Arango-Argoty Gustavo, Haghighi Marzieh, Sun Gerald J, Choe Elizabeth Y, Markovets Aleksandra, Barrett J Carl, Lai Zhongwu, Jacob Etai
Oncology R&D, AstraZeneca, Waltham, MA, USA.
NPJ Precis Oncol. 2025 May 20;9(1):147. doi: 10.1038/s41698-025-00921-w.
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.
循环肿瘤DNA是癌症诊断中的关键生物标志物，但其准确解读需要仔细考虑克隆性造血（CH），这可能导致游离DNA出现变异，并可能掩盖真正的肿瘤来源信号。在缺乏匹配的白细胞测序的情况下，准确检测血浆样本中CH来源的体细胞变异仍然具有挑战性。在此，我们提出了一个开源机器学习框架（MetaCH），它可以将仅来自血浆样本的cfDNA变异分类为CH或肿瘤来源，分类率超过了当前的先进水平。
