Methods for DNA methylation analysis and applications in colon cancer

Mutation Research 693 (2010) 84–93

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Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis journal homepage: www.elsevier.com/locate/molmut Community address: www.elsevier.com/locate/mutres

Review

Methods for DNA methylation analysis and applications in colon cancer Mireia Jordà, Miguel A. Peinado ∗ Institut de Medicina Predictiva i Personalitzada del cancer (IMPPC), Badalona, Catalonia, Spain

a r t i c l e

i n f o

Article history: Received 6 July 2009 Received in revised form 14 June 2010 Accepted 17 June 2010 Available online 25 June 2010 Keywords: 5-Methyltcytosine DNA methylation methods Colorectal cancer Clinical marker

a b s t r a c t It is well established that epigenetic events, in an intimate cooperation with genetic events, are involved in every step of tumorigenesis. DNA methylation, which in mammals takes place in the cytosines that precede a guanine (CpG dinucleotide), is the most well-characterized epigenetic mark. The study of aberrant DNA methylation patterns, such as hypermethylation of CpG islands and global genomic hypomethylation, are common issues in the studies on all types of cancer, and as in other areas of molecular oncology, colorectal cancer has become a privileged target. Besides the great variety of technologies available for the analysis of DNA methylation, most methods are based on three principles: methylation-sensitive enzymes, bisulphite conversion of unmethylated cytosines and immunoprecipitation of 5-methylcytosines. By combining each one of these principles with other genomic methodologies, a large range of approaches aimed at the analysis of methylation from one specific CpG site to a large number of sequences on the genome scale and suitable for different research needs have been developed. The goal of this review is to describe the most widely used methylation methods in the study of cancer, as well as the potential clinical applications of DNA methylation biomarkers in colorectal cancer. © 2010 Elsevier B.V. All rights reserved.

Contents 1. 2.

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Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methodologies for DNA methylation analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Sequence specific analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Genome-wide analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Applications in colorectal cancer research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Early diagnosis of cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Prognosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Treatment planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Concluding remarks and future directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conflict of interest statement. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1. Introduction For decades, scientists have focused on the understanding of the origins of cancer trying to dissect the sequence of events that deregulate the complex networks governing homeostasis in multicellular organisms. Cancer is a disease that ultimately alters gene expression. In this context, although we have more precise maps of

∗ Corresponding author at: Institut de Medicina Predictiva i Personalitzada del Càncer (IMPPC), Ctra. Can Ruti, Camí de les Escoles s/n, 08916 Badalona, Barcelona, Spain. Tel.: +34 935543050; fax: +34 934651472. E-mail address: [email protected] (M.A. Peinado). 0027-5107/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.mrfmmm.2010.06.010

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the human genome [1], this has proved insufficient to understand how genetic programs are read and translated into transcriptional patterns in normal and pathological cells. A large body of data indicates that information other than that encoded within the DNA sequence is required. This “other” information, termed epigenetic (which literally means “over genetic”), is defined as the heritable changes in gene expression that are not caused by alterations in the DNA sequence [2]. Actually, it has been widely demonstrated that epigenetic events, altogether with genetic events, play a crucial role in tumor progression. Epigenetic alterations provide an alternative mechanism to genetic inactivation of some tumor-suppressor genes, and, for a growing number of genes, epigenetic inactivation represents the only inactivating mechanism [3,4].

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The most common epigenetic modification at DNA level is methylation, which in mammalian cells occurs predominantly in the cytosines that precede guanines [5], although non-CpG methylation has also been reported [6]. Much of the human genome is CpG depleted, which is thought to be due to the high rate of mutation from methylated CpG form to TpG/CpA [7,8], with the exception of CpG islands. CpG islands are short genomic regions (500 bp to a few kb) [9,10] located in the proximal promoter region of approximately 75% of human genes and usually “protected” from methylation as most of them are unmethylated at all stages of development and in all tissue types [5,11]. A small proportion of CpG islands becomes methylated during normal physiological processes, such as genomic imprinting and X chromosome inactivation, and when this happens the associated gene is stably silent, and therefore DNA methylation has been considered as a mark of long-term repression [5,11]. On the contrary, most of CpG dinucleotides outside CpG islands are methylated, especially those found in repeat DNA elements which make up to 45% of the genome and contain the majority of 5-methylcytosines [12]. In fact, one of the primary functions of DNA methylation, apart from control gene expression, is to repress the activity of transposon elements, whether because methylated transposon promoters are inactive or because over time 5mC->T mutations destroy transposons [12]. This plays an important role in the maintenance of the genome integrity so that it prevents chromosomal instability, translocations and gene disruption [13,14]. Moreover, we now know that DNA methylation occurs in a complex chromatin environment. It not only alters the interaction of some transcription factors to DNA [15,16] but also recruits methyl-DNA-binding proteins and modifying chromatin enzymes such as histone deacetylases which results in conformational changes of the chromatin repressing the expression of the nearby gene [17,18]. Therefore the human genome can be divided into two compartments: the unmethylated one, mostly consisting of CpG islands, promoters and first exons, and the methylated one that includes repetitive DNA and non-regulating regions [19]. These methylation patterns are maintained during cell division by DNA methyltransferases [11], but methylation of some CpG islands in healthy tissues increases with age [20,21], while the global level of 5methylcytosines decreases [22]. These opposite events also occur in tumor cells but are much more marked [23,24]. For this reason, it is said that cancer is not only a genetic disease but also epigenetic. The first finding of aberrant DNA methylation in human cancer was a general loss of methylation [25,26], which has later been reported to be mainly due to the demethylation of repetitive sequences [27,28] generating chromosomal instability [13]. Then, as a paradox, hypermethylation at individual loci was observed. The first discovery of methylation in the CpG island of a tumorsuppressor gene in human cancer was that of the Retinoblastoma (Rb) gene [29], causing its transcriptional repression, and since then the discoveries of hypermethylated CpG island in tumoral cells has increased exponentially [30–32]. More recently it has been also observed that some microRNA (miRNA) genes are inactivated by methylation [33]. In recent years, the study of DNA methylation and its role in tumorigenesis has become one of the hottest issues in molecular oncology. For this reason, an increasing number of techniques designed for its analysis have emerged. Our aim in this review is to describe the most frequently used methodologies for DNA methylation study in cancer research and some of their applications with special emphasis in colorectal cancer. Colorectal cancer (CRC) is the second cause of death by cancer in the industrialized countries (World Health Organization) and is one of the best studied models of tumor progression [34]. This model has been reviewed later suffering some modifications, such as the important role of aberrant methylation events [35], but it is still regarded as the pro-

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totypical example of tumorigenesis. In this review we will expose and discuss the major studies and most significant findings on DNA methylation in colon cancer. 2. Methodologies for DNA methylation analyses DNA methylation research can be approached from several points of view and to achieve different objectives. This is why a wide range of methods have been developed to generate quantitative and qualitative information on DNA methylation [36–38] (Table 1). The earliest studies on DNA methylation were aimed at determining the overall levels of 5-methylcytosines in the genome by hydrolyzing DNA chemically and quantifying the hydrolyzed products by high performance liquid chromatography (HPLC) [39]. This approach has been optimized through the years by modifying the hydrolysis step [40,41]. The development of the capillary electrophoresis techniques gave rise to high performance capillary electrophoresis (HPCE), which is faster, cheaper, and more sensitive [42]. Although high performance separation techniques allow an accurate quantification of global DNA methylation, they require a high amount of sample and expensive and sophisticated equipment not always available. Furthermore, in the last years epigenetic research has focused on the analysis of the degree of methylation of particular sequences, so that alternative approaches have been designed. Actually, the detection of DNA methylation is mainly based on the use of three strategies: the digestion of DNA with methylation-sensitive or insensitive restriction enzymes, the chemical modification of DNA by bisulphite, and the purification of the methylated fraction of the genome using antibodies. The combination of these approaches among them and with other procedures, such as fingerprinting or sequencing, has given rise to a large assortment of techniques for DNA methylation analysis (Fig. 1). 2.1. Sequence specific analyses Initially, the study of the methylation pattern of individual and specific sequences was almost entirely based on the use of methylation-sensitive and insensitive enzymes [43] even though these approaches have some drawbacks, from incomplete DNA digestion to the limitation of the endonuclease cleavage sites. Once the DNA is digested with a sensitive-methylation restriction enzyme whose recognition site is located inside the sequence of interest, the methylation state can be determined by Southern blot procedure using a specific probe [44]: if the sequence of interest is unmethylated, the enzyme will be able to cut it, so two bands will be detected; on the contrary, if the sequence is methylated, it will not be cleaved and only one band will be observed. This approach, although reliable, is awkward and requires a considerable amount of DNA. To overcome these limitations digested DNA can also be analyzed by PCR using specific primers [45]: as only unmethylated restriction sites will be cut, only methylated sequences will be amplified by PCR. This strategy requires a smaller amount of DNA and is more sensitive, but it is prone to false-positive results due to incomplete digestion. More recently, methylation-sensitive enzymes have been successfully used in genome-wide methylation analyses and marker discovery approaches (see below). The discovery of sodium bisulphite DNA conversion produced a great revolution in the study of DNA methylation since it reacts selectively with unmethylated cytosines converting them to uracils but not with methylated cytosines [46,47]. This reaction is highly single-strand dependent and cannot be performed on double strand DNA, so it requires a prior denaturation of DNA [48]. This is a crucial step of the method, so a partial denaturation

