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Record 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 on 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

Bibliographic record

VenueBlood · 2025
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsLeukemiaMyeloidDiseaseMyeloid leukemiaHaematopoiesisSomatic cellGermline mutationHematopoietic stem cell transplantationSomatic evolution in cancerAllele

Abstract

fetched live from 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.

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 score0.002
Threshold uncertainty score0.007

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)0.0020.000

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 head0.006
GPT teacher head0.219
Teacher spread0.213 · 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".

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Citations4
Published2025
Admission routes1
Has abstractyes

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