Building upon the foundation of liquid biopsy utility for the early detection of cancer, analysis of genome-wide cell-free DNA fragmentation with machine learning classification and modeling can also extend to the identification of liver cirrhosis and other chronic diseases, according to findings published in Science Translational Medicine.
The researchers used whole-genome sequencing to examine cell-free DNA fragmentomes in 1,576 individuals.
Analysis focused on fragment size as well as distribution of the fragments within the entire genome.
They also developed a machine learning classifier to detect signatures of early liver disease, advanced fibrosis, and cirrhosis across the vast amount of fragmentomes.
The classifier was tested in a discovery (n = 423) and validation cohort (n = 221), and demonstrated limited cross-reactivity for other diseases and high sensitivity.
Then, a machine learning model was created using cell-free DNA fragmentomes to predict the survival of patients with several of the identified diseases.
The fragmentome can serve as a foundation for building different classifiers for different diseases, and importantly, these classifiers are disease-specific and do not cross-react.
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