• MeDIP;
  • DNA hypermethylation;
  • MYCN;
  • neuroblastoma


  1. Top of page
  2. Abstract
  3. Material and Method
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information

The downregulation of specific genes through DNA hypermethylation is a major hallmark of cancer, although the extent and genomic distribution of hypermethylation occurring within cancer genomes is poorly understood. We report on the first genome-wide analysis of DNA methylation alterations in different neuroblastic tumor subtypes and cell lines, revealing higher order organization and clinically relevant alterations of the epigenome. The methylation status of 33,485 discrete loci representing all annotated CpG islands and RefSeq gene promoters was assessed in primary neuroblastic tumors and cell lines. A comparison of genes that were hypermethylated exclusively in the clinically favorable ganglioneuroma/ganglioneuroblastoma tumors revealed that nine genes were associated with poor clinical outcome when overexpressed in the unfavorable neuroblastoma (NB) tumors. Moreover, an integrated DNA methylation and copy number analysis identified 80 genes that were recurrently concomitantly deleted and hypermethylated in NB, with 37 reactivated by 5-aza-deoxycytidine. Lower expression of four of these genes was correlated with poor clinical outcome, further implicating their inactivation in aggressive disease pathogenesis. Analysis of genome-wide hypermethylation patterns revealed 70 recurrent large-scale blocks of contiguously hypermethylated promoters/CpG islands, up to 590 kb in length, with a distribution bias toward telomeric regions. Genome-wide hypermethylation events in neuroblastic tumors are extensive and frequently occur in large-scale blocks with a significant bias toward telomeric regions, indicating that some methylation alterations have occurred in a coordinated manner. Our results indicate that methylation contributes toward the clinicopathological features of neuroblastic tumors, revealing numerous genes associated with poor patient survival in NB.

Neuroblastoma (NB) is a childhood neuroblastic tumor arising from precursor cells of the sympathetic nervous system. These tumors are composed largely of immature neuroblasts and display a broad clinical spectrum ranging from rapid advancement and death due to disease to spontaneous regression.1 The diverse clinical behavior of NB is mirrored by extensive heterogeneity in the genetic abnormalities exhibited in the tumors, with MYCN amplification or deletion of chromosome 11q material representing distinct tumor subtypes with generally unfavorable clinical outcomes. In contrast, tumors that are characterized primarily by hyperdiploidy and few structural chromosome abnormalities have a more clinically favorable outcome. Other neuroblastic tumors include benign ganglioneuroma, which are composed primarily of Schwannian stromal and mature ganglion cells, and an intermediate tumor, ganglioneuroblastoma (GNB), which is stromal rich but also possess immature neuroblasts. GNB tumors are clinically less aggressive than NB.

Aberrant hypermethylation of cytidine bases within gene promoter regions is a well-known mechanism for the transcriptional silencing of tumor suppressor genes in many forms of cancer.2, 3 Studies based on a limited number of candidate genes indicate that aberrant hypermethylation is a clinically relevant event in NB tumors. For example, CASP8, a key regulator of apoptosis, and RASSF1A, a well-documented tumor suppressor gene, have been noted to undergo frequent hypermethylation in clinically unfavorable subtypes of NB.4–10 Most significantly, the methylation status of a limited set of genes indicated that patterns of methylation might prove useful for discriminating clinical risk groups of NB.4, 11, 12 There have also been reports indicating that some genes might become methylated in a coordinated manner, a “methylator phenotype,”11, 13 and that genes with methylated promoter regions display some level of clustering.7 A genome-wide assessment of DNA hypermethylation in either NB or the clinically more favorable neuroblastic tumors, however, has never been carried out.

Here, using genome-wide DNA methylation profiling, we identify DNA methylation differences found at gene promoter regions that might contribute to the clinicopathological differences between NB, GN and GNB tumors. Recently, it has also become apparent that recurrent large-scale blocks of contiguously methylated CpG islands occur in several types of cancer, including colorectal,14, 15 breast,16 astrocytoma17 and Wilm's tumors.18 In this report, we demonstrate for the first time that these contiguously hypermethylated regions also occur in neuroblastic tumors and that they have a highly biased distribution toward the terminal regions of chromosomes.

Material and Method

  1. Top of page
  2. Abstract
  3. Material and Method
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information

Cell culture

The Kelly, SK-N-BE and SK-N-AS cell lines were obtained from the European Collection of Animal Cell Cultures (Porton Down, UK), while LAN-5, CHLA-20 and CHLA-42 were obtained from Dr. P. Reynolds, Children Hospital of Los Angeles. The NGP cell line was obtained from Cancer Therapy and Research Centre, San Antonio, TX. SK-N-AS cells were grown in EMEM supplemented with 1% nonessential amino acids, 2 mM glutamine, 10% fetal bovine serum and penicillin/streptomycin. Kelly and NGP cells were cultured in RPMI-1640 supplemented with 2 mM glutamine, 10% fetal bovine serum and penicillin/streptomycin. LAN-6, CHLA-20 and CHLA-42 cells were grown in IMDM supplemented with 20% fetal bovine serum, 5 ml of insulin/transferrin/seleneium and penicillin/streptomycin. SK-N-BE cells were cultured in Ham's F12 (220 ml), EMEM (220 ml), 10% FBS, 2.5 ml nonessential amino acids, 2.5 ml L-glutamine and 5 ml penicillin/streptomycin. All cell culture reagents were obtained from GIBCO.

