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Background Breast malignancy is the most frequently diagnosed malignancy in women worldwide, and the diagnosis relies on invasive examinations. However, most clinical breast changes in women are benign, and invasive diagnostic approaches cause unnecessary suffering for the patients. Thus, a novel noninvasive approach for discriminating malignant breast lesions from benign lesions is needed. Methods We performed cell-free DNA (cfDNA) sequencing on plasma samples from 173 malignant breast lesion patients, 158 benign breast lesion patients, and 102 healthy women. We then analyzed the cfDNA-based nucleosome profiles, which reflect the various tissues of origin and transcription factor activities. Moreover, by using machine learning classifiers along with the cfDNA sequencing data, we built classifiers for discriminating benign from malignant breast lesions. Receiver operating characteristic curve analyses were used to evaluate the performance of the classifiers. Results cfDNA-based nucleosome profiles reflected the various tissues of origin and transcription factor activities in benign and malignant breast lesions. The cfDNA-based transcription factor activities and breast malignancy-specific transcription factor-binding site accessibility profiles could accurately distinguish benign and malignant breast lesions, with area under the curve values of 0.777 and 0.824, respectively. Conclusions Our proof-of-principle study established a methodology for noninvasively discriminating benign from malignant breast lesions.

Additional file 6: All additional figures.

Background Noninvasive monitoring of fetal development and the early detection of pregnancy-associated complications is challenging, largely due to the lack of information about the molecular spectrum during pregnancy. Recently, cell-free DNA in plasma was found to reflect the global nucleosome footprint and status of gene expression, and showed potential for noninvasive health monitoring during pregnancy. Objective(s) We aimed to test the relationships between plasma cell-free DNA profiles and pregnancy biology, and evaluate the use of cell-free DNA profile as a non-invasive method for physiological and pathological status monitoring during pregnancy. Study Design We used genome cell-free DNA sequencing data generated from non-invasive prenatal testing in a total of 2937 pregnant women. For each physiological and pathological conditions, features of cell-free DNA profile were identified using the discovery cohort, and support vector machines classifiers were built and evaluated using independent training and validation cohorts. Results We established nucleosome occupancy profiles at transcription start sites in different gestational trimesters, demonstrated the relationships between gene expression and cell-free DNA coverage at transcription start sites, and showed that the cell-free DNA profiles at transcription start sites represented the biological processes of pregnancy. In addition, using cell-free DNA data, nucleosome profiles of transcription factor binding sites were identified to reflect transcription factor footprint, which may help to reveal the molecular mechanisms underlying pregnancy. Finally, by using machine learning models on low coverage non-invasive prenatal testing data, we evaluated the use of cell-free DNA nucleosome profiles for distinguishing gestational trimesters, fetal gender, and fetal trisomy 21, and highlighted its potential utility for predicting physiological and pathological fetal conditions by using low coverage non-invasive prenatal testing data. Conclusion(s) Our analyses profiled nucleosome footprints and regulatory networks during pregnancy and established a noninvasive, proof-of-principle methodology for health monitoring during pregnancy.