Open Access

Two independent epigenetic biomarkers predict survival in neuroblastoma

  • Yania Yáñez1Email author,
  • Elena Grau1,
  • Virginia C Rodríguez-Cortez2,
  • David Hervás3,
  • Enrique Vidal4,
  • Rosa Noguera5,
  • Miguel Hernández6,
  • Vanessa Segura1,
  • Adela Cañete1,
  • Ana Conesa4,
  • Jaime Font de Mora7 and
  • Victoria Castel1
Clinical EpigeneticsThe official journal of the Clinical Epigenetics Society20157:16

https://doi.org/10.1186/s13148-015-0054-8

Received: 9 September 2014

Accepted: 9 February 2015

Published: 27 February 2015

Abstract

Background

Neuroblastoma (NB) is the most common extracranial pediatric solid tumor with a highly variable clinical course, ranging from spontaneous regression to life-threatening disease. Survival rates for high-risk NB patients remain disappointingly low despite multimodal treatment. Thus, there is an urgent clinical need for additional biomarkers to improve risk stratification, treatment management, and survival rates in children with aggressive NB.

Results

Using gene promoter methylation analysis in 48 neuroblastoma tumors with microarray technology, we found a strong association between survival and gene promoter hypermethylation (P = 0.036). Hypermethylation of 70 genes significantly differentiated high-risk survivor patients from those who died during follow-up time. Sixteen genes with relevant roles in cancer biology were further validated in an additional cohort of 83 neuroblastoma tumors by bisulfite pyrosequencing. High promoter methylation rates of these genes were found in patients with metastatic tumors (either stage metastatic (M) or metastatic special (MS)), 18 months or older at first diagnosis, MYCN amplification, relapsed, and dead. Notably, the degree of methylation of retinoblastoma 1 (RB1) and teratocarcinoma-derived growth factor 1 (TDGF1) predicts event-free and overall survival independently of the established risk factors. In addition, low RB1 mRNA expression levels associate with poor prognosis suggesting that promoter methylation could contribute to the transcriptional silencing of this gene in NB.

Conclusions

We found a new epigenetic signature predictive for NB patients’ outcome: the methylation status of RB1 and TDGF1 associate with poorer survival. This information is useful to assess prognosis and improve treatment selection.

Keywords

NeuroblastomaEpigenetic biomarkersPromoter methylation

Background

Neuroblastoma originates from the sympathico-adrenal lineage of the neural crest and is the most common extracranial solid tumor in early childhood. This tumor exhibits contrasting patterns of clinical behavior ranging from spontaneous remission to rapid tumor progression and death. Prognosis classically depends on age at diagnosis [1], tumor stage [2], MYCN oncogene amplification status [3,4], and histology in a lesser extent [5]. Several well-characterized genetic abnormalities associated with neuroblastoma (NB) have been used to predict outcome, for example, DNA content [6], gain of chromosome arm 17q [7], or deletion of chromosome arm 1p [8] and 11q [9,10]. However, current knowledge of the molecular features of NB is not sufficient to explain the observed clinical heterogeneity.

There is an important body of work on NB to find robust biomarkers that could help to improve the standard response criteria and, consequently, patients’ survival. In this regard, several mRNA and miRNA classifiers have been established [11-13]. A recent study from SIOPEN shows that levels of tyrosine hydroxylase (TH) and paired-like homeobox 2B (PHOX2B) or doublecortin (DCX) mRNA in peripheral blood and bone marrow at diagnosis are independent predictors of survival [14]. Notably, high levels of TH and PHOX2B mRNA in peripheral blood identify ultrahigh-risk NB patients. Despite these new discoveries, survival rates in children with high-risk NB remain disappointingly low.

Epigenetic modifications, particularly the methylation of the 5′ position of cytosines, within CpGs dinucleotides at gene promoter regions, are essential regulatory mechanisms for normal cell development and may modulate gene expression without altering the DNA sequence. In the last decade, DNA-methylation studies have focused on identifying epigenetically modified genes to further understand NB pathogenesis and to find prognostic methylation markers. In this regard, global methylation studies have demonstrated that a methylator phenotype, characterized by the methylation of multiple CpG islands, is a hallmark of NB with poor prognosis [15]. Several tumor suppressor genes such as caspase 8 (CASP8), Ras association (RalGDS/AF-6) domain family member 1 (RASSF1A), cycling D2 (CCND2), CD44, O-6-methylguanine-DNA methyltransferase (MGMT), and PYD and CARD domain containing (TMS1), have been shown to be silenced in NB by aberrant hypermethylation of their promoters [16-18]. In general, screening studies in NB showed that frequently methylated genes are related to apoptotic pathways as well as to cell cycle regulation.

