Aberrant methylation of candidate tumor suppressor genes in neuroblastoma

Aberrant methylation of candidate tumor suppressor genes in neuroblastoma

Cancer Letters 273 (2009) 336–346 Contents lists available at ScienceDirect Cancer Letters journal homepage: www.elsevier.com/locate/canlet Aberran...

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Cancer Letters 273 (2009) 336–346

Contents lists available at ScienceDirect

Cancer Letters journal homepage: www.elsevier.com/locate/canlet

Aberrant methylation of candidate tumor suppressor genes in neuroblastoma Jasmien Hoebeeck a, Evi Michels a, Filip Pattyn a, Valérie Combaret b, Joëlle Vermeulen a, Nurten Yigit a, Claire Hoyoux c, Geneviève Laureys d, Anne De Paepe a, Frank Speleman a, Jo Vandesompele a,* a

Center for Medical Genetics, Ghent University Hospital, Ghent, Belgium Molecular Oncology Unit, Centre Léon Bérard, Lyon, France c Department of Pediatric Oncology, Hôpital de la Citadelle, Liège, Belgium d Department of Pediatric Hematology and Oncology, Ghent University Hospital, Ghent, Belgium b

a r t i c l e

i n f o

Article history: Received 19 June 2008 Received in revised form 19 July 2008 Accepted 18 August 2008

Keywords: Neuroblastoma Methylation MSP

a b s t r a c t CpG island hypermethylation has been recognized as an alternative mechanism for tumor suppressor gene inactivation. In this study, we performed methylation-specific PCR (MSP) to investigate the methylation status of 10 selected tumor suppressor genes in neuroblastoma. Seven of the investigated genes (CD44, RASSF1A, CASP8, PTEN, ZMYND10, CDH1, PRDM2) showed high frequencies (P30%) of methylation in 33 neuroblastoma cell lines. In 42 primary neuroblastoma tumors, the frequencies of methylation were 69%, CD44; 71%, RASSF1A; 56%, CASP8; 25%, PTEN; 15%, ZMYND10; 8%, CDH1; and 0%, PRDM2. Furthermore, CASP8 and CDH1 hypermethylation was significantly associated with poor event-free survival. Meta-analysis of 115 neuroblastoma tumors demonstrated a significant correlation between CASP8 methylation and MYCN amplification. In addition, there was a correlation between ZMYND10 methylation and MYCN amplification. The MSP data, together with optimized mRNA re-expression experiments (in terms of concentration and time of treatment and use of proper reference genes) further strengthen the notion that epigenetic alterations could play a significant role in NB oncogenesis. This study thus warrants the need for a global profiling of gene promoter hypermethylation to identify genome-wide aberrantly methylated genes in order to further understand neuroblastoma pathogenesis and to identify prognostic methylation markers. Ó 2008 Elsevier Ireland Ltd. All rights reserved.

1. Introduction Neuroblastoma is a childhood tumor originating from sympathetic nervous system cells. The molecular basis of neuroblastoma development and progression is still poorly understood. The best-characterized genetic alterations include amplification of the proto-oncogene MYCN, gain of chromosome arm 17q and losses of 1p, 3p, and 11q. Based on these genetic aberrations, neuroblastoma is currently classified into three major genetic subgroups * Corresponding author. Tel.: +32 9 3325187; fax: +32 9 3326549. E-mail address: [email protected] (J. Vandesompele). 0304-3835/$ - see front matter Ó 2008 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.canlet.2008.08.019

[1–3]. Many studies focused on the identification of culprit tumor suppressor genes located within regions of recurrent loss in order to gain insight into the molecular defects governing neuroblastoma development. Thus far, no bona fide tumor suppressor genes with a classic 2-hit inactivation have been identified. Other strategies including gene expression profiling also remained unsuccessful with respect to the identification of genes and pathways implicated in neuroblastoma. As presently, epigenetic modifications, such as DNA methylation or chromatin modifications, are considered as an integral part of the process of malignant transformation and progression of cancer cells [4–7] further studies of the neuroblastoma

