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DNA Methylation Heterogeneity Patterns in Breast Cancer Cell Lines DNA Methylation Heterogeneity Patterns in Breast Cancer Cell Lines The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Sun, Shuying, Sunny Tian, Karina Bertelsmann, Linda Yu, and Shuying Sun. “DNA Methylation Heterogeneity Patterns in Breast Cancer Cell Lines.” Cancer Informatics (September 2016): 1. © 2016 the authors, publisher and licensee Libertas Academica Limited As Published http://dx.doi.org/10.4137/cin.s40300 Publisher Libertas Academica, Ltd. Version Final published version Citable link http://hdl.handle.net/1721.1/108129 Terms of Use Creative Commons Attribution-NonCommercial 3.0 Unported Detailed Terms https://creativecommons.org/licenses/by-nc/3.0/ DNA Methylation Heterogeneity Patterns in Breast Cancer Cell Lines Sunny Tian1, Karina Bertelsmann2, Linda Yu3 and Shuying Sun4 1Massachusetts Institute of Technology, Cambridge, MA, USA. 2Clear Creek High School, League City, TX, USA. 3St. John’s School, Houston, TX, USA. 4Department of Mathematics, Texas State University, San Marcos, TX, USA. Supplementary Issue: Computer Simulation, Bioinformatics, and Statistical Analysis of Cancer Data and Processes (A) ABSTR ACT: Heterogeneous DNA methylation patterns are linked to tumor growth. In order to study DNA methylation heterogeneity patterns for breast cancer cell lines, we comparatively study four metrics: variance, I2 statistic, entropy, and methylation state. Using the categorical metric methylation state, we select the two most heterogeneous states to identify genes that directly affect tumor suppressor genes and high- or moderate-risk breast cancer genes. Utilizing the Gene Set Enrichment Analysis software and the ConsensusPath Database visualization tool, we generate integrated gene networks to study biological relations of heterogeneous genes. This analysis has allowed us to contribute 19 potential breast cancer biomarker genes to cancer databases by locating “hub genes” – heterogeneous genes of significant biological interactions, selected from numerous cancer modules. We have discovered a con- siderable relationship between these hub genes and heterogeneously methylated oncogenes. Our results have many implications for further heterogeneity analyses of methylation patterns and early detection of breast cancer susceptibility. KEYWORDS: DNA methylation, heterogeneity, and hub genes SUpplEMENT: Computer Simulation, Bioinformatics, and Statistical Analysis of Cancer COmpETinG INTERESTS: Authors disclose no potential conflicts of interest. Data and Processes (A) CORREspOndEncE: [email protected] CITATION: Tian et al. DNA Methylation Heterogeneity Patterns in Breast Cancer Cell Lines. Cancer Informatics 2016:15(S4) 1–9 doi: 10.4137/CIN.S40300. COpyriGHT: © the authors, publisher and licensee Libertas Academica Limited. This is an open-access article distributed under the terms of the Creative Commons CC-BY-NC TYPE: Original Research 3.0 License. RECEIVED: June 15, 2016. RESUbmiTTED: August 07, 2016. AccEPTED FOR Paper subject to independent expert blind peer review. All editorial decisions made PUblicaTION: August 13, 2016. by independent academic editor. Upon submission manuscript was subject to anti- plagiarism scanning. Prior to publication all authors have given signed confirmation of AcadEmic EdiTOR: J. T. Efird, Editor in Chief agreement to article publication and compliance with all applicable ethical and legal PEER REVIEW: Two peer reviewers contributed to the peer review report. Reviewers’ requirements, including the accuracy of author and contributor information, disclosure of reports totaled 860 words, excluding any confidential comments to the academic editor. competing interests and funding sources, compliance with ethical requirements relating to human and animal study participants, and compliance with any copyright requirements FUndinG: This work was supported by Dr. Shuying Sun’s start-up funds and the of third parties. This journal is a member of the Committee on Publication Ethics (COPE). Research Enhancement Program provided by Texas State University. The authors confirm that the funder had no influence over the study design, content of the article, or Published by Libertas Academica. Learn more about this journal. selection of this journal. Introduction as hundreds of gigabytes or even terabytes and have complex Cancer is one of the leading causes of death worldwide. There biological and technical structures.7 In this paper, we focus were approximately 1.7 million new cases of cancer and over on studying cancer methylation heterogeneity patterns for 585,000 cancer-related deaths in the U.S. alone in 2014.1 breast cancer cell lines. Detailed information about DNA Around one in eight women will be diagnosed with