Epigenetic Regulation in Cancer Metastasis

Chapter 28

Epigenetic Regulation in Cancer Metastasis H. Wang*,†, Y. Zhang*, A. Kriska*, H. Chen*,** *Department of Food Science and Human Nutrition, University of Illinois at Urbana-Champaign, Urbana, IL, United States **Division of Nutritional Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, United States † Department of Human Genetics, University of California at Los Angeles, Los Angeles, CA, United States

Chapter Outline Overview of Cancer Metastasis Epigenetic Control Molecular Pathways of Epithelial-toMesenchymal Transition DNA Methylation and EMT Histone Modification and EMT microRNA Regulation of EMT Long Noncoding RNA Regulation of EMT Anoikis Resistance

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Epigenetic Regulations of Apoptosis Through FLIP and Caspase 8 Anchorage-Independent Growth Extravasation and Colonization Epigenome-Wide Big Data Analysis for Mechanistic Understanding of Metastasis Abbreviations References

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OVERVIEW OF CANCER METASTASIS Metastasis is the growth and development of tumors at distant sites other than the primary cancer location. The majority of deaths from cancer occurs due to metastasized tumors and not due to the growth of the primary tumor. Cancer of an epithelial origin accounts for over 90% of all cancers. Metastasis of the epithelial cancer involves the primary tumor cells that have successfully gone through the multistep process in a step-wide fashion: gaining mobility to leave the primary site and invading into endothelia, surviving transportation through circulation, and invading and colonizing at a distant target site [1]. Metastasis involves continuous progression from the initial dissemination to the final establishment of the distant metastatic tumor (Fig. 28.1). Although metastatic cancer remains the greatest challenge in cancer cure, the molecular basis underlying the phenotypic switch from primary tumor cells to metastatic cells are being elucidated. Tumor invasion is an elaborate and critical step that initiates the metastasis process. During tumor invasion, the original epithelial features of cell–cell and cell–matrix are disrupted, allowing the gain of mobility to occur simultaneously or subsequently. Proteins that are involved in cell adhesion, including structural proteins and regulatory factors, are regulated tightly under normal conditions to ensure structural integration of epithelial tissues. In tumor progression, however, the regulatory network is dysfunctional, resulting in morphological transition of the original epithelial cells. The transition resembles a normal developmental plasticity and wound healing process called epithelial-to-mesenchymal transition (EMT) [2]. Being morphologically distinct, cells completing EMT become nonpolarized, spindle-shaped, and fibroblast-like. Molecularly, EMT is characterized by the loss of epithelial-specific cadherins and the presence of mesenchymal intermediate filament protein vimentin. The cell–cell, cell–matrix junction and polarity are largely maintained by a complex system of intercellular glycoproteins to achieve selective adhesion [3]. E-cadherin itself was originally identified as one of the transmembrane glycoproteins that hold the junctions together. Loss of E-cadherin by transcriptional repression directly induces EMT [4]. Many signaling pathways have now been shown to interact intricately and regulate E-cadherin protein, making it the center of interactions among metastasis suppressors and oncogenes. Dysregulation of related oncogenes and metastasis suppressor genes induces EMT. For example, it has been shown that loss of transcription factor RUNX3 results in EMT of gastric epithelial cells [5]. Tumor cells completing EMT acquire the physical requirements to dissolve basal membranes and invade stromal compartments. They now possess molecular signatures of invasiveness. Medical Epigenetics. http://dx.doi.org/10.1016/B978-0-12-803239-8.00028-4 Copyright © 2016 Elsevier Inc. All rights reserved.

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FIGURE 28.1  Outline of epigenetic regulations of metastasis. Top panel illustrates the metastasis cascade from primary tumor to distant macrometastasis. Bottom panel summarizes the various mechanisms of epigenetic regulation that control gene expression through regulation of transcription. Ac, acetyl group; M, methyl group.

In order to escape the local environment in solid tumor and to be transported in the circulation, metastatic tumor cells need to lose all of the epithelial features, including polarity and anchorage-dependence. Loss of anchorage in epithelial cells induces a special apoptosis called anoikis. Over 90% of solid tumors are of epithelial origin, meaning that the majority of primary tumor cells are anchorage-dependent. To survive in the circulation, tumor cells need to become anchorage independent. Therefore one of the key features of the surviving, circulating tumor cells is anoikis resistance. Indeed, an analysis of gene expression of anchorage independent growth identified an anchorage-independent signature that can be used to predict metastatic potential [6]. Anoikis resistance comes mainly from the acquired capability of defying the apoptotic signaling that is induced by the loss of cell–cell and/or cell–matrix interaction. When cells are attached to the extracellular matrix (ECM) or each other, they are protected by prosurvival pathways that suppress apoptosis [7]. Interruption of the contact through dysfunctional adhering molecules would, under normal conditions, trigger anoikis. Cancer cells achieve anoikis resistance by adapting to metastasis-promoting environments and responding to factors that promote survival. For instance, it is summarized in a review by Tan et al. that several suppressors of anoikis were upregulated and a promoter of anoikis was downregulated in metastatic cancer cells, together conferring anoikis resistance [8]. For the surviving and circulating tumor cells, adhesion and extravasation need to be successfully completed at the target tissue site for metastasis to occur. These steps are sometimes collectively called “homing.” Homing and even organ/tissuespecific homing occur with passive trapping by physical features of local vasculature [9], as well as with active interactions between tissue specific factors and adaptive circulating cancer cells [10]. For example, a bioinformatics analysis of cell adhesion molecules led to the identification of a set of unique cell adhesion molecules that is associated with tissue specific metastasis from pancreatic and prostate cancer cells [10]. Subsequent colonization of the invading cancer cells to a target tissue/organ, though extremely inefficient, is the result of interactions between the local microenvironments and the adaptations of the newly arrived tumor cells. Originally proposed as a “seed and soil” concept, the interactions are highly tissue-specific and adaptive. Indeed, analysis of breast cancer metastasis has reviewed a signature panel of genes for tissue-specific metastasis [11]. It is also a generalizable process that has to be adopted by many other cancer cells in order to successfully colonize and metastasize to a specific target tissue,

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which differs drastically from the primary site where the cells originate from. At the same time, unique tissue specific environments also contribute greatly to the colonization of invading tumor cells. For example, photon microscopic imaging of both lung and liver in mice after injection of the same murine mammary tumor cells revealed drastically different speeds as well as levels of extravasation in these two organs, indicating highly specific target organ–tumor cell interaction during metastasis [12].

