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<p>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.</p>

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Epigenomic diagnosis and prognosis of Acute Myeloid Leukemia

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月29日

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  • Francisco Marchi,
  • Vivek M. Shastri,
  • Richard J. Marrero,
  • Nam H. K. Nguyen,
  • Antonella Öttl,
  • Ann-Kathrin Schade,
  • Marieke Landwehr,
  • Olga Krali,
  • Jessica Nordlund,
  • Matin Ghavami,
  • Fernando Sckaff,
  • Vikash K. Mansinghka,
  • Xueyuan Cao,
  • William Slayton,
  • Petr Starostik,
  • Christopher R. Cogle,
  • Raul C. Ribeiro,
  • Jeffrey E. Rubnitz,
  • Jeffery Klco,
  • Abdelrahman Elsayed,
  • Alan S. Gamis,
  • Timothy J. Triche Jr.,
  • Rhonda Ries,
  • E. Anders Kolb,
  • Richard Aplenc,
  • Todd Alonzo,
  • Stanley Pounds,
  • Soheil Meshinchi,
  • Jatinder K. Lamba

摘要

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.