J. Hoebeeck et al. / Cancer Letters 273 (2009) 336–346

epigenome are needed. In human cancer, many genes implicated in pathways controlling growth, genomic stability, and survival were reported to be silenced by promoter hypermethylation. Of fundamental and possible also therapeutic interest is the fact that genes silenced by DNA methylation can be reactivated by treatment with demethylating agents, such as 5-aza-20 -deoxycytidine (DAC). The methyltransferase inhibitor DAC is a cytosine analogue and is incorporated into DNA during replication. Incorporation triggers covalent binding and inhibition of DNA methyltransferase (DNMT), leading to genome wide demethylation. In addition to CpG hypermethylation based gene silencing, compact and inactive chromatin characterized by the presence of hypoacetylated histones and methylation of specific lysines residues also mediates gene silencing. The level of acetylation of histones depends on the activity of histone deacetylase (HDAC) and histone acetyltransferase enzymes. The HDAC inhibitor trichostatin A can be used to reactivate genes that were silenced due to condensed chromatin. In order to further asses the role of epigenetic modifications in neuroblastoma, we selected 10 genes for analysis of promoter hypermethylation in primary neuroblastoma tumors and cell lines using methylation-specific PCR (MSP). In a second step, the effect of the methylation status on gene expression was investigated through measurement of the mRNA expression before and after treatment with a demethylating agent and a histone deacetylase inhibitor in 22 NB cell lines. To achieve this, we optimized the workflow of a re-expression experiment by determining optimal concentration and duration of treatment and by validating the reference genes for proper normalization of the gene expression data. 2. Materials and methods 2.1. Neuroblastoma tumors and cell lines Forty-two primary neuroblastoma tumor samples (at least 60% tumor cells) were collected prior to therapy at the Ghent University Hospital (Ghent, Belgium). The patients were randomly selected. Median age at diagnosis was 17.8 months and median follow-up time for survivors (n = 24) was 76.4 months. Twelve tumors were classified as stage 1, four as stage 2, six as stage 3, 18 as stage 4, and two as stage 4S according to the International Neuroblastoma Staging System [8]. An overview of the clinical and biological parameters of the patients is given in Table 1. In addition, 33 well-characterized neuroblastoma cell lines were included in this study [2,9– 13]. DNA was isolated using the QIAamp DNA mini kit (Qiagen). 2.2. Methylation-specific PCR (MSP) MSP was performed according to Herman et al. [14], with minor modifications. Briefly, 1 lg of genomic DNA was denaturated by NaOH in a volume of 20 ll (final concentration 0.4 M). Five microliters of 30 mM hydroquinone and 156 ll of 5 M sodium bisulfite (pH 5.0) and 5 ll water were added and incubated at 50 °C for 16 h. DNA samples

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were purified using Microcon-100 columns (Millipore) and eluted into 50 ll water. Modification was completed by NaOH (final concentration 0.3 M) treatment for 5 min at room temperature, followed by precipitation. Bisulfite modified DNA was resuspended in 30 ll water. MSP was performed using primers shown in Table 2. Newly developed primers were designed using the web-based MSP design software MethPrimer (http://www.urogene.org/ methprimer) [15], followed by in silico specificity assessment using our in-house-developed methBLAST software (http://medgen.ugent.be/methblast/, [16]). All primers are available in the public methPrimerDB database (http:// medgen.ugent.be/methprimerdb/, [16]) (see Table 2). PCR was carried out in a 50 ll reaction containing 50 ng bisulfite modified DNA, 1 Platinum Taq PCR buffer (Invitrogen), 6 mM MgCl2, 200 lM of each dNTP, 1.25 U Platinum Taq polymerase (Invitrogen), 300 nM of each primer. Three percent of DMSO was added to increase specificity for the MSP reactions of CD44, PTEN, ZMYND10, PRDM2, ROBO1 and TP73. The PCR cycling conditions consisted of an initial enzyme activation step at 93 °C for 4 min, followed by 35– 40 cycles of denaturation of 30 s at 93 °C, annealing of 30 s at annealing temperature shown in Table 2, extension for 30 s at 72 °C, followed by a final extension step of 4 min at 72 °C. For PCR detection of RASSF1A (both methylated (M) and unmethylated (U) alleles) and for the methylated alleles of PTEN, a touchdown protocol was used. The PCR cycling conditions consisted of an initial enzyme activation step at 93 °C for 4 min, followed by six (RASSF1A U) or seven (RASSF1A M and PTEN M) touchdown cycles of 20 s at 93 °C, 40 s at temperature X °C shown in Table 2, extension for 20 s at 72 °C, X-1 °C per cycle and then 35 cycles of 20 s at 93 °C, 30 s at X-6 °C (RASSF1A U) or X-7 °C (RASSF1A M and PTEN M) and 30 s at 72 °C and finally, an extension step of 4 min at 72 °C. PCR products were loaded onto a 2% TBE agarose gel, stained with ethidium bromide, and visualized under UV illumination. SssI methylase (New England Biolabs, Beverly, MA, USA) treated DNA (M-DNA), following the manufacturer’s instructions and normal human genomic DNA were used as a positive and negative control for methylation after bisulfite modification, respectively. Samples were scored as methylated when an amplification product was clearly visible using the primers specific for the methylated allele. If neither the methylated nor the unmethylated product was present, results were excluded. 2.3. Statistical analysis of MSP data Univariate survival analysis was performed with the Kaplan–Meier method and log-rank statistic using SPSS 15.0 software to estimate overall (OS) and event-free survival (EFS). Event-free survival was defined as the time between initial diagnosis and relapse or death of disease, or time between diagnosis and last follow-up if no event had occurred. The relationship between the methylation status and clinico-genetic parameters was determined using Fisher’s exact test. Power analysis was performed with the STPLAN software version 4.3 (Houston, TX: Department of Biostatistics, The University of Texas M.D. Anderson Cancer Center).