5-Aza-deoxycytidine treatment

NB cells were incubated with 10 ml cell culture media containing 5-aza-2′-deoxycytidine (DAC; Sigma-Aldrich, Cat No. A3656-10MG) at a final concentration of 2 μg/ml as used by Kron et al.19 for 72 hr, including a change of media after 48 hr.

Neuroblastoma tumor samples

NB tumor samples were obtained from Our Lady's Children's Hospital, Crumlin, Ireland. Each tumor was characterized by aCGH by previously described methods20 using the 72k 4-plex array from NimbleGen. This work was approved by the Research Ethics Committee of the Royal College of Surgeons on October 16, 2007 (application no. REC241) and by the Research Ethics Committee of Our Lady's Children's Hospital on August 5, 2008 (application no. GEN/70/07).

Methylated DNA immunoprecipitation

The protocol used for methylation analysis was as previously described by Murphy et al.21 Briefly, reactions were incubated overnight with 10 μg of anti-5′-methylcytidine antibody (BI-MECY-1000; Eurogentec) and immunoprecipitated using Dynabeads (112-02D, Invitrogen) and a magnetic particle concentrator (DynaMag™, Cat. No. 123.21D, Invitrogen). The MeDIP DNA and reference control were differentially labeled and hybridized to a CpG Island promoter plus array from Roche NimbleGen (Human Meth 385K Prom Plus CpG; Cat. No. 05543622001) according to NimbleGen DNA methylation analysis protocol version 6.0. Arrays were scanned using the Axon 4000B scanner, and the data were processed using the GenePix Pro 6.0. Image analysis and peak detection were performed using the methylation application in Nimblescan Version 2.4. Methylated peaks were identified using the following parameters; sliding window of 750bp, p-value minimum cutoff (−log10) of 2.0 and a minimum of two probes per peak. Resulting data files were visualized using SignalMap 1.9. Methylation microarray data have been deposited at (Accession E-TABM-965).

Q-PCR validation of MeDIP

The MeDIP experiments were validated using a relative quantification approach using SYBR Green master mix (Applied Biosystems, Part No. 4309155). Previously published primers22 for H19 imprinted control region and the H3b unmethylated region were obtained from MWG Biotech. The relative level of enrichment (RQ) was calculated for each of the target regions using the comparative Ct method. A negative control of immunoprecipitated DNA using normal mouse IgG (Santa cruz Antibodies, Cat No. SC-2025) was also included. All Q-PCR analysis was carried out on the 7900HT Applied Biosystems real-time PCR.

Gene expression analysis

Double-stranded cDNA was synthesized from total RNA using the SuperScript Double-Stranded cDNA Synthesis Kit (Invitrogen, Cat. no. 11917-020) according to the manufacturer's instructions. cDNA was labeled overnight using the NimbleGen One-Color DNA Labeling Kit (Cat. no. 05223555001). The Homo sapiens 4 x 72K gene expression array from Roche NimbleGen was used in this study. mRNA expression microarray data have been deposited at (Experiment Accession No. E-MEXP-2557; Array Design Accession No. A-MEXP-1785). qPCR analysis was carried out using 20 ng of DAC-treated and untreated cDNA in triplicate on the 7900 HT Fast Realtime System (Applied Biosystems) from Kelly and SK-N-AS for validation of gene reexpression after DAC treatment for the following genes: TRAK1 (Hs00209162_m1), CCNB3 (Hs00364460_m1), ZNF560 (Hs01074077_m1), PRSS8 (Hs00173606_m1), BMP8B (Hs00236942_m1) and COL1A2 (Hs00236942_m1). B-Actin (Hs99999903_m1) was used for normalization. A relative fold change in expression was carried out using the comparative cycle threshold method (2−ΔΔCT).

Bioinformatic analysis

Compilation, preprocessing and analysis of genomic methylation data and integrative analysis of methylation and chromosomal copy number data were performed using in-house developed Java (v1.6) software. Hierarchical cluster analysis, dendrogram generation and plotting were performed using R (v2.10.1) cluster package and custom scripts. Gene ontology (GO) analysis was carried out using the DAVID online database.23 Ideogram generation was carried out using Idiographica web-based software.24

Bisulfite sequencing

A total of 500 ng of DNA from selected cell lines and tumors was bisulfite converted using the EZ DNA-methylation Gold kit (Cat. No. D5005 and D5006, Zymo) using the alternative conversion reaction 2 as per manufacturer's instructions. PCR primers were designed using methyl primer express ( PCR was carried out using 20 ng of the bisulfite-treated DNA, 10 pmol of each primer, 25 μl of PyroMark 2× PCR master mix and 5 μl of CoralLoad Concentrate 10× provided in the PyroMark PCR kit (Cat. No. 978703), along with a 1:5 dilution of DMSO. PCR cycling conditions were as follows: 95°C for 10 min, 94°C for 30 sec, 58–60°C for 30 sec, 72°C for 30 sec (35 cycles) followed by 72°C for 10 min. PCR products were purified using the QIAquick PCR purification kit (Cat. No. 28104, Qiagen), as per manufacturer's instructions, and sequenced in the forward direction at MWG biotech. Resulting electropherograms were analyzed using the BIQ analyzer.25


