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

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Date

2018

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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.

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