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Table 1 DNA methylation methodologiesa . Method

Detection technique

Resolution

References

Quantification of overall 5-methylcytosines HPLC HPLC separation HPCE HPCE separation

Absorbance Absorbance

5-mC genomic content 5-mC genomic content

[39] [42]

Sequence specific analyses Southern blot

Methylation-sensitive RE digestion Methylation-sensitive RE digestion Bisulphite conversion Bisulphite conversion Bisulphite conversion Bisulphite conversion Bisulphite conversion Bisulphite conversion Bisulphite conversion + MS restriction enzymes Bisulphite conversion Bisulphite conversion

Hybridization

Single recognition site of the RE used Single recognition site of the RE used Each CpG of the amplicon Each CpG of the amplicon Few CpGs inside the primers Few CpGs inside the primers Few CpGs inside the primers Average methylation of amplicon Single recognition site of the RE used Single CpG site Relative average methylation of amplicon

[44]

Methylation-sensitive RE digestion Methylation-sensitive RE digestion Methylation-sensitive RE digestion Methylation-sensitive RE digestion MBD affinity

2D-electrophoresis

[61]

DMH

Methylation-sensitive RE digestion

Microarray

MSO microarray

Bisulphite conversion

Microarray

MeDIP-chip

5mC antibody

Microarray

Expression microarray

5-aza-dC

Microarray

MeDIP-seq

5mC antibody

High-throughput sequencing

Recognition sites of the RE used within the genome Recognition sites of the RE used within the genome Recognition sites of the RE used within the genome Recognition sites of the RE used within the genome Recognition sites of the RE used within the genome Recognition sites of the RE used within the probes of the microarray Hihg-throughput; few CpGs inside the probes Average methylation of the sequences purified by MeDIP and hybridized on the chip Gene expression depending on methylation Average methylation of the sequences purified by MeDIP

PCR amplification Bisulphite sequencing PyroMeth MSP MethyLight Headloop-PCR Melting curves COBRA MS-SnuPE MS-SSCA Genome-wide analyses RLGS AP-MS-PCR MCA AIMS MeCP2 binding column

Methylation discrimination principle

PCR/hybridization Sanger sequencing Pyrosequencing PCR Fluorescence real-time PCR PCR Fluorescence real-time PCR PCR SnuPE SSCA

Southern blot RDA Fingerprinting SPM

[45] [46,47] [52] [53] [54] [55] [56] [57] [110] [111]

[112] [62] [65] [67] [69]

[72] [75]

[76] [82,83]

a Abbreviations: HPLC, high performance liquid chromatography; HPCE, high performance capillary electrophoresis; 5mC, 5-methylcytosine; RE, restriction enzyme; COBRA, combined bisulphite restriction analysis; MS-SnuPE, methylation-sensitive single nucleotide primer extension; MS-SSCA, methylation-sensitive single conformational analysis; RLGS, restriction landmark genomic scanning; AP-MS-PCR, arbitrarily primed methylation-sensitive-PCR; MCA, methylated CpG island amplification; RDA, representational difference analysis; MeCP2, CpG-methyl-binding protein 2; MBD, CpG-methyl-binding domain; SPM, segregation of partially melted molecules; AIMS, amplification of intermethylated sites; DMH, differential methylation hybridization; MSO, methylated specific oligonucleotide; MeDiP, methyl-DNA immunoprecipitation; 5-aza-dC, 5-aza-2 -deoxycitidine.

can cause an incomplete transformation and consequently artifacts [49]. The reaction is the basis for differentiating methylated DNA from unmethylated DNA, and its combination with other methods allows defining the methylation state of particular sequences. All bisulphite-associated strategies require PCR amplification of the transformed DNA (which incorporates T for U) and the design of target specific methylation-dependent primers. However, the method of analysis of the amplified PCR products can vary depending on the degree of specificity and detail of methylation required giving rise to a large assortment of techniques, which have been very well reviewed [50]. Therefore, this review will concentrate on those techniques most applied in cancer research. Since the first application of bisulphite DNA treatment for the study of methylation, genomic sequencing has become the standard method for this kind of analysis, as it determines the methylation state of each cytosine of the target sequence. After the modification of DNA, the fragment of interest is amplified by PCR using primers that do not contain any CpG site to avoid any discrimination between different methylated templates. Then the methylation of the PCR product is read by scoring the remaining

cytosine residues in the sequence. The sample can either be directly sequenced, so the result will represent an average methylation level of the population, or be cloned into a plasmid followed by the sequencing of individual clones, which is informative at singlemolecule [46,47]. This approach has been helpful in the study of the DNA methylation state of some genes associated with cancer such as adenomatous polyposis coli (APC) in colorectal cancer [32]. More recently bisulphite treatment has been coupled with pyrosequencing, which is a sequence-by-synthesis approach based on the luminometric detection of pyrophosphate release following nucleotide incorporation [51]. This method, named PyroMeth, is more sensitive and accurate since the analysis is performed by real-time sequencing [52]. Bisulphite-modified DNA can also be analyzed by PCR. In fact, the most widely used method to detect CpG-island methylation is methylation-specific PCR (MSP) [53], which is simple, sensitive (to 0.1% methylated alleles of a given CpG-rich locus) and rapid. Moreover, it requires small quantities of DNA allowing the study of samples such as paraffin-embedded or microdissected tissues. It is based on the design of primers containing CpG dinucleotides that

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Fig. 1. Main DNA methylation methodologies classified by the principle of CpG methylation discrimination: bisulphite conversion, methylation-sensitive enzymes and 5mC immunopreciptiation, and the method of resolution.

anneal specifically with either the unmethylated or the methylated version of the bisulphite-transformed sequence, which is the critical and complex step of the procedure. It is important to consider that bisulphite-converted DNA is not self-complementary, so that primers that are designed to amplify the top strand of a particular sequence will be different from those that are designed to amplify the bottom strand. A quantitative version of MSP is MethyLight [54], which uses fluorescence-based real time PCR and requires no further manipulation after the PCR step. This method specifically employs TaqMan technology, based on the design of three oligonucleotides: specific forward and reverse PCR primers and the fluorogenic probe hybridization, which offers the opportunity for several detection strategies so that the sequence discrimination can occur at the level of the PCR amplification process and/or at the level of the fluorogenic probe hybridization. Fluorescence detection results in a great increase of the sensitivity since it detects a single methylated allele in 105 unmethylated alleles. Headloop PCR is another highly sensitive method in which it is possible to selectively suppress amplification of unmethylated sequences by using hairpin primers that cause looping back and extension on sequences derived from DNA not methylated at CpG sites [55]. Another PCR-based method is fluorescence melting-curve analysis which provides average methylation levels of a specific amplicon (from real-time PCR melting profile) [56]. This assay allows discriminating methylated from unmethylated sequences because the former has a higher melting temperature due to the presence of cytosines instead of thymines. It is a very simple

method to give an overall estimate of methylation but with a limited sensitivity. Combined bisulphite restriction analysis (COBRA) method [57] is based on the fact that bisulphite DNA conversion can lead to the methylation-dependent creation of new restriction enzyme sites or to the elimination of pre-existing sites, so that some endonucleases are able to distinguish methylated from unmethylated sequences when digesting PCR products of bisulphite-treated DNA. It provides semi-quantitative data since the level of methylation is linearly correlated with the relative proportions of digested and undigested products. Like bisulphite sequencing, the primers used in the PCR reaction do not contain CpG dinucleotides. The average relative amounts of digested products can be quantified by hybridization with labeled oligonucleotides and phosphoimager detection. Although it is a quantitative and sensitive approach, it is limited to restriction enzyme recognition site and can be affected by incomplete bisulphite conversion and/or partial digestion. A mass spectrometric sensitive approach has been also developed that is appropriate for detection of methylation, for the discrimination between methylated and non-methylated samples, and for the identification of differentially methylated sites through quantitative analysis of methylation [58–60]. The method employs a T7-promoter-tagged PCR amplification of bisulphite-converted DNA, followed by generation of a single-stranded RNA molecule and subsequent base-specific cleavage (3 to either rUTP or rCTP) by RNase A. The mixture of cleavage products differing in length and mass are analyzed by MALDI-TOF-mass spectrometry.

