Overview of the study design. The plasma cfDNA concentration of all 862 individuals from the study cohort was measured. The fragmentomic features of the cfDNA were analyzed, including the size profile and end motif distributions. The contribution of nucleases was explored using the cleavage profile end motif patterns, and protein quantification of DNASE1L3 in plasma. A machine learning model was trained using fragmentomic features to predict cfDNA concentration. We further explored whether a similar model could be applied to study fractional DNA concentration in predicting the fetal and tumor fraction of plasma cfDNA in pregnant subjects and patients with HCC, respectively.