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Table 1 Clinical characteristics of neuroblastoma patients and genetic alterations and stage of their tumor Sample

Stage

1pa

3pa

11qa

MYCNb

Agec

Statusd

OSe

Eventf

EFSg

W91–239 T97–96 01T130 T99–84 T98–47 T94–64 T96–82 T98–33 T98–17 T99–9 T98–104 01T149 T97–26 01T198 T94–40 01T146 W90–160 T99–119 T00–121 01T96 T98–24 T96–21 T00–35 T96–81 T95–60 T01–25 01T28 01T143 01T94 T95–24 W91–5 T01–31 SL–32 T95–4 W92–145 T99–13 W93–64 T97–10 SL–28 T94–25 01T208 01T15

1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4S 4S

No No No No No No No No No No No No No No No No Loss Loss No No No No Loss No No Loss Loss No No Loss Loss No Loss No – No Loss Loss – No Loss No

No No No No No No No No No No No Loss No Loss Loss No No No No Loss No No No Loss No No No No No Loss No No No No – No No No – No No No

No No No No No No No No – – – Loss No No – No No No No No No No No Loss No No No No No Loss No No Loss No – No No No – No No No

No No No No No No No No No No No No No No No No Amp Amp No No No No Amp No No Amp Amp No Amp Amp No No No No No No amp No – No No No

6.6 18.4 1.6 1.9 65.0 23.2 10.0 3.0 46.6 5.7 0.9 8.0 14.1 0.1 10.4 15.5 20.1 22.4 7.5 43.8 17.3 7.5 11.7 27.67 49.9 29.5 25.4 1.3 26.4 57.5 113.9 31.5 62.9 56.7 100.7 54.6 34.0 16.9 39.6 3.3 8.5 3.7

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 DOT 0 1 1 1 1 1 0 0 1 1 0 DOT 1 1 0 1 1 1 1 0 0

159.4 104.2 31.4 70.7 103.2 133.8 113.9 97.0 78.8 84.6 74.1 52.2 112.3 53.1 159.4 61.3 21.0 17.8 69.0 64.0 – 107.6 13.6 31.8 18.7 5.5 9.9 65.8 66.1 24.8 27.0 11.7 – 21.5 19.2 87.2 28.0 9.4 23.6 71.2 51.5 72.6

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 1 1 1 1 1 0 0 1 1 1 – 1 1 0 1 1 1 1 0 0

23.2 104.2 31.4 70.7 103.2 133.8 113.9 97.0 78.8 84.6 74.1 52.2 112.3 53.1 159.4 61.3 14.4 15.1 34.2 64.0 6.4 107.6 9.4 29.3 13.1 – – 65.8 66.1 9.2 22.8 8.9 – 18.2 – 87.2 25.7 – 19.0 51.6 51.5 72.6

a b c d e f g

No, no loss, –, not tested or not informative. Amp, amplification; no, no amplification; –, not tested. age at diagnosis (in months). 0, alive; 1, dead; DOT, death of toxicity. Overall survival (in months from the date of diagnosis till disease-related death or last follow-up). 1, tumor progression, relapse or death; 0, no event; –, not available. Event-free survival (months) (see Section 2); –, not available.