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Table 2 Illustrative examples of hypermethylated genes in colorectal cancer detected using different strategies. Gene name

Function

Discovery method

References

hMLH1 P16INK4 /CDKN2A DUSP4 SFRP1 CPLX2 PTGIS SLC5A8 TPEF/HPP1 HOXD1

DNA mismatch repair Cyclin-dependent kinase inhibitor Dual specificity phosphatase 4 Secreted frizzled-related protein 1 Complexin 2 Prostacyclin synthase Sodium transporter Transmembrane protein Homeobox gene

Candidate gene approach Candidate gene approach MCA—microarray Reexpression after 5-aza-dC treatment AUMA AIMS RLGS MS-AP-PCR MeDIP

[87] [31] [64] [76] [113] [95] [114] [115] [94]

2.2. Genome-wide analyses Most of previous techniques can be combined with “display” approaches at genomic scale such as fingerprinting or microarrays to allow the assessment of multiple sequences or the screening of novel markers. A few examples of hypermethylated genes discovered by genome-wide approaches are shown in Table 2. The restriction landmark genomic scanning (RLGS) [61] was one of the earliest approaches used for genome-wide analyses. Genomic DNA is digested with a rare sensitive-methylation restriction enzyme, radioactively end labeled at the cleavage sites and size-fractionated in one dimension. Then the fractionated DNA is further digested with a more frequently occurring endonuclease and resolved in the second dimension. The method gives rise to a two-dimensional profile with thousands of spots representing different unmethylated sequences, whose location and intensity indicate its locus and the copy number of the corresponding restriction site, respectively. The profiles of different samples can be compared to detect methylation differences and the spots of interest can be isolated, cloned and identified (reviewed in chapter “DNA fingerprinting techniques to detect genetic and epigenetic alterations in colorectal cancer” in this issue). Another genome-wide method based on the use of sensitive and insensitive-methylation endonucleases is methylated CpG island amplification (MCA) [62]. DNA is digested with the sensitive enzyme SmaI (CCCGGG), which cuts unmethylated sites leaving blunt ends, and followed by the digestion with the isoschyzomer XmaI, which cleaves the remaining methylated sites but leaves overhanging ends to which specific adaptors are ligated. The last step is a PCR amplification with primers that anneal the XmaI adaptor sequence. Since about 70–80% CpG islands contain two close SmaI/XmaI sites, the amplified products are enriched in this kind of sequences. In MCA differentially methylated sequences are identified by representational difference analysis (RDA) [63], which is a subtraction technique, but the identification of the hypermethylated sequences is a laborious task [62], although more recently it has been coupled to microarrays [64]. Amplification of intermethylated sites (AIMS) [65] is another method based on MCA approach but in this case the ligated sequences are amplified by PCR using adaptor-specific primers extended at the 3 end to reduce the complexity of the sample. PCR products are resolved in denaturing polyacrylamide-sequencing gels generating readable fingerprints that consist of multiple anonymous bands that represent the methylome of the cell (reviewed in chapter “DNA fingerprinting techniques to detect genetic and epigenetic alterations in colorectal cancer” in this issue). Although the isolation and identification of the sequences with differential methylation is arduous, it is a suitable approach for the comparison of a large series of samples and when coupled with microarrays, genomewide results may be obtained (Fig. 2). By modifying the enzymes and the primers it is possible to enrich the sample representation in a particular type of sequences. For example, AUMA (amplification of unmethylated Alu’s) technique amplifies preferentially unmethy-

lated Alu elements by means of a unique digestion with SmaI and using an adaptor-specific primer with the nucleotides TT at the 3 end, since a considerable fraction of Alu’s contain the consensus sequence AACCCGGG [66]. An original method to isolate methylated CpG-rich regions employs affinity chromatography of a fragment of the methylCpG-binding domain of MeCP2 [67]. Firstly, DNA is digested with methylation-sensitive enzymes and is passed over the affinity chromatography to be fractionated according to its degree of methylation. Fragments of interest are then cloned and subjected to segregation of partially melted molecules (SPM) analysis [68]. Multiple methods for the study of DNA methylation at the genome scale involve the use of microarrays. In the last years many high-quality commercial arrays have been made widely available, these include lithographic (Affymetrix), adaptive lithographic (NimbleGen), inkjet (Agilent) and bead arrays (Illumina). The first array used for methylation studies contained 276 known CpG island sequences [69] and was hybridized with a sample enriched in CpG islands obtained by the digestion of genomic DNA with MseI (TTAA) and the sensitive-methylation endonucleases BstUI (CGCG) and HpaII (CCGG), as described in [67]. This method, called differential methylation hybridization (DMH), was later improved to include 1104 CpG islands sequences in the microarray [70]. Variations of the technique have also been reported in which a different sensitive-methylation endonuclease has been used, McrBc [71]. It should be noted that its specificity relies on the efficient digestion of genomic DNA, so a partial digestion could lead to false-positive results. Microarrays for the analysis of bisulphite-treated DNA have also been designed [72]. In this case, pairs of oligonucleotides with sequences annealing either the unmethylated or the methylated version of the DNA regions of interest are immobilized as different spots on one array, and each probe can interrogate one or more CpGs, lending this system remarkable flexibility. Samples to hybridize are prepared by PCR amplification of a bisulphitemodified DNA using primers that do not contain CpG sites so that methylated and unmethylated regions are amplified equally. The ratio of methylated and unmethylated DNA in the sample is determined comparing the hybridization to methylated versus unmethylated oligonucleotides. Employing this approach, called methylation-specific oligonucleotide (MSO) microarray, any region of the genome can be analyzed, but each region must be amplified individually by PCR. More recently, techniques based on chromatin immunoprecipitation using ChIP-on-chip approach have provided new insights in the epigenomic profiling. For example, DNA immunoprecipitated with an antibody that recognizes CpG-methyl-binding domains (MBDs), which have a high affinity for binding methylated CpG sites [73], has been hybridized on CpG islands microarrays [74]. A new methylated DNA purification technology based on the direct immunoprecipitation of methylated DNA, named methyl-DNA immunoprecipitation (MeDIP) [75], has been developed. This assay uses a monoclonal antibody that recognizes 5-methylcitosines

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Fig. 2. Identification of hypermethylated genomic regions by amplification of intermethylated sites (AIMS) coupled to microarrays. AIMS products obtained from paired normal and tumor tissues were labeled with Cy3 and Cy5 fluorochromes respectively and competitively hybridized to Agilent CpG islands arrays. Red bars indicate hypermethylation in the tumor, green bars indicate hypomethylation in the tumor. (A) A hypermethylation in the colon cancer is denoted by over-representation of the tumor label in most oligonucleotides representing a given CpG island. (B) Identification of concurrent hypermethylation in contiguous CpG islands, that may indicate a region spanning 145 kb undergoing Long Range Epigenetic Silencing (LRES).

allowing purification of methylated DNA, but the enrichment is biased towards CpG-rich regions so that its sensitivity is very low outside CpG islands. The methylation state of known sequences can then be analyzed by PCR, but actually this approach is most used for genome-wide analyses in combination with microarrays (e.g. CpG islands microarrays or promoter microarrays) [75]. A different strategy is gene expression profiling by using cDNA microarrays to search for genes that are reexpressed after the treatment of the cells with the demethylating agent 5-aza-2 deoxycitidine (5-aza-dC) [76]. It is important to validate the results of the screens at both level of gene expression by quantitative PCR and promoter methylation by MSP or bisulphite sequencing. With the advent of next generation sequencing, newest and most promising approaches for genome-wide analyses are being developed. Various technologies exist, such as the fluorescent nucleotide-based system developed by Solexa (now Illumina) [77] or the high-throughput pyrosequencing approach developed by 454 Life Sciences [78]. All these methods allow the parallel sequencing of large amounts of sequences (thousands of millions) more quickly and at a lower cost than conventional methods. Some of the advantages of these approaches are that they avoid cloning, PCR amplification (except the amplification inherent in the sequencing strategy) and hybridization steps which can introduce biases. Moreover, they are largely quantitative since counts of sequence reads are proportional to the representation of the different methylation states. Although large genomes rich in repetitive elements represent a challenge in the application of direct high-throughput sequencing of bisulphite-converted genomic DNA, advances in sequencing methods and the development of bioinformatic tools have allowed the generation of single-base resolution maps of human methylomes [79,80]. An indiscriminate application of such

approaches to DNA methylation studies is nowadays unfeasible and probably questionable. Nevertheless, the generation of these high resolution DNA methylation maps for different cell types, including pathological situations, is likely to represent a milestone in epigenetic studies of similar impact as the sequencing of the human genome. Reduced complexity approaches using massive and parallel sequencing represent a practical alternative when a high number of samples must be analyzed. For instance high-throughput sequencing of known bisulphite-treated amplicons [81] and of MeDIP selected fractions (MeDIP-seq) [82,83]. 3. Applications in colorectal cancer research Since the finding of the first hypermethylated CpG island associated with the tumor-suppressor gene Rb [29], most of the DNA methylation studies have been biased towards the compartment of the genome constituted by CpG islands and regulatory regions. In recent years the mapping of an increasing number of hypermethylated CpG islands in cancer has revealed unique profiles that define each neoplasia [84,85]. Using different approaches (Table 2), a high number of hypermethylated and silenced genes in colon cancer have been identified, most of them being tumor-suppressor genes [37]. Examples include the tumor-suppressor gene adenomatous polyposis coli (APC). Germ-line mutations in APC gene are associated with hereditary familial adenomatous polyposis (FAP) and somatic mutations are common in sporadic colorectal tumors, but in recent years it has also been reported that methylation of the APC promoter constitutes an alternative mechanism for gene inactivation in colon and in other gastrointestinal tumors [32]. Like APC mutation, its aberrant hypermethylation occurs early in colorectal carcinogenesis.

