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DNA Methylation Marks in Peripheral Blood and the Risk of Developing Mature B Cell Neoplasms

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DNA Methylation Marks in Peripheral Blood and the Risk of Developing Mature B Cell Neoplasms DNA methylation marks in peripheral blood and the risk of developing mature B cell neoplasms Nicole Wong Doo A thesis in fulfillment of the requirements for the degree of Doctor of Philosophy School of Population and Global Health The University of Melbourne 2018 ORCID ID: 0000-0003-3725-3397 0 Blank 1 Abstract Dysregulation of DNA methylation is a feature of mature B cell neoplasms (MBCN) but it is not known whether methylation changes can be detected in blood-derived DNA prior to MBCN diagnosis. In this prospective cohort study, peripheral blood was collected from healthy participants at recruitment (1990-1994). Participants who were subsequently diagnosed with MBCN (chronic lymphocytic lymphoma, B cell non-Hodgkin lymphoma and myeloma) up to 2012 were matched to the same number of controls based on age, seX, ethnicity, and type of blood sample (Guthrie cards, mononuclear cells, buffy coats). DNA methylation was measured using the Infinium®HumanMethylation450 BeadChip. Peripheral blood DNA was collected from 438 matched case-control pairs, a median of 10.6 years prior to diagnosis with MBCN. A series of analytical approaches was used in order to evaluate whether there was a distinct methylation profile associated with MBCN. First, global methylation analysis was performed, identifying increased methylation in CpG island and promoter-associated CpGs and widespread hypomethylation. Second, conditional logistic regression was used to identify differentially methylated CpG sites (DMPs) and kernel smoothing was used to identify differentially methylated regions (DMRs). Third, differential methylation variability, considered to be a distinctive feature in cancer, was assessed. In total, 1,338 DMPs were identified, of which 90 had gain of methylation in CpG sites associated with homeoboX genes and 1,248 had loss of methylation in CpG sites associated with MAPK signaling pathway genes and genes involved in chemokine signaling pathways. There were 9,857 DMRs, with a cluster of 151 DMRs located in a 3.8kb region on 6p21.3, corresponding to the major histocompatibility locus. Differential methylation variability analysis identified 144 novel CpG sites distinctively located outside CpG islands. Conclusion: Distinctive changes in peripheral blood DNA methylation can be detected many years prior to diagnosis with MBCN, suggesting that changes in DNA methylation are an early epigenetic event. This contributes to our understanding of the timing of methylation changes in the development of MBCN. 2 Blank 3 Declaration This is to certify that: (i) the thesis comprises only my original work towards the PhD eXcept where indicated, (ii) due acknowledgement has been made in the teXt to all other material used, (iii) the thesis is less than 100,000 words in length, eXclusive of table, maps, bibliographies, appendices and footnotes. 4 Blank 5 Preface (i) The work contained within this thesis was performed as a collaboration with Graham G. Giles, Dallas R. English and John L. Hopper at the School of Population and Global Health, University of Melbourne and the Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, who established the Melbourne Collaborative Cohort Study (MCCS); Melissa C. Southey, JiHoon E. Joo and Ee Ming Wong at the Genetic Epidemiology Laboratory, University of Melbourne who performed the DNA methylation assay; Enes Makalic, Daniel F. Schmidt and Chol- Hee Jung who performed the bioinformatics analysis. The component of the work which I contributed as original research was to assist in planning the nested study design, wholly planning and designing the statistical analyses, wholly interpreting the results and writing of the manuscript. The regional methylation analysis was performed by JiHoon E. Joo, according to specifications outlined by me and I interpreted the analysis in full. The differential variability analysis was performed by Dr Pierre Antoine-Dugué, and I interpreted the results. (ii) No portion of this thesis has been submitted for other qualifications (iii) No portion of this thesis was carried out prior to enrolment in the degree (iv) No third party editorial assistance was provided in preparation of the thesis (v) For the publication included in this thesis, the roles of the authors are as follows: Enes Makalic – biostatistical analysis of array data; JiHoon E. Joo – DNA methylation assay; Claire M Vajdic – contribution to manuscript; Daniel F. Schmidt – biostatistical analysis of array data; Ee Ming Wong – DNA methylation assay; Gianluca Severi – contribution to study design and review of manuscript; Daniel J. Park – contribution to manuscript; Jessica Chung – contribution to developing bioinformatics pipeline; Laura Baglietto – contribution to study design and review of manuscript; Henry M. Prince – contribution to manuscript; John F. Seymour – contribution to manuscript; Constantine Tam – contribution to manuscript; John L. Hopper – contribution to study design; Dallas R. English – contribution to study design; Dallas R. English – contribution to study design; Roger L. Milne – contribution to manuscript; Simon J. Harrison – contribution to manuscript; Melissa C. Southey – DNA methylation assay and contribution to study design; Graham G. Giles – contribution to study design and manuscript. (vii) The MCCS was funded by VicHealth and Cancer Council Victoria. The MCCS was further supported by Australian NHMRC grants 209057, 251553 and 504711 and by infrastructure provided by Cancer Council Victoria. i Blank ii Acknowledgements Nicholas Brennan – for your patience and understanding Ella & Luca – you embody the forces of nature and time Elsa & Victor Wong Doo – a lifetime of support and encouragement Colleagues at Concord Hospital – for your unquestioning support iii Blank iv Table of Contents Abstract ................................................................................................................. 2 Declaration ............................................................................................................ 4 Preface ................................................................................................................... i Acknowledgements ............................................................................................... iii Table of Contents ................................................................................................... v List of Tables ......................................................................................................... vii List of Figures ....................................................................................................... viii List of Abbreviations ............................................................................................ 10 Acknowledgements ..................................................... Error! Bookmark not defined. 1 Introduction .................................................................................................. 12 2 Background .................................................................................................... 14 2.1 Mature B cell neoplasms – Background ............................................................... 14 2.2 DNA Methylation ................................................................................................ 26 2.3 Differential methylation as a marker of cancer risk ............................................. 40 2.4 Measuring DNA methylation ............................................................................... 41 2.5 Measures of differential methylation .................................................................. 46 2.6 Biological challenges in measuring DNA methylation .......................................... 47 3 Study Design .................................................................................................. 50 3.1 Melbourne Collaborative Cohort Study ............................................................... 50 3.2 Nested Case-Control Study, participant selection ................................................ 51 4 Methods ......................................................................................................... 55 4.1 DNA source and sample collection ...................................................................... 55 4.2 DNA Extraction and Bisulfite conversion ............................................................. 55 4.3 DNA methylation measurement ......................................................................... 56 4.4 Data processing .................................................................................................. 56 4.5 CpG site selection ............................................................................................... 57 4.6 Assembly of Candidate Genes ............................................................................. 58 5 Results ........................................................................................................... 59 5.1 Global DNA Methylation ..................................................................................... 59 5.2 Differentially methylated positions ..................................................................... 72 Background ..................................................................................................................... 72 Analysis ........................................................................................................................... 72 Results ...........................................................................................................................
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