ARTICLE

Clustering of Gene Hypermethylation Associated With Clinical Risk Groups in Neuroblastoma

Miguel Alaminos, Veronica Davalos, Nai-Kong V. Cheung, William L. Gerald, Manel Esteller

Affiliations of authors: Cancer Epigenetics Laboratory, Molecular Pathology Program, Spanish National Cancer Centre (CNIO), Madrid, Spain (MA, VD, ME); Department of Pediatrics (NKVC) and Department of Pathology (WLG), Memorial Sloan-Kettering Cancer Center, New York, NY

Correspondence to: Manel Esteller, MD, PhD, Cancer Epigenetics Laboratory, Spanish National Cancer Centre (CNIO), Melchor Fernandez Almagro 3, 28029 Madrid, Spain (e-mail: mesteller{at}cnio.es)


    ABSTRACT
 Top
 Notes
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
 References
 
Background: Neuroblastoma is the most common extracranial solid malignancy in infancy and childhood, but the biological factors involved in its development and progression are still unclear. Transcriptional silencing of tumor suppressor genes mediated by hypermethylation of promoter CpG islands is a hallmark of human tumors. We addressed the clinical relevance of promoter hypermethylation in neuroblastoma. Methods: We examined the methylation status of 45 candidate genes representative of many cellular pathways in 10 neuroblastoma cell lines and of 10 of these genes in 145 tumor samples (118 of them were primary neuroblastomas). We used Fisher's exact test to examine the association of CpG island methylation and clinical subgroups and Kaplan–Meier analysis to determine the association between methylation and survival in primary tumors. Cluster analysis was used to group cell lines and tumors by gene methylation status. Bonferroni-corrected statistical tests were two-sided. Results: Clustering of neuroblastoma cell lines on the basis of hypermethylation distinguished lines with MYCN amplification (a negative prognostic factor) from those without it (P = .012). Promoter hypermethylation of the developmental gene HOXA9 was associated with mortality in noninfant patients (P = .04) and in tumors lacking MYCN amplification (P = .023). Hypermethylation of the proapoptotic gene TMS1 and the cell cycle gene CCND2 was associated with stage 4–progressing tumors (P<.001), but the genes were never methylated in stage 4S tumors, which undergo spontaneous regression. Hypermethylation of the differentiation gene RAR{beta}2 was associated with patient survival (P = .032). Unsupervised hierarchical cluster analysis of all tumors based on methylation of the 10 genes separated several clinically relevant groups of tumors. Conclusions: Profiling the status of CpG island hypermethylation in human primary neuroblastomas may have clinicopathologic value.



    INTRODUCTION
 Top
 Notes
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
 References
 
Neuroblastoma is the most common extracranial solid cancer diagnosed during infancy and childhood (1,2). The prevalence is about one case in 7000 live births, which corresponds to approximately 700 new cases per year in the United States (3). Mortality approaches 30%–60% overall, even with the most aggressive treatments (35), although prognosis is highly variable depending on the age of the patient at diagnosis, the stage of the disease, and several other biological features, such as amplification of the MYCN oncogene (3). Children (>12 months at diagnosis) with stage 4 or MYCN-amplified stage 3 tumors are at high risk of mortality (>60%); children with non–MYCN-amplified local–regional tumors (i.e., stages 1, 2, and 3) and infants (<12 months at diagnosis) with stage 4S disease are generally at low risk of mortality; and infants with stage 4 disease and children with stage 3 disease without MYCN amplification are at intermediate risk. Mortality is less than 10% for patients in the low-risk group (who are often treated with surgery alone) and for those in the intermediate-risk group (who are treated with chemotherapy and surgery) (610) but is more than 60% for patients with high-risk disease (11). In a substantial proportion of patients (>10% of all neuroblastomas), however, the tumors undergo complete spontaneous regression in the absence of any therapeutic intervention (2,12). Regression is especially likely in stage 4S tumors, a special disease corresponding to infant patients whose primary tumors have spread to the liver, skin, or bone marrow (13,12). Spontaneous involution and maturation of stage 4S neuroblastoma is a unique feature of this type of neuroblastoma and is a well-documented and frequent phenomenon (13,14). However, the incidence of spontaneous regression in local–regional neuroblastoma is at least 50% in the absence of any therapeutic intervention (15,16). Some neuroblastomas differentiate into benign lesions, termed ganglioneuromas (17).

Little is known about the molecular basis of the clinical diversity of this disease, prompting efforts to uncover new prognostic biomarkers. The best-characterized genetic aberrations in neuroblastoma tumors are MYCN amplification, loss of heterozygosity at 1p36, and chromosome gain of 17q. MYCN amplification occurs in about 25% of neuroblastomas and is associated with advanced-stage disease, rapid tumor progression, and low survival rates (18,19). However, these aberrations have been found in total in only 25%–50% of neuroblastic tumors (5,20). Thus, new molecular markers for neuroblastoma prognosis are needed.