EPIGENETIC CONTROL Epigenetic regulation of gene expression occurs largely through chemical modifications of DNA and of histone tails. These do not alter DNA sequence, but instead control the transcription of target genes. Epigenetics specifically regulates gene expression results in either silenced regions of the genome (heterochromatin) or actively transcribed regions of the genome (euchromatin). Substantial changes in epigenetic modifications occur during tumorigenesis [13]. Of note, numerous epigenetic mechanisms have been studied intensively, including DNA methylation, histone modifications, micro and long noncoding RNAs, and chromatin architecture. DNA methylation is evident in the predisposition of stem cells that contain polycomb–repressive complex occupied genes to acquire DNA methylation abnormalities in aging and cancer [14]. In 1975, DNA methylation was proposed to be responsible for the stable maintenance of gene expression through mitotic cell division [15]. Since then, numerous studies proved this concept and DNA methylation is now recognized to be one of the primary contributors to the stability of gene expression. DNA methylation is a postreplication modification, which is predominantly found in cytosine of the CpG dinucleotide sequence sites. Specifically, a silent chromatin state is established by DNA methylation through blocking transcription and collaborating proteins, which modify nucleosomes [16]. DNA methylation is generally regulated by the action of DNA methyltransferases (DNMT). In addition, DNA methylation can be inhibited by 5′-aza-2′-deoxycytidine, a nucleotide analog that can bind DNMTs, which results in hypomethylation and increased mRNA expression [17]. Unlike DNA methylation, histone tails can be modified in multiple ways, including acetylation, methylation, ubiquitination, and phosphorylation. Acetylation of histones at lysine residues is catalyzed by histone acetyltransferases (HATs), and is associated with the activation of gene transcription. Deacetylation of histones is associated with the silencing of transcription, and is catalyzed by histone deacetylases (HDACs) [18]. Overall, the acetylation of histones marks active transcriptionally competent regions, while hypoacetylated histones are found in transcriptionally inactive euchromatic or heterochromatic regions. Histone methylation regulates gene transcription depending on the site of modification. Methylation of lysine 9 on the N terminus of histone H3 (H3K9Me) is typically a hallmark of silent DNA and is globally distributed throughout heterochromatic regions such as centromeres and telomeres [19]. However, methylation of lysine 4 of histone 3 (H3K4Me) activates gene transcription and is primarily found in promoter regions [19]. Lysine can be modified by mono-, di-, and tri-methylation. The enormous variation is thus likely to result in a diversity of possible combinations of different modifications. Emerging evidence shows that noncoding RNAs, including microRNA and long noncoding RNA (LncRNA), are key regulators of transcription. microRNAs are endogenously expressed ∼20 nt-lncRNAs, which have been shown negatively regulating transcription of target genes [20]. miRNAs can bind to the complementary sequences of target mRNA at the specific miRNA recognition elements (MREs) of the 3′ UTR through a “seed” region [21]. The binding results in cleavage of target mRNA and inhibition of translation. It has been shown that tumor metastasis exerts a unique phenotype of miRNA alteration [22]. Another class of noncoding RNAs, lncRNAs (by definition, >200 nt in length), is also associated with carcinogenesis [23,24]. Epigenetic perturbations have been identified in numerous situations and aspects during cancer metastasis. This chapter will critically evaluate the key steps during the complex process of metastasis in an attempt to illustrate the potential mechanisms that involve epigenetic modifications along the process (Fig. 28.1).

MOLECULAR PATHWAYS OF EPITHELIAL-TO-MESENCHYMAL TRANSITION The molecular pathway of EMT in tumor metastasis is characterized by three substantial elements including EMT effectors, core regulators, and inducers [1]. The molecules that execute the EMT process are considered to be EMT effectors. EMT is characterized as the transcription repression of E-cadherin and downregulation of α-catenin and γ-catenin [25]. Additionally, the major intermediate filament switches from cytokeratin to the mesenchymal intermediate-filament vimentin in tumor metastasis [26]. It has been shown that EMT facilitates breast cancer cell metastasis through an induction of fibronectin [27]. Moreover, autocrine platelet-derived growth factor receptor (PDGFR) signaling is required in the promotion

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of mammary epithelial cell metastasis in response to EMT induction [28]. Twist, an EMT inducer, upregulates N-cadherin in gastric carcinomas [29]. Another biomarker of EMT in tumor metastasis is the alteration of expression of integrins due to relocation of cells from above the basement membrane into the extracellular matrix [30]. It has been shown that CD44+ cells switch to EMT cells in order to promote prostate cancer cell metastasis [31]. Furthermore, loss of neural cell adhesion molecular (NCAM), a member of the immunoglobulin-like cell adhesion molecule Ig-CAM triggers tumor cells to disseminate due to loss of function of cell–cell adhesion molecules [3]. Transcription factors that orchestrate the EMT program are core EMT regulators. A number of factors that transcriptionally repress E-cadherin drive EMT process in tumor metastasis. Snail zinc finger family, including Snail1 and Snail2, can bind to the promoter in order to repress the transcription of E-cadherin in the tumor metastasis [32]. It has been shown that coexpression of EMT-associated transcription factor ZEB1/ZEB2 decreases transcription of E-cadherin in head and neck cancers [33]. Additionally, cells without Claudin-3 and Claudin-4 exert tumor metastasis phenotype similar to ovarian carcinoma cells induced by EMT [34]. Moreover, repression of ZO-1 has been shown to promote signaling events and tumor invasion through regulation of tight junction in breast cancer cells [35]. Basic helix–loop–helix (bHLH) transcription factors, Twist1 and Twist2, are positively related to EMT-induced tumor metastasis [36]. EMT inducers are generally referred to as extracellular cues that activate the EMT program. EMT can be triggered by the interplay of extracellular signals and many secreted factors such as transforming growth factorβ (TGFβ), fibroblast growth factor (FGF), epidermal growth factor (EGF), hepatocyte growth factor (HGF), and PDGF [37]. Among many of these signaling pathways, the Wnt, TGFβ, Hedgehog, and nuclear factor kB (NFkB) signaling pathways have been shown to be critical for the induction of EMT phenotype [38]. Activation of canonical Wnt signaling pathway induces EMT through repression of E-cadherin in tumor metastasis, which results in cell invasion [39]. It has been shown that EMT coincides with the increased Wnt receptor Frizzled2 (Fzd2) and its ligands Wnt5a/b in metastatic liver, lung, colon, and breast cancer cell lines and in high-grade tumors [40]. Additionally, Notch signaling pathway is critical in regulation of EMT induction in tumor metastasis [41]. Moreover, receptor tyrosine kinase (RTK) signaling pathway through PI3KAkt also plays a part in TGFβ-mediated EMT induction in tumor metastasis [42]. Additionally, matrix metalloproteinase (MMPs) facilitate EMT-associated tumor progression in breast cancer cells through direct effect and enhanced EMT process [43].