  1. Top of page
  2. Abstract
  3. Material and Method
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information

DNA methylation analysis of neuroblastoma cell lines

To initially assess the overall frequency of hypermethylation, methylated DNA immunoprecipitation (MeDIP) was carried out in duplicate on seven NB cell lines comprising four MYCN-amplified (NGP, Kelly, SK-N-BE and LAN-5) and three MYCN-unamplified (SK-N-AS, CHLA-20, CHLA-42) lines. Enrichment of immunoprecipitated DNA was confirmed using a qPCR-based assay for the imprinted H19 locus compared to the unmethylated H3B locus for NB cell lines. The fold enrichment for H19 compared to H3B, using the comparative Ct method, ranged from 60- to 150-fold for the NB cell lines tested (Supporting Information Fig. S1a). MeDIP and input DNA were hybridized to a microarray covering 33,485 loci representing all annotated CpG islands and promoter regions, and a pair-wise comparison of MeDIP replicates for each cell line resulted in Pearson correlation coefficients ranging from 0.82 to 0.93 (Supporting Information Figs. S1b–S1h). Unsupervised hierarchical clustering of the raw log2 ratio values for each experiment also revealed that replicate experiments grouped together (Supporting Information Fig. S1i), further validating our experimental approach. In addition, bisulfite sequencing of PCR products from Kelly and SK-N-AS cell lines indicated no false-positive results and a false-negative frequency of ∼10% (Supporting Information Table S1).

Peaks of hypermethylation identified using the NimbleScan 2.4 software in each experiment were mapped to genes using a window of −2 kb to +500 bp of the transcription start site. The average number of hypermethylated loci (including gene promoters and CpG islands) detected in the NB cell lines was 4,585 (ranging from 3,805 to 5,197), which corresponds to 13.7% of the fraction of the genome interrogated (considering 33,485 regions were analyzed on the microarray). A comparison of hypermethylated loci across an increasing number of cell lines provided the following results: four cell lines, 3,200 common peaks; five cell lines, 2,210 common peaks, six cell lines, 1,405 common peaks and seven cell lines, 772 common peaks. Genes that were commonly methylated across cell lines and have been previously implicated in cancer development included TNFα, COL1A2, TGFBR1, TRADD and HIC1.

Reactivation of genes after treatment with 5′-aza-2-deoxycytidine

To determine how promoter region hypermethylation has influenced mRNA transcription at genome-wide level, mRNA expression microarray analysis was carried out on three cell lines (Kelly, SK-N-AS and NGP) before and after treatment with the demethylating agent DAC. Table 1 displays the number of genes in each cell line expressed >5-, 6-, 7-, 8-, 9- and 10-fold after DAC treatment. Reexpression was confirmed by Taqman qPCR for a selected number of methylated genes (Supporting information Fig. S2). Of the genes that were most significantly reexpressed (>10-fold), only 12–31%, depending upon the cell line, actually exhibited hypermethylation at their promoter regions before DAC exposure. Although the promoters of some of the reexpressed genes might not have had a sufficient density of CpG methylation to be registered as hypermethylated using MeDIP, many were likely reexpressed as a consequence of secondary events. In this regards, it is noteworthy that approximately an equal number of genes were downregulated in response to DAC, most likely as a consequence of secondary events such as miRNA activation or the activation of transcriptional repressors.

Table 1. Total number of genes reexpressed after DAC treatment
inline image

Epigenetic analysis and stratification of primary tumors

To further extend our methylation analysis and to determine how the sites of hypermethylation in cell lines relate to DNA methylation in vivo, 28 primary tumors (prechemotherapy), which included six ganglioneuroma (GN), four GNB and a NB group consisting of two hyperdiploid tumors, seven MYCN-amplified tumors and nine chromosome 11q tumors, were analyzed. Details of tumor characteristics for NB samples are summarized in Supporting Information Table S2. An average of 2,636 methylated loci was detected in GN/GNB samples (range: 2,285–3,092), whereas the average number of methylated loci identified in the NB group was 2,729 (range: 1,462–3,721). Unsupervised hierarchical clustering of the methylation data revealed a distinct split between the GN/GNB and the NB group (Fig. 1). This approach, however, was unable to resolve differences in distinct genetic subtypes of NB (e.g., MYCN amplified versus 11q tumors).

thumbnail image

Figure 1. Epigenetic discrimination of neuroblastic tumors. (a) Dendrogram of unsupervised hierarchical clustering of primary neuroblastic tumors. Two main groups comprising of ganglioneuroma/ganlgioneuroblastoma and neuroblastoma samples are displayed. A mouse IgG-negative control MeDIP was performed and is displayed to the far left of the dendrogram. MYCN amplified, low stage and chromosome 11q deleted are represented with the abbreviations MNA, LS and 11q, respectively. (b) Examples of pair-wise comparisons based on log2 ratio data from GNB compared to GN, NB compared to a GN and two NB samples compared to each other. (c) Log 2 MeDIP enrichment ratios for four neuroblastic tumors within the FXR1 promoter region. Samples GNB29 and GN222 displayed a statistically significant enrichment for methylated sequence, which was not detected in the neuroblastoma samples (NB276 and NB283). The FXR1 transcript, associated CpG island and tiled sequence present on the microarray are displayed in the lower panels.

Download figure to PowerPoint

Supporting Information Table S3 lists the genes that were hypermethylated in ≥9/10 GN/GNB tumors and ≤2/18 NB or ≥15/18 NB and ≤1/10 GN/GNB tumors. In total, 259 loci (including gene promoters and CpG Islands) were hypermethylated in GN/GNB tumors relative to NB, whereas there were only 15 loci hypermethylated in the NB group relative to these other types of neuroblastic tumors. For genes hypermethylated in more than 90% of GN/GNB, GO analysis was performed using the DAVID Functional Annotation Tool.23 Functional enrichment was assessed using the Panther Biological Process and Molecular Function databases and corrected for multiple comparisons (Benjamini). Statistically significant terms identified included signaling molecules (p = 0.04) and cell communication (p = 0.0019).