Several studies have shown that human neuroblastoma cell lines display high cell diversity in culture, with three prominent cell types (21,22). Neuroblastic, or N-type, cells are small, rounded, rapidly proliferating neuroblastoma cells. S-type, or stromal, cells are flattened, highly adherent cells that express some proteins characteristic of melanoblasts, immature glial or Schwann cells, meningeal cells, or ectomesenchymal smooth-muscle cells. S-type neuroblastoma cell lines are rarely tumorigenic, and they are, in general, unable to form tumors in athymic mice (21,23). Finally, I-type cells are morphologically intermediate between N- and S-type cells and possess biochemical characteristics of both. The I-type cells have the highest malignant potential and form large tumors when implanted in mice (21).

A new hallmark of human cancer has recently emerged: aberrant methylation of promoter-associated CpG islands of tumor suppressor genes, leading to gene silencing (2426). Epigenetic silencing of tumor suppressor genes plays an important role in the pathogenesis of most cancers, and alterations of the normal DNA methylation pattern may lead to the development and progression of all common forms of human cancer (2426). Hypermethylation of normally unmethylated promoter-associated CpG islands has been shown to be important for transcriptional repression of numerous genes with a role in preventing tumor growth and development (2426). Hypermethylation markers have several potential advantages over classical genetic factors: The number of genes undergoing epigenetic silencing includes all major cellular pathways; the total number of such genes is increasing every year; with a few selected hypermethylation markers, it is possible to cover 100% of tumors (26,27); and assays for DNA methylation are easily and readily standardizable (28,29).

A few potential tumor suppressor genes have been described as frequently hypermethylated and silenced in pediatric neuroblastomas, including caspase-8 (CASP8) and the tumor necrosis factor receptors DR4, DR5, DcR1, and DcR2 (3033). However, a comprehensive methylation profiling of a large series of neuroblastic tumors has not been carried out to date. In this study, we investigated the hypothesis that the CpG island hypermethylation profile from patients with neuroblastoma would be useful for the clinicopathologic classification of these patients. To test this hypothesis, we first determined the methylation pattern of 45 candidate genes in 10 neuroblastoma cell lines. We then analyzed a large cohort of 145 human neuroblastic tumors for which both clinical (e.g., age, outcome, treatment regime) and analytical (MYCN status, microarray analysis, chromosome 1 and 17 allelotyping) data had been previously established (4,3436). Finally, we assessed whether the clustering of gene hypermethylation had clinical value in discrimination between high- and low-risk neuroblastoma patients.


    SUBJECTS AND METHODS
 Top
 Notes
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
 References
 
Neuroblastoma Cell Lines and Tumor Specimens

We studied 10 neuroblastoma cell lines: LAI-5S (MYCN-amplified, S-type); LAI-55N (MYCN-amplified, N-type); LAN-1 (MYCN-amplified, N-type); SH-IN (non–MYCN-amplified, I-type); SH-EP1 (non–MYCN-amplified, S-type) and SK-N-BE(2)C (an S-type, MYCN-amplified variant of neuroblastoma established from a relapse tumor sample), all of which were kindly provided by R. Ross and B. Spengler of Fordham University (Fordham, NY); SK-N-JD (MYCN-amplified, I-type) and SK-N-ER (non–MYCN-amplified, I-type), which were derived in our laboratory (Nai-Kong V. Cheung, Memorial Sloan-Kettering Cancer Center, New York, NY); and SK-N-AS (non–MYCN-amplified, S-type; derived from a bone marrow metastasis); and IMR-32 (MYCN-amplified, N-type; derived from a primary tumor), which were purchased from the American Type Culture Collection (ATCC; Manassas, VA). Cells were cultured and passaged in RPMI or Dulbecco's modified Eagle medium with 10% fetal calf serum.

The tumor specimens consisted of 145 neuroblastic tumors obtained at the time of surgery and were immediately snap-frozen at Memorial Sloan-Kettering Cancer Center. The clinicopathologic features of these samples, including MYCN copy number and 1p36 and 17q status, have been previously described (4,3436). The neuroblastic tumors included 118 original primary tumors and 27 relapse samples corresponding to the same patients from whom primary tumor specimens were also available. Only original primary tumors were used for survival studies; relapse samples were used for determination of the percentage of hypermethylated tumors among the global cohort of neuroblastomas. Patients were staged according to the International Neuroblastoma Staging System (INSS) (37) and included nine patients with ganglioneuromas, nine with stage 1 neuroblastoma tumors, 20 with stage 2 tumors, 26 with stage 3 tumors, 73 with stage 4 tumors, and eight with stage 4S tumors. One hundred seven patients were noninfant (≥12 months old at diagnosis), 36 were infants (<1 year old), and two patients were of unknown age at diagnosis. After a mean follow-up of 76.5 months, 74 patients were alive and 44 patients had died of the disease. Sections stained with hematoxylin–eosin that corresponded to all samples were reviewed before DNA extraction to ensure high tumor cell content. The institutional review boards at Memorial Sloan-Kettering Cancer Center and the Spanish National Cancer Centre (CNIO) approved this research.