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Other early events are hypermethylation of O(6)-methylguanine DNA methyltransferase (MGMT) [86] and human mut L homologue 1 (hMLH1) [87] genes, which are implicated in DNA repair, and the cell cycle inhibitor P16/CDKN2A gene [31]. As in other cancers, global hypomethylation mostly affecting repetitive elements is also a common event in colon cancer, but the hypomethylation of specific regions has also been reported. One example is S100A4, a metastasis-associated gene that is hypomethylated in some colon cancer cell lines resulting in its reactivation [88]. One of the most direct applications of epigenetic alterations in clinical settings is its use as markers either for diagnosis or for prognosis. Global genomic DNA methylation content has an important role in cancer and its measurement in tumor cells has also been considered as a prognostic indicator [89,90]. Nevertheless, most of the studies have addressed the analysis of genes with an altered pattern of methylation in tumor cells. DNA methylation markers have several advantages. Firstly, methylation analysis employs DNA, which is chemically more stable than RNA or protein, and furthermore DNA methylation is one of the most stable molecular marks. These two features altogether facilitate the study of DNA methylation and make it very suitable for clinical use. On the other hand, it has been demonstrated for some genes that an identical pattern of DNA methylation in cancer cells and in circulating DNA in bodily fluids of the same patient can be detected [91,92], opening the possibility of developing noninvasive diagnostic assays. At the same time, and as a paradox, DNA methylation, unlike genetic events, is readily reversible [93], which means an attractive therapeutic target. The earliest studies to find tumor markers with aberrant methylation were focused on candidate gene approaches. For example, the hypermethylated genes previously mentioned (APC, hMLH1, MGMT, p16) were found in this way since it had already been demonstrated their key role in tumorigenesis. In the recent years the search for novel DNA methylation tumor markers is mostly performed using genome-wide techniques such as AIMS or MedIP coupled to microarrays [64,94]. For example, AIMS analyses allowed to identify some recurrent putative colon cancer markers such as prostacyclin synthase (PTGIS) gene, which was found to be methylated in 43 out of 100 colorectal cancers and in several tumor cell lines [95]. Once the best tumor markers are determined, it is crucial for clinical applications to be able to detect these markers by means of rapid, sensitive, automatable and cost-effective methods. Probably MSP is the technique that better fulfills these criteria. Virtually all strategies for the sensitive detection of cancer-specific DNA methylation patterns rely on the principle of MSP [53] or fluorescence-based variants, such as MethyLight [54].

3.1. Early diagnosis of cancer The early detection of a cancer is critical for its successful treatment. There are several options for screening colorectal cancer: the faecal occult blood testing (FOBT), colonoscopy and flexible sigmoidoscopy, the last two being more efficient but very invasive. If we bear in mind that DNA methylation can be detected in serum/plasma and other bodily fluids draining or surrounding a tumor site by sensitive techniques such as MSP, we can consider DNA methylation markers as a very powerful noninvasive tool for diagnosis. Since aberrant methylation events occur early during tumorigenesis, genes with an altered methylation pattern have a potential applicability for the detection of cancer in the earlystages. For example, hypermethylation of p16, MGMT and APC has been observed at equal frequencies in early (<1 cm diameter) and late adenomas [31,32,96].

Moreover, cytosine methylation underlies many of the early genetic events described in colorectal tumorigenesis. For instance, the majority of p53 mutations are caused by deamination of aberrantly methylated CpG sites [97]. Silencing of MGMT by methylation is believed to predispose to the subsequent acquisition of guanine-to-adenine point mutations in K-ras and p53 [86]. To date no single hypermethylated gene has been identified to be specific enough for colon cancer diagnosis, but all studies indicate that a panel of several methylated genes will be required to detect the disease at sufficient specificity and sensitivity. Most of the candidate genes have been analyzed in tumoral tissue but the purpose is to find good diagnosis markers that can be detected in body fluids, and only those methylation markers that are always unmethylated in normal “healthy” patients should be included in this panel. Most of the diagnostic studies in colon cancer have been performed in serum, plasma and stool. For example, p16INK4a was found methylated in 38% (20 of 52) of CRC tissues, and among the 20 patients with aberrant methylation in the tumor tissues, similar changes were also detected in the serum (70%). On the contrary, no methylated p16 was detected in the peripheral serum of the 32 CRC patients without these changes in the tumor, neither in 34 patients with adenomatous polyps nor in 10 healthy controls. This assay offers a potential means for the serum-based detection of CRC patients [98]. Other hypermethylated genes which have been proposed as diagnostic markers are APC, hMLH1, MGMT, HIC1, SRRP2 and EN1 [37]. In most of these approaches, MSP is the method of choice due to its high sensitivity, but a recent comparison of different approaches has determined that melting-curve analysis offers a better sensitivity/false-positive ratio [99]. In summary, multiple studies indicate that noninvasive detection of specific epigenetic markers represents a promising alternative for the early diagnosis of cancer. Nevertheless we are still far from having a standardized panel of markers genes for early detection and diagnosis of colon cancer. 3.2. Prognosis A huge number of attempts have been made to identify useful molecular predictors of the disease’s outcome. Epigenetics is not an exception and currently, several genes regulated by DNA methylation have been assessed for their positive prognostic potential in colon cancer. For example, p16 methylation in serum was significantly associated with later Dukes’ stage [98,100]. p16 has also been analyzed in plasma for prognosis and results indicate that it may be used to identify patients with a high risk of recurrence [101]. Another gene with prognostic value is Id4, a member of the inhibitor of DNA binding (Id) family proteins that inhibit DNA binding of basic helix–loop–helix transcription factors. It was found to be methylated in several CRC cell lines and tumor tissue. Id4 methylation status of primary CRCs significantly correlated with histopathological tumor grade, and survival was significantly poorer in patients with this hypermethylated gene [102]. 3.3. Treatment planning Tumors are often divided into subtypes which determine the choice of the therapeutic course, so identification of methylation markers that assist in defining the subtype could be useful for deciding the therapy. It is likely that a panel of genes, rather than individual genes, will provide a more specific and sensitive assay for tumor classification. To this end, some genome-wide analyses are being performed. For example, Shen and collaborators [103] analyzed 27 promoter-associated CpG islands, selected in base to prior studies and several genetic alterations, in a large series of tumors,

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and integrating all the information they classified colon cancer in three molecularly different subclasses [103]. On the other hand, several studies have been published which correlate the methylation state of a gene or a set of genes with the responsiveness of cancer to some treatments. Most of these studies have employed MSP technique [104]. For example, hMLH1 expression and methylation status were analyzed for evaluating the prognosis of patients receiving 5-fluorouracil (5-FU) therapy after a curative surgical resection, but the results indicated that while hMLH1 expression could be helpful, methylation state was not [105]. The use of alkylating agents, such as carmustine, has met with chemoresistance in CRC. In the majority of cancers, direct repair of resulting DNA adducts by MGMT is believed to mediate this resistance [106], but in 30–40% of CRCs, MGMT expression is silenced by promoter hypermethylation [86]. Experimental evidence suggests that tumors with hypermethylated MGMT but not hMLH1 are more likely to respond to chemotherapy with alkylating agents [107]. Similarly, tumors that have hypermethylated COX-2 [108] are unlikely to respond to chemopreventative or adjuvant therapy with celecoxib, a selective COX-2 inhibitor reported to reduce the polyp burden in patients with familial adenomatous polyposis [109]. 4. Concluding remarks and future directions In summary, a large number of methodologies are available for the analysis of DNA methylation, but all of them are based in one of these three main principles: sensitive-methylation endonucleases, bisulphite treatment and purification of methylated DNA by affinity/specific antibodies. The election of one or another method determines the kind of information obtained (qualitative, quantitative, resolution, etc) (Fig. 1), therefore it is critical to choose the approach that better fits the planned objectives of the research. Moreover, many of these methodologies can be combined among them opening even more the assortment of possibilities. The application of all these methods has made the discovery of epigenetically altered genes in cancer possible, and furthermore the determination of specific methylation profiles for the different types of neoplasias. In colorectal cancer, a growing number of the DNA methylation aberrations constitute potential markers of disease (for instance for diagnosis or prognosis) and promising targets in therapeutic strategies. Technological developments have contributed enormously to these advances, but the successful application of many of them still depends on the availability of reproducible, sensitive, rapid and cost-effective methods for DNA methylation analysis. Two main fronts for the development and application of DNA methylation techniques can be foreseen in colon cancer studies: (1) the detection of DNA methylation markers in body fluids, such as serum or stool, representing a potential noninvasive screening approach; and (2) the generation of DNA methylation maps at sequence level and with a high processivity and sensitivity, that will contribute to draft the dynamic epigenomes of intestine cells and of the malignant transformation. Conflict of interest statement The authors declare no competing financial interests. Acknowledgments We apologize to colleagues whose work could not be cited owing to space limitations. Research in the laboratory of MJ and MAP is supported by grants from the Ministerio de Ciencia e Inno-

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vación (SAF2008/1409 and Consolider-Ingenio 2010 CSD2006/49) and Fundación Merck-Serono.

