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Enregistrement W4417018929 · doi:10.1182/blood-2025-1387

Prevalence and significance of cytoses in clonal hematpoiesis

2025· article· en· W4417018929 sur OpenAlexaff
Virginia O. Volpe, Keishla Marie Arce-Ruiz, Yating Wang, Caitlyn Vlasschaert, Md Mesbah Uddin, Pradeep Natarajan, Daniel J. DeAngelo, Alexander G. Bick, Marlise R. Luskin, R. Coleman Lindsley, Shai Shimony, Lachelle D. Weeks

Notice bibliographique

RevueBlood · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensQueen's University
Organismes subventionnairesnon disponible
Mots-clésBasophiliaCytopeniaContext (archaeology)ExomeMyelodysplastic syndromesCohortThrombocytosisMyeloidExome sequencing

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction Clonal hematopoiesis (CH) is the age-related clonal expansion of hematopoietic progenitor cells, often caused by mutations in genetic drivers of myeloid neoplasia (MN). Clonal hematopoiesis of indeterminate potential (CHIP) and clonal cytopenia of uncertain significance (CCUS) are formally defined CH subtypes, acknowledged by the World Health Organization (WHO) and the International Consensus Classification (ICC) as MN precursors. CHIP is defined by the presence of mutations in MN driver genes at a variant allele fraction ≥2% in individuals without cytopenias. CCUS is used when a person possessing the molecular characteristics of CHIP also has persistent and unexplained cytopenias without clinical or morphological features of MN. At diagnosis, MNs may exist with various combinations of cytopenias and cytoses. However, cytoses are not considered in commonly used CH nosology and remain largely underexplored in the context of CH. We utilized the UK Biobank to determine the prevalence of cytoses among individuals with CHIP and CCUS and evaluated how cytoses influence incident MN risk. An independent clinical cohort from the Dana-Farber Hematologic Malignancies Data Repository (HMDR) was used for validation. Methods In a cohort of 470,960 UK Biobank participants without prior history of hematologic malignancy, 29,385 were classified as having CHIP or CCUS based upon variant calls from whole exome sequencing and blood counts. Cytoses were defined based on the presence of neutrophilia (neutrophils ≥ 6K/uL), monocytosis (monocytes ≥ 1K/uL), eosinophilia (eosinophils ≥ 0.5K/uL), basophilia (basophils ≥ 0.3K/uL), thrombocytosis (platelets ≥ 450K/uL), and erythrocytosis (hemoglobin ≥ 17g/dL). Prevalences were summarized descriptively and age- and sex- adjusted binomial logistic regression was used for statistical comparisons. Using a competing risk approach, with death as the competing event, we compared the cumulative incidences of MN between groups with and without cytoses subclassified by CH status or clonal hematopoiesis risk score (CHRS)-defined risk groups. Hazard ratios for MN were determined by age- and sex- adjusted cox proportional hazard models. Results Cytoses were more common in CHIP/CCUS (n = 4,549, 15.5%) compared to individuals without CHIP or CCUS (n = 58,619, 13.3%), OR 1.2 (95% confidence interval 1.15-1.23), p < 0.0001. Most CHIP/CCUS cytoses cases only involved 1 lineage (n = 4041, 88.8%). The prevalence of individuals with ≥ 1 cytosis was similar in CHIP (n = 4160, 15.1%) and CCUS (389, 16.6%), p = 0.084. Neutrophilia was the most common cytosis, detected in 11.5% of all individuals with CHIP/CCUS (n = 3391) and representing 74.5% of CHIP/CCUS cytoses cases. Cytoses were associated with a higher likelihood of mutations in JAK2 (OR 11.5, 95%CI 8.69-15.3, p < 0.0001), SRSF2 (OR 1.79, 95% CI 1.4-2.3, p < 0.0001), SF3B1 (OR 1.59, 95% CI 1.16-2.16, p = 0.0035), and ASXL1 (1.32, 95% CI 1.20-1.46, p <0.0001). The 10-year cumulative incidence of MN was higher for CHIP/CCUS with cytosis compared to without (4.6% vs 1.2%, hazard ratio = 5.86, 95% CI 5.32-6.45, p < 0.0001). This was driven by incident MPNs (51.4% of incident MNs), though 30.5% and 18.1% of incident MNs were AML and MDS. As 47% of CHRS-defined high-risk CHIP/CCUS had ≥1 cytosis, we investigated the impact of cytoses on CHRS risk estimates. For all CHRS risk strata, the cumulative incidence of MN in CHIP/CCUS with cytoses was significantly higher compared to without (10-year risk of MN: 66.3% vs 44.3% in high-risk CHIP/CCUS, 9.36% vs 3.78% in intermediate-risk CHIP/CCUS, and 4.98% vs 1.17% in low-risk CHIP/CCUS (p<0.001). Findings in the HMDR validated these trends, with unexplained cytoses being present in 27.1% of clinical CHIP/CCUS cases and similarly associated with an increased risk of MNs, particularly MPN and MDS/MPN overlap syndromes. Conclusion Cytoses are common among individuals labeled as CHIP or CCUS using current classification strategies. CHIP/CCUS with cytoses has a distinct molecular distribution driven by JAK2, splicing factors, and ASXL1 and is associated with a greater risk of incident MN, driven by MPNs, than estimated using the CHRS. These observations suggest clonal cytosis is a clinical entity distinct from CHIP and CCUS. Precise classification of myeloid precursor states is essential to refine risk stratification tools and improve selection of at-risk populations for early interception clinical trials to prevent MN.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,005
Score d'incertitude au seuil0,012

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0040,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,014
Tête enseignante GPT0,293
Écart entre enseignants0,280 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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