Analysis of Promoter-Associated CpG Island Methylation

We selected 45 candidate genes with a CpG island located in the 5'-regulatory region for the DNA methylation analysis (see Table 1). All of these genes had been previously described as undergoing methylation-associated gene silencing in other tumor types (2426,38). DNA methylation status of the promoter-associated CpG islands was determined by methylation-specific polymerase chain reaction (PCR) (39). In brief, 1 µg of DNA was denatured by NaOH and modified by sodium bisulfite, which chemically converts unmethylated cytosines to uracil but does not affect methylated cytosines; a PCR using primers specific for either the methylated or the unmethylated DNA was subsequently carried out. Primer sequences for all genes are available on request. Normal lymphocyte DNA treated in vitro with SssI methyltransferase was used as a positive control for methylated alleles, and DNA from normal adrenal medulla and untreated normal lymphocytes was used as a negative control for methylated alleles. Each PCR product was loaded directly onto 2% agarose gels and separated by electrophoresis; the gels were then stained with ethidium bromide and visualized under UV illumination.


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Table 1. Methylation analysis of a panel of 45 genes in 10 neuroblastoma cell lines*

 
Reverse Transcription–PCR and Demethylation Agent 5-aza-2'-deoxycytidine

For all 45 genes selected for analysis, an association between hypermethylation and gene silencing has been previously established (2426,38). We confirmed this association for neuroblastoma by using reverse transcription (RT)–PCR to determine the relationship between CpG island hypermethylation and RNA expression in four neuroblastoma cell lines. Total RNA was extracted using the Qiagen RNeasy System (Qiagen, Mississauga, Ontario, Canada), according to the manufacturer's recommendations. RNA concentration was determined by measuring absorbance at 260 nm, and quality was verified by the integrity of 28S and 18S rRNA after ethidium bromide staining of total RNA samples subjected to 0.8% agarose gel electrophoresis. Total cDNA was synthesized with avian RNAse H reverse transcriptase (ThermoScript RT; Invitrogen Life Technologies, Carlsbad, CA). RT–PCR was performed using 2 µg of total cellular RNA to generate cDNA. Primers were designed between different exons to avoid DNA amplification; the sequences are available on request. RT–PCR for the GAPDH (glyceraldehyde-3-phosphate dehydrogenase) gene served as a control for RNA loading. Ten microliters of each PCR reaction was directly loaded onto 2% agarose gels and separated by electrophoresis; the gels were stained with ethidium bromide and visualized under UV illumination. To restore silenced gene expression of methylated genes, we treated cell lines for 72 hours with the demethylating agent 5-aza-2'-deoxycytidine (Sigma) at a concentration of 2 µM, as described previously (40).

Statistical Analysis and Clustering of CpG Island Promoter Hypermethylation

Comparisons for statistical significance of the association between single-locus CpG island hypermethylation and clinical subgroups were performed with Fisher's exact test. To determine the association between hypermethylation of individual genes and survival, we used the log-rank test of Kaplan–Meier analysis (41). Cox regression analysis with proportional hazards [proportionality was tested by the method of Grambsch and Therneau (42)] was used to identify associations between hypermethylation of groups of genes and survival. The method used for the Cox analysis was the forward stepwise likelihood ratio. The Kruskal–Wallis test was used to identify the statistical significance of differences among three groups being compared, and the Kendall {tau} test was used to determine the correlation between two nonparametric distributions. For individual tests, a two-sided P value less than .05 was considered statistically significant. For multiple comparisons, a Bonferroni-adjusted significance level of .001 was considered because up to 50 statistical tests were used at the same time. To carry out the previously described statistical analysis, we used SPSS 11.5 software. To identify genes whose methylation was statistically significantly associated with specific groups of patients, we used the Significance Analysis for Microarrays (SAM) function of the program TIGR MeV (MultiExperiment Viewer 2.2; The Institute for Genomic Research, Rockville, MD) (43). We used a {delta} value of 0.1710 with 5000 permutations of the values. The program is available at http://www.tigr.org/software/tm4 (last accessed: June 16, 2004).