Each gene that was hypermethylated in GN/GNB tumors but not in NB was assessed for associations with patient overall survival using a set of 88 NB tumors that were analyzed using Affymetrix u133p2 arrays.26 In total, nine genes (FXR1, EIF4G1, UBE2D3, SPTBN2, NME2, CSPG3, NFIB, CIRH1A and ELFN2) were identified in which high expression correlated with poor overall survival (p < 0.01; Bonferonni corrected for multiple comparisons; Supporting Information Table S3 and Supporting Information Fig. S3). Figure 1c illustrates differential methylation at the FXR1 promoter region in representative GN/GNB versus NB tumors. Examples of cancer-related genes that are hypermethylated exclusively in the GN/GNB include EIF4G1 and NME2. These results suggest that epigenetic silencing of these genes contributes toward a more favorable tumor subtype.

This approach also allowed identification of genes that display differential methylation patterns between the cell lines and the NB tumors. Supporting Information Table S4 lists 267 loci that were hypermethylated in all cell lines but in less than 2/18 primary NB tumors. Only five loci were hypermethylated in ≥ 15/18 primary tumors that were not hypermethylated in any cell lines.

Identification of large-scale regions of hypermethylation

To determine if large-scale regions of hypermethylation were present in neuroblastic tumors or cell lines similar to the regions identified in colon14 and breast16 cancer, we identified sites that had a minimum of four contiguously hypermethylated CpG islands or promoter regions and that were recurrent (minimum of two independent samples). In total, 70 chromosomal regions were identified that had four to a maximum of 11 contiguously hypermethylated sites. The size of these regions ranged from 12.5 to 590.5 kb, with a mean length of 96.4 kb. These large-scale regions of hypermethylation, along with the genes that map within them, are summarized in Figure 2 and Supporting Information Table S5. Two sites on chromosome 11p (ASCL2, TSPAN32 and CD81; CDKN1C, SLC22A18 and SLC22A18AS) and another on chromosome 18 (TCEB3C) contain genes known to be imprinted, as documented in the online Catalogue of Imprinted Genes (,27 thus further validating our approach. Another site included the HOXD family on chromosome 2, which was also identified as a region of large-scale DNA methylation in breast cancer,16 indicating that these regions transcend different forms of cancer.

thumbnail image

Figure 2. Identification of genome-wide methylation blocks in neuroblastoma. Regions of consecutive hypermethylation are highlighted in blue dots across chromosomes. Gene density across each chromosome is also depicted.

Download figure to PowerPoint

Interestingly, upon mapping the large-scale blocks of hypermethylation, a clear distribution bias toward the terminal ends of chromosomes was observed. In total, 14 and 22 blocks mapped to within regions <1 and <2 Mb from the telomeric ends of chromosomes, respectively. The p values associated with finding such numbers of blocks in these limited regions by random chance were p = 2.1e-14 and p = 5.0e-18, respectively. In addition, no distribution bias in the array coverage could account for such an overrepresentation of contiguous hypermethylation within terminal ends of chromosomes in NB.

Integrated DNA methylation and DNA copy number analysis

A well-established mechanism to inactivate tumor suppressor genes involves allelic loss of one copy and hypermethylation of the remaining copy. To investigate this, recurrent concomitant regions of DNA hypermethylation and DNA copy number loss were assessed by integrating methylation profiles of the NB primary tumors with aCGH analysis. In total, 29 genes were both hypermethylated and deleted in a minimum of three MYCN-amplified (MNA) tumor subtypes. Loss of 1p36 is often associated with amplification of MYCN, and as one might expect, considerable numbers of genes mapping to the 1p deletion region were identified in the MNA subgroup. Surprisingly, genes that were concomitantly hypermethylated and deleted on chromosome 19 were only identified from the MNA tumor subgroup. Moreover, the genes identified on chromosome 19 were clustered within a 1.5 Mb region, with one gene (PRG2) occurring within a block of contiguously hypermethylated sites (Supporting Information Table S5). For the 11q subtype, 51 genes were identified that were concomitantly deleted and hypermethylated in a minimum of three tumors. The majority of these genes was mapped to chromosome 11q, as well as chromosome 3p, which is commonly codeleted in 11q tumors.

To determine if the hypermethylation affecting the hemizygous allele might be functionally significant, we determined if DAC treatment resulted in transcriptional activation of these loci in cell lines. Thirty-seven of the 80 genes (46%) that were concomitantly hypermethylated and deleted in the primary tumors were reexpressed by greater than 1.5-fold in at least one cell line, as determined by microarray analysis and validated by qPCR on selected loci (summarized in Fig. 3a). A set of 88 NB tumors analyzed by u133p2 expression arrays indicated that low expression of four of these genes (DDX6, FBXO44, CDC42 and CAPN5) that were concomitantly deleted/hypermethylated was significantly associated with poor overall patient survival (p < 0.01; Bonferonni corrected for multiple comparisons) (Fig. 3b).

thumbnail image

Figure 3. Genes with alleles that were hypermethylated and deleted in primary tumors. (a) Chromosomal map of genes that are reexpressed in cell lines treated with DAC is displayed. Asterisks indicate genes that are significantly (p < 0.01) associated with poor overall survival. (b) Kaplan-Meier curves for concurrently deleted and hypermethylated genes that are associated with poor patient survival (Bonferroni corrected for multiple comparisons), when expressed at lower than median levels in NB tumors (n = 88).