DNA Methylation and EMT It has been shown that promoter DNA methylation results in loss of E-cadherin expression, which is associated with EMT transition in breast cancer cell lines [44]. Additionally, TGFβ induced EMT and altered global DNA methylation pattern as the promoter of numerous genes in canine kidney and human breast cancer cells [45]. It has been shown that global DNA methylation is altered in the EMT-associated gene set based on the E-cadherin differential expression in nonsmall cell lung cancer cell lines [46]. EMT altered DNA methylation is a general effect. It has been shown that aberrant DNA methylation at the 5′ CpG island of CDH1 gene encoding E-cadherin occurs in EMT process in different human carcinoma types including oral carcinoma [47]. Additionally, DNA methylation on the promoter CpG island of CDH1 gene is increased during the progression of breast and hepatocellular cancers [48]. A demethylating agent, 5-azacytidine has been shown to restore the expression of E-cadherin in many cancer cells including human breast and prostate cancer cell lines [49]. Moreover, the restoration of expression of E-cadherin by 5-azacytidine has been shown to be associated with increased cell aggradation, reduced motility, and results in inhibited metastasis in breast cancer cells [50]. DNA methylation has also been shown to regulate EMT through an alternative mechanism, which is hypomethylation of specific transcription factors in cancer stem cells [51]. Forkhead box protein C1 (FOXO1) is demethylated and highly expressed in CD44+ cells, which is associated with repressed expression of E-cadherin and induced EMT [51]. Furthermore, the potential mechanism of increasing DNA hypermethylation on the promoters of multiple family members of cadherin genes is associated with DNMT1, which causes the transformation of lung epithelial cells [52]. The induction of EMT has been shown to be accompanied by DNA hypermethylation of other genes including estrogen receptor and Twist in basal-like breast cancer cell [53]. DNA methylation has been shown to interact with other epigenetic modifications including histone modification and microRNA regulation. DNA methylation has been shown to regulate EMT-inducing miR-200c and miR-141 expression [54,55]. Additionally, global alteration of DNA methylation and histone modification reveal the EMT-associated characteristics in prostate cancer cells [56]. The potential mechanism of DNA hypermethylation on the promoter CpG islands of CDH1 can be attributed to the binding of methyl-CpG binding proteins including MeCP2 and MBD2. The binding of these nuclear factors results in the recruitment of HDACs and histone deacetylation, increased histone methylation of histone H3 lysine 9 in various cancer cell lines [57].

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Histone Modification and EMT Histone modifications have been shown to be related to the EMT process during cancer development. The following sections discuss the different histone modifications associated with the EMT process in cancer development including histone acetylation and methylation respectively. It has been shown that trichostatin A (TSA), a HDAC inhibitor, induces E-cadherin in breast cancer cells [58]. The induction of E-cadherin is attributed to the synergistic effects of HNF3 with AML and p300, and results in inhibition of the metastatic potential of breast cancer cells. Mechanistically, it has been shown that SNAIL represses E-cadherin by the recruitment of a protein complex that includes HDAC1, HDAC3, and mSin3A. Consequently, SNAIL decreases acetylation of histone H3 and H4 and increases methylation of histone H3 lysine 9 at the promoter region of E-cadherin gene in mammalian cells [59]. Another corepressor complex, including C-terminal binding protein (CtBP), and both HDACs and HMTs, facilitates the inhibition of E-cadherin transcription and promotes tumorigenesis [60]. Moreover, SNAIL2/SLUG binds to the E-boxes on the promoter of E-cadherin, and recruits a repressor complex including CtBP/HDAC3 and HDAC3 complex in mammalian cancer cells [61]. Metastasis tumor antigen family proteins (MTAs), including MTA1, MTA2, and MTA3, are components of chromatin remodeling and deacetylation (NuRD) complex, which have been shown to be expressed in different stages of breast cancer progression and may be associated with the EMT process [62]. Additionally, it has been shown that MTA3 is involved in the estrogen-dependent component of the Mi-2/NuRD transcriptional corepressor complex in breast epithelial cells, suggesting that MTA3 plays a key role in the estrogen-dependent pathway regulating EMT process and breast cancer metastasis [63]. The loss of MTA3 causes altered expression of the transcriptional repressor Snail2, and results in loss of E-cadherin expression in breast cancer cells. Histone methylation is another critical epigenetic regulation of transcription. It has been shown that the enhancer of zeste homolog 2 (EZH2), part of polycomb-repressive complex 2 (PRC2), is involved in the progression of many cancers including breast cancer [64]. Additionally, EZH2 is involved in increased histone H3 methylation of lysine 27 and results in E-cadherin silencing during EMT and cancer metastasis in prostate and breast cancer cell lines [65]. Histone demethylase KDM6B, also known as JMJD3, demethylates histone H3 lysine 27 and is induced by TGFβ. Demethylation of SNAIL1 by KDM6B promotes EMT in breast cancer cells [66]. Additionally, upregulation of KDM6B is positively associated with mesenchymal markers and negatively associated with epithelial markers, promoting EMT by inducing a master transcription regulator, SLUG, in kidney cancer cells [67]. Finally, KDM6B can be activated by repression of miR-941 and this promotes EMT in hepatoma cells [68]. Histone demethylase KDM4B, also known as JMJD2B, demethylates histone H3 lysine 9, and has been shown to promote EMT through binding with β-catenin at the promoter of vimentin in gastric cancer cells [69]. G9a, a methyltransferase, increases the methylation of H3 lysine 9 and recruits DNA methylation by DNMTs through interaction with SNAIL on the promoter of E-cadherin, and results in activation of EMT in breast cancer cell lines [70]. Additionally, knockdown of G9a inhibits cell migration and invasion in vitro in breast cancer cells and in vivo in tumor metastasis. Increased di-methylation of H3 lysine 9 and decreased acetylation of histone H3 lysine 9 have been shown to be induced by TGFβ treatment, which induces EMT process in head and neck squamous cell carcinomas [71]. SET9, also known as KMT5A, induces histone H3 lysine 20 monomethylation and has been shown to interact with TWIST-promoted EMT, and binds on the promoter of E-cadherin in vivo and in vitro in breast cancer cells [72]. Lysine specific demethylase 1 (LSD1, as known as KDM1) can remove mono- and di-methylation of histone H3 lysine 4, which has been proven to be a component of NuRD complex and interplay with deacetylation of histone H3 [73]. In in vitro breast cancer cells, LSD1 blocks cancer cells metastasis and inhibits in vivo TGFβ-induced EMT in breast cancer carcinoma [73]. Additionally, LSD1 interplays with other methylations. It has been shown that loss of LSD1 function decreases di-methylation of histone H3 lysine 9 and increases tri-methylation of histone H3 lysine 4 and lysine 36 [74]. Moreover, JARID1B/ PLU-1, a member of Jmji/ARID family demethylates histone H3 lysine 4, which belongs to the LSD1/NuRD complex and involves in transcription repression of an epithelial derived chemokine, CCL14 in breast cancer cells [75].