2.4. 5-Aza-20 -deoxycytine and trichostatin A drug treatment and RNA isolation Neuroblastoma cell lines were grown in RPMI 1640 growth medium (Invitrogen) supplemented with 10% FCS at 37 °C and 5% CO2. To select the optimal treatment conditions for demethylation, three neuroblastoma cell lines (NGP, SK-N-AS, and IMR-32) were grown in the presence of different concentrations of 5-aza-20 -deoxycytine (DAC; 0, 1, 3, and 10 lM) during different durations of treatment (3, 5, and 7 days). The medium was changed every 2 days, along with supplementing fresh DAC. For the final study to assess reactivation of gene expression, 22 NB cell lines were plated at day 0 and treated 24 h later with either 3 lM DAC (Sigma) for 3 days, or with 3 lM DAC for 3 days

and by 500 nM trichostatin A (TSA) for the last 12 h. Medium was changed on day 2 with addition of new drugs. In parallel, untreated controls were also prepared. Cells (treated and untreated) were harvested and RNA was extracted for real-time quantitative PCR. RNA extraction was performed with the RNeasy Mini kit (Qiagen) according to the manufacturer, accompanied by RNase free DNase treatment on column (Qiagen). 2.5. Real-time quantitative PCR (qPCR) Following an additional RNAse free DNase treatment, cDNA was synthesized using the iScript cDNA synthesis kit from Bio-Rad (Hercules, CA, USA) (http://medgen.ugent.be/CMGG/protocols). mRNA expression was

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J. Hoebeeck et al. / Cancer Letters 273 (2009) 336–346 Table 2 Primer sequences and PCR characteristics for methylation analysis Gene

Locus

IDa

Forward primer (50 –30 )

Reverse primer (50 –30 )

Size

Ta (°C)

Reference

TP73

1p36

4

M U

GGACGTAGCGAAATCGGGGTTC AGGGGATGTAGTGAAATTGGGGTTT

M U

ACCCCGAACATCGACGTCCG ATCACAACCCCAAACATCAACATCCA

60 69

68 59

[50]

PRMD2

1p36.2

15

M U

GTGGTGGTTATTGGGCGACGGC TGGTGGTTATTGGGTGATGGT

M U

GCTATTTCGCCGACCCCGACG ACTATTTCACCAACCCCAAGA

176 169

69 58

[51]

CASP8

2q33-34

2

M U

TAGGGGATTCGGACATTGCGA TAGGGGATTTGGAGATTGTGA

M U

CGTATATCTACATTCGAAACGA CCATATATATCTACATTCAAAACAA

321 322

61 60

[23]

ROBO1

3p14.2

6

M U

TTTTTCGGAAATTTTGGGGTC TTTGGAAATTTTGGGGTTGG

M U

ATTTTCAAAACAATTACTCGTCGAC ATTTTCAAAACAATTACTCATCAAC

110 113

63 63



ZMYND10

3p21.3

11

M U

TTCGTGGGTTATAGTTCGAGAAAGCG TTTGTGGGTTATAGTTTGAGAAAGTG

M U

AACGAATTAACCGCGCCTACGC AACAAATTAACCACACCTACAC

156 156

62 56

[24]

RASSF1A

3p21.3

10

M U

CGAGAGCGCGTTTAGTTTCGTT GGGGGTTTTGTGAGAGTGTGTTT

M U

CGATTAAACCCGTACTTCGCTAA CCCAATTAAACCCATACTTCACTAA

194 204

65 64



PTEN

10q23.31

5

M U

GTTTGGGGATTTTTTTTTCGC TATTAGTTTGGGGATTTTTTTTTTGT

M U

AACCCTTCCTACGCCGCG CCCAACCCTTCCTACACCACA

200 200

68 60

[52]

CD44

11p13

9

M U

TTTTCGAGGTAGTTTTATTGTTTAGC TTGAGGTAGTTTTATTGTTTAGTGG

M U

AAACTTATCCATAATATCCGAAACG AAACTTATCCATAATATCCAAAACAAA

158 155

60 58



CDH1

16q21.1

7

M U

GGTGAATTTTTAGTTAATTAGCGGTAC GGTAGGTGAATTTTTAGTTAATTAGTGGTA

M U

CATAACTAACCGAAAACGCCG ACCCATAACTAACCAAAAACACCA

204 211

64 61

[53]

DCC

18q21.3

3

M U

CGTTGTTCGCGATTTTTGGTTTC GTTGTTGTTGTTTGTGATTTTTGGTTTT

M U

ACCGATTACTTAAAAATACGCG CCACTTACCAATTACTTAAAAATACACA

134 145

66.5 64

[31]

a

methPrimerDB ID (http://medgen.ugent.be/methprimerdb/), U, unmethylated; M, methylated; Ta, annealing temperature.

