ESTIMATION AND ANALYSIS OF CELL-SPECIFIC DNA METHYLATION FROM BISULFITE-SEQUENCING DATA

Abstract

DNA methylation is the best understood heritable gene regulatory mechanism that does not involve direct modification of DNA sequence itself. Cells with different methylation profiles (over temporal or micro-environmental dimensions) may exhibit different phenotypic properties. In cancer, heterogeneity across cells in the tumor microenvironment presents significant challenges to treatment. In particular, epigenetic heterogeneity is discernible among tumor cells, and it is believed to impact the growth properties and treatment resistance of tumors. Existing computational methods used to study the epigenetic composition of cell populations are based on the analysis of DNA methylation modifications at multiple consecutive genomic loci spanned by single DNA sequencing reads. These approaches have yielded great insight into how cell populations differ epigenetically across different tissues. However, they only provide a general summary of the epigenetic composition of these cell populations without providing cell-specific methylation patterns over longer genomic spans to perform a comprehensive analysis of the epigenetic heterogeneity of cell populations. In this dissertation, we address this challenge by proposing two computational methods called methylFlow and MCFDiff. In methylFlow, we propose a novel method based on network flow algorithms to reconstruct cell-specific methylation profiles using reads obtained from sequencing bisulfite-converted DNA.We reveal the methylation profile of underlying clones in a heterogeneous cell population including the methylation patterns and their corresponding abundance within the population. In MCFDiff, we propose a statistical model that leverages the identified cell-specific methylation profiles (from methylFlow) to determine regions of differential methylation composition (RDMCs) between multiple phenotypic groups, in particular, between tumor and paired normal tissue. In MCFDiff, we can systematically exclude the tumor tissue impurities and increase the accuracy in detecting the regions with differential methylation composition in normal and tumor samples. Profiling the changes between normal and tumor samples according to the reconstructed methylation profile of underling clone in different samples leads us to the discovery of de novo epigenetic markers and a better understanding about the effect of epigenetic heterogeneity in cancer dynamics from the initiation, progression to metastasis, and relapse.