microRNA Regulation of EMT Recent investigations have demonstrated that numerous microRNA have been associated with tumor metastasis. miR-205 and the miR-200 family, such as miR-200a, miR-200b, miR-200c, miR-141, and miR-429, have been shown to be downregulated by TGFβ-induced EMT in madin darby canine kidney (MDCK) epithelial cells [76]. Additionally, it has been shown that these miRNAs cooperatively inhibit expression of ZEB1 and SIP1 and result in upregulation of E-cadherin transcription and inhibition of tumor metastasis in mammalian cancer cells [76]. On the other hand, the regulation of tumor

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metastasis by the miR-200 family has been shown to target different stages in breast cancer metastasis. It has been shown that ectopically inducing an miR-200 family member to 4TO7 cells, a breast cancer cell line not expressing miR-200 increases MET process and results in increased metastases in lung and liver [77]. Collectively, these results indicate that the miR-200 family suppresses EMT and cancer cell dissemination, but increases metastatic colonization after tumor cells have already disseminated to distant organs [77]. The regulation of EMT in tumor metastasis by miRNAs is a complicated process involving interplay of different miRNAs. The miR-200 family can be regulated by miR-103/107 [78]. It has been shown that miR-103/107 inhibits microRNAs biosynthesis by targeting Dicer, which decreases the miR-200 family and results in increased EMT process [78]. Downregulation of E-cadherin expression is regulated by miR-9 in breast cancer cells. Overexpression of miR-9 decreases E-cadherin and results in activation of β-catenin signaling and increased expression of vascular endothelial growth factor (VEGF) in breast cancer cells [79]. Additionally, it has been shown that miR-221/222 complex promotes the EMT process through downregulation of trichorhinophalangeal syndrome type 1 (TRPS1), which represses ZEB2 in breast cancer cells [80]. miR-30a inhibits EMT by downregulation of Snail, a repressor of E-cadherin on the promoter of CDH1 in nonsmall lung cancer cells [81]. It has been shown that knockdown of miR-155 inhibits TGFβ-induced EMT through repression of RhoA in breast cancer cells [82]. Knockdown of miR-204 increases TGFβ receptor 2 and Snail2 and decreases Claudins, which results in promotion of EMT in retinal pigment epithelium [83]. miR-10b has been shown to be positively associated with mesenchymal features in breast cancer cells [84]. miRNA can also suppress metastasis. miR-335 has been shown to suppress metastasis through targeting progenitor cell transcription factor SOX4 and extracellular matrix component tenascin C in human breast cancer cells [85].

Long Noncoding RNA Regulation of EMT Transcriptome analysis reveals that two lncRNAs, lnc-LCE5A-1 and lnc-KCTD6-3 are negatively associated with poor survival in head and neck squamous cell carcinoma patients [86]. It has been shown that overexpression of linc-ROR induces the EMT process in human mammary epithelial cells [87]. Additionally, linc-ROR prevents degradation of ZEB2 by targeting miR-205 and results in induction of EMT [87]. Linc00617, a human ortholog of evolutional-conserved lncRNA (TUNA in mouse), promotes the EMT process through activation of the transcription of Sox2 in breast cancer cells [88]. Loss of lncRNA-HIT (HOXA transcript induced by TGFβ) has been shown to inhibit the EMT process, while an overexpression of lncRNA-HIT increases TGFβ-induced EMT in breast cancer cells [89]. It has been shown that an lncRNA regulates the EMT process by targeting HNF-1β-induced miR-200 expression in renal epithelial cells [90]. An lncRNA, HOTAIR has been shown to be positively associated with induced EMT through interaction with STAT3 in various cancers, including colon cancer cells [91]. Additionally, it has been shown that HOTAIR promotes tumor metastasis through induction of VEGF, MMP-9, and EMT-related genes in cervical cancer cells [92]. In summary, epigenetic regulation during EMT is proven to be closely related to various cancers. The investigations of these regulations involving DNA methylation, histone tail modifications, as well as micro and other noncoding RNA offer insights into the etiology of EMT during dissemination at the primary tumor location. Understanding of epigenetic mechanisms involved in this key step during metastasis could lead to development of specific treatments and therapeutics for prevention of the spread of cancer.

ANOIKIS RESISTANCE Cancer cells that become malignant are also cells that have developed pathways that resist anoikis. This allows the cells to survive after detachment, while traveling in the lymphatic and circulatory systems. Recent advances in research show that d­ etachment can be sensed by integrin receptors which act as mediators of cell–ECM interactions, enabling the cell to transduce anoikis signals. Surviving detachment during this time in the lymphatic and circulatory systems is the key step in traveling through the body and reaching the potential target tissue sites [93]. Developing an anchorage-independent signature would predict the potential for metastasis. Using microarray techniques, Mori et al. illustrated an anchorage-independent growth signature that included deregulated mitochondrial function. More importantly, this signature matched metastatic human tumor samples, showing that anchorage independence correlates very closely with metastasis in tissue samples [6]. The caveolin (CAV1) gene plays a role in anoikis-resistance by both promoting cell cycle progression and enhancing anchorage-independent growth. CAV1 functions as a membrane adaptor, linking an integrin alpha subunit to tyrosine kinase FYN. This kinase then activates a downstream pathway that is critical in the Ras-ERK pathway for promoting the cell cycle [94]. Shown in human breast cancer cell models, expression of caveolin-1 enhances matrix-independent cell survival

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through a survival mechanism that involves upregulated IGF-IR, IGF-I, and the PI3K/AKt pathways. In addition to matrixindependent survival, caveolin-1 prevents activation of the detachment-induced p53 pathway [95]. CAV1 also interacts with a tyrosine kinase receptor, TrkB, functioning to activate proliferation, differentiation, and survival of the retinal and glial cells. This interaction changes cells to become more resistant to anoikis. By implementing similar pathways in other epithelial cells through TrKB and the PI(3)Ks, cells that are supposed to go through anoikis will instead proliferate due to new anchorage independent growth, supported by the TrkB system [7].

