Graduation Year


Document Type




Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Biology (Cell Biology, Microbiology, Molecular Biology)

Major Professor

Liang Wang, M.D., Ph.D.

Committee Member

Alvaro Monteiro, Ph.D.

Committee Member

Theresa Boyle, M.D., Ph.D.

Committee Member

Jong Park, Ph.D.

Committee Member

Mingxiang Teng, Ph.D.


Cancer biomarker, cfMBD-seq, DNA methylation profiling, Liquid biopsies


Early detection of cancer is believed as one of the best solutions to improve the therapeutic outcomes and overall survival of cancer patients. Analysis of circulating nucleic acids in bodily fluids, referred to as “liquid biopsies”, is rapidly gaining prominence for this purpose. Cell-free DNA (cfDNA) methylation has emerged as a promising biomarker for early cancer detection, tumor type classification, and treatment response monitoring. Currently, most cfDNA methylation profiling technologies are based on bisulfite conversion, while enrichment-based methods such as cfMeDIP-seq are beginning to show potential. To expand the use of enrichment-based methods in cfDNA methylation profiling, here, we report an ultra-low input method based on methyl-CpG binding proteins capture, termed cfMBD-seq. We optimized the conditions of cfMBD capture by adjusting the amount of MethylCap protein along with using methylated filler DNA. Our data showed high genome-wide correlation between cfMBD-seq with 1 ng input and the standard MBD-seq (>1000 ng input). Compared with the most commonly used HM450K assay, our results showed that cfMBD-seq reliably detected 94% of the methylated CpG islands detected by HM450K, while correctly classifying 98% of non-methylated sites (AUC=0.995). We also found that cfMBD-seq outperforms cfMeDIP-seq in the enrichment of high-CpG-density regions such as CpG islands, which play an important role in the regulation of normal biological functions and diseases. To identify the clinical feasibility of cfMBD-seq, we applied cfMBD-seq to profile the cfDNA methylome using plasma samples from colorectal (N=13), lung (N=12), pancreatic (N=12) cancer patients, and non-cancer controls (N=16). We identified 1759, 1783, and 1548 differentially hypermethylated CpG islands (DMCGIs) in lung, colorectal, and pancreatic cancer patients, respectively. Interestingly, the vast majority of DMCGIs were overlapped with aberrant methylation changes in the corresponding primary tumor tissues, indicating that DMCGIs detected by cfMBD-seq were mainly driven by tumor-specific DNA methylation patterns. From the overlapping DMCGIs, we carried out machine learning analyses and identified a set of discriminating methylation signatures that had robust performance in cancer detection and classification. Overall, our study demonstrates that cfMBD-seq is a powerful tool for sensitive detection of tumor-derived epigenomic signals in cfDNA. Our findings will help to expand on existing blood-based molecular diagnostic tests and identify novel methylation biomarkers for early cancer detection and classification.