~15–40% of aCML patients. The molecular signature of CNL is a driver mutation in colony‐stimulating factor 3 receptor—classically T618I, documented in >80% of cases. Atypical CML harbors a complex genomic backdrop with high rates of recurrent somatic mutations in ASXL1 , SETBP1 , TET2 , SRSF2 , EZH2 , and less frequently in ETNK1 . Leukemic transformation rates are ~10–25% and 30–40% for CNL and aCML, respectively. Overall survival is poor: 15–31 months in CNL and 12–20 months in aCML. The Mayo Clinic CNL risk model for survival stratifies patients according to platelets <160 × 10 9 /L (2 points), leukocytes >60 × 10 9 /L (1 point), and ASXL1 mutation (1 point); distinguishing low‐ (0–1 points) versus high‐risk (2–4 points) categories. The Mayo Clinic aCML risk model attributes 1 point each for: age >67 years, hemoglobin <10 g/dL, and TET2 mutation, delineating low‐ (0–1 risk factor) and high‐risk (≥2 risk factors) subgroups. Management is risk‐driven and symptom‐directed, with no current standard of care. Most commonly used agents include hydroxyurea, interferon, Janus kinase inhibitors, and hypomethylating agents, though none are disease‐modifying. Hematopoietic stem cell transplant is the only potentially curative modality and should be considered in eligible patients. Recent genetic profiling has disclosed CBL , CEBPA , EZH2 , NRAS , TET2 , and U2AF1 to represent high‐risk mutations in both entities. Actionable mutations ( NRAS/KRAS , ETNK1 ) have also been identified, supporting novel agents targeting involved pathways. Preclinical and clinical studies evaluating new drugs (e.g., fedratinib, phase 2) and combinations are detailed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.961
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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 head0.020
GPT teacher head0.335
Teacher spread0.315 · 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 teacher head, not a consensus.

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

Citations25
Published2024
Admission routes1
Has abstractyes

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