References [1] E.S. Lander, L.M. Linton, B. Birren, C. Nusbaum, M.C. Zody, J. Baldwin, K. Devon, K. Dewar, M. Doyle, W. FitzHugh, R. Funke, D. Gage, K. Harris, A. Heaford, J. Howland, L. Kann, J. Lehoczky, R. LeVine, P. McEwan, K. McKernan, J. Meldrim, J.P. Mesirov, C. Miranda, W. Morris, J. Naylor, C. Raymond, M. Rosetti, R. Santos, A. Sheridan, C. Sougnez, N. Stange-Thomann, N. Stojanovic, A. Subramanian, D. Wyman, J. Rogers, J. Sulston, R. Ainscough, S. Beck, D. Bentley, J. Burton, C. Clee, N. Carter, A. Coulson, R. Deadman, P. Deloukas, A. Dunham, I. Dunham, R. Durbin, L. French, D. Grafham, S. Gregory, T. Hubbard, S. Humphray, A. Hunt, M. Jones, C. Lloyd, A. McMurray, L. Matthews, S. Mercer, S. Milne, J.C. Mullikin, A. Mungall, R. Plumb, M. Ross, R. Shownkeen, S. Sims, R.H. Waterston, R.K. Wilson, L.W. Hillier, J.D. McPherson, M.A. Marra, E.R. Mardis, L.A. Fulton, A.T. Chinwalla, K.H. Pepin, W.R. Gish, S.L. Chissoe, M.C. Wendl, K.D. Delehaunty, T.L. Miner, A. Delehaunty, J.B. Kramer, L.L. Cook, R.S. Fulton, D.L. Johnson, P.J. Minx, S.W. Clifton, T. Hawkins, E. Branscomb, P. Predki, P. Richardson, S. Wenning, T. Slezak, N. Doggett, J.F. Cheng, A. Olsen, S. Lucas, C. Elkin, E. Uberbacher, M. Frazier, R.A. Gibbs, D.M. Muzny, S.E. Scherer, J.B. Bouck, E.J. Sodergren, K.C. Worley, C.M. Rives, J.H. Gorrell, M.L. Metzker, S.L. Naylor, R.S. Kucherlapati, D.L. Nelson, G.M. Weinstock, Y. Sakaki, A. Fujiyama, M. Hattori, T. Yada, A. Toyoda, T. Itoh, C. Kawagoe, H. Watanabe, Y. Totoki, T. Taylor, J. Weissenbach, R. Heilig, W. Saurin, F. Artiguenave, P. Brottier, T. Bruls, E. Pelletier, C. Robert, P. Wincker, D.R. Smith, L. Doucette-Stamm, M. Rubenfield, K. Weinstock, H.M. Lee, J. Dubois, A. Rosenthal, M. Platzer, G. Nyakatura, S. Taudien, A. Rump, H. Yang, J. Yu, J. Wang, G. Huang, J. Gu, L. Hood, L. Rowen, A. Madan, S. Qin, R.W. Davis, N.A. Federspiel, A.P. Abola, M.J. Proctor, R.M. Myers, J. Schmutz, M. Dickson, J. Grimwood, D.R. Cox, M.V. Olson, R. Kaul, N. Shimizu, K. Kawasaki, S. Minoshima, G.A. Evans, M. Athanasiou, R. Schultz, B.A. Roe, F. Chen, H. Pan, J. Ramser, H. Lehrach, R. Reinhardt, W.R. McCombie, M. de la Bastide, N. Dedhia, H. Blocker, K. Hornischer, G. Nordsiek, R. Agarwala, L. Aravind, J.A. Bailey, A. Bateman, S. Batzoglou, E. Birney, P. Bork, D.G. Brown, C.B. Burge, L. Cerutti, H.C. Chen, D. Church, M. Clamp, R.R. Copley, T. Doerks, S.R. Eddy, E.E. Eichler, T.S. Furey, J. Galagan, J.G. Gilbert, C. Harmon, Y. Hayashizaki, D. Haussler, H. Hermjakob, K. Hokamp, W. Jang, L.S. Johnson, T.A. Jones, S. Kasif, A. Kaspryzk, S. Kennedy, W.J. Kent, P. Kitts, E.V. Koonin, I. Korf, D. Kulp, D. Lancet, T.M. Lowe, A. McLysaght, T. Mikkelsen, J.V. Moran, N. Mulder, V.J. Pollara, C.P. Ponting, G. Schuler, J. Schultz, G. Slater, A.F. Smit, E. Stupka, J. Szustakowski, D. ThierryMieg, J. Thierry-Mieg, L. Wagner, J. Wallis, R. Wheeler, A. Williams, Y.I. Wolf, K.H. Wolfe, S.P. Yang, R.F. Yeh, F. Collins, M.S. Guyer, J. Peterson, A. Felsenfeld, K.A. Wetterstrand, A. Patrinos, M.J. Morgan, P. de Jong, J.J. Catanese, K. Osoegawa, H. Shizuya, S. Choi, Y.J. Chen, Initial sequencing and analysis of the human genome, Nature 409 (2001) 860–921. [2] A. Bird, DNA methylation patterns and epigenetic memory, Genes Dev. 16 (2002) 6–21. [3] P.A. Jones, S.B. Baylin, The fundamental role of epigenetic events in cancer, Nat. Rev. Genet. 3 (2002) 415–428. [4] J.G. Herman, S.B. Baylin, Gene silencing in cancer in association with promoter hypermethylation, N. Engl. J. Med. 349 (2003) 2042–2054. [5] A.P. Bird, CpG-rich islands and the function of DNA methylation, Nature 321 (1986) 209–213. [6] B.H. Ramsahoye, D. Biniszkiewicz, F. Lyko, V. Clark, A.P. Bird, R. Jaenisch, NonCpG methylation is prevalent in embryonic stem cells and may be mediated by DNA methyltransferase 3a, Proc. Natl. Acad. Sci. U. S. A. 97 (2000) 5237–5242. [7] J. Sved, A. Bird, The expected equilibrium of the CpG dinucleotide in vertebrate genomes under a mutation model, Proc. Natl. Acad. Sci. U. S. A. 87 (1990) 4692–4696. [8] M.F. Chan, R. van Amerongen, T. Nijjar, E. Cuppen, P.A. Jones, P.W. Laird, Reduced rates of gene loss, gene silencing, and gene mutation in Dnmt1deficient embryonic stem cells, Mol. Cell. Biol. 21 (2001) 7587–7600. [9] M Gardiner-Garden, M. Frommer, CpG islands in vertebrate genomes, J. Mol. Biol. 196 (1987) 261–282. [10] D. Takai, P.A. Jones, Comprehensive analysis of CpG islands in human chromosomes 21 and 22, Proc. Natl. Acad. Sci. U. S. A. 99 (2002) 3740–3745. [11] M.G. Goll, T.H. Bestor, Eukaryotic cytosine methyltransferases, Annu. Rev. Biochem. 74 (2005) 481–514. [12] J.A. Yoder, C.P. Walsh, T.H. Bestor, Cytosine methylation and the ecology of intragenomic parasites, Trends Genet. 13 (1997) 335–340. [13] A. Eden, F. Gaudet, A. Waghmare, R. Jaenisch, Chromosomal instability and tumors promoted by DNA hypomethylation, Science 300 (2003) 455. [14] J. Rodriguez, J. Frigola, E. Vendrell, R.A. Risques, M.F. Fraga, C. Morales, V. Moreno, M. Esteller, G. Capella, M. Ribas, M.A. Peinado, Chromosomal instability correlates with genome-wide DNA demethylation in human primary colorectal cancers, Cancer Res. 66 (2006) 8462–9468. [15] S.M. Iguchi-Ariga, W. Schaffner, CpG methylation of the cAMP-responsive enhancer/promoter sequence TGACGTCA abolishes specific factor binding as well as transcriptional activation, Genes Dev. 3 (1989) 612–619. [16] P.L. Molloy, F. Watt, DNA methylation and specific protein–DNA interactions, Philos. Trans. R. Soc. Lond. B: Biol. Sci. 326 (1990) 267–275. [17] X. Nan, H.H. Ng, C.A. Johnson, C.D. Laherty, B.M. Turner, R.N. Eisenman, A. Bird, Transcriptional repression by the methyl-CpG-binding protein MeCP2 involves a histone deacetylase complex, Nature 393 (1998) 386–389.

