HomeScience & TechSolving the major obstacles in testing cell-free DNA (cfDNA) called as liquid...

Solving the major obstacles in testing cell-free DNA (cfDNA) called as liquid biopsy

A team of researchers led by Xianghong Jasmine Zhou, PhD, professor of pathology and laboratory medicine at the UCLA David Geffen School of Medicine, has made significant progress in solving one of the major obstacles in testing cell-free DNA (cfDNA), popularly known as liquid biopsy.

They discovered specific methylation patterns that are unique to each tissue, which can help identify the exact tissue or organ involved in the cfDNA abnormalities detected by testing, a major obstacle to accurate disease diagnosis and monitoring.

Cell-free DNA holds great promise for disease identification and monitoring. However, reliable measurement of tissue-derived cfDNA using existing methods has proven difficult, as has determining the tissue origin of the cfDNA fragments found in these assays.

In a new study published in the Proceedings of the National Academy of Sciences (PNAS), a peer-reviewed journal of the National Academy of Sciences (NAS), the team developed a comprehensive, high-resolution methylation atlas based on a large dataset of 521 non-cancerous tissue samples representing 29 major human tissue types.

They called the approach cfSort and showed that they successfully identified specific methylation patterns unique to each tissue at the fragment level and confirmed these findings using additional datasets.

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

“We showed that cfSort outperformed existing methods in terms of accuracy and limit of detection: more accurate estimation of the tissue fraction and resolution of a lower level of tissue-derived cfDNA,” said first author Shuo Li. “Furthermore, cfSort has demonstrated near-perfect robustness to unseen local fluctuations in tissue composition, suggesting its broad applicability across diverse individuals.”

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