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 () and Weibin Cheng ()
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Xiao Zhou: Tsinghua University
Zhen Cheng: Tsinghua University
Mingyu Dong: Tsinghua University
Qi Liu: Tsinghua University
Weiyang Yang: Tsinghua University
Min Liu: Tsinghua University
Junzhang Tian: Guangdong Second Provincial General Hospital
Weibin Cheng: Guangdong Second Provincial General Hospital

Nature Communications, 2022, vol. 13, issue 1, 1-13

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.

Date: 2022
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DOI: 10.1038/s41467-022-35320-3

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