92

M. Jordà, M.A. Peinado / Mutation Research 693 (2010) 84–93

[18] A.P. Bird, A.P. Wolffe, Methylation-induced repression—belts, braces, and chromatin, Cell 99 (1999) 451–454. [19] R.A Rollins, F. Haghighi, J.R. Edwards, R. Das, M.Q. Zhang, J. Ju, T.H. Bestor, Large-scale structure of genomic methylation patterns, Genome Res. 16 (2006) 157–163. [20] N. Ahuja, Q. Li, A.L. Mohan, S.B. Baylin, J.P. Issa, Aging and DNA methylation in colorectal mucosa and cancer, Cancer Res. 58 (1998) 5489–5494. [21] J.P. Issa, Y.L. Ottaviano, P. Celano, S.R. Hamilton, N.E. Davidson, S.B. Baylin, Methylation of the oestrogen receptor CpG island links ageing and neoplasia in human colon, Nat. Genet. 7 (1994) 536–540. [22] E.G. Hoal-van Helden, P.D. van Helden, Age-related methylation changes in DNA may reflect the proliferative potential of organs, Mutat. Res. 219 (1989) 263–266. [23] M.A. Gama-Sosa, V.A. Slagel, R.W. Trewyn, R. Oxenhandler, K.C. Kuo, C.W. Gehrke, M. Ehrlich, The 5-methylcytosine content of DNA from human tumors, Nucleic Acids Res. 11 (1983) 6883–6894. [24] P.A. Jones, P.W. Laird, Cancer epigenetics comes of age, Nat. Genet. 21 (1999) 163–167. [25] A.P. Feinberg, B. Vogelstein, Hypomethylation distinguishes genes of some human cancers from their normal counterparts, Nature 301 (1983) 89–92. [26] J.N. Lapeyre, F.F. Becker, 5-Methylcytosine content of nuclear DNA during chemical hepatocarcinogenesis and in carcinomas which result, Biochem. Biophys. Res. Commun. 87 (1979) 698–705. [27] D.J. Weisenberger, M. Campan, T.I. Long, M. Kim, C. Woods, E. Fiala, M. Ehrlich, P.W. Laird, Analysis of repetitive element DNA methylation by MethyLight, Nucleic Acids Res. 33 (2005) 6823–6836, Print 2005. [28] A. Narayan, W. Ji, X.Y. Zhang, A. Marrogi, J.R. Graff, S.B. Baylin, M. Ehrlich, Hypomethylation of pericentromeric DNA in breast adenocarcinomas, Int. J. Cancer 77 (1998) 833–838. [29] V. Greger, E. Passarge, W. Hopping, E. Messmer, B. Horsthemke, Epigenetic changes may contribute to the formation and spontaneous regression of retinoblastoma, Hum. Genet. 83 (1989) 155–158. [30] J.G. Herman, F. Latif, Y. Weng, M.I. Lerman, B. Zbar, S. Liu, D. Samid, D.S. Duan, J.R. Gnarra, W.M. Linehan, et al., Silencing of the VHL tumor-suppressor gene by DNA methylation in renal carcinoma, Proc. Natl. Acad. Sci. U. S. A. 91 (1994) 9700–9704. [31] J.G. Herman, A. Merlo, L. Mao, R.G. Lapidus, J.P. Issa, N.E. Davidson, D. Sidransky, S.B. Baylin, Inactivation of the CDKN2/p16/MTS1 gene is frequently associated with aberrant DNA methylation in all common human cancers, Cancer Res. 55 (1995) 4525–4530. [32] M. Esteller, A. Sparks, M. Toyota, M. Sanchez-Cespedes, G. Capella, M.A. Peinado, S. Gonzalez, G. Tarafa, D. Sidransky, S.J. Meltzer, S.B. Baylin, J.G. Herman, Analysis of adenomatous polyposis coli promoter hypermethylation in human cancer, Cancer Res. 60 (2000) 4366–4371. [33] A. Lujambio, S. Ropero, E. Ballestar, M.F. Fraga, C. Cerrato, F. Setien, S. Casado, A. Suarez-Gauthier, M. Sanchez-Cespedes, A. Git, I. Spiteri, P.P. Das, C. Caldas, E. Miska, M. Esteller, Genetic unmasking of an epigenetically silenced microRNA in human cancer cells, Cancer Res. 67 (2007) 1424–1429. [34] B. Vogelstein, E.R. Fearon, S.R. Hamilton, S.E. Kern, A.C. Preisinger, M. Leppert, Y. Nakamura, R. White, A.M. Smits, J.L. Bos, Genetic alterations during colorectal-tumor development, N. Engl. J. Med. 319 (1988) 525–532. [35] K.W. Kinzler, B. Vogelstein, Lessons from hereditary colorectal cancer, Cell 87 (1996) 159–170. [36] M.F. Fraga, M. Esteller, DNA methylation: a profile of methods and applications, Biotechniques 33 (2002) 632, 634, 636–649. [37] M. Esteller, Cancer epigenomics: DNA methylomes and histone-modification maps, Nat. Rev. Genet. 8 (2007) 286–298. [38] D. Zilberman, The human promoter methylome, Nat. Genet. 39 (2007) 442–443. [39] D. Eick, H.J. Fritz, W. Doerfler, Quantitative determination of 5-methylcytosine in DNA by reverse-phase high-performance liquid chromatography, Anal. Biochem. 135 (1983) 165–171. [40] J. Catania, B.C. Keenan, G.P. Margison, D.S. Fairweather, Determination of 5-methylcytosine by acid hydrolysis of DNA with hydrofluoric acid, Anal. Biochem. 167 (1987) 347–351. [41] M. Ehrlich, M.A. Gama-Sosa, L.H. Huang, R.M. Midgett, K.C. Kuo, R.A. McCune, C. Gehrke, Amount and distribution of 5-methylcytosine in human DNA from different types of tissues of cells, Nucleic Acids Res. 10 (1982) 2709–2721. [42] M.F. Fraga, R. Rodriguez, M.J. Canal, Rapid quantification of DNA methylation by high performance capillary electrophoresis, Electrophoresis 21 (2000) 2990–2994. [43] H. Cedar, A. Solage, G. Glaser, A. Razin, Direct detection of methylated cytosine in DNA by use of the restriction enzyme MspI, Nucleic Acids Res. 6 (1979) 2125–2132. [44] F. Gounari, G.R. Banks, K. Khazaie, P.A. Jeggo, R. Holliday, Gene reactivation: a tool for the isolation of mammalian DNA methylation mutants, Genes Dev. 1 (1987) 899–912. [45] L.I. Kutueva, V.V. Ashapkin, B.F. Vanyushin, The methylation pattern of a cytosine DNA-methyltransferase gene in Arabidopsis thaliana plants, Biochem. Mol. Biol. Int. 40 (1996) 347–353. [46] M. Frommer, L.E. McDonald, D.S. Millar, C.M. Collis, F. Watt, G.W. Grigg, P.L. Molloy, C.L. Paul, A genomic sequencing protocol that yields a positive display of 5-methylcytosine residues in individual DNA strands, Proc. Natl. Acad. Sci. U. S. A. 89 (1992) 1827–1831. [47] S.J. Clark, J. Harrison, C.L. Paul, M. Frommer, High sensitivity mapping of methylated cytosines, Nucleic Acids Res. 22 (1994) 2990–2997.

