2022,
Nature Communications.
DOI:
10.1038/s41467-022-35320-3
Tumor fractions deciphered from circulating cell-free DNA methylation for cancer early diagnosis
Xiao Zhou
, Zhen Cheng
, Mingyu Dong
, Qi Liu
, Weiyang Yang
, Min Liu
, Junzhang Tian
, Weibin Cheng
Abstract:
Abstract Tumor-derived circulating cell-free DNA (cfDNA) provides critical clues for cancer early diagnosis, yet it often suffers from low sensitivity. Here, we present a cancer early diagnosis approach using tumor fractions deciphered from circulating cfDNA methylation signatures. We show that the estimated fractions of tumor-derived cfDNA from cancer patients increase significantly as cancer progresses in two independent datasets. Employing the predicted tumor fractions, we establish a Bayesian diagnostic model in which training samples are only derived from late-stage patients and healthy individuals. When validated on early-stage patients and healthy individuals, this model exhibits a sensitivity of 86.1% for cancer early detection and an average accuracy of 76.9% for tumor localization at a specificity of 94.7%. By highlighting the potential of tumor fractions on cancer early diagnosis, our approach can be further applied to cancer screening and tumor progression monitoring.
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2023-01-31 23:13:00
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#paper doi：https://doi.org/10.1038/s41467-022-35320-3
Tumor fractions deciphered from circulating cell-free DNA methylation for cancer early diagnosis. Nat Commun 13, 7694 (2022)
本文是清华大学团队开发的使用 cfDNA 甲基化特征来构建SRFD-Bayes诊断模型，通过去卷积混合甲基化特征来估计cfDNA的肿瘤的起源组织 (TOO)，用于预测原发性肿瘤的位置和对癌症早期诊断。本文分为三个部分，使用肿瘤和正常样本甲基化数据模拟 cfdna 数据；甲基化标记物选择，使用半参考反卷积（SRFD）从血浆cfDNA甲基化谱中学习的参考数据库， 构建SRFD-Bayes 模型；在早期患者和健康个体上验证时，该模型对癌症早期检测的敏感性为86.1%，对肿瘤定位的平均准确性为76.9%，特异性为94.7%。