Epigenetic Regulations of Apoptosis Through FLIP and Caspase 8 Activation of the death receptor activated pathway during the detachment induces anoikis. This is mediated through the activation of a series of caspases, including the master activator Caspase-8. Block of the activation of the pathway, on the other hand, is linked to anoikis resistance. Overexpression of one of such inhibitors of the pathway, Fas-associated death domain-like interleukin-1-converting enzyme-like inhibitory protein (FLIP), has been shown to prevent anoikis and FLIP overexpression has been linked to higher mortalities in cancers of the prostate, breast, and lung [96,97]. Using human pancreatic cancer cells as a model, involvement of epigenetic regulation of the FLIP-caspase signaling has been investigated [98]. In the pancreatic cancer cells the resistance to cell death comes as the result of the overexpressed Bcl-XL, a major antiapoptotic factor. Similarly, high expression of FLIP is also observed in these cancer cells. Inhibition of histone acetylation by using an HDAC inhibitor sodium butyrate markedly reduced expression of FLIP, rendering reactivation of apoptosis pathways in these cells. Moreover sodium butyrate treatment sensitized cells to treatment of CD11, an agonistic antibody to Fas, resulting in the activation of caspase-8 as well as the extrinsic pathway of apoptosis. It is worth pointing out that sodium butyrate may represent a good adjuvant in chemo-, radio-, and immunotherapies in pancreatic cancer to inhibit anoikis resistance during the therapies. In glioblastoma, HDAC inhibitors get the cells ready to undergo tumor necrosis factor-related apoptosis-inducing ligand (TRAIL)–induced apoptosis. This uses the same pathway found in anoikis. The HDAC inhibitor, MS2975, increased TRAIL-induced apoptosis most likely due to c-myc-controlled regulation of FLIP [99]. MS275 reduced expression of both splice variants of FLIP, FLIPL, and FLIPs. Caspase 8, without FLIP regulation, results in the TRAIL process and caspase-dependent apoptosis. Indeed this HDAC inhibitor is not binding near FLIP, it causes c-myc to bind to the FLIP promoter, which in turn decreases the activity of the promoter. Lower expression of FLIP results in more caspase-dependent apoptosis [99]. It has been shown that gene expression of caspase-8 is under direct epigenetic control as well. In hepatocellular carcinoma, Cho et al. demonstrated that promotor methylation was negatively correlated to immunohistochemical signal of the gene product caspase-8. Further, promoter methylation of the caspase-8 gene is inversely correlated with apoptotic index [100]. Therefore, the aberrant DNA methylation of caspase-8 promoter may help explain the resistance to apoptosis in these cancer cells.

Anchorage-Independent Growth Anchorage-independent growth is a hallmark of anoikis resistance and the path to further steps in metastasis. Cell culture models using a semisolid medium to replicate anchorage-independent growth are employed to test factors that are regulated epigenetically that lead to anoikis resistance through anchorage-independent growth. Using a component found in turmeric called curcumin, Guo et al. have shown how anchorage-independent growth can be epigenetically regulated. Previously, curcumin was shown to regulate the activity of histone acetyltransferases (HATs), HDACs, and, most recently, DNA methyltransferases (DNMTs) [101]. Previous studies showed that promoter CpG methylation induced silencing of a tumor suppressor gene, deleted in lung and esophageal cancer 1 (DLEC1), which is prevalent in many cancers including gastric cancer [102]. In cell lines of these cancers, the DLEC1 promoter is aberrantly hypermethylated. Specifically, HT29 colon cancer cells were grown in a semisolid medium to establish anchorage-independent growth. Treatment with curcumin inhibited the colony formation by the established anchorage-independent growth [103]. This is related to the capability for curcumin to decrease promoter methylation of the DLEC1 gene and induce transcription of the DLEC1 gene. Treatment with curcumin decreased methylation on the promoter region of DLEC1. This is the potential consequence of altered DNA methyltransferase expression by curcumin [103]. More importantly, some of the histone modifying enzymes were also changed by the curcumin treatment, indicating an interacting network of epigenetic modifications is in place during the anchorage independent growth. Most malignant and early metastatic phenotypes are thought to be under the Warburg effect. This means that cells have altered metabolisms to go through glycolysis, even when oxygen is present. Normal cells in our body will go through

