Document Type
Article
Publication Date
7-29-2025
Identifier
DOI: 10.1038/s41467-025-62005-4; PMCID: PMC12307604
Abstract
Despite the critical role of DNA methylation, clinical implementations harnessing its promise have not been described in acute myeloid leukemia. Utilizing DNA methylation from 3314 leukemia patient samples across 11 harmonized cohorts, we describe the Acute Leukemia Methylome Atlas, which includes robust models capable of accurately predicting AML subtypes. A genome-wide prognostic model as well as a targeted panel of 38 CpGs significantly predict five-year survival in our pediatric and adult test cohorts. To accelerate rapid clinical utility, we develop a specimen-to-result protocol that uses long-read nanopore sequencing and machine learning to characterize patients' whole genomes and epigenomes. Clinical validation on patient samples confirms high concordance between epigenomic signatures and genomic lesions, though uniquely rare karyotypes remained challenging due to limited available training data. These results unveil the potential for increased affordability, speed, and accuracy for patients in need of complex molecular diagnosis and prognosis.
Journal Title
Nat Commun
Volume
16
Issue
1
First Page
6961
Last Page
6961
MeSH Keywords
Humans; Leukemia, Myeloid, Acute; Prognosis; DNA Methylation; Epigenomics; Adult; Child; Female; Male; Adolescent; Machine Learning; CpG Islands; Middle Aged; Child, Preschool; Young Adult; Epigenesis, Genetic; Cohort Studies
PubMed ID
40730747
Keywords
Acute Myeloid Leukemia; Prognosis; DNA Methylation; Epigenomics; Machine Learning; CpG Islands; Genetic Epigenesis; Cohort Studies
Recommended Citation
Marchi F, Shastri VM, Marrero RJ, et al. Epigenomic diagnosis and prognosis of Acute Myeloid Leukemia. Nat Commun. 2025;16(1):6961. Published 2025 Jul 29. doi:10.1038/s41467-025-62005-4
Comments
Grants and funding
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Publisher's Link: https://www.nature.com/articles/s41467-025-62005-4