循环细胞游离 DNA (cfDNA) 作为癌症筛查和诊断的非侵入性生物标志物被广泛探索。解码 cfDNA 中起源细胞的能力将为病理生理机制提供生物学见解，帮助癌症表征并指导临床管理和随访。我们开发了一种基于 DNA 甲基化特征的反卷积算法 MetDecode，用于癌症组织起源识别。我们从头开始构建了参考图谱，并发布了结直肠癌、乳腺癌、卵巢癌和宫颈癌以及血细胞衍生实体的全基因组甲基化测序数据。 MetDecode 使用从输入 cfDNA 甲基化概况中动态学习的甲基化模式对图谱中不存在的贡献者进行建模。此外，我们的模型考虑了每个标记区域的覆盖范围，以减轻潜在的噪声源。计算机模拟实验显示，cfDNA 中肿瘤组织贡献的检测限低至 2.88%。 MetDecode 产生的 Pearson 相关系数高于 0.95，并且在模拟中优于其他方法（p < 0.001；T 检验；单方面）。在癌症患者的血浆 cfDNA 谱中，MetDecode 在 84.2% 的病例中分配了正确的组织来源。总之，MetDecode 可以通过准确估计多个组织的贡献来揭示 cfDNA 池成分的变化，同时提供不完善的参考图集。MetDecode 可在 https://github.com/JorisVermeeschLab/MetDecode 上获取。补充数据可在以下位置获取：在线生物信息学。© 作者 2024。由牛津大学出版社出版。

Circulating-cell free DNA (cfDNA) is widely explored as a non-invasive biomarker for cancer screening and diagnosis. The ability to decode the cells of origin in cfDNA would provide biological insights into pathophysiological mechanisms, aiding in cancer characterization and directing clinical management and follow-up.We developed a DNA methylation signature-based deconvolution algorithm, MetDecode, for cancer tissue origin identification. We built a reference atlas exploiting de novo and published whole-genome methylation sequencing data for colorectal, breast, ovarian and cervical cancer, and blood-cell-derived entities. MetDecode models the contributors absent in the atlas with methylation patterns learnt on-the-fly from the input cfDNA methylation profiles. Additionally, our model accounts for the coverage of each marker region to alleviate potential sources of noise. In-silico experiments showed a limit of detection down to 2.88% of tumour tissue contribution in cfDNA. MetDecode produced Pearson correlation coefficients above 0.95 and outperformed other methods in simulations (p < 0.001; T-test; one-sided). In plasma cfDNA profiles from cancer patients, MetDecode assigned the correct tissue-of-origin in 84.2% of cases. In conclusion, MetDecode can unravel alterations in the cfDNA pool components by accurately estimating the contribution of multiple tissues, while supplied with an imperfect reference atlas.MetDecode is available at https://github.com/JorisVermeeschLab/MetDecode.Supplementary data are available at Bioinformatics online.© The Author(s) 2024. Published by Oxford University Press.