We performed cluster analysis using the Self-Organizing Hierarchical Neural Network SOTA (44), an unsupervised neural network with a binary tree topology, which combines the advantages of divisive and customizable methods. The analysis was performed in a blinded manner; that is, the tumor type corresponding to each sample was decoded only at the end of the process. Cell lines with correlated methylation profiles and genes with similar methylation patterns across cell lines were identified with the linear correlation coefficient, which was taken as a measure of similarity or complete correlation distance between values. The output file was visualized as a binary tree with the TREEVIEW program. The matrix values were transformed into a graded color pattern (red through black to green [appears as lighter gray] or gray scale [appears as darker gray]), representing the mean adjusted ratio of each point of the matrix. The scale below the dendrogram depicts the correlation coefficient, as represented by the length of the branches connecting pairs of nodes. For validation of the cluster trees, leave-one-out cross-validation was used. The cluster analysis programs are available at http://gepas.bioinfo.cnio.es/cgi-bin/sotarray (last accessed: June 16, 2004).


    RESULTS
 Top
 Notes
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
 References
 
CpG Island Hypermethylation Profile of Human Neuroblastoma Cell Lines

We selected 45 candidate genes for the initial screening of hypermethylated promoter CpG islands (Table 1). These genes were chosen because they had previously been described as undergoing hypermethylation in other tumor types, methylation at these CpG sites is associated with gene silencing, their CpG islands are unmethylated in normal tissues, they are known to be (or are good candidates to be) tumor suppressors, and they cover all cellular pathways (DNA repair, apoptosis, cell cycle, adherence, differentiation, and development) (2426,38).

The complete spectrum of CpG island hypermethylation events observed in the 10 neuroblastoma cell lines is shown in Table 1. Representative examples of the DNA methylation analysis are shown in Fig. 1, A (color versions of figures are available in online data supplement [http://jncicancerspectrum.oupjournals.org/jnci/content/vol96/issue16]). Thirty-four (76%) of the 45 genes included in the study showed promoter methylation in at least one of the tested lines, whereas no promoter methylation was evident for the remaining 11 (24%) genes (Table 1). Eighteen genes were hypermethylated in at least 50% of the tested lines. None of the 45 genes studied was methylated in control tissue (i.e., adrenal medulla and lymphocytes). The highest CpG island methylation rate (30 [67%] of the 45 genes showed promoter methylation) corresponded to LAI-5S, an MYCN-amplified line, and the lowest methylation rate (16% of the genes showed promoter methylation) was observed in SK-N-AS, a non–MYCN-amplified cell line. Most interesting was that the percentage of methylated genes was higher in MYCN-amplified neuroblastoma lines (37.3%) than in cell lines with a normal MYCN copy number (26%) (P = .0124 for Fisher's exact test).



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Fig. 1. Methylation-specific PCR analysis of 45 genes with promoter-associated CpG islands in 10 neuroblastoma cell lines. A) Illustrative methylation-specific polymerase chain reaction (PCR) results for seven of the genes studied. U = PCR product with primers for unmethylated DNA; M = PCR product with primers specific for methylated DNA; NL = normal adrenal medulla DNA (used as a control of unmethylated genes); IVD = in vitro–methylated normal lymphocyte DNA (positive control for methylated genes). B) Unsupervised hierarchical cluster analysis of all 45 genes in the neuroblastoma cell lines. The analysis separated the lines in two main branches: low-proliferative and high-proliferative cells. Darker gray boxes show methylated status, and lighter gray boxes show unmethylated status. White boxes correspond to homozygous deletions. The dendrogram shown at left represents the hierarchical relationship among the genes, arranged in a binary tree. C) Illustrative examples of RNA expression analysis in neuroblastoma cell lines as assessed by reverse transcription–PCR in untreated cell lines (C) and cells treated with the demethylating agent 5-aza-2'-deoxycytidine (5AZA). Expression of the GAPDH (glyceraldehyde-3-phosphate dehydrogenase) gene was used as a control for RNA loading. Gene full names and abbreviations described in Table 1.

 
Unsupervised hierarchical cluster analysis of all neuroblastoma cell lines using CpG island methylation analysis for all 45 genes clustered the cell lines in two branches (Fig. 1, B) that appeared to differ in malignant potential. One dichotomous branch of the cluster tree contained four MYCN-amplified neuroblastoma cell lines, including LAN-1, LAI-55N, SK-N-JD, and LAI-5S, and the non–MYCN-amplified line SK-N-ER (which had been established from an extremely aggressive local–regional tumor) (35). The other three non–MYCN-amplified cell lines (SK-N-AS, SH-EP1, and SH-IN) and the other two MYCN-amplified cell lines [IMR-32 and SK-N-BE(2)C] were in the other branch. The percentage of MYCN-amplified lines in the first group was higher than in the second group (P<.001 by Fisher's exact test. Furthermore, four N- and I-type lines were included in the first branch, whereas the second branch was enriched in lines of the less malignant S type (three of five lines). The average doubling time for the cell lines in the first (higher malignant potential) branch was 33 hours, and that of the cell lines in the second branch (lower malignant potential) was 50.6 hours (44,45) (unpublished data). The lines in the more malignant branch had higher expression (4261.7 versus 3037.8 fluorescent units) of the cell proliferation marker proliferating cell nuclear antigen (PCNA) (47), based on expression levels determined in our previous microarray study (36). The difference in PCNA expression between the two groups of cell lines (1223.9 fluorescent units) was statistically significant according to the Mann–Whitney U test (95% CI = 914.5 to 1533.3; P<.01). Despite the exploratory nature of the analysis, the use of leave-one-out cross-validation carried out on the 10 cell lines showed that the results were not altered with the exclusion of any of the lines from the analysis.