Download figure to PowerPoint


  1. Top of page
  2. Abstract
  3. Material and Method
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information

To date, studies assessing methylation changes in NB have concentrated on a select number of candidate genes,5–7, 9, 10, 28, 29 but a genome-wide analysis of promoter region and/or CpG island DNA methylation has been lacking. We provide the first genome-wide assessment of DNA methylation patterns in NB, GN and GNB neuroblastic tumors. Our analysis indicates that recurrent large-scale blocks of contiguously hypermethylated promoter/CpG island sites (n = 70) occur in the tumors, and that there is a significant bias for these sites toward telomeric ends (31% of the blocks occurring <2 Mb from telomere). Chromosome 19 displayed the highest number of methylation blocks (eight blocks in total), which may be due to the greater gene density of this chromosome. The blocks mapping to imprinted regions (n = 3) or chromosome X (n = 4) are possibly related to normal developmental processes, which leaves a total of 63 methylation blocks that are potentially disease related. In contrast, DNA methylation analysis of three human chromosomes in normal tissue identified a significant correlation for methylation of regions only over distances ≤1,000 bp,30 suggesting that larger methylated regions may be disease specific.

A number of groups have also noted similar clusters of hypermethylated sites in other forms of cancer,14–18 but to the best of our knowledge, our report is the first demonstrating significant overrepresentation of methylated blocks toward the telomeres. In some cancer cells, particularly those that are telomerase active, subtelomeric repeats exhibit significant levels of methylation,31 and it is tempting to speculate that the mechanism leading to hypermethylation of these repeats can extend methylation further proximal. Interestingly, one of the hypermethylated blocks in NB included the HOXD family on chromosome 2, which was also identified as a region of large-scale DNA methylation in breast cancer16 and in brain tumors,17 indicating that at least one of these blocks occurs in multiple forms of cancer. As the characterization of these large regions of methylation has only been carried out in a very limited number of cancers, future studies should focus on determining the level of similarity of our 63 regions across different cancer types to elucidate further their relevance to cancer development.

NB, GN and GNB tumors differ greatly in their histopathological characteristics and in their clinical aggressiveness. Here, we demonstrate that large differences in the DNA methylation status of numerous loci also exist between the clinically unfavorable NB and more favorable GN/GNB tumor groups, allowing the stratification of both groups on the basis of DNA methylation profiling. The number of genes that are consistently hypermethylated in the GN/GNB group relative to NB is far greater (227 genes) than the opposite comparison (11 genes) even though there were approximately the same average number of hypermethylated sites per tumor in both categories. The reason for this bias is likely due to the extensive heterogeneity of the NB tumor group, consisting of at least three distinct genetic subtypes, MYCN amplified, 11q- and hyperdiploid. An analysis of a larger tumor cohort would likely identify additional hypermethylated sites specific to NB subtypes.

Of the genes that were hypermethylated in NB tumor group and not in the GN/GNB, the lower expression of one gene PCID2 correlated with poor survival in the 88 tumor set (p = 0.011).

This gene codes for a 399 amino acid protein containing a PCI domain and has no assigned specific function (Swiss-Prot:Q5JVF3), making it an interesting novel NB candidate gene for further follow-up. ATXN2 was also predominantly methylated in the NB tumors compared to the GN/GNB group. Wiedemeyer et al.32 have previously reported that the ectopic expression of wild-type ataxin-2 in SHEP Tet21N NB cells has a profound effect on the susceptibility of the cells to undergo apoptosis. Our analysis suggests that ATXN2 undergoes methylation in the more aggressive primary NB tumors, which possibly leads to an increased resistance to apoptosis.

Overexpression of NME2, a nucleoside diphosphate kinase that has been previously associated with an aggressive disease course in NB,33 was one of the genes identified as being hypermethylated in the GN/GNB tumors but not in NB. Overexpression of this gene was significantly associated with poor survival in our NB tumor cohort. Other genes that were selectively methylated in the GN/GNB group included EIF4G1 and TRIM32. High levels of EIF4G1 protein have been associated with the formation of tumor cell emboli, which promote invasion in breast cancer,34 and it has also been shown to be highly overexpressed in advanced squamous cell carcinoma.35 Interestingly, high expression of EIF4G1 in our tumor cohort was also associated with poor survival. TRIM32 expression is elevated in human head and neck cell carcinoma, and it has also been shown that overexpression of this gene leads to enhanced cell growth and transforming ability by promoting degradation of ABI2.36

It can be hypothesized that genes that are hypermethylated exclusively in GN/GNB contribute to the more benign phenotype of this group. An important caveat to this hypothesis is that the GN/GNB tumors have much larger amounts of Schwann cells, thus the methylation profiles could be representative of a cell type that might be of nonmalignant origin,37 although Mora et al.38 have suggested that these cells are of tumor origin. Nevertheless, the stromal cell component seems to contribute to a less aggressive phenotype,39 and the hypermethylation of specific genes within these cells could be contributing to this phenomenon. Further studies involving DNA methylation profiling of isolated cell types will have to be carried out to further address this issue.