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oxidative phosphorylation and the TCA cycle instead of repeating the first major step in metabolism (glycolysis) repeatedly. In other words, these cancer cells undergo glucose fermentation even when there is enough oxygen to go through respiration. Specific to mitochondrial function and biogenesis, Kang et al. showed that, as part of the anchorage-independent signature, the mitochondrial transcription factor Tfam had increased expression. Tfam is responsible for gene expression related to mitochondrial biogenesis. Taken together, anchorage-independent growth shows a mitochondrial biogenesis phenotype [104]. Examining the anchorage-independent growth phenotype using three metabolic pathways showed that glycolytic genes were not upregulated, TCA cycle genes showed an upward trend, and pentose phosphate pathways were significantly upregulated. Further examination showed that this phenotype showed overexpression of peroxisome proliferator-activated receptor-y coactivator 1a (PGC1a). Overexpression of this gene increased mitochondrial biogenesis. Using breast cancer cell models, Mori confirmed that the PGC1a overexpression has a strong correlation with anchorage-independent growth [6]. Moreover, PGC1a has been shown to be under epigenetic regulations such as methylation and acetylation [64,105] Overexpression of the histone methytransferase EZH2 is detected in most malignant human tumors. Ferraro et al. found that both EZH2 mRNA and protein expression were found in metastatic cell lines. They also found that inhibiting protein kinases ERK and AKT in metastatic colon cancer cell lines decreases expression of EZH2. More importantly, reducing EZH2 expression prevents the cells from going through anoikis as readily [106]. EZH2 overexpression leads to poor prognosis in many aggressive cancers [64,107]. EZH2 is responsible for forming the Polycomb Repressive Complex 2 (PRC2), which binds to other gene promoters and results in repression through tri-methylation of lysine 27 at histone H3 (H3K27me3). In fact, decreasing expression of EZH2 seems to slow the growth of some invasive breast cancers [108]. EZH2 is thought to target E-cadherin, Wnt antagonist genes, and a metastatic suppressor gene RKIP. In other conditions to induce anchorage-independent growth, they found high expression of EZH2 in colon cancer cell lines, with the expression localized toward the colonic crypts. Ferraro also tested human tumor samples and found EZH2 to be overexpressed in the tumor samples. In tumor samples presenting metastasis, EZH2 expression was greater. Ferraro et al. showed that in cell lines, EZH2 mRNA levels were increased while there were low levels of E-cadherin. E-cadherin functions in both the cell–cell adherent junctions and in the ECM basement membrane. Its gene encodes 2 subunits that can form 24 different heterodimers [109]. The different heterodimers, called integrins, perform a range of functions without overlap. One integrin in particular, ITGa2, is epigenetically controlled through EZH2 and H3K26me3 changes [106]. In addition to functioning with the PRC2, EZH2 works in conjunction with MYC to repress microRNAs (miRNAs). Briefly, miRNAs play a role in cancer progression, chemotherapy resistance, and carcinogenesis because they can regulate inflammation in a tumor microenvironment. The details given in the further sections of the chapter show how some miRNAs contribute to cancer metastasis through anoikis. miR-29 is repressed when EZH2 forms a complex with HDAC3 to induce MYC, which in turn stops miR-29. Through an interesting positive feedback loop, MYC increases expression of EZH2 by stopping the inhibitory miR-26a from acting on EZH2, while EZH2 increases MYC expression by stopping the inhibitory miR-494 from acting on MYC. Zhang restored function of miR-29 through an epigenetic drug, DZNep, which circumvented the positive feedback loop, inhibiting oncogenic signaling pathways and slowing tumor growth in vivo. Aberrant miRNAs expression is linked to initiation and progression of cancers including breast, lung, pancreas, and prostate [22]. Acting as tumor oncogenes or tumor suppressors, miRNAs can be viewed as cancer biomarkers. Derouet et al. previously surveyed miRNAs and found 568 which were either significantly up- or downregulated after neoadjuvant therapy. Two such miRNAs, miR-135b and miR-145, are of interest to cancer progression and metastasis. mir-135b has been shown to act as a tumor promoter in colon cancer and lung cancer. The other, miR-145, has been shown to be a tumor suppressor. In esophageal squamous cell carcinoma (ESCC), miR-145 expression is downregulated but, when expressed, cell proliferation and invasion is inhibited. Contradictory to this, in human studies miR-145 expression is linked to worse prognosis in esophageal adenocarcinoma (EAC) [110]. Exploring this difference in EAC and ESCC, Derouet used cell lines for both cancers to test cell proliferation, anoikis, and cell adhesion. In ESCC, miR-145 expression led to more cleavage of PARP and caspases. These are key features of the anoikis pathway, showing that miR-145 expression has some tumor suppressor ability, especially by promoting anoikis. In the other cancer, EAC, it has been shown that miR-145 expression was protecting cells from anoikis. This protection allowed cell survival in the detached state, which allows the cells to migrate through the circulatory and lymph systems to create sites of metastasis. Taken together, miR-145 has an opposite effect in the two cancers studied [111]. Because miR-145 upregulation can be induced by neoadjuvant therapy, it is critical to understand levels of miR-145 in patients of EAC, which can tell clinicians various things such as risk for cancer recurrence, and evaluate risks of chemotherapy more appropriately. Because EMT activation precedes anoikis, genes causing this transition and anoikis breakdown are being investigated in various cancers. One such protein, Hsp90, was shown to promote anoikis breakdown by interacting with EZH2. Hsp90 can cause MEK/ERK to cause transcriptional upregulation in EZH2, and Hsp90 can also cause increased EZH2 EMT

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activity. Examples of the Hsp90-EZH2 EMT activity include tumor cell motility and anoikis resistance. Right now, the only explanation of EZH2 upregulation due to Hsp90 is a novel unknown epigenetic mechanism [112]. Without activating anoikis machinery, any detached cell can freely flow through the circulatory system and create distant sites of metastasis by inappropriately attaching to other tissues and resuming growth. Metastatic cancer cells have acquired anoikis resistance through the initial invasion process. Currently, breast and colon cancers are the most common cancers that are related to anoikis [113]. Anoikis resistance is most often preceded by anchorage-independent growth, a process that allows the cells to survive without receiving appropriate signals from the ECM. This process can be regulated by a myriad of epigenetic events, and is critical to the understanding, detecting, prognosis, and treatment of many cancers.

EXTRAVASATION AND COLONIZATION Clinical metastases appear more histologically epithelial in phenotype rather than mesenchymal, therefore challenging the view of essential mesenchymal phenotype. It is then postulated that surviving, circulating tumor cells need to go through the reversal of EMT (termed mesenchymal-to-epithelial transition, MET) and convert back to be epithelial again in order to extravasate and colonize at the target organ. A study using bladder cancer cells as a model showed that more metastatic sublines of the cancer exhibit a “cuboidal” morphology resembling typical epithelial cells [114]. Further analysis of specific fibroblast growth factors confirmed that these metastatic cells acquired epithelial characteristics [114]. At the target tissue site, interactions between circulating tumor cells and the local endothelial cells result in the expression of chemokines, cytokines, and adhesion molecules, contributing to the recruitment of supporting immune cells [115]. The recruited monocytes and neutrophils then facilitate the tumor cell extravasation to the specific tissue [116]. Subsequently, transcription of membrane proteins selectins and integrins are activated to further promote the cadherin expression during the process [117,118]. Tumor cell metastasis and colonization at the targeting organ and tissue are not solely determined by cancer cells, but also by the target tissue microenvironment including local cells of the target organ, namely stromal cells and parenchymal cells. It has been shown that in breast cancer patients with bone metastasis, bone marrow microenvironment exhibits a significant shift toward a profile favoring extravasation with higher levels of cell adhesion molecules and growth promoting factors [ICAM-1 and VCAM-1, platelet derived growth factor (PDGF), and macrophage migration inhibitory factor (MIF), etc.] [119]. It is also reported that in a mouse experimental liver metastasis from colon cancer, when liver is compromised by the induction of hepatic steatosis by high fat diet, hepatic metastatic burden is increased [120]. This is accompanied with infiltrating inflammatory cells and apoptosis, illustrating the close association of modulation of specific factors in host tissue and invading metastatic cells. Recent advances in understanding target organ microenvironment in metastasis has shown that postextravasation survival in the target organ is key to colonization and metastasis. By investigating interactions between hepatocytes and breast and prostate cancer cells, Chao et al. showed that the key cellular adhesion molecule E-cadherin has been reexpressed in the cancer cells, enabling them to attach to hepatocytes better [121]. This could be the mechanism contributing to the establishment of colonization. Although the mechanisms of extravasation and colonization are still in the process of being illustrated, many aspects in the regulations of individual factors involved in this process have been gradually identified. The following section will provide evidence of some of the epigenetic modulations during tumor extravasation and colonization. Many metastasis suppressor genes are involved in the extravasation and colonization process. One such factor, NDRG1 (also known as Drg-1), was first identified as being silenced in metastatic colon cancer cells when compared to primary colon cancer cells [122]. Decreased mRNA expression of NDRG1 was also significantly reduced in breast cancer patients with lymph node or bone metastasis as compared to those with localized breast cancer [123]. Overexpression of NDRG1 in highly metastatic colon cancer cell line SW620 induced a morphological change that resembles MET, a reverse process to EMT that is essential to the subsequent extravasation and colonization. More importantly, overexpression of NDRG1 reduced the liver metastasis of colon cancer cells from 75% in control to 23% in the NDRG1-overexpressed animals in an experimental metastasis model in mice [122]. Looking specifically into the regulation of NDRG1 in its silencing in the metastatic cell line revealed epigenetic modifications. For example, compared to the low metastatic cell line SW480, there is increased histone H3 lysing 4 methylation in the gene body of NDRG1 in SW620 [124]. In fact, coordinated histone modifications at NDRG1 gene were observed to be changed in SW620, highly correlating to its silencing in the metastatic cell line. The combined effects from the coordinated histone modifications may be the key to the regulation of NDRG1 gene expression during the transformation to metastatic state. microRNAs are small, noncoding RNAs that play key roles in the regulation of gene expression. Profiling of microRNA expression between highly metastatic human breast cancer cell lines and nonmetastatic cell lines revealed specific panels of microRNA that are associated with metastasis [125]. One of the microRNAs that is repressed in the metastatic cells is