examined by an optimized two-step real-time quantitative PCR assay [17]. The primers were designed with PrimerExpress 2.0 software (Applied Biosystems, Foster City, CA, USA) and are available in the public RTPrimerDB database [18–19] (http://medgen.ugent.be/rtprimerdb/) (gene (RTPrimerDB-ID): HPRT1 (5), YWHAZ (7), GAPDH (3), and HMBS (4), SDHA (7), CASP8 (86), PTEN (363), ZMYND10 (3490), DCC (3491), RASSF1A (3489)). Reactions were run on an iCycler iQ (Bio-Rad). The results were imported into the relative quantification software qBase (http://medgen.ugent.be/qbase/) [20] for further analysis. The transcription levels were normalized using the geometric mean of four stably expressed reference genes (HPRT1, YWHAZ, GAPDH, and HMBS) [17]. A stably expressed reference gene is a control gene that is not variable in expression in the type of cells under investigation and in response to the experimental treatment, as determined by the geNorm algorithm [17]. 3. Results and discussion 3.1. Methylation status in neuroblastoma We selected 10 genes (PRDM2, TP73, CASP8, ZMYND10, RASSF1A, ROBO1, PTEN, CD44, CDH1, and DCC) that previously have been described for undergoing methylationassociated silencing in cancer or that were of interest in neuroblastoma based on their chromosomal location. Five of these genes are located in recurrent regions of loss in neuroblastoma, i.e., PRDM2 and TP73 on 1p, RASSF1A and ZMYND10 on 3p, and DCC on 18q. At the time of study design, silencing through promoter hypermethylation of four genes (i.e., RASSF1A, CASP8, CDH1, and TP73) had been previously investigated in neuroblastoma [21–23]. These

genes were included in order to validate the reported frequencies of methylation in our patient and cell line cohort. Meanwhile, additional studies investigating the methylation status of PRDM2, ZMYND10, PTEN, and CD44 in NB have been published [24–27]. To our knowledge, this report is the first study analyzing ROBO1 and DCC methylation in NB. The ROBO1 gene is located on 3p12 and encodes an axon guidance receptor, a member of the NCAM cell adhesion family receptors. Aberrant methylation has been described in breast, cervical and colorectal cancer [28–30]. The DCC gene (18q21) is involved in neuronal development and absent or reduced mRNA and protein expression has been reported in both neuroblastoma cell lines and primary tumors. However, mutation detection frequencies of DCC are low in NB suggesting that other mechanisms for inactivation in addition to deletion may play a role. In other tumor types such as gastric [31] and breast [32] cancer, oral squamous cell carcinoma [33] and acute lymphoblastic leukemia [34], abnormal DCC methylation has been described. We used MSP to establish the frequency of methylation for these 10 genes in neuroblastoma tumors and cell lines. An overview of the results is given in Fig. 1 and Table 3. Overall, higher methylation frequencies were observed in cell lines compared to primary tumors, in keeping with other reports [35–37]. This could be explained by the fact that neuroblastoma cell lines are mainly derived from more aggressive tumors and this could reflect in a higher number of methylated genes. However, we cannot exclude additional in vitro effects. Nevertheless, genes that show high methylation frequencies in neuroblastoma cell lines are also detected to be methylated in primary tumors. All but five neuroblastoma tumors (88%) presented with methylation in at least one of the investigated regions. Se-

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Fig. 1. Methylation status of 10 genes in a panel of 33 neuroblastoma cell lines. Abbreviations are as follows: (a) amp, amplification; no, no amplification; (b) no, no loss; –, not tested.

Table 3 Summary of methylation-specific PCR results in neuroblastoma tumors Gene

# Samples

U

M

% Me

ROBO1 PRMD2 TP73 DCC CDH1 ZMYND10 PTEN CASP8 RASSF1A CD44

41 41 42 41 40 40 20 36 41 32

41 41 42 38 37 34 15 16 12 10

0 0 0 3 3 6 5 20 29 22

0 0 0 7 8 15 25 56 71 69

U, unmethylated, M, methylated; % Me, percentage of methylated samples for each gene.

ven of 10 genes showed methylation in at least one of the investigated tumor samples (DCC, CDH1, ZMYND10, PTEN, CASP8, RASSF1A, and CD44), while no methylation was found for the remaining three genes. In tumors, a high frequency of methylation was found for CD44 (69%), CASP8 (56%), and RASSF1A (71%). Intermediate to low methylation frequencies were found for PTEN (25%), ZMYND10 (15%), DCC (7%), and CDH1 (8%). While PRDM2 and ROBO1 were methylated in 30% and 3% of cell lines, respectively, no methylation could be detected in primary tumor samples. TP73 showed no methylation in neuroblastoma tumors or cell lines. Representative examples of the MSP analysis are shown in Fig. 2. For many tumor samples, we detect