DAC, commonly known as decitabine, causes reactivation of epigenetically silenced genes and is a widely reported method to validate potential gene targets to establish their silencing as a consequence of DNA methylation.40 Genome-wide screens for epigenetically silenced genes using expression arrays have been used previously to identify novel target genes in various cancer types.41 In our study, we observe a substantial number of genes that are reexpressed after DAC treatment; however, only 30% of these genes exhibited substantial promoter region methylation before DAC treatment. In addition, DAC treatment also led to greater than 5-fold downregulation of similar numbers of genes. Exposing cells to DAC clearly causes a rampant cascade of secondary events, resulting from activation of transcription factors or miRNAs, which makes interpretation of expression microarrays exceedingly complicated. In agreement with our results, most DAC studies based on small numbers of genes have also reported only partial overlap of genes that are hypermethylated and that are reexpressed in response to DAC.42, 43In vitro studies involving DAC are further complicated by the fact that there were significantly more peaks of hypermethylation detected in cell lines when compared to primary tumors, consistent with the observation of Smiraglia et al.44 on other types of cancer. Our results indicate that the DNA methylation status for specific genes in cell lines is not necessarily indicative of the situation in primary tumors. Nevertheless, a more precise understanding of the effects of DAC (decitabine) on gene expression is certainly warranted given that it is an FDA-approved cancer therapeutic.45

Our integrated analysis of genome-wide DNA methylation, aCGH and mRNA expression profiles has identified numerous new candidate loci for potential functional studies, and the value of a similar approach was also demonstrated in the analysis of osteosarcoma cell lines by Sadikovic et al.46 An interesting candidate identified from our study is RBP7 (CRBP IV), a cellular retinol-binding protein gene on chromosome 1p36.22, which was concomitantly deleted and hypermethylated in ∼50% of MYCN-amplified tumors and reexpressed in cell lines treated with DAC. This locus was also shown to be hypermethylated and reactivated by DAC treatment in nasopharyngeal carcinoma cell lines and might confer resistance to retinoic responsiveness.47 In addition, we show that promoter region hypermethylation of CDC42 is an alternative method of silencing this gene, which was previously shown to be downregulated by 1p deletion and repressed by MYCN binding.48 Ectopic upregulation of this locus causes NB cells to undergo differentiation.

In conclusion, through the integration of genomic, epigenetic and expression data, we have identified differentially methylated genes related to more aggressive neuroblastic tumor phenotypes and large-scale blocks of contiguously hypermethylated sites with an enrichment at telomeric regions. Consistent and widespread differences in methylation patterns between neuroblastic tumor subtypes indicate that epigenetic differences contribute to the phenotypic characteristics of these tumors. The identification of concurrent hypermethylation/deletion inactivation events in genes that are related to poor survival in NB warrants functional follow-up to assess their exact involvement in disease mechanism.


  1. Top of page
  2. Abstract
  3. Material and Method
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information

PGB is supported by a postdoctoral fellowship from the Irish Research Council for Science, Engineering and Technology.