CpG Island Methylation, Loss of Expression, and Gene Reactivation Using Demethylating Agents

Although the association between CpG island methylation and loss of expression has been previously demonstrated for all the genes included in this study (2426,38) in tumors and cancer cell lines, we sought to establish this relationship in the neuroblastoma cell lines. Therefore, we determined the expression levels of eight genes (HOXA9, TMS1, SYK, DR4, p16INK4a, RIZ, LMX1, and CCND2) that were methylated in a relatively large number of the neuroblastoma cell lines in relation to their CpG island methylation status. For this analysis we used four different cell lines (SK-N-ER, SK-N-JD, LAN-1, and LAI-5S). For all eight genes, the presence of CpG island methylation was associated with loss of gene expression, whereas in those cell lines harboring an unmethylated CpG island, each gene was expressed (Fig. 1, C). To further study the relevance of CpG island methylation in gene silencing, we treated these cell lines with the demethylating agent 5-aza-2'-deoxycytidine. In all cases where CpG island hypermethylation was associated with loss of gene expression, the addition of the demethylating drug restored gene expression.

CpG Island Hypermethylation Portrait of Human Neuroblastic Tumors

Once the profile of CpG island hypermethylation in the neuroblastoma cell lines was known, we sought to determine the hypermethylation profile and potential consequences in a large set of human neuroblastic tumors (n = 145) (Fig. 2, A). For this analysis, we used 10 of the 45 genes: the tumor necrosis factor receptor DR4, the proapoptotic gene TMS1, the differentiating gene RAR{beta}2, the tyrosine kinase SYK, the histone methyltransferase RIZ1, folate hydrolase 1 (FOLH1), the cell cycle regulators p16INK4a and cyclin D2 (CCND2), and the homeobox-containing genes LMX-1 and HOXA9. These genes were chosen because they were frequently methylated across the spectrum of neuroblastoma cell lines (most of them in more than 25% of the cell lines), covered different cellular pathways (e.g., apoptosis, signal transduction, differentiation), were located in hotspots of loss of heterozygosity in neuroblastoma (such as RIZ1, which is at 1p36, and p16INK4a, which is at 9p21) (3,48), or had been previously found to be expressed at reduced levels in neuroblastoma (such as CCND2, HOXA9, and SYK) (36). The hypermethylation frequency for each of the 10 genes is depicted in Fig. 2, B. DR4 was the most frequently hypermethylated gene (in 72 [50%] of 145 tumors) and RAR{beta}2 the least frequently hypermethylated (in 10 [7%] of 145 tumors). Only six tumors (4%) showed no evidence of hypermethylation of any of the 10 genes analyzed; thus, a case-informative incidence of 96% (139 of 145) was obtained. The distribution of cases by the number of genes hypermethylated is shown in Fig. 2, C.



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Fig. 2. Methylation-specific polymerase chain reaction (PCR) analysis of 145 neuroblastic tumors. (A) Representative results. U = PCR product with primers for unmethylated DNA; M = PCR product with primers specific for methylated DNA. B) Histogram representing the percentage of tumors showing methylation for the 10 genes as indicated. C) Histogram representing groups of tumors according to the number of genes hypermethylated (0 = 0 of 10 genes; 1 = 1 of 10 genes, etc.). Gene full names and abbreviations described in Table 1.

 
We next related the hypermethylation of specific genes with classical clinicopathologic parameters. First, we determined that the cases studied are representative of the general population of neuroblastic tumors. Indeed, as expected, noninfant patients in the cohort showed worse survival than infant patients (log-rank P = .006) (Fig. 3, A; Table 2), and patients with MYCN-amplified tumors had poorer survival than patients without MYCN amplification (log-rank P<.001) (Fig. 3, B; Table 2). The gene-by-gene analysis showed that hypermethylation of the homeobox gene HOXA9 was associated with a lower overall survival for noninfant patients (log-rank P =.040) (Fig. 3, A; Table 2) and for patients of all ages with non–MYCN-amplified tumors (log-rank P =.023) (Fig. 3, B; Table 2).