both unmethylated and methylated alleles in the same investigated locus. These mixed results can be explained either by heterogeneity of methylation and by contamination of normal cells. In general, these methylation frequencies match closely with previously reported findings. Similar methylation frequencies in both cell lines and tumors were found for CDH1 and RASSF1A [22,38–43]. In contrast, large differences were found for CD44 (tumors: 69% vs. 0%; cell lines: 90% vs. 33%) and for PTEN (tumors: 25% vs. 0%; cell lines: 88% vs. 0%) [26–27,44]. Lower methylation frequencies were observed in tumors for ZMYND10 (15% vs. 34–54%) and PRDM2 (0% vs. 25%) while comparable results were described in cell lines [24–25,40–41,44]. For CASP8, lower frequencies (67–92% vs. 97%) were reported in general in neuroblastoma cell lines [23,25,38,40–41,43–46]. For some genes, significant lower or higher methylation frequencies were reported in cell lines or tumors. This can possibly be explained by use of different primer pairs and targeted promoter regions that were investigated (e.g., PTEN, Supplementary Figure 1 created using methGraph (http:// medgen.ugent.be/methgraph/; Lefever et al., in preparation), or by use of a different methylation analysis method. 3.2. Methylation status and clinico-genetic parameters Univariate Kaplan–Meier analysis with log rank testing demonstrated that MYCN amplification, 1p deletion, age at diagnosis >12 months, and high-stage tumor (stages 3

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Fig. 2. Representative results of the MSP analysis of genes CASP8, PRDM2, and PTEN. Abbreviations are as follows: U, unmethylated allele; M, methylated allele; 50 bp, 50bp DNA ladder; HgDNA, human genomic DNA; M-DNA, SssI treated positive control for methylation; NTC, no template control; T, tumor sample; C, cell line.

and 4) correlate significantly (P < 0.01) with poor survival. Confirmation of predictive power of those known markers demonstrates that our patient cohort is a representative sample of the neuroblastoma patient population. Next, we examined the relationship between the methylation status of genes and clinico-genetic parameters, including stage, age at diagnosis >12 months, overall and event-free sur-

vival time, survival status, 1p, 3p and 11q loss and MYCN amplification (Table 4). Kaplan–Meier survival analysis showed that hypermethylation of CASP8 and CDH1 was correlated with poor event-free survival (log rank P = 0.016 and P = 0.038, respectively). The methylation status of the other genes was not correlated with overall or event-free survival. Furthermore, there appears to be an association

Table 4 Cross table: relationship between the methylation status of genes and clinico-genetic parameters RASSF1A

CD44

CASP8

PTEN

UMa

Mb

Pc

UM

M

P

UM

M

P

Tumor stage 1, 2, 4S 3, 4

5 7

13 16

1

5 5

9 13

0.71

10 6

6 14

Age at diagnosis <1 year >1 year

4 8

13 16

0.73

4 6

8 14

1

9 7

Status Alive Dead

6 5

18 10

0.72

5 4

12 9

1

Event Yes No

6 6

16 12

0.74

5 4

11 11

MYCN gene Single copy Amplification

9 2

24 5

1

9 1

Chromosome 1p Normal Deletion

9 2

20 8

0.69

Chromosome 3p Normal Deletion

9 2

24 4

Chromosome 11q Normal 10 Deletion 1

22 3

a b c

ZMYND10

CDH1

DCC

UM

M

P

UM

M

P

UM

M

P

UM

M

P

0.091

7 8

2 3

1

16 17

1 5

0.21

16 21

0 3

0.26

16 22

1 2

1

5 15

0.087

7 8

1 4

0.60

15 18

1 5

0.37

14 23

1 2

1

14 24

2 1

0.55

12 3

9 10

0.079

9 4

2 3

0.33

21 11

2 3

0.35

21 14

1 2

0.56

22 15

2 1

1

1

12 3

8 12

0.037

7 7

2 3

1

19 13

2 4

0.38

21 15

0 3

0.89

20 17

1 2

0.60

16 5

0.63

14 2

16 3

1

15 0

4 1

0.25

28 4

3 3

0.063

29 8

2 0

1

30 7

2 1

0.50

6 4

15 6

0.69

12 4

14 4

1

13 1

3 2

0.16

25 7

3 2

0.58

25 11

2 0

1

26 10

2 1

1

0.55

7 3

19 2

0.30

14 2

14 4

0.66

12 2

4 1

1

27 5

4 1

1

30 6

2 0

1

30 6

3 0

1

1

5 2

20 1

0.15

12 2

15 2

1

9 2

4 1

1

26 3

4 1

0.49

29 4

2 0

1

29 4

3 0

1

Unmethylated. Methylated. P-value, Fisher’s exact test.