  1. Top of page
  2. Abstract
  3. Material and Method
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information
  • 1
    Brodeur GM. Neuroblastoma: biological insights into a clinical enigma. Nat Rev Cancer 2003; 3: 20316.
  • 2
    Esteller M, Corn PG, Baylin SB, Herman JG. A gene hypermethylation profile of human cancer. Cancer Res 2001; 61: 32259.
  • 3
    Esteller M. CpG island hypermethylation and tumor suppressor genes: a booming present, a brighter future. Oncogene 2002; 21: 542740.
  • 4
    Alaminos M, Davalos V, Cheung NK, Gerald WL, Esteller M. Clustering of gene hypermethylation associated with clinical risk groups in neuroblastoma. J Natl Cancer Inst 2004; 96: 120819.
  • 5
    Astuti D, Agathanggelou A, Honorio S, Dallol A, Martinsson T, Kogner P, Cummins C, Neumann HP, Voutilainen R, Dahia P, Eng C, Maher ER, et al. RASSF1A promoter region CpG island hypermethylation in phaeochromocytomas and neuroblastoma tumours. Oncogene 2001; 20: 75737.
  • 6
    Eggert A, Grotzer MA, Zuzak TJ, Wiewrodt BR, Ikegaki N, Brodeur GM. Resistance to TRAIL-induced apoptosis in neuroblastoma cells correlates with a loss of caspase-8 expression. Med Pediatr Oncol 2000; 35: 6037.
  • 7
    van Noesel MM, van Bezouw S, Voute PA, Herman JG, Pieters R, Versteeg R. Clustering of hypermethylated genes in neuroblastoma. Genes Chromosomes Cancer 2003; 38: 22633.
  • 8
    Liu J, Bi G, Wen P, Yang W, Ren X, Tang T, Xie C, Dong W, Jiang G. Down-regulation of CD44 contributes to the differentiation of HL-60 cells induced by ATRA or HMBA. Cell Mol Immunol 2007; 4: 5963.
  • 9
    Yang Q, Zage P, Kagan D, Tian Y, Seshadri R, Salwen HR, Liu S, Chlenski A, Cohn SL. Association of epigenetic inactivation of RASSF1A with poor outcome in human neuroblastoma. Clin Cancer Res 2004; 10: 8493500.
  • 10
    Michalowski MB, de Fraipont F, Plantaz D, Michelland S, Combaret V, Favrot MC. Methylation of tumor-suppressor genes in neuroblastoma: the RASSF1A gene is almost always methylated in primary tumors. Pediatr Blood Cancer 2008; 50: 2932.
  • 11
    Weber M, Davies JJ, Wittig D, Oakeley EJ, Haase M, Lam WL, Schubeler D. Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells. Nat Genet 2005; 37: 85362.
  • 12
    Banelli B, Gelvi I, Di Vinci A, Scaruffi P, Casciano I, Allemanni G, Bonassi S, Tonini GP, Romani M. Distinct CpG methylation profiles characterize different clinical groups of neuroblastic tumors. Oncogene 2005; 24: 561928.
  • 13
    Abe M, Westermann F, Nakagawara A, Takato T, Schwab M, Ushijima T. Marked and independent prognostic significance of the CpG island methylator phenotype in neuroblastomas. Cancer Lett 2007; 247: 2538.
  • 14
    Frigola J, Song J, Stirzaker C, Hinshelwood RA, Peinado MA, Clark SJ. Epigenetic remodeling in colorectal cancer results in coordinate gene suppression across an entire chromosome band. Nat Genet 2006; 38: 5409.
  • 15
    Hitchins MP, Lin VA, Buckle A, Cheong K, Halani N, Ku S, Kwok CT, Packham D, Suter CM, Meagher A, Stirzaker C, Clark S, et al. Epigenetic inactivation of a cluster of genes flanking MLH1 in microsatellite-unstable colorectal cancer. Cancer Res 2007; 67: 910716.
  • 16
    Novak P, Jensen T, Oshiro MM, Watts GS, Kim CJ, Futscher BW. Agglomerative epigenetic aberrations are a common event in human breast cancer. Cancer Res 2008; 68: 861625.
  • 17
    Wu X, Rauch TA, Zhong X, Bennett WP, Latif F, Krex D, Pfeifer GP. CpG island hypermethylation in human astrocytomas. Cancer Res 2010; 70: 271827.
  • 18
    Dallosso AR, Hancock AL, Szemes M, Moorwood K, Chilukamarri L, Tsai HH, Sarkar A, Barasch J, Vuononvirta R, Jones C, Pritchard-Jones K, Royer-Pokora B, et al. Frequent long-range epigenetic silencing of protocadherin gene clusters on chromosome 5q31 in Wilms' tumor. PLoS Genet 2009; 5: e1000745.
  • 19
    Kron K, Pethe V, Briollais L, Sadikovic B, Ozcelik H, Sunderji A, Venkateswaran V, Pinthus J, Fleshner N, van der Kwast T, Bapat B. Discovery of novel hypermethylated genes in prostate cancer using genomic CpG island microarrays. PLoS One 2009; 4: e4830.
  • 20
    Selzer RR, Richmond TA, Pofahl NJ, Green RD, Eis PS, Nair P, Brothman AR, Stallings RL. Analysis of chromosome breakpoints in neuroblastoma at sub-kilobase resolution using fine-tiling oligonucleotide array CGH. Genes Chromosomes Cancer 2005; 44: 30519.
  • 21
    Murphy DM, Buckley PG, Bryan K, Das S, Alcock L, Foley NH, Prenter S, Bray I, Watters KM, Higgins D, Stallings RL. Global MYCN transcription factor binding analysis in neuroblastoma reveals association with distinct E-Box motifs and regions of DNA hypermethylation. PLoS One 2009; 4: e8154.
  • 22
    Weber M, Hellmann I, Stadler MB, Ramos L, Paabo S, Rebhan M, Schubeler D. Distribution, silencing potential and evolutionary impact of promoter DNA methylation in the human genome. Nat Genet 2007; 39: 45766.
  • 23
    Dennis G, Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA. DAVID: database for annotation, visualization, and integrated discovery. Genome Biol 2003; 4: P3.
  • 24
    Kin T, Ono Y. Idiographica: a general-purpose web application to build idiograms on-demand for human, mouse and rat. Bioinformatics 2007; 23: 2945.
  • 25
    Bock C, Reither S, Mikeska T, Paulsen M, Walter J, Lengauer T. BiQ analyzer: visualization and quality control for DNA methylation data from bisulfite sequencing. Bioinformatics 2005; 21: 40678.
  • 26
    Geerts D, Koster J, Albert D, Koomoa DL, Feith DJ, Pegg AE, Volckmann R, Caron H, Versteeg R, Bachmann AS. The polyamine metabolism genes ornithine decarboxylase and antizyme 2 predict aggressive behavior in neuroblastomas with and without MYCN amplification. Int J Cancer 2010; 126: 201224.
  • 27