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Fig. 3. Survival curves for the different comparison described in the text. A) Top panel: Kaplan–Meier survival curves for the comparison of infant (<12 months old at diagnosis) versus noninfant (≥12 months old at diagnosis) patients (P = .006). Lower panel: Survival of cases with HOXA9 promoter methylation versus cases with unmethylated HOXA9 for noninfant patients (P =.040). B) Top panel: Kaplan–Meier analysis for comparison of MYCN-amplified versus non–MYCN-amplified tumors (P<.001). Lower panel: Comparison of cases with HOXA9 promoter hypermethylation versus tumors with unmethylated HOXA9 for non–MYCN-amplified cases (P = .023). C) Significance analysis for Microarrays (SAM) for all neuroblastic tumors and for the 10 genes selected. The three genes showing positive significant association with survival are shown ({delta} = 0.1710; q value = 33.3333). D) Kaplan–Meier survival curves of cases with promoter-associated CpG island hypermethylation of the gene RAR{beta}2 versus cases with no methylation of this gene. U = unmethylated; M = methylated.

 

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Table 2. Statistical significance of associations between CpG island methylation of HOXA9, TMS1, CCND2, and RAR{beta}2 and clinicopathologic parameters*

 
Additional genes (DR4, p16INK4a, TMS1, FOLH1, and CCND2) were also more likely to be hypermethylated in tumors from patients that died, whereas the other genes (SYK, LMX1, RAR{beta}2, and RIZ1) were more likely to be hypermethylated in samples from patients who survived (Table 3). None of these associations was statistically significant. However, the combination of HOXA9 hypermethylation with that of the other genes for which hypermethylation is associated with poor outcome resulted in a considerable increase in prognostic power. For example, neuroblastic patients harboring hypermethylated HOXA9, TMS1, and CCND2 had shorter survival times than patients in which these three genes were unmethylated (Cox regression multivariate analysis P = .021; SAM {delta} value of 0.1710 and q value of 33.3333 for each gene; Fig. 3, C), although the differences are not statistically significant for the Bonferroni-adjusted P value of .001.


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Table 3. Percentage of promoter-associated CpG island hypermethylation of 10 genes in 145 neuroblastic tumors*

 
The hypermethylation of the proapoptotic gene TMS1 and of the cell cycle gene CCND2 displayed additional and specific clinical values: Each one was a good predictor of clinical progression of metastatic neuroblastoma in infant patients because these genes were more likely to be methylated in stage 4 infant tumors than in stage 4S cases (P<.001 from Fisher's exact test for the comparison of stage 4 infants versus stage 4S infants for both genes; Tables 2 and 3). Conversely, an unmethylated promoter of any of these two genes appears to "mark" those metastatic neuroblastomas that undergo spontaneous regression, which is one of the unique and most characteristic features of this tumor type (neuroblastoma stage 4S) (3,12).

Finally, a third methylation biomarker was identified with this analysis: the RAR{beta}2 gene was more likely to be hypermethylated in ganglioneuromas, which are benign neuroblastic tumors, than in classical malignant neuroblastoma (P = .032 by Fisher's exact test; Tables 2 and 3). The pathologic distinction between these tumors is important in clinical practice because ganglioneuromas have a much better outcome than the typical neuroblastoma (49) (P = .034 for the Kaplan–Meier survival analysis; Fig. 3, D). The potential beneficial prognostic association of hypermethylated RAR{beta}2 is also underscored by the observation that all 10 patients displaying this epigenetic aberration have survived, whereas 40.7% (55 of 135) of patients with unmethylated RAR{beta}2 have died.

Several clinicopathologically significant candidate molecular lesions have been previously reported for neuroblastoma. Therefore, we sought to establish whether any of the hypermethylation markers identified in this study could serve as copredictors of clinical outcome. MYCN amplification was more frequent in tumors with TMS1 or CCND2 hypermethylation (P≤.001 by Fisher's exact test); 1p36 loss was more common in tumors with TMS1 methylation (P = .0311 by Fisher's exact test, 95% CI = .030 to .324); and the gain of 17q was more frequent in tumors displaying FOLH1 or CCND2 hypermethylation (P≤.001 by Fisher's exact test) (Table 2). However, the single hypermethylation marker that was statistically significantly associated with poor outcome in our study, HOXA9, was an independent factor of poor prognosis that was not associated with MYCN gene amplification, 1p36 loss, or 17q gain.

