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<p>Circulating tumor DNA is a critical biomarker in cancer diagnostics, but its accurate interpretation requires careful consideration of clonal hematopoiesis (CH), which can contribute to variants in cell-free DNA and potentially obscure true tumor-derived signals. Accurate detection of somatic variants of CH origin in plasma samples remains challenging in the absence of matched white blood cells sequencing. Here we present an open-source machine learning framework (MetaCH) which classifies variants in cfDNA from plasma-only samples as CH or tumor origin, surpassing state-of-the-art classification rates.</p>

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An artificial intelligence-based model for prediction of clonal hematopoiesis variants in cell-free DNA samples

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月20日

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  • Gustavo Arango-Argoty,
  • Marzieh Haghighi,
  • Gerald J. Sun,
  • Elizabeth Y. Choe,
  • Aleksandra Markovets,
  • J. Carl Barrett,
  • Zhongwu Lai,
  • Etai Jacob

摘要

Circulating tumor DNA is a critical biomarker in cancer diagnostics, but its accurate interpretation requires careful consideration of clonal hematopoiesis (CH), which can contribute to variants in cell-free DNA and potentially obscure true tumor-derived signals. Accurate detection of somatic variants of CH origin in plasma samples remains challenging in the absence of matched white blood cells sequencing. Here we present an open-source machine learning framework (MetaCH) which classifies variants in cfDNA from plasma-only samples as CH or tumor origin, surpassing state-of-the-art classification rates.