基于无细胞 DNA (cfDNA) 的检测在检测早期癌症信号方面具有巨大潜力，但确定癌症信号的组织起源 (TOO) 仍然是一项具有挑战性的任务。在这里，我们研究了甲基化图谱对低深度 cfDNA 样本中 TOO 检测的贡献。我们利用来自五种肿瘤组织（乳腺癌、结直肠癌）的全基因组亚硫酸氢盐测序（WGBS）数据构建了肿瘤特异性甲基化图谱（TSMA）。 、胃癌、肝癌和肺癌）和配对白细胞（WBC）。 TSMA 与非负最小二乘矩阵分解 (NNLS) 反卷积算法结合使用来识别 WGBS 样本中肿瘤组织类型的丰度。我们表明，TSMA 对肿瘤组织效果良好，但由于存在大量 WBC 衍生 DNA，因此对 cfDNA 样本效果不佳。为了构建 TOO 模型，我们采用了多模态策略，并将 TSMA 的反卷积分数与 cfDNA 其他特征的组合用作输入。我们的最终模型由使用反卷积分数和全基因组甲基化密度的图卷积神经网络组成特征，在 239 个低深度 cfDNA 样本的验证数据集中实现了 69% 的准确率。 总之，我们已经证明，我们的 TSMA 与其他 cfDNA 特征相结合可以改善低深度 cfDNA 样本中的 TOO 检测。 © 2024。作者。

Cell free DNA (cfDNA)-based assays hold great potential in detecting early cancer signals yet determining the tissue-of-origin (TOO) for cancer signals remains a challenging task. Here, we investigated the contribution of a methylation atlas to TOO detection in low depth cfDNA samples.We constructed a tumor-specific methylation atlas (TSMA) using whole-genome bisulfite sequencing (WGBS) data from five types of tumor tissues (breast, colorectal, gastric, liver and lung cancer) and paired white blood cells (WBC). TSMA was used with a non-negative least square matrix factorization (NNLS) deconvolution algorithm to identify the abundance of tumor tissue types in a WGBS sample. We showed that TSMA worked well with tumor tissue but struggled with cfDNA samples due to the overwhelming amount of WBC-derived DNA. To construct a model for TOO, we adopted the multi-modal strategy and used as inputs the combination of deconvolution scores from TSMA with other features of cfDNA.Our final model comprised of a graph convolutional neural network using deconvolution scores and genome-wide methylation density features, which achieved an accuracy of 69% in a held-out validation dataset of 239 low-depth cfDNA samples.In conclusion, we have demonstrated that our TSMA in combination with other cfDNA features can improve TOO detection in low-depth cfDNA samples.© 2024. The Author(s).