Finally, we aimed to define a global CpG island hypermethylation pattern that could classify human neuroblastic tumors in a similar fashion to that of expression microarrays in leukemia, for example (50). For this purpose, we developed an unsupervised, average-linkage, nonvalidated, hierarchical cluster analysis of all neuroblastic tumors. The unveiled dendrogram clustered the tumors in two different branches, with the second branch further subdivided into two major groups. Although the separation is not completely clean, these three groups correspond in general to distinct clinically relevant classes of tumors (Fig. 4). The first dichotomous branch of the cluster tree corresponded mainly to stroma-rich low-risk cases, which accounted for 75% of the tumors that clustered in that group, including the ganglioneuromas (group 1 of Fig. 4). The second branch of the tree, corresponding to groups 2 and 3 of Fig. 4, to which most of the cases were assigned, was subdivided in two large dichotomous sub-branches: One (group 2) was enriched in high-risk patients, and the other (group 3) was enriched in a higher number of local–regional cases. Group 2 includes mostly highly aggressive neuroblastomas and noninfant stage 4 tumors and accounted for 51% of the tumors included in this sub-branch. The most benign neuroblastic tumors, such as ganglioneuromas, were almost completely excluded from this group 2 (2.5%, 1 of 39 cases). Group 3 was defined by a predominance of local–regional tumors, which represented 60.2% (59 of 98) of the cases in this group. Six genes (HOXA9, TMS1, DR4, SYK, RIZ, and RAR{beta}2), including those methylation markers with potential as single-gene outcome predictors, showed different hypermethylation rates in each of the three branches (Kruskal–Wallis P = .03), supporting the accuracy of the cluster.



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Fig. 4. Unsupervised hierarchical cluster analysis of all neuroblastic tumors for the 10 genes selected, showing the three main branches with their corresponding clinicopathological characteristics. Boxes with dark gray shading represent methylated genes, and boxes with light gray shading represent unmethylated genes. International Neuroblastoma Staging System (INSS) stages are shown after the tumor identification number. A = alive; D = dead; I = infant; NI = noninfant.

 

    DISCUSSION
 Top
 Notes
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
 References
 
Here, we have provided a comprehensive gene CpG island methylation profile of one of the largest cohorts of neuroblastic tumors in the biomedical literature. Initial clustering analysis of CpG island methylation of 45 candidate genes was able to classify 10 neuroblastoma cell lines in two relevant groups: higher and lower malignant potential. One group was defined by an enrichment of MYCN-amplified samples of aggressive cell phenotypes (intermediate and neuroblastic) with lower doubling times and higher PCNA expression; the other dichotomous group included mostly non–MYCN-amplified samples of more benign cell behavior (stromal type) with higher doubling times and lower PCNA expression. We then translated our cell line CpG island clustering data to a cohort of 145 neuroblastic tumors, using just 10 of the most informative genes. The dendrogram, which was obtained in an unsupervised hierarchical manner, revealed two main branches: one with a high percentage of stroma-rich, low-risk tumors and another containing mostly stroma-poor neoplasms. Most important, this second branch was subdivided, with one branch having a high percentage of highly aggressive tumors and the other being enriched in low-risk, local–regional neuroblastomas. Finally, we found several statistically significant single-gene associations with clinical parameters. Methylation of the HOXA9 gene was associated with mortality in noninfant patients and in patients without MYCN amplification; the TMS1 and CCND2 genes were methylated in stage 4 but not stage 4S tumors; and methylation of RAR{beta}2 was associated with improved patient survival.

The single-gene associations, if confirmed in independent validation sets, could contribute to better prediction of clinical outcomes. For example, promoter hypermethylation of HOXA9, RAR{beta}2, CCND2, or TMS1 showed prognostic power in this study. The presence of promoter hypermethylation at the HOXA9 gene was associated with a subgroup of patients with worse overall survival in two types of neuroblastic patients: noninfant cases and non–MYCN-amplified cases across all ages. These two groups are clinically extremely relevant, because there are currently no clear clinical or pathologic parameters that can accurately predict the outcome of these subgroups (3,5,20). HOXA9 methylation was independent of clinical stage and was distributed homogeneously among stages. HOXA9 encodes a homeodomain-containing transcription factor that provides cells with specific positional identities on the anterior–posterior axis (51). HOXA9 expression has been found to be dysregulated in non–small-cell lung tumors (52) and leukemia (53). Interestingly, genetic aberrations at the MEIS1 gene, which codes for a protein that binds to HOXA9, has been recently reported in neuroblastoma (54).

Another clinically relevant issue in neuroblastoma is the classification of stage 4 versus stage 4S tumors in infant patients. Two hypermethylated genes, the proapoptotic gene TMS1 and the cell cycle regulator CCND2, were identified in our study as putative markers of stage 4 tumors, i.e., tumors that will not undergo spontaneous regression and will lead to patient death if untreated (although survival is near 90% with a mild chemotherapeutic regimen in patients with unamplified MYCN). In these stage 4S tumors, TMS1 and CCND2 were never methylated. TMS1 codes for a protein containing a COOH-terminal caspase recruitment domain, a recently described protein interaction motif that is found in apoptotic signaling molecules (55). Ectopic expression of TMS1 has been shown to induce apoptosis and inhibit survival of human cancer cells (55). Our data therefore raise the possibility that methylation-mediated silencing of TMS1 may confer a survival advantage in neuroblastoma by allowing tumor cells to evade apoptosis. Furthermore, because TMS1 promoter hypermethylation has also been found in other tumor types (56), its clinical relevance warrants further analysis. CCND2 has also been frequently reported to be hypermethylated in human neoplasia (57). CCND2, in addition to its customary role in the G1–S transition during the cell cycle, may also have growth-inhibitory effects, as evidenced by its ability to induce a senescence-like phenotype (58).

