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<p>Cerebrospinal fluid (CSF) liquid biopsies serve as a rich source of tumor-derived cell-free DNA (cfDNA) for evaluating persons with central nervous system (CNS) tumors. However, challenges stemming from trace cfDNA yields and low mutational burden have hindered sensitivity, whereas first-generation clinical assays have relied on genetic alterations as biomarkers. Leveraging the diagnostic utility of DNA methylation classification in CNS tumors, we developed M-PACT (methylation-based predictive algorithm for CNS tumors), a robust deep neural network that accurately classifies tumors from subnanogram-input cfDNA methylomes. Across embryonal CNS tumor benchmarking (<i>n</i> = 79) and validation (<i>n</i> = 58) cohorts, M-PACT achieved 92% and 88% accuracy, respectively. We further showcase M-PACT utility in nonembryonal CNS tumors, balanced tumor genomes and nonmalignant CSF. Beyond classification, this workflow enables methylation-based cellular deconvolution and sensitive copy-number variation detection. Altogether, we provide a blueprint for CNS tumor classification from low-input cfDNA methylomes, motivating prospective validation for future clinical implementation.</p>

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M-PACT leverages cell-free DNA methylomes to achieve robust classification of pediatric brain tumors

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月17日

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  • Kyle S. Smith,
  • Tom T. Fischer,
  • Katie Han,
  • Anna Kostecka,
  • Hong Lin,
  • Daniel Senfter,
  • Taha Soliman,
  • Natalia Stepien,
  • Stefanie Volz,
  • Nathalie Schwarz,
  • Tatjana Wedig,
  • Sibylle Madlener,
  • Christine Haberler,
  • Sandeep K. Dhanda,
  • Santhosh A. Upadhyaya,
  • Patrick R. Blackburn,
  • Maria T. Schmook,
  • Judith de Bont,
  • Hannu Haapasalo,
  • Justina Dargvainiene,
  • Frank Leypoldt,
  • Stefan M. Pfister,
  • Esther Hulleman,
  • Brent A. Orr,
  • Amar Gajjar,
  • Giles W. Robinson,
  • Joonas Haapasalo,
  • Kristiina Nordfors,
  • Johannes Gojo,
  • Kristian W. Pajtler,
  • Kendra K. Maass,
  • Paul A. Northcott

摘要

Cerebrospinal fluid (CSF) liquid biopsies serve as a rich source of tumor-derived cell-free DNA (cfDNA) for evaluating persons with central nervous system (CNS) tumors. However, challenges stemming from trace cfDNA yields and low mutational burden have hindered sensitivity, whereas first-generation clinical assays have relied on genetic alterations as biomarkers. Leveraging the diagnostic utility of DNA methylation classification in CNS tumors, we developed M-PACT (methylation-based predictive algorithm for CNS tumors), a robust deep neural network that accurately classifies tumors from subnanogram-input cfDNA methylomes. Across embryonal CNS tumor benchmarking (n = 79) and validation (n = 58) cohorts, M-PACT achieved 92% and 88% accuracy, respectively. We further showcase M-PACT utility in nonembryonal CNS tumors, balanced tumor genomes and nonmalignant CSF. Beyond classification, this workflow enables methylation-based cellular deconvolution and sensitive copy-number variation detection. Altogether, we provide a blueprint for CNS tumor classification from low-input cfDNA methylomes, motivating prospective validation for future clinical implementation.