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miR-155. More importantly, overexpression of miR-155 in the highly metastatic breast cancer cells greatly decreased the endpoint tumor burden in the lung, without affecting proliferation, apoptosis, and morphology of the metastatic cells. As the model specifically tests for the lung establishment of metastasis from breast cancer, this result confirmed that miR-155 might be responsible for the controlling of extravasation and colonization steps of metastasis. Functional analysis of targets of miR-155 further identified clusters of proteins that are involved in lipid metabolism, cellular movement, cell-to-cell signaling, cellular growth, and cell survival [125]. A series of investigations on another microRNA, miR-214, in several cancers illustrated a network of regulatory pathways that are centered on this key regulator microRNA (reviewed by Penna et al. [126]). Cross examination of this microRNA in several cancer types highlighted the commonality of the regulatory network, which potentially could lead to more in-depth understanding of the mechanisms involved in the establishment of distant metastasis. In particular, miR-214 is upregulated in various metastatic cancers including gastric cancer [127]. Further, miR-214 directs several lines of interactions with key regulators of target tissue invasion and metastasis. This includes the involvement of PTEN in gastric cancer [127], interactions with endothelial cells, and extravasation in melanoma [128], and clustering with other small RNAs such as miR-199 subtypes [129]. Overall, by systematically analyzing the interactions of microRNA and their downstream target pathways, one hopes to be able to 1 day illustrate the mechanisms that underline the phenotypical and physiological consequences of extravasation and colonization.

EPIGENOME-WIDE BIG DATA ANALYSIS FOR MECHANISTIC UNDERSTANDING OF METASTASIS As next-generation sequencing (NGS) technology advances rapidly, cancer research enters a new era of big data analysis. Based on high-throughput sequencing detection methods, NGS brings evolutional changes in the way scientists study genome and epigenome. Unlike previous single-site specific detections, NGS provides a wide choice of applications that allow scientists to discover the changes on the entire genome of the species of interest. Using big data analytics to identify abnormalities during cancer progression in basic and clinical research and in diagnostic applications becomes more and more popular. RNA-seq is a deep-sequencing technology that is used to profile transcriptome. Compared to microarray, cDNA, or EST sequencing, RNA-seq offers critical advantages. First of all, instead of hybridization or Sanger sequencing, RNA-seq is based on high-throughput sequencing technology. In some cases, it does not rely on existing genomic sequences, making it very effective to reveal genomic information for organisms without determined genomes. Secondly, RNA-seq provides a single-base resolution on precise locations. Moreover, RNA-seq produces much lower background signal noise compared to microarray. It has a wide detection range of transcript abundance, making the quantification of transcripts with extreme high or low abundance accurate. The results of RNA-seq are highly reproducible for either biological or technical replicates [130]. RNA-seq also requires fewer RNA samples. Because of the benefits offered by RNA-seq, this approach has become popular among scientists in the field of cancer research. The cost of RNA-seq has decreased exponentially due to the advancement of technologies, also making it more available and practical for basic and clinical research. RNA-seq is applied to identify the overall and gene-specific alterations of expression profile at the same time in various cancer types and stages. One study identified novel splicing alterations in different breast cancer subtypes using RNAseq [131]. In this study, researchers discovered novel subtype-specific differential spliced isoforms and genes. They also reported that the predominant splicing types in breast cancers were exon skip and intron retention. Deregulations on expression of primary transcripts and promoter switch in breast cancers compared to normal breast tissues were also identified. Another study discovered enriched Wnt2 expression in pancreatic circulating tumor cells and metastatic ascites cells in a mouse model using single-molecule RNA-seq method [132]. They also identified that noncanonical Wnt signaling was enriched in the blood of human patients. In another study, researchers applied the whole transcriptome RNA-seq analysis to investigate tumorigenesis and metastasis of malignant melanoma [133]. They discovered asymmetrical gene expression patterns on chromosomes 9, 11, and 14 among normal epidermal melanocytes, nonmetastatic melanoma cells, and metastatic melanoma cells. These distinct patterns suggested their associations to tumorigenesis and metastasis. In a study of metastatic castration-resistant prostate cancer (CRPC), Sowalsky et al. conducted RNA-seq on a panel of CRPC bone marrow biopsy samples from androgen deprivation therapy-treated patients [134]. Genome-side analysis showed that mutations were present in a series of genes with prostate cancer relevance. The results indicated that there was a greater proportion of unspliced RNA on some of the genes in CRPC samples, compared to normal prostate epithelial tissues, in untreated primary prostate cancer. RNA-seq can also be used to identify disease biomarkers and assess cancer risk. One study profiled the whole transcriptome from formalin-fixed paraffin-embedded breast tumor tissues using optimized RNA-seq library chemistry

