New tool improves liquid biopsy

Cell-free DNA has significant potential in disease detection and monitoring. However, accurately quantifying tissue-derived cfDNA has proven challenging with current methods, among them determining the tissue origin of cfDNA fragments detected in these tests.

In a new study, the team developed a comprehensive and high-resolution methylation atlas based on a vast dataset of 521 noncancerous tissue samples representing 29 major types of human tissues. They call the approach cfSort and showed it successfully identified specific methylation patterns unique to each tissue at the fragment level and validated these findings using additional datasets.

Going further, the team illustrated the clinical applications of cfSort through two potential uses: aiding in disease diagnosis and monitoring treatment side effects. By estimating the tissue-derived cfDNA fraction using cfSort, they were able to assess and predict clinical outcomes in patients.

Reference: Comprehensive tissue deconvolution of cell-free DNA by deep learning for disease diagnosis and monitoring, July 3, 2023,120 (28) e2305236120 DOI: 10.1073/pnas.2305236120