DNA methylation is an epigenetic mechanism that is related to mammalian cellular differentiation, gene expression regulation, and disease. In several studies, DNA methylation has been identified as an effective marker to identify differences between cells. In this review, we introduce single-cell DNA-methylation profiling methods, including experimental strategies and approaches to computational data analysis. Furthermore, the blind spots of the basic analysis and recent alternatives are briefly described. In addition, we introduce well-known applications and discuss future development.

Background Postoperative minimal residual disease (MRD) detection using circulating-tumour DNA (ctDNA) requires a highly sensitive analysis platform. We have developed a tumour-informed, hybrid-capture ctDNA sequencing MRD assay. Methods Personalised target-capture panels for ctDNA detection were designed using individual variants identified in tumour whole-exome sequencing of each patient. MRD status was determined using ultra-high-depth sequencing data of plasma cell-free DNA. The MRD positivity and its association with clinical outcome were analysed in Stage II or III colorectal cancer (CRC). Results In 98 CRC patients, personalised panels for ctDNA sequencing were built from tumour data, including a median of 185 variants per patient. In silico simulation showed that increasing the number of target variants increases MRD detection sensitivity in low fractions (<0.01%). At postoperative 3-week, 21.4% of patients were positive for MRD by ctDNA. Postoperative positive MRD was strongly associated with poor disease-free survival (DFS) (adjusted hazard ratio 8.40, 95% confidence interval 3.49–20.2). Patients with a negative conversion of MRD after adjuvant therapy showed significantly better DFS (P < 0.001). Conclusion Tumour-informed, hybrid-capture-based ctDNA assay monitoring a large number of patient-specific mutations is a sensitive strategy for MRD detection to predict recurrence in CRC.

Poly(ADP-ribose) polymerase inhibitors have been shown dramatic responses in patients with BRCAness. However, clinical studies have been limited to breast cancer patients with germline mutations. Here, we describe a patient with metastatic breast cancer who had a rare BRCA1 somatic mutation (BRCA1 c.4336G>T (p.E1446*)) detected by cell-free DNA analysis after failing standard therapies. This tier III variant of unknown significance was predicted to be a pathogenic variant in our assessment, leading us to consider off-label treatment with olaparib.The patient responded well to olaparib for several months, with a decrease in allele frequency of this BRCA1 somatic mutation in cfDNA. Olaparib resistance subsequently developed with an increase in the allele frequency and new BRCA1 reversion mutations. To our knowledge, this is the first report confirming BRCA1 c.4336G>T (p.E1446*) as a mutation sensitive to olaparib in breast cancer and describing the dynamic changes in the associated mutations using liquid biopsy.

Background The factors affecting cardioprotective collateral circulation are still incompletely understood. Recently, characteristics, such as CpG methylation of cell-free DNA (cfDNA), have been reported as markers with clinical utility. The aim of this study was to evaluate whether cfDNA methylation patterns are associated with the grade of coronary collateral circulation (CCC). Result In this case–control study, clinical and angiographic data were obtained from 143 patients (mean age, 58 years, male 71%) with chronic total coronary occlusion. Enzymatic methyl-sequencing (EM-seq) libraries were prepared using the cfDNA extracted from the plasma. Data were processed to obtain the average methylation fraction (AMF) tables of genomic regions from which blacklisted regions were removed. Unsupervised analysis of the obtained AMF values showed that some of the changes in methylation were due to CCC. Through random forest preparation process, 256 differentially methylated region (DMR) candidates showing strong association with CCC were selected. A random forest classifier was then constructed, and the area under the curve of the receiver operating characteristic curve indicated an appropriate predictive function for CCC. Finally, 20 DMRs were identified to have significantly different AMF values between the good and poor CCC groups. Particularly, the good CCC group exhibited hypomethylated DMRs. Pathway analysis revealed five pathways, including TGF-beta signaling, to be associated with good CCC. Conclusion These data have demonstrated that differential hypomethylation was identified in dozens of cfDNA regions in patients with good CCC. Our results support the clinical utility of noninvasively obtained epigenetic signatures for predicting collateral circulation in patients with vascular diseases.