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and bioinformatics methods [135]. Sinicropi et al. discovered RNA biomarkers for disease recurrence risk in a cohort study of 136 patients. Another study identified novel biomarkers of nonsmall cell lung cancer (NSCLC) by comparing the gene expression profile of NSCLC and normal lung tissues from 88 male patients using RNA-seq [136]. Chromatin immunoprecipitation combined with NGS (ChIP-seq) is a genomewide approach to analyze associations of DNA-binding proteins, histone modifications, or nucleosomes to DNA sequences. It combines the ChIP with massive parallel DNA sequencing. ChIP-seq is a powerful tool to determine the changes of epigenomic features. Unlike its arraybased precursor ChIP-chip, ChIP-seq sequenced the DNA fragments directly without hybridization on an array [137]. The biggest improvement is that ChIP-seq provides base pair resolution, therefore has higher resolution than ChIP-chip. ChIPseq doesn’t require hybridization steps therefore it has minimal background noise. And its genome coverage is not limited by the probes designed for ChIP-chip. Chip-seq is used to profile histone modifications and transcriptional regulations induced by treatments and potential biomarkers in cancers. One cohort study applied ChIP-seq to detect changes in estrogen receptor (ERα) binding and histone methylation in tumor specimens from metastatic ERα-positive breast cancer patients who received aromatase inhibitors (AI) [138]. The dataset included profiles of ERα binding and trimethylation at histone H3 lysine 4 (H3K4me3), and trimethylation at histone H3 lysine 27 (H3K27me3) on patients’ DNA samples. Using combined clinical data with ChIP-seq data, researchers in this study identified that ERα and H3K27me3 profiles predicted the treatment outcomes upon first-line AI treatment for breast cancer. In another study on androgen-independent prostate cancer cells, ChIP-seq was used to detect the genomic distribution of lysine-specific demethylase 1 (LSD1) in PC-3M-luc cells [139]. In this study, researchers also identified LSD1-regulated genes using RNA-seq on cells treated with either siRNA against LSD1 or a control. Combining the result from ChIP-seq and RNA-seq, this study revealed a novel mechanism by which LSD1 controls metastasis and identified an LSD1-regulated potential therapeutic target to treat metastatic prostate cancer. A more recent study applied ChIP-seq to profile the genomewide binding of a transcription factor Oct4 in human lung adenocarcinoma cell lines with stable expression of Oct4 [140]. After the Oct4-bond genes were discovered, researchers conducted pathway analysis, and reported that these genes enriched in cellular pathways related to cell growth and proliferation, cell death and survival, and cellular movement. Moreover, the results showed that Oct4-bond sites were often in the promoter and enhancer region of genes that were critical in cancer progression. It was shown in this study that PTEN was a novel Oct-repressed gene, and the downregulation of PTEN by Oct4 led to AKT-signaling activation and drug-resistance. Lung cancer patients with high Oct4, low PTEN, and high TNC expression profiles correlated with poor prognosis. Taken together, this study suggested that efficient repression on Oct4 or Oct4-regulated genes might facilitate the development of a therapeutic approach for lung cancer. Methylated DNA immunoprecipitation followed by NGS (MeDIP-seq) is another NGS application for examining epigenomic changes besides ChIP-seq. It is a relatively new large-scale method to detect viable DNA methylation changes. MeDIP-seq isolates the DNA fragments with 5-methylcytosine (5mC) by immunoprecipitation, and inputs the enriched fragments to NGS to map the profile of methylation levels on a whole genomewide scale. Compared to array-based DNA methylation detection methods, such as Infinium HumanMethylation450 BeadChip, MeDIP-seq provides a wider range of detection on the genome of interest. It maps DNA methylation not only in reference sequence regions, but also in nonreference sequence regions and repetitive elements, which may present critical alterations in diseases such as cancer [141]. Nearly 50% of human genome derived from transposal elements and about half of all CpGs reside in the repetitive sequence of the genome. It was shown that the dysregulation of methylation in repetitive DNA sequences played a role in cancer development [142]. MeDIP-seq provides 96% coverage of transposal elements while the HumanMetylation450 BeadChip provides less than 2% coverage, making it a much more effective approach to detect methylome alteration in cancers [141]. MeDIP-seq analysis was used to profile association between DNA methylation changes and morphological changes during cancer development. One study applied MeDIP-seq to assess DNA methylation profile on a genomewide scale in eight human breast cancer cell lines, and normal human mammary epithelium [143]. Their results indicated that, compared to the normal breast cells, the cancer cells had significantly reduced methylation in CpG-poor regions. It also reported that the epithelial-mesenchymal transition induction was correlated with a dramatic loss of hypermethylated CpG islands (CGIs), and an increase of hypomethylated CGIs. This study provided evidence of how methylome changes impact tumorigenesis. A cohort study profiled the methylome patterns in 53 benign prostate tissues and 51 prostate tumor tissues, and reported about 150,000 cancer-related DNA methylation changes [144]. Moreover, they identified transmembrane protease serine 2 (TMPRSS2)/E26 oncogene homolog (ERG) rearrangement-based methylome changes. Their results revealed genomewide DNA methylation alterations in TMPRSS2/ERG fusion-negative prostate tumors, and suggested a mechanism of epigenetic regulation during tumor formation. MeDIP-seq was also used to identify the epigenetic signature of cancers. To assess the DNA methylation changes during the initiation of colon cancer, Grimm et al. analyzed the methylome of APCMin adenoma using MeDIP-seq [145]. They

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reported that the silence of tumor suppressor genes by increased DNA methylation, which often occurred in colon cancer, did not happen frequently in the intestinal adenoma. They also identified a set of differentially methylated regions that was conserved between human colon cancer and mouse adenoma. This study may facilitate the establishment of therapeutic epigenetic biomarkers for intestinal cancers. Besides the applications that are discussed here, there are other big data analytic tools. Every approach has its advantages and disadvantages, making it difficult to conclude which method is exclusively better than the others. Combining two or more methods will ideally increase the credibility of results, however it also raises the expenses dramatically. One should choose the type of applications carefully based on the purpose of study, the expected outcomes, the estimated budget, and turnover time of the procedure. Overall, big data analysis has wide applications in basic and clinical research, and promising potential in disease diagnostic and therapeutic interventions. It is currently a hot spot of cancer research and still under constant and rapid development. When the technologies and mathematic methodologies for interpreting the data become more mature and robust, it will be the future standard procedure for cancer diagnostic and treatment.

ABBREVIATIONS ChIP-seq DNMT ECM EMT HAT HDAC LncRNA MeDIP-seq MET miRNA NGS TSA

Chromatin immunoprecipitation DNA methyltransferase Extracellular matrix Epithelial-to-mesenchymal transition Histone acetyltransferase Histone deacetylase Long noncoding RNA Methylated DNA immunoprecipitation coupled sequencing Mesenchymal-to-epithelial transition micro RNA Next-generation sequencing Trichostatin A

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