Tumor detection by analysis of both symmetric- and hemi-methylation of plasma cell-free DNA

Xu Hua, Hui Zhou, Hui-Chen Wu, Julia Furnari, Corina P. Kotidis, Raul Rabadan, Jeanine M. Genkinger, Jeffrey N. Bruce, Peter Canoll, Regina M. Santella and Zhiguo Zhang ()
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Xu Hua: Columbia University Irving Medical Center
Hui Zhou: Columbia University Irving Medical Center
Hui-Chen Wu: Columbia University Irving Medical Center
Julia Furnari: Columbia University Irving Medical Center
Corina P. Kotidis: Columbia University Irving Medical Center
Raul Rabadan: Columbia University Irving Medical Center
Jeanine M. Genkinger: Columbia University Irving Medical Center
Jeffrey N. Bruce: Columbia University Irving Medical Center
Peter Canoll: Columbia University Irving Medical Center
Regina M. Santella: Columbia University Irving Medical Center
Zhiguo Zhang: Columbia University Irving Medical Center

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: Abstract Aberrant DNA methylation patterns have been used for cancer detection. However, DNA hemi-methylation, present at about 10% CpG dinucleotides, has been less well studied. Here we show that a majority of differentially hemi-methylated regions (DHMRs) in liver tumor DNA or plasma cells free (cf) DNA do not overlap with differentially methylated regions (DMRs) of the same samples, indicating that DHMRs could serve as independent biomarkers. Furthermore, we analyzed the cfDNA methylomes of 215 samples from individuals with liver or brain cancer and individuals without cancer (controls), and trained machine learning models using DMRs, DHMRs or both. The models incorporated with both DMRs and DHMRs show a superior performance compared to models trained with DMRs or DHMRs, with AUROC being 0.978, 0.990, and 0.983 in distinguishing control, liver and brain cancer, respectively, in a validation cohort. This study supports the potential of utilizing both DMRs and DHMRs for multi-cancer detection.

Date: 2024
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DOI: 10.1038/s41467-024-50471-1

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