[48] R. Shapiro, V. DiFate, M. Welcher, Deamination of cytosine derivatives by bisulfite. Mechanism of the reaction, J. Am. Chem. Soc. 96 (1974) 906–912. [49] T. Rein, H. Zorbas, M.L. DePamphilis, Active mammalian replication origins are associated with a high-density cluster of mCpG dinucleotides, Mol. Cell. Biol. 17 (1997) 416–426. [50] S.J. Clark, A. Statham, C. Stirzaker, P.L. Molloy, M. Frommer, DNA methylation: bisulphite modification and analysis, Nat. Protoc. 1 (2006) 2353–2364. [51] M. Ronaghi, M. Uhlen, P. Nyren, A sequencing method based on real-time pyrophosphate, Science 281 (1998) 363, 365. [52] K. Uhlmann, A. Brinckmann, M.R. Toliat, H. Ritter, P. Nurnberg, Evaluation of a potential epigenetic biomarker by quantitative methyl-single nucleotide polymorphism analysis, Electrophoresis 23 (2002) 4072–4079. [53] J.G. Herman, J.R. Graff, S. Myohanen, B.D. Nelkin, S.B. Baylin, Methylationspecific PCR: a novel PCR assay for methylation status of CpG islands, Proc. Natl. Acad. Sci. U. S. A. 93 (1996) 9821–9826. [54] C.A. Eads, K.D. Danenberg, K. Kawakami, L.B. Saltz, C. Blake, D. Shibata, P.V. Danenberg, P.W. Laird, MethyLight: a high-throughput assay to measure DNA methylation, Nucleic Acids Res. 28 (2000) E32. [55] K.N. Rand, T. Ho, W. Qu, S.M. Mitchell, R. White, S.J. Clark, P.L. Molloy, Headloop suppression PCR and its application to selective amplification of methylated DNA sequences, Nucleic Acids Res. 33 (2005) e127. [56] J. Worm, A. Aggerholm, P. Guldberg, In-tube DNA methylation profiling by fluorescence melting curve analysis, Clin. Chem. 47 (2001) 1183–1189. [57] Z. Xiong, P.W. Laird, COBRA: a sensitive and quantitative DNA methylation assay, Nucleic Acids Res. 25 (1997) 2532–2534. [58] M. Ehrich, M.R. Nelson, P. Stanssens, M. Zabeau, T. Liloglou, G. Xinarianos, C.R. Cantor, J.K. Field, D. van den Boom, Quantitative high-throughput analysis of DNA methylation patterns by base-specific cleavage and mass spectrometry, Proc. Natl. Acad. Sci. U. S. A. 102 (2005) 15785–15790. [59] P. Schatz, D. Dietrich, M. Schuster, Rapid analysis of CpG methylation patterns using RNase T1 cleavage and MALDI-TOF, Nucleic Acids Res. 32 (2004) e167. [60] I.G. Gut, DNA analysis by MALDI-TOF mass spectrometry, Hum. Mutat. 23 (2004) 437–441. [61] I. Hatada, Y. Hayashizaki, S. Hirotsune, H. Komatsubara, T. Mukai, A genomic scanning method for higher organisms using restriction sites as landmarks, Proc. Natl. Acad. Sci. U. S. A. 88 (1991) 9523–9527. [62] M. Toyota, C. Ho, N. Ahuja, K.W. Jair, Q. Li, M. Ohe-Toyota, S.B. Baylin, J.P. Issa, Identification of differentially methylated sequences in colorectal cancer by methylated CpG island amplification, Cancer Res. 59 (1999) 2307–2312. [63] N. Lisitsyn, M. Wigler, Cloning the differences between two complex genomes, Science 259 (1993) 946–951. [64] M.R. Estecio, P.S. Yan, A.E. Ibrahim, C.S. Tellez, L. Shen, T.H. Huang, J.P. Issa, High-throughput methylation profiling by MCA coupled to CpG island microarray, Genome Res. 17 (2007) 1529–1536. [65] J. Frigola, M. Ribas, R.A. Risques, M.A. Peinado, Methylome profiling of cancer cells by amplification of inter-methylated sites (AIMS), Nucleic Acids Res. 30 (2002) e28. [66] J. Rodriguez, L. Vives, M. Jorda, C. Morales, M. Munoz, E. Vendrell, M.A. Peinado, Genome-wide tracking of unmethylated DNA Alu repeats in normal and cancer cells, Nucleic Acids Res. 36 (2008) 770–784. [67] S.H. Cross, J.A. Charlton, X. Nan, A.P. Bird, Purification of CpG islands using a methylated DNA binding column, Nat. Genet. 6 (1994) 236–244. [68] M. Shiraishi, Y.H. Chuu, T. Sekiya, Isolation of DNA fragments associated with methylated CpG islands in human adenocarcinomas of the lung using a methylated DNA binding column and denaturing gradient gel electrophoresis, Proc. Natl. Acad. Sci. U. S. A. 96 (1999) 2913–2918. [69] T.H. Huang, M.R. Perry, D.E. Laux, Methylation profiling of CpG islands in human breast cancer cells, Hum. Mol. Genet. 8 (1999) 459–470. [70] P.S. Yan, M.R. Perry, D.E. Laux, A.L. Asare, C.W. Caldwell, T.H. Huang, CpG island arrays: an application toward deciphering epigenetic signatures of breast cancer, Clin. Cancer Res. 6 (2000) 1432–1438. [71] M. Nouzova, N. Holtan, M.M. Oshiro, R.B. Isett, J.L. Munoz-Rodriguez, A.F. List, M.L. Narro, S.J. Miller, N.C. Merchant, B.W. Futscher, Epigenomic changes during leukemia cell differentiation: analysis of histone acetylation and cytosine methylation using CpG island microarrays, J. Pharmacol. Exp. Ther. 311 (2004) 968–981. [72] R.S. Gitan, H. Shi, C.M. Chen, P.S. Yan, T.H. Huang, Methylation-specific oligonucleotide microarray: a new potential for high-throughput methylation analysis, Genome Res. 12 (2002) 158–164. [73] L. Lopez-Serra, E. Ballestar, M.F. Fraga, M. Alaminos, F. Setien, M. Esteller, A profile of methyl-CpG binding domain protein occupancy of hypermethylated promoter CpG islands of tumor suppressor genes in human cancer, Cancer Res. 66 (2006) 8342–8346. [74] E. Ballestar, M.F. Paz, L. Valle, S. Wei, M.F. Fraga, J. Espada, J.C. Cigudosa, T.H. Huang, M. Esteller, Methyl-CpG binding proteins identify novel sites of epigenetic inactivation in human cancer, EMBO J. 22 (2003) 6335–6345. [75] M. Weber, J.J. Davies, D. Wittig, E.J. Oakeley, M. Haase, W.L. Lam, D. Schubeler, Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells, Nat. Genet. 37 (2005) 853–862. [76] H. Suzuki, E. Gabrielson, W. Chen, R. Anbazhagan, M. van Engeland, M.P. Weijenberg, J.G. Herman, S.B. Baylin, A genomic screen for genes upregulated by demethylation and histone deacetylase inhibition in human colorectal cancer, Nat. Genet. 31 (2002) 141–149. [77] D.R. Bentley, Whole-genome re-sequencing, Curr. Opin. Genet. Dev. 16 (2006) 545–552.

M. Jordà, M.A. Peinado / Mutation Research 693 (2010) 84–93 [78] M. Margulies, M. Egholm, W.E. Altman, S. Attiya, J.S. Bader, L.A. Bemben, J. Berka, M.S. Braverman, Y.J. Chen, Z. Chen, S.B. Dewell, L. Du, J.M. Fierro, X.V. Gomes, B.C. Godwin, W. He, S. Helgesen, C.H. Ho, G.P. Irzyk, S.C. Jando, M.L. Alenquer, T.P. Jarvie, K.B. Jirage, J.B. Kim, J.R. Knight, J.R. Lanza, J.H. Leamon, S.M. Lefkowitz, M. Lei, J. Li, K.L. Lohman, H. Lu, V.B. Makhijani, K.E. McDade, M.P. McKenna, E.W. Myers, E. Nickerson, J.R. Nobile, R. Plant, B.P. Puc, M.T. Ronan, G.T. Roth, G.J. Sarkis, J.F. Simons, J.W. Simpson, M. Srinivasan, K.R. Tartaro, A. Tomasz, K.A. Vogt, G.A. Volkmer, S.H. Wang, Y. Wang, M.P. Weiner, P. Yu, R.F. Begley, J.M. Rothberg, Genome sequencing in microfabricated highdensity picolitre reactors, Nature 437 (2005) 376–380. [79] R. Lister, M. Pelizzola, R.H. Dowen, R.D. Hawkins, G. Hon, J. Tonti-Filippini, J.R. Nery, L. Lee, Z. Ye, Q.M. Ngo, L. Edsall, J. Antosiewicz-Bourget, R. Stewart, V. Ruotti, A.H. Millar, J.A. Thomson, B. Ren, J.R. Ecker, Human DNA methylomes at base resolution show widespread epigenomic differences, Nature 462 (2009) 315–322. [80] L. Laurent, E. Wong, G. Li, T. Huynh, A. Tsirigos, C.T. Ong, H.M. Low, K.W. Kin Sung, I. Rigoutsos, J. Loring, C.L. Wei, Dynamic changes in the human methylome during differentiation, Genome Res. 20 (2010) 320–331. [81] Y. Korshunova, R.K. Maloney, N. Lakey, R.W. Citek, B. Bacher, A. Budiman, J.M. Ordway, W.R. McCombie, J. Leon, J.A. Jeddeloh, J.D. McPherson, Massively parallel bisulphite pyrosequencing reveals the molecular complexity of breast cancer-associated cytosine-methylation patterns obtained from tissue and serum DNA, Genome Res. 18 (2008) 19–29. [82] K.R. Pomraning, K.M. Smith, M. Freitag, Genome-wide high throughput analysis of DNA methylation in eukaryotes, Methods (2008). [83] T.A. Down, V.K. Rakyan, D.J. Turner, P. Flicek, H. Li, E. Kulesha, S. Graf, N. Johnson, J. Herrero, E.M. Tomazou, N.P. Thorne, L. Backdahl, M. Herberth, K.L. Howe, D.K. Jackson, M.M. Miretti, J.C. Marioni, E. Birney, T.J. Hubbard, R. Durbin, S. Tavare, S. Beck, A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis, Nat. Biotechnol. 26 (2008) 779–785. [84] J.F. Costello, M.C. Fruhwald, D.J. Smiraglia, L.J. Rush, G.P. Robertson, X. Gao, F.A. Wright, J.D. Feramisco, P. Peltomaki, J.C. Lang, D.E. Schuller, L. Yu, C.D. Bloomfield, M.A. Caligiuri, A. Yates, R. Nishikawa, H. Su Huang, N.J. Petrelli, X. Zhang, M.S. O’Dorisio, W.A. Held, W.K. Cavenee, C. Plass, Aberrant CpG-island methylation has non-random and tumour-type-specific patterns, Nat. Genet. 24 (2000) 132–138. [85] M. Esteller, P.G. Corn, S.B. Baylin, J.G. Herman, A gene hypermethylation profile of human cancer, Cancer Res. 61 (2001) 3225–3229. [86] M. Esteller, M. Toyota, M. Sanchez-Cespedes, G. Capella, M.A. Peinado, D.N. Watkins, J.P. Issa, D. Sidransky, S.B. Baylin, J.G. Herman, Inactivation of the DNA repair gene O6-methylguanine-DNA methyltransferase by promoter hypermethylation is associated with G to A mutations in K-ras in colorectal tumorigenesis, Cancer Res. 60 (2000) 2368–2371. [87] M.F. Kane, M. Loda, G.M. Gaida, J. Lipman, R. Mishra, H. Goldman, J.M. Jessup, R. Kolodner, Methylation of the hMLH1 promoter correlates with lack of expression of hMLH1 in sporadic colon tumors and mismatch repair-defective human tumor cell lines, Cancer Res. 57 (1997) 808–811. [88] N. Nakamura, K. Takenaga, Hypomethylation of the metastasis-associated S100A4 gene correlates with gene activation in human colon adenocarcinoma cell lines, Clin. Exp. Metastasis 16 (1998) 471–479. [89] J. Frigola, X. Sole, M.F. Paz, V. Moreno, M. Esteller, G. Capella, M.A. Peinado, Differential DNA hypermethylation and hypomethylation signatures in colorectal cancer, Hum. Mol. Genet. 14 (2005) 319–326. [90] S. Ogino, K. Nosho, G.J. Kirkner, T. Kawasaki, A.T. Chan, E.S. Schernhammer, E.L. Giovannucci, C.S. Fuchs, A cohort study of tumoral LINE-1 hypomethylation and prognosis in colon cancer, J. Natl. Cancer Inst. 100 (2008) 1734–1738. [91] W.A. Palmisano, K.K. Divine, G. Saccomanno, F.D. Gilliland, S.B. Baylin, J.G. Herman, S.A. Belinsky, Predicting lung cancer by detecting aberrant promoter methylation in sputum, Cancer Res. 60 (2000) 5954–5958. [92] P. Cairns, M. Esteller, J.G. Herman, M. Schoenberg, C. Jeronimo, M. SanchezCespedes, N.H. Chow, M. Grasso, L. Wu, W.B. Westra, D. Sidransky, Molecular detection of prostate cancer in urine by GSTP1 hypermethylation, Clin. Cancer Res. 7 (2001) 2727–2730. [93] A.R. Karpf, D.A. Jones, Reactivating the expression of methylation silenced genes in human cancer, Oncogene 21 (2002) 5496–5503. [94] F.V. Jacinto, E. Ballestar, S. Ropero, M. Esteller, Discovery of epigenetically silenced genes by methylated DNA immunoprecipitation in colon cancer cells, Cancer Res. 67 (2007) 11481–11486. ˜ [95] J. Frigola, M. Munoz, S.J. Clark, V. Moreno, G. Capella, M.A. Peinado, Hypermethylation of the prostacyclin synthase (PTGIS) promoter is a frequent event in colorectal cancer and associated with aneuploidy, Oncogene 24 (2005) 7320–7326.