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between CASP8 methylation and the occurrence of an event (P = 0.037). In order to end the long lasting controversy concerning the correlation between CASP8 promoter methylation and MYCN amplification, we performed a metaanalysis of three studies in which the same primer pairs were used to study the methylation status of CASP8 (this study, [23,45]). The individual studies could demonstrate only borderline significance or no significance for this correlation. Our meta-analysis, including 115 neuroblastoma tumors, demonstrated that CASP8 methylation and MYCN amplification are significantly correlated (Fisher’s exact test: P = 0.0062), whereby a higher frequency of CASP8 methylation in MYCN amplified versus non-amplified samples is observed (66% vs. 36%). Our results also indicate that there is a trend towards an association between ZMYND10 methylation and MYCN amplification (Fisher’s exact test: P = 0.063). For the other genes, no significant correlation between methylation status and the clinico-genetic parameter could be demonstrated. As the power of our study is limited, we cannot exclude weak associations. 3.3. Re-expression of genes after DAC and TSA treatment In order to ascertain whether promoter hypermethylation of the tested genes resulted in silencing, we treated neuroblastoma cell lines with a demethylating agent (5-aza-20 -deoxycytidine, DAC) and a histon-deacetylase inhibitor (trichostatin A, TSA). A pilot study was designed to determine the optimal treatment conditions with

Fig. 3. A representative example of relative mRNA expression levels of the stably expressed reference gene (HMBS) and a methylated gene (RASSF1A) in neuroblastoma cell line SK-N-AS treated with DAC at different concentrations (0, 1, 3, and 10 lM) and durations (3, 5, and 7 days) (untreated cells day 3 are rescaled to 1). A clear time and dosedependent response is visible for RASSF1A reactivation. Error bars denote standard error of the mean (duplicated PCRs, gene of interest divided by geometric mean of four reference genes).

Fig. 4. Relative mRNA expression levels of candidate reference genes GAPDH, HMBS, HPRT1, SDHA, and YWHAZ in four representative neuroblastoma cell lines (before (0) and after treatment with DAC alone (3 lM, 3 days) or combined DAC (3 lM, 3 days) and TSA (500 nM, last 12 h)) demonstrates that expression of SDHA is consistently influenced by treatment, while for GAPDH, HMBS, HPRT1, and YWHAZ normal variation in mRNA expression is observed (untreated cells are rescaled to 1).

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respect to duration and dose of the treatment. Based on the mRNA re-expression analysis of RASSF1A and CASP8, two known methylated genes in neuroblastoma, we decided to treat the neuroblastoma cells with 3 lM DAC for 3 days. At these conditions, re-expression of methylated genes is sufficiently high. Higher inductions were observed for the 10 lM treatments for one week, but these conditions are likely to induce a more prominent stress response (Fig. 3). In order to accurately normalize the mRNA expression levels, we first evaluated five candidate reference genes (i.e., HPRT1, YWHAZ, HMBS, SDHA, and GAPDH) that were previously shown to be stably expressed in untreated neu-

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roblastoma cells [17]. To this purpose, we determined their relative expression in 22 different neuroblastoma cells with or without treatment and analyzed the data using the geNorm applet for Microsoft Excel [17]. Based on a robust gene stability measurement algorithm, the geNorm program determines the most stably expressed reference genes from a set of tested candidate reference genes in a given sample panel. Following this approach, we determined that four reference genes are required for normalization in DAC and TSA treated neuroblastoma cells and that the reference gene SDHA was highly induced in the treated cells (Fig. 4). In this study, we again demonstrate

Fig. 5. Representative examples of mRNA expression analysis for CASP8, PTEN, and ZMYND10 in cell lines untreated (0) or treated with DAC alone (DAC) or in combination with TSA (DAC+TSA) (lowest expression of the represented cell lines is rescaled to 1). U, unmethylated; M, methylated.