breast methylation is illustrated below. cancer in their lifetime, and hundreds of thousands of people DNA methylation occurs when a methyl group (-CH3) in the U.S. are diagnosed with this disease each year.2 Because covalently bonds to a cytosine in the dinucleotide 5′-CpG-3′ cancer springs from the rapid growth of abnormal cells, effec- (or the fifth nucleotide).8 When a cytosine is linked to a gua- tive early detection and screening are principal to overcoming nine by a phosphodiester bond, a CG or CpG site is formed. this disease. Therefore, medical researchers have been con- CpG islands are genomic regions that are rich in CpG sites. ducting genetic and epigenetic research to find potential bio- These islands often overlap with transcription start sites of markers for early detection, screening, and treatment.3–5 genes, as well as intergenic regions and gene bodies.9 DNA Traditional pathological examination of tumors tends methylation plays an important role in regulating gene expres- to rely on needle biopsy, a procedure that analyzes tiny frac- sion by directly preventing transcription factor binding.9 DNA tions of cells that may not sufficiently represent the tumor methylation near transcription starting sites may block initia- mass in heterogeneous cells. This means that important dis- tion and methylation in centromeres and other repeat regions. ease details and features may be overlooked. Furthermore, DNA methylation is also likely to have a role in both chromo- increasing evidence indicates greater heterogeneity of cancer somal and genome stability through suppressing expression of cells when compared to normal cells. Although some research transposable elements.10 has been done on cancer heterogeneity patterns using DNA Heterogeneous or differential methylation means that sequencing data,6 this topic is relatively new and challenging there is a large amount of methylation variation or difference because genome-wide DNA sequencing datasets are as large among different samples of one group (eg, cancer patients) CANCER INFORMATicS 2016:15(S4) 1 Tian et al or between two groups (eg, cancer patients and normal on differential methylation identification.11 In the context of individuals).11–14 For instance, comparing methylation ratios testing if a population variance is equal to a specific value, − 1 2 2 2 ()ns 2 at each cytosine base (mC-ratio) in different cancer patients σσ=∼= χ 2 H : 0,Q 2 n−1. ThisI statistic takes the Q reveals heterogeneous methylation patterns. It is important to 0 σ 0 identify the genes or regions that have heterogeneous methy- value and looks at it relative to its degree of freedom, thereby lation patterns across different samples or patients. Generally accounting for the sample size. speaking, researchers are aware of the existence of methyla- Entropy score. Entropy may be used to measure the tion heterogeneity, but it is unknown what the exact heteroge- randomness and heterogeneity level among different samples neity patterns are. It is also unclear how many genes have such and determine if methylation levels vary from person to per- hetero geneous patterns and what the impact these heteroge- son among cancer patients. We will calculate an entropy score neous genes may have, especially in relation to cancer genes. for each CpG site as defined by the method named Quantita- In order to address the above questions, we conduct a bioin- tive Differentially Methylated Regions (QDMR) below.14 Let = formatics analysis. More detailed explanations of all steps and mi (mi,1, mi,2, …, mi,s, …, mi,N) be the methylation levels at results are introduced in the following sections. CpG site i and across N samples, where mi,s represents the methylation level in sample s. The sum of methylation levels of Methods N CpG site i in N samples is ∑mis, , and the ratio of methylation Four metrics for methylation heterogeneity analysis. s=1 In some genomic regions, DNA methylation levels are hetero- level of CpG site i in samples relative to the total value is defined geneous across different cancer patients or samples. The het- N = erogeneity pattern may be due to different methylation events as the relative methylation probability pmis,,is/ ∑mis, . =1 within cancer cells. In order to gain some intuitive under- s standing of the methylation heterogeneity or variation pat- Let Mi be the median for methylation levels in N samples at terns, we first calculate the mean and standard deviation at CpG site i and Si be the absolute distance |mi,s – Mi|. Thus, for selected CpG sites across all cell lines. Our exploratory analy- each sample s, a uniform measure of distance from the cen- mM− sis of mean and standard deviation of the methylation levels = is, i ε = ter is defined as uis, +ε , where 0.0001 is used
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