Our results, in addition to providing possible insights into the mechanisms that underlie the association of silencing of HOXA9, TMS1, and CCND2 with neuroblastoma, potentially provide additional clinically relevant information. That is, the combination of HOXA9 hypermethylation with that of TMS1 and CCND2 was associated with worse overall survival, although the association was not statistically significant. If this association is confirmed in subsequent studies of an independent test set of tumors, then it may turn out that the elucidation of particular "methylotypes" for any given tumor may be useful for prognosis in human cancer.

Not all of the hypermethylation events were associated with poor patient outcome. RAR{beta}2 CpG island promoter methylation was associated with ganglioneuroma, a benign form of neuroblastic tumors, and was absent from the malignant neoplasms. Consistent with this finding, patients with RAR{beta}2 methylation were more likely to survive than patients without it. The association of RAR{beta}2 methylation with survival could have a double value: diagnostic, because they support the histopathologic classification of neuroblastic tumors, and prognostic, because ganglioneuromas have an excellent prognosis (100% overall survival), in contrast with the approximately 60% survival in cases with unmethylated RAR{beta}2. RAR{beta}2 function may provide an additional translational consequence. Retinoic acid plays an important role in cell development and differentiation, acting primarily via nuclear receptors encoded by retinoic acid receptor genes, such as RAR{beta}2. Consequently, retinoids have been postulated as potential chemopreventive and chemotherapeutic agents for human cancer (59,60). In neuroblastoma, it has been demonstrated that in vitro retinoid treatment induces differentiation and decreased growth rate (61,62), and some clinical studies have demonstrated (11,63) or aim to demonstrate its potential clinical utility. Thus, the determination of the RAR{beta}2 methylation status may have a helpful role in the accurate classification of neuroblastic tumors.

Genetic markers, such as MYCN amplification or single hypermethylation markers, as we have shown, may provide useful information for the biologic and pathologic classification of neuroblastomas. An important goal is to provide a global profile of molecular alterations with clinicopathologic value. One way to accomplish this is with microarray analysis of global gene expression patterns, as has been reported for breast cancer (64,65), leukemia (50), and lymphomas (66). In neuroblastoma, for example, a differential profile of gene expression exists between those cases with or without MYCN overexpression (36). Although microarray analysis provides an enormous amount of information and the potential to lead to improved clinical interventions, this approach is expensive, is time-consuming, uses a highly sophisticated technology, and (the most limiting step) requires the extraction of high-quality RNA, precluding the use of old archive material, for example.

Molecular profiling of human tumors by clustering of hypermethylated genes offers a technique complementary to microarray expression analysis. Rather than mRNA, the starting point is DNA, which can be obtained from paraffin-embedded sections; the amount of material required for the analysis is small, and the procedure can be carried out at lower cost. In this study, we provide a potential proof of principle for this approach in neuroblastic cell lines and tumors. Although the cluster analysis did not completely separate all the possible risk groups from the neuroblastic tumors, the distinction among the three groups that were identified is clinically useful for the management of neuroblastoma. The findings of our study, if validated in independent cohorts of cases, warrant further research in this area with the development of prospective clinical trials.

This exploratory study has, therefore, demonstrated the potential value of profiling CpG island hypermethylation. The next issues to address for the creation of a molecular classification of human tumors, including neuroblastic neoplasia, by methylation pattern include the establishment of methods for quantification of the degree of methylation and procedures to automate analysis. Important steps in standardizing these assays have been accomplished, for example, in the model of hypermethylation of the glutathione S-transferase pi (GSTP1) gene in prostate cancer using a quantitative fluorogenic real-time methylation-specific PCR assay (28,29). Furthermore, the recent development and preliminary use of microarray platforms for gene CpG islands and promoters to study differences in DNA methylation (6770) may provide a necessary normalization value between different laboratories for the future establishment of these promising markers in a clinical setting.


    NOTES
 Top
 Notes
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
 References
 
Supported by the Health and Science Departments of the Spanish government. Dr. Alaminos is the recipient of a Postdoctoral Fellowship from the Spanish National Cancer Centre (CNIO)—Caja Madrid. We thank Dr. Kevin Petrie for his critical review of the manuscript.


    REFERENCES
 Top
 Notes
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
 References
 

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Manuscript received December 29, 2003; revised May 24, 2004; accepted June 9, 2004.


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