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the necessity of carefully determining which and how many reference genes are required for accurate gene expression profiling. After having established suitable reference genes, we performed qPCR-based expression analysis for five randomly selected genes of the 10 genes that were tested with MSP. Reactivation or upregulation of CASP8 expression was found in all investigated cell lines that were methylated at the intragenic regulatory region between exons 2 and 3, except for NGP and SK-N-AS (Fig. 5). Interestingly, Banelli et al. reported that silencing of CASP8 does not depend directly from hypermethylation of this region [47]. Rather, it likely reflects the epigenetic inactivation of genes that transactivate CASP8. Re-expression of RASSF1A after treatment with DAC was detected in most but not all neuroblastoma cell lines. Probably, a more intense treatment (higher dose, longer period) is needed to reactivate expression in those cell lines for which the current treatment did not result in re-expression. Presently, we could not confirm an association between CpG island methylation and loss of gene expression for DCC and ZMYND10. In contrast to DAC treatment alone, gene expression of ZMYND10 was upregulated when cells were treated with both DAC and TSA (Fig. 5) indicating that histone modifications are also involved. Finally, expression of the PTEN gene was not influenced by treatment suggesting that the observed hypermethylation might not be linked to the mRNA expression status. These data show that complementary experiments are needed when investigating the silencing of a putative candidate tumor suppressor gene through methylation. In addition to the screening of the promoter region for abnormal methylation, investigating its influence on gene expression is also required. However, interpreting these mRNA expression data is not always straightforward. Some genes are completely silenced and show no expression in the cancer cell, while other genes present with only reduced expression compared to unmethylated cells. In addition, small experimental and technical variation in gene expression measurement may occur. For these reasons and because of the considerable sensitivity of an optimal real-time quantitative PCR, we considered a gene to be upregulated after treatment when a more than 2.5-fold increase in mRNA expression was observed. In the present study, a NB cell line panel was treated in which we can first evaluate mRNA expression before and after treatment with DAC alone or in combination with TSA. In a subsequent step, bisulfite sequencing of the promoter region of re-activated genes should be performed to correlate silencing of gene-expression with hypermethylation of specific CpG dinucleotides in the CpG island. Based on these results, we will be able to determine the region of interest for further analysis using MSP in a cohort of NB tumor samples. In future research, this methylome analysis will aid in the identification of genes which contribute to neuroblastoma pathogenesis through methylation and gene silencing. 3.4. Conclusion and perspectives In this study, we report on the gene specific methylation analysis in neuroblastoma of positional and functional

candidate genes for which promoter hypermethylation is known in other tumor types. Our MSP data together with the re-expression experiments demonstrated that important tumor suppressor genes are aberrantly methylated suggesting a role for DNA methylation in neuroblastoma pathogenesis. Nowadays, evidence is emerging that subclass discovery through profiling of gene methylation offers opportunities for diagnostic and prognostic stratification of cancer patients (The Cancer Epigenome Project, [48– 49]). Integration of methylation markers in risk stratification of cancer patients seems a powerful approach to improve therapeutic decision making. Most likely, this strategy will result in better survival rates and avoid unnecessary overtreatment of certain patients. For this reason and encouraged by the findings of the present study, we have embarked on genome-wide profiling of methylated genes in NB using the here described platform for validation. 4. Conflicts of interest statement The authors declare no competing financial or other interests with regard to the submitted manuscript. Acknowledgements We thank Peter Degrave and Geert De Vos for cell culturing. Jasmien Hoebeeck is supported by the Vlaamse Liga tegen Kanker by a grant of the Stichting Emmanuel van der Schueren and by a grant of the Ghent University (BOF 01P07406). Filip Pattyn is supported by the Vlaamse Liga tegen Kanker by a grant of the Stichting Emmanuel van der Schueren. Joëlle Vermeulen is supported by the Belgian Kid’s Fund. Jo Vandesompele is a postdoctoral researcher with the Foundation for Scientific Research, Flanders (FWO). This study was supported by the Kinderkankerfonds, the Fund for Scientific Research Flanders (Krediet aan Navorsers 1.5.243.05), the ‘‘Stichting tegen Kanker” Project No. 365B0107, GOA-Grant 12051203, FWO-Grant G.0185.04. This text presents research results of the Belgian program of Interuniversity Poles of attraction initiated by the Belgian State, Prime Minister’s Office, Science Policy Programming (IUAP) and the European 6th framework programme EET-pipeline. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.canlet.2008. 08.019. References [1] G.M. Brodeur, Neuroblastoma: biological insights into a clinical enigma, Nat. Rev. Cancer 3 (2003) 203–216. [2] E. Michels, J. Vandesompele, K. De Preter, J. Hoebeeck, J. Vermeulen, A. Schramm, J.J. Molenaar, B. Menten, B. Marques, R.L. Stallings, V. Combaret, C. Devalck, A. De Paepe, R. Versteeg, A. Eggert, G. Laureys, N. Van Roy, F. Speleman, ArrayCGH-based classification of neuroblastoma into genomic subgroups, Genes Chromosomes Cancer 46 (2007) 1098–1108.

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