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Enregistrement W4417014103 · doi:10.1182/blood-2025-8

Sickle cell disease is associated with early-onset clonal hematopoiesis involving DNA damage response pathway mutations

2025· article· en· W4417014103 sur OpenAlexaff
Lachelle D. Weeks, Courtney D. Fitzhugh, Sam Pollock, Miriam A. Osei, Micah Rickles-Young, H. Moses Murdock, Megan Townsend, Christopher W. Reilly, Carla Luana Dinardo, Éster Cerdeira Sabino, Mark Fleharty, Matthew DeFelice, Azeet Narayan, Donna Neuberg, Robert Redd, Lakshmanan Krishnamurti, David A. Williams, Pablo Bartolucci, Carrie Cibulskis, Julia Smith, Milena Batchvarova, Nancy Asomaning, Brian Custer, Kolapo Oyebola, Carlo Brugnara, Russell E. Ware, Vivien Sheehan, Jeffrey Glassberg, Shannon Kelly, Allison E. Ashley‐Koch, Marilyn J. Telen, Guillaume Lettre, Niall J. Lennon, R. Coleman Lindsley

Notice bibliographique

RevueBlood · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueHemoglobinopathies and Related Disorders
Établissements canadiensUniversité de Montréal
Organismes subventionnairesnon disponible
Mots-clésLeukemiaMyeloidDiseaseMyeloid leukemiaHaematopoiesisSomatic cellGermline mutationHematopoietic stem cell transplantationSomatic evolution in cancerAllele

Résumé

récupéré en direct d'OpenAlex

Abstract Background: Individuals with sickle cell disease (SCD) face an elevated risk of myeloid leukemias. Recently, myelodysplastic syndrome and acute myeloid leukemia have emerged as complications of curative SCD therapies, including gene therapy and allogeneic hematopoietic cell transplantation (HCT). Leukemias arising in SCD have been reported to harbor somatic TP53 mutations, and post-HCT TP53-mutant leukemias have been traced to low-level TP53 clones detectable pre-HCT. These findings suggest that SCD itself may predispose patients to high-risk clonal hematopoiesis (CH). Prior studies of CH in SCD used sequencing methods with limited sensitivity, yielding conflicting conclusions and potentially missing small, clinically relevant clones. In this multinational cohort, we defined CH prevalence, age distribution, and mutational profiles in SCD relative to non-SCD controls and other hemoglobinopathies. Methods: We analyzed archived blood DNA from 7,283 individuals across 17 cohorts in 4 countries: 3,885 with SCD (SS, SC, Sꞵ0, Sꞵ+), 3,398 without SCD (AA, AS, AC), and 188 with beta-thalassemia. Using duplex sequencing, we identified somatic CH variants at ≥0.001 variant allele fraction (VAF), germline variants in leukemia predisposition genes, and HBB genotypes. CH was analyzed by gene and in pre-specified biological groups: DNMT3A/TET2 (DT-CH) and DNA damage response (DDR-CH: TP53, PPM1D, CHEK2, ATM). We used binomial logistic regression (age- and sex-adjusted) to compare the prevalence of CH in SCD vs non-SCD controls. Results: We detected 6,661 CH variants in 2,673 individuals (median VAF=0.002). CH occurred earlier in SCD and was more prevalent in SCD cases compared to non-SCD controls among those aged 0-19 years [10.6% (95% CI: 9.1, 12.2) vs 3.5% (2.4, 5.0); p <0.0001]. This was driven by a selectively increased prevalence of DDR-CH in SCD [SCD: 3.3% (2.5, 4.3) vs non-SCD: 0.6% (0.2, 1.5), p = 0.0012] which extended across adult age groups (20-29 years: 3.5% vs 0.9%; 30-39 years: 7.7% vs 1.8%; 40-49 years: 15.1% vs 5.5%). Within DDR-CH, PPM1D was enriched in SCD compared to controls (36.4% vs 21.8%; p<0.0001); ATM (4.9% vs 9.5%; p=0.016) and CHEK2 (26.7% vs 34.1%; p=0.03) were underrepresented; and TP53 was similar (32.0% vs. 34.7%; p=0.45). To further evaluate the onset of CH in children with SCD, we performed serial sequencing of 148 participants enrolled in the BabyHUG trial (age 0.6 to 1.4 years) with follow-up samples obtained between 3 and 11 years of age. We detected CH, including DDR-CH, in 4.7% of children at baseline, all of which persisted in subsequent samples. Among those without CH at baseline, 3.9% developed incident CH during follow-up. In contrast to DDR-CH, DT-CH prevalence was higher in SCD among the youngest population [0-19: 6.4% (5.2, 7.7) vs 2.5 (1.5, 3.9), p<0.0001] but progressively decreased with advancing age relative to non-SCD controls. Among older individuals, (age ≥50 years), the prevalence of DT-CH was significantly lower in those with SCD than in those without SCD [54.4% (47.7, 61.0) vs 76.9% (74, 79.6), p<0.0001]. To determine whether sickle cell trait also had increased CH prevalence, we compared individuals with AA to those with AS/AC genotypes. CH prevalence was not higher in AS/AC compared to AA: overall CH (OR 1.01, p=0.95), DDR-CH (OR 0.99, p=0.50), DT-CH (OR 0.92, p=0.38). Then, to evaluate whether the association of SCD with early-onset CH was generalizable to other beta hemoglobinopathies, we analyzed the CH prevalence in pediatric beta-thalassemia patients (n=166). CH prevalence in beta-thalassemia was similar to AA controls (OR 1.37, p=0.49) and lower than SCD (OR 0.41, p=0.027). We observed no DDR-CH in beta-thalassemia. The prevalence of pathogenic/likely pathogenic germline variants in leukemia predisposition genes, such as DDX41, TERT, GATA2, and RUNX1 was similar across all evaluated HBB genotypes.Conclusions: Using deep targeted sequencing, we demonstrate that SCD is associated with a predisposition to early onset high-risk CH. Individuals with SCD exhibit markedly increased prevalence of DDR pathway mutations compared to controls, with the earliest clones detectable in infancy. This precocious DDR-CH is specific to SCD and not observed in individuals with sickle cell trait or beta-thalassemia. These findings provide a plausible mechanistic basis for the elevated relative risk of myeloid leukemias in SCD and therapy-related leukemias as a complication of curative therapies.

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)0,000
score de la tête « metaresearch » (Gemma)0,001
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 enseignants0,002
Score d'incertitude au seuil0,007

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
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 savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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 Opus0,006
Tête enseignante GPT0,219
Écart entre enseignants0,213 · 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

Citations4
Publié2025
Routes d'admission1
Résumé présentoui

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