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<p>Acute leukemia requires precise molecular classification and urgent treatment. However, standard-of-care diagnostic tests are time-intensive and do not capture the full spectrum of acute leukemia heterogeneity. Here, we developed a framework to classify acute leukemia using genome-wide DNA methylation profiling. We first assembled a comprehensive reference cohort (<i>n</i> = 2,540 samples) and defined 38 methylation classes. Methylation-based classification matched standard-pathology lineage classification in most cases and revealed heterogeneity in addition to that captured by genetic categories. Using this reference, we developed a neural network (MARLIN; <Emphasis Type="Underline">m</Emphasis>ethylation- and <Emphasis Type="Underline">A</Emphasis>I-guided <Emphasis Type="Underline">r</Emphasis>apid <Emphasis Type="Underline">l</Emphasis>eukemia subtype <Emphasis Type="Underline">in</Emphasis>ference) for acute leukemia classification from sparse DNA methylation profiles. In retrospective cohorts profiled by nanopore sequencing, high-confidence predictions were concordant with conventional diagnoses in 25 out of 26 cases. Real-time MARLIN classification in patients with suspected acute leukemia provided accurate predictions in five out of five cases, which were typically generated within 2 h of sample receipt. In summary, we present a framework for rapid acute leukemia classification that complements and enhances standard-of-care diagnostics.</p>

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Rapid epigenomic classification of acute leukemia

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月22日

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  • Til L. Steinicke,
  • Salvatore Benfatto,
  • Maria R. Capilla-Guerra,
  • Andre B. Monteleone,
  • Jonathan H. Young,
  • Subha Shankar,
  • Phillip D. Michaels,
  • Harrison K. Tsai,
  • Jonathan D. Good,
  • Antonia Kreso,
  • Peter van Galen,
  • Christoph Schliemann,
  • Evan C. Chen,
  • Gabriel K. Griffin,
  • Volker Hovestadt

摘要

Acute leukemia requires precise molecular classification and urgent treatment. However, standard-of-care diagnostic tests are time-intensive and do not capture the full spectrum of acute leukemia heterogeneity. Here, we developed a framework to classify acute leukemia using genome-wide DNA methylation profiling. We first assembled a comprehensive reference cohort (n = 2,540 samples) and defined 38 methylation classes. Methylation-based classification matched standard-pathology lineage classification in most cases and revealed heterogeneity in addition to that captured by genetic categories. Using this reference, we developed a neural network (MARLIN; methylation- and AI-guided rapid leukemia subtype inference) for acute leukemia classification from sparse DNA methylation profiles. In retrospective cohorts profiled by nanopore sequencing, high-confidence predictions were concordant with conventional diagnoses in 25 out of 26 cases. Real-time MARLIN classification in patients with suspected acute leukemia provided accurate predictions in five out of five cases, which were typically generated within 2 h of sample receipt. In summary, we present a framework for rapid acute leukemia classification that complements and enhances standard-of-care diagnostics.