    Morison IM, Ramsay JP, Spencer HG. A census of mammalian imprinting. Trends Genet 2005; 21: 45765.
  • 28
    Michalowski MB, de Fraipont F, Michelland S, Entz-Werle N, Grill J, Pasquier B, Favrot MC, Plantaz D. Methylation of RASSF1A and TRAIL pathway-related genes is frequent in childhood intracranial ependymomas and benign choroid plexus papilloma. Cancer Genet Cytogenet 2006; 166: 7481.
  • 29
    Nair PN, McArdle L, Cornell J, Cohn SL, Stallings RL. High-resolution analysis of 3p deletion in neuroblastoma and differential methylation of the SEMA3B tumor suppressor gene. Cancer Genet Cytogenet 2007; 174: 10010.
  • 30
    Eckhardt F, Lewin J, Cortese R, Rakyan VK, Attwood J, Burger M, Burton J, Cox TV, Davies R, Down TA, Haefliger C, Horton R, et al. DNA methylation profiling of human chromosomes 6, 20 and 22. Nat Genet 2006; 38: 137885.
  • 31
    Ng LJ, Cropley JE, Pickett HA, Reddel RR, Suter CM. Telomerase activity is associated with an increase in DNA methylation at the proximal subtelomere and a reduction in telomeric transcription. Nucleic Acids Res 2009; 37: 11529.
  • 32
    Wiedemeyer R, Westermann F, Wittke I, Nowock J, Schwab M. Ataxin-2 promotes apoptosis of human neuroblastoma cells. Oncogene 2003; 22: 40111.
  • 33
    Leone A, Seeger RC, Hong CM, Hu YY, Arboleda MJ, Brodeur GM, Stram D, Slamon DJ, Steeg PS. Evidence for nm23 RNA overexpression, DNA amplification and mutation in aggressive childhood neuroblastomas. Oncogene 1993; 8: 85565.
  • 34
    Silvera D, Arju R, Darvishian F, Levine PH, Zolfaghari L, Goldberg J, Hochman T, Formenti SC, Schneider RJ. Essential role for eIF4GI overexpression in the pathogenesis of inflammatory breast cancer. Nat Cell Biol 2009; 11: 9038.
  • 35
    Comtesse N, Keller A, Diesinger I, Bauer C, Kayser K, Huwer H, Lenhof HP, Meese E. Frequent overexpression of the genes FXR1, CLAPM1 and EIF4G located on amplicon 3q26-27 in squamous cell carcinoma of the lung. Int J Cancer 2007; 120: 253844.
  • 36
    Kano S, Miyajima N, Fukuda S, Hatakeyama S. Tripartite motif protein 32 facilitates cell growth and migration via degradation of Abl-interactor 2. Cancer Res 2008; 68: 557280.
  • 37
    Ambros IM, Zellner A, Stock C, Amann G, Gadner H, Ambros PF. Proof of the reactive nature of the Schwann cell in neuroblastoma and its clinical implications. Prog Clin Biol Res 1994; 385: 3317.
  • 38
    Mora J, Cheung NK, Juan G, Illei P, Cheung I, Akram M, Chi S, Ladanyi M, Cordon-Cardo C, Gerald WL. Neuroblastic and Schwannian stromal cells of neuroblastoma are derived from a tumoral progenitor cell. Cancer Res 2001; 61: 68928.
  • 39
    Chlenski A, Liu S, Crawford SE, Volpert OV, DeVries GH, Evangelista A, Yang Q, Salwen HR, Farrer R, Bray J, Cohn SL. SPARC is a key Schwannian-derived inhibitor controlling neuroblastoma tumor angiogenesis. Cancer Res 2002; 62: 735763.
  • 40
    Cairns P. 5'-azacytidine expression arrays. Methods Mol Biol 2009; 507: 16574.
  • 41
    Ibanez de Caceres I, Dulaimi E, Hoffman AM, Al-Saleem T, Uzzo RG, Cairns P. Identification of novel target genes by an epigenetic reactivation screen of renal cancer. Cancer Res 2006; 66: 50218.
  • 42
    Fujikane T, Nishikawa N, Toyota M, Suzuki H, Nojima M, Maruyama R, Ashida M, Ohe-Toyota M, Kai M, Nishidate T, Sasaki Y, Ohmura T, et al. Genomic screening for genes upregulated by demethylation revealed novel targets of epigenetic silencing in breast cancer. Breast Cancer Res Treat 2010; 122: 699710.
  • 43
    Ostrow KL, Park HL, Hoque MO, Kim MS, Liu J, Argani P, Westra W, Van Criekinge W, Sidransky D. Pharmacologic unmasking of epigenetically silenced genes in breast cancer. Clin Cancer Res 2009; 15: 118491.
  • 44
    Smiraglia DJ, Rush LJ, Fruhwald MC, Dai Z, Held WA, Costello JF, Lang JC, Eng C, Li B, Wright FA, Caligiuri MA, Plass C. Excessive CpG island hypermethylation in cancer cell lines versus primary human malignancies. Hum Mol Genet 2001; 10: 141319.
  • 45
    Issa JP, Kantarjian HM. Targeting DNA methylation. Clin Cancer Res 2009; 15: 393846.
  • 46
    Sadikovic B, Yoshimoto M, Al-Romaih K, Maire G, Zielenska M, Squire JA. In vitro analysis of integrated global high-resolution DNA methylation profiling with genomic imbalance and gene expression in osteosarcoma. PLoS One 2008; 3: e2834.
  • 47
    Kwong J, Lo KW, Chow LS, To KF, Choy KW, Chan FL, Mok SC, Huang DP. Epigenetic silencing of cellular retinol-binding proteins in nasopharyngeal carcinoma. Neoplasia 2005; 7: 6774.
  • 48
    Valentijn LJ, Koppen A, van Asperen R, Root HA, Haneveld F, Versteeg R. Inhibition of a new differentiation pathway in neuroblastoma by copy number defects of N-myc, Cdc42, and nm23 genes. Cancer Res 2005; 65: 313645.

Supporting Information

  1. Top of page
  2. Abstract
  3. Material and Method
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information

Additional Supporting Information may be found in the online version of this article.

IJC_25584_sm_suppinfofigs1.tif2230KSupporting Information Figure 1
IJC_25584_sm_suppinfofigs2.tif146KSupporting Information Figure 2
IJC_25584_sm_suppinfofigs3.tif1229KSupporting Information Figure 3
IJC_25584_sm_suppinfotable1.doc56KSupplementary Table S1. Bi-sulphite sequencing of selected loci
IJC_25584_sm_suppinfotable2.doc49KSupporting Information Table S2. Summary of neuroblastoma tumor characteristics
IJC_25584_sm_suppinfotable3.doc236KSupporting Information Table S3. Loci differentially methylated between GN/GNB and NB
IJC_25584_sm_suppinfotable4.doc235KSupporting Information Table S4. Loci differentially methylated between NB cell lines and primary NB tumors
IJC_25584_sm_suppinfotable5.doc111KSupporting Information Table S5. Blocks of consecutive methylated sites across the genome

Please note: Wiley Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.