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Methylome Analysis: From Computation Workflow Development to Implementation in a Breast Cancer Prevention Trial

Frankhouser, David E
http://rave.ohiolink.edu/etdc/view?acc_num=osu1512052081030923

Abstract Details

2017, Doctor of Philosophy, Ohio State University, Biomedical Sciences.
Cancer research is rapidly advancing toward more personalized treatments and diagnostics. The major driving force of this advancement are the technological developments of sequencing which has resulted in greatly reduced costs. The democratization of sequencing assays has produced a unique set of challenges that must be addressed in order to successfully conduct clinical research. DNA methylation (DNAm) has increasingly been assayed by sequencing due to its role in human disease and cancers, including breast cancer. DNAm is an epigenetic modification and primarily functions to regulate gene expression. In high-risk breast cancer subtypes, the focus of our most recent research, DNAm is able to predict survival for subtypes with no molecular markers of risk. There are many sequencing assays for DNAm, but the two most common approaches are methylation capture (MethylCap-seq) and bisulfite conversion (BS-seq). As with any sequencing analyte, choosing the appropriate approach for a study is made more difficult by the continual improvements made to the methods and the sequencing technologies. Additionally, each DNAm assay requires a unique data analysis workflow in order to derive valid conclusions. In this work, we will describe a novel computational quantification method for MethylCap-seq and the study design and analysis of a BS-seq experiment as applied to a preventative therapy in breast cancer. Together, these results demonstrate the development and planning needed for the successful implementation of a sequencing assay in clinical research. MethylCap-seq is an approach that uses a protein that binds to DNAm to capture methylated DNA fragments. A region with a large number of captured fragments is interpreted as having high DNAm. Therefore, quantification is based on the number of fragments. Although MethylCap-seq is an inexpensive genome-wide assay, it does have a few limitations. The one we sought to address was the limited resolution of the data. Biologically, DNAm occurs at a single nucleotide; however, the resolution of MethylCap-seq is the fragment which is several hundred nucleotides. To improve the quantification of MethylCap-seq data, we developed a novel computational method. Our method, PrEMeR-CG, imputes a more accurate DNAm quantification at single nucleotide resolution. Since development, PrEMeR-CG has been used to make discoveries in multiple clinical studies. Unlike MethylCap-seq, BS-seq provides DNAm quantification at nucleotide resolution and is considered the gold-standard of DNAm sequencing assays. We utilized BS-seq to assess DNAm for an on-going clinical trial that aims to investigate the efficacy of high-dose polyunsaturated fatty acids (PUFA) treatment to prevent breast cancer in subtypes that have no preventative therapies. This work is motivated by the anti-inflammatory effect observed from PUFA treatment and by pro-cancer effects of inflammation in breast cancer. Our hypothesis is that anti-inflammatory effects of PUFA are mediated by DNAm changes in the breast tissue. In this work, we present two achievements that advance the research of PUFA treatment. First, we describe the design and the approach to investigate DNAm in the breast tissue in an on-going phase II clinical trial of PUFA treatment. One important result from this process was that we developed a novel inflammation related breast cancer specific candidate gene list for interrogating DNAm. Second, we report DNAm changes due to PUFA treatment in the PBMCs of peripheral blood. Our data showed that PUFA treatment resulted in specific DNAm changes in inflammation related pathways. Additionally, we identified potential inflammation related genes that we will follow up as treatment response biomarkers.
Lisa Yee, MD (Advisor)
Qianben Wang, PhD (Advisor)
Ralf Bundschuh, PhD (Committee Member)
Amanda Toland, PhD (Committee Member)
111 p.
Bioinformatics; Biomedical Research
DNA methylation; breast cancer; bioinformatics; next-generation sequencing; omega-3 fatty acids; bisulfite; methylcap-seq; clinical research; biomarker; computational analysis; cancer prevention; treatment; therapy; study design

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Citations

  • Frankhouser, D. E. (2017). Methylome Analysis: From Computation Workflow Development to Implementation in a Breast Cancer Prevention Trial [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1512052081030923

    APA Style (7th edition)

  • Frankhouser, David. Methylome Analysis: From Computation Workflow Development to Implementation in a Breast Cancer Prevention Trial. 2017. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1512052081030923.

    MLA Style (8th edition)

  • Frankhouser, David. "Methylome Analysis: From Computation Workflow Development to Implementation in a Breast Cancer Prevention Trial." Doctoral dissertation, Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1512052081030923

    Chicago Manual of Style (17th edition)

osu1512052081030923
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This open access ETD is published by The Ohio State University and OhioLINK.