93

[96] M. Esteller, J. Garcia-Foncillas, E. Andion, S.N. Goodman, O.F. Hidalgo, V. Vanaclocha, S.B. Baylin, J.G. Herman, Inactivation of the DNA-repair gene MGMT and the clinical response of gliomas to alkylating agents, N. Engl. J. Med. 343 (2000) 1350–1354. [97] A.N. Magewu, P.A. Jones, Ubiquitous and tenacious methylation of the CpG site in codon 248 of the p53 gene may explain its frequent appearance as a mutational hot spot in human cancer, Mol. Cell. Biol. 14 (1994) 4225–4232. [98] H.Z. Zou, B.M. Yu, Z.W. Wang, J.Y. Sun, H. Cang, F. Gao, D.H. Li, R. Zhao, G.G. Feng, J. Yi, Detection of aberrant p16 methylation in the serum of colorectal cancer patients, Clin. Cancer Res. 8 (2002) 188–191. [99] R. Mayor, L. Casadome, D. Azuara, V. Moreno, S.J. Clark, G. Capella, M.A. Peinado, Long-range epigenetic silencing at 2q14.2 affects most human colorectal cancers and may have application as a non-invasive biomarker of disease, Br. J. Cancer 100 (2009) 1534–1539. [100] M. Esteller, S. Gonzalez, R.A. Risques, E. Marcuello, R. Mangues, J.R. Germa, J.G. Herman, G. Capella, M.A. Peinado, K-ras and p16 aberrations confer poor prognosis in human colorectal cancer, J. Clin. Oncol. 19 (2001) 299–304. [101] T. Lecomte, A. Berger, F. Zinzindohoue, S. Micard, B. Landi, H. Blons, P. Beaune, P.H. Cugnenc, P. Laurent-Puig, Detection of free-circulating tumor-associated DNA in plasma of colorectal cancer patients and its association with prognosis, Int. J. Cancer 100 (2002) 542–548. [102] N. Umetani, H. Takeuchi, A. Fujimoto, M. Shinozaki, A.J. Bilchik, D.S. Hoon, Epigenetic inactivation of ID4 in colorectal carcinomas correlates with poor differentiation and unfavorable prognosis, Clin. Cancer Res. 10 (2004) 7475–7483. [103] L. Shen, M. Toyota, Y. Kondo, E. Lin, L. Zhang, Y. Guo, N.S. Hernandez, X. Chen, S. Ahmed, K. Konishi, S.R. Hamilton, J.P. Issa, Integrated genetic and epigenetic analysis identifies three different subclasses of colon cancer, Proc. Natl. Acad. Sci. U. S. A. 104 (2007) 18654–18659. [104] M. Esteller, S. Tortola, M. Toyota, G. Capella, M.A. Peinado, S.B. Baylin, J.G. Herman, Hypermethylation-associated inactivation of p14(ARF) is independent of p16(INK4a) methylation and p53 mutational status, Cancer Res. 60 (2000) 129–133. [105] T. Ide, Y. Kitajima, K. Ohtaka, M. Mitsuno, Y. Nakafusa, K. Miyazaki, Expression of the hMLH1 gene is a possible predictor for the clinical response to 5-fluorouracil after a surgical resection in colorectal cancer, Oncol. Rep. 19 (2008) 1571–1576. [106] S.G. Chaney, A. Sancar, DNA repair: enzymatic mechanisms and relevance to drug response, J. Natl. Cancer Inst. 88 (1996) 1346–1360. [107] H. Kawate, R. Itoh, K. Sakumi, Y. Nakabeppu, T. Tsuzuki, F. Ide, T. Ishikawa, T. Noda, H. Nawata, M. Sekiguchi, A defect in a single allele of the Mlh1 gene causes dissociation of the killing and tumorigenic actions of an alkylating carcinogen in methyltransferase-deficient mice, Carcinogenesis 21 (2000) 301–305. [108] M. Toyota, L. Shen, M. Ohe-Toyota, S.R. Hamilton, F.A. Sinicrope, J.P. Issa, Aberrant methylation of the cyclooxygenase 2 CpG island in colorectal tumors, Cancer Res. 60 (2000) 4044–4048. [109] G. Steinbach, P.M. Lynch, R.K. Phillips, M.H. Wallace, E. Hawk, G.B. Gordon, N. Wakabayashi, B. Saunders, Y. Shen, T. Fujimura, L.K. Su, B. Levin, The effect of celecoxib, a cyclooxygenase-2 inhibitor, in familial adenomatous polyposis, N. Engl. J. Med. 342 (2000) 1946–1952. [110] M.L Gonzalgo, P.A. Jones, Quantitative methylation analysis using methylation-sensitive single-nucleotide primer extension (Ms-SNuPE), Methods 27 (2002) 128–133. [111] T. Bianco, D. Hussey, A. Dobrovic, Methylation-sensitive, single-strand conformation analysis (MS-SSCA): a rapid method to screen for and analyze methylation, Hum. Mutat. 14 (1999) 289–293. [112] M.L Gonzalgo, G. Liang, C.H. Spruck, J.M. Zingg, W.M. Rideout, P.A. Jones, Identification and characterization of differentially methylated regions of genomic DNA by methylation-sensitive arbitrarily primed PCR, Cancer Res. 57 (1997) 594–599. ˜ [113] J. Rodriguez, M. Munoz, L. Vives, C.G. Frangou, M. Groudine, M.A. Peinado, Bivalent domains enforce transcriptional memory of DNA methylated genes in cancer cells, Proc. Natl. Acad. Sci. U. S. A. 105 (2008) 19809–19814. [114] H. Li, L. Myeroff, D. Smiraglia, M.F. Romero, T.P. Pretlow, L. Kasturi, J. Lutterbaugh, R.M. Rerko, G. Casey, J.P. Issa, J. Willis, J.K. Willson, C. Plass, S.D. Markowitz, SLC5A8, a sodium transporter, is a tumor suppressor gene silenced by methylation in human colon aberrant crypt foci and cancers, Proc. Natl. Acad. Sci. U. S. A. 100 (2003) 8412–8417. [115] G. Liang, K.D. Robertson, C. Talmadge, J. Sumegi, P.A. Jones, The gene for a novel transmembrane protein containing epidermal growth factor and follistatin domains is frequently hypermethylated in human tumor cells, Cancer Res. 60 (2000) 4907–4912.