Recent studies in oncology research have increased genome coverage allowing the identification of new epigenetic biomarkers. In NB, two recent studies have investigated DNA methylation patterns using genome-wide technologies [19,20]. However, the number of clinically relevant epigenetic biomarkers is still very low. In here, we used methylation microarrays to identify robust and independent epigenetic biomarkers in NB.

Results

Genome-wide promoter methylation screening

With the purpose of identifying DNA methylation biomarkers, we first analyzed 48 primary NB tumors using the Infinium HumanMethylation27 BeadChip microarray. Clinical, biological, and follow-up data are summarized in Table 1. Patients were classified following the International Neuroblastoma Risk Group Staging System (INRGSS) guidelines as 10 L1 (stage 1 localized); 16 L2 (stage 2 localized), 18 M (metastatic), and 4 MS (metastatic special). Twenty patients relapsed: 1 L1 presented a local relapse and is alive and disease free, 7 L2 (5 presented local relapses, 1 metastatic and 1 combined, 4 of them died of disease); 11 M stages (2 presented local relapses, 3 presented metastatic relapses and 6 combined relapses, all of them died of disease). One MS stage presented a metastatic relapse and died due to disease progression.
Table 1

Clinical and biological data from the 48 patients included in the microarray analysis

 

INRG staging system

Characteristics

L1

L2

M

MS

Total

Number of patients

10

16

18

4

48

Pre-treatment risk group

  Very low + low

10

2

0

3

15

  Intermediate

0

10

2

0

12

  High

0

4

16

1

21

Age at diagnostic in months

  Median

11.1

18.6

27.6

4

16.1

  Range

1.5-22.7

4.8-109.3

6.6-79.8

1.9-6.7

1.5-109.3

Patients over 18 months at diagnostic

4

9

11

0

24

Sex

  Female

6

8

6

2

22

  Male

4

8

12

2

26

Primary site

  Adrenal

1

5

9

3

18

  Abdominal

4

5

2

0

11

  Cervical

0

1

0

1

2

  Thoracic

2

0

4

0

6

  Cervical-thoracic

0

1

0

0

1

  Thoracic-abdominal

1

1

0

0

2

  Pelvic

1

0

0

0

1

  Other sites

1

3

3

0

7

Protocol of treatment

  LNESG I

2

0

0

0

2

  LNESG II

1

0

0

0

1

  EUNS

0

2

0

0

2

  INES

3

5

2

2

12

  HR-NBL-1

0

4

9

0

13

  Other (national protocols)

4

4

7

2

17

MYCN status

  Amplified (%)

0 (0%)

4 (25%)

8 (44%)

1 (25%)

13 (27%)

  Not amplified (%)

10 (100%)

12 (75%)

10 (56%)

3 (75%)

35 (73)

1p status

  Normal (%)

8 (80%)

8 (50%)

8 (44%)

3 (75%)

27 (56%)

  Deleted (%)

0 (0%)

4 (25%)

7 (39%)

0 (0%)

11 (23%)

  Not determined (%)

2 (20%)

4 (25%)

3 (17%)

1 (25%)

10 (21%)

Patients with relapse (%)

1 (10%)

7 (44%)

11 (61%)

1 (25%)

20 (42%)

Type of relapse

  Local

1

5

2

0

8

  Metastatic

0

1

3

1

5

  Local + metastatic

0

1

6

0

7

Dead (%)

0 (0%)

5 (32%)

12 (67%)

1 (25%)

18 (38%)

Cause of death

  Disease progression

0

4

11

1

16

  Other

0

1

1

0

2

Time of follow-up (month)

  Median

152.1

93.5

45.4

129.3

117.4

  Range

87.8-208.3

9.4-206.5

3.4-161.2

17.2-214.9

3.4-214.9

Data distribution according to the methylation probe intensity from the Illumina array was shown to be bimodal (Figure 1A). Using minAS, a method for feature selection in multivariate data, cutoff values for data discretization were defined as follows: ≤0.3 hypomethylated ‘0’, >0.3 < 0.7 intermediate ‘0.5’, and >0.7 hypermethylated ‘1’.
Figure 1

DNA promoter methylation profiling from 48 NB primary tumors. (A) Data distribution according to the probe intensity. (B) Association between gene promoter hypermethylation and patients’ status. (C) Heat map representation of DNA promoter methylation data of the 70 genes differentially methylated in the cohort of 48 NB patients.

In order to understand to which extent gene promoter methylation is relevant to NB, we analyzed differences in the methylation levels among different patient subgroups. The most striking result of this analysis revealed a significant association between poor survival and global gene promoter hypermethylation (P = 0.036) (Figure 1B). Patients who died during follow-up time had higher promoter hypermethylation rates than survivors (median follow-up time for survivors of 12 years). No association was detected between survival and global hypomethylation of gene promoters. Other types of subgrouping (by age at diagnosis, MYCN status, or relapse) did not reveal any significant changes. However, comparing patients at different NB stages (L1 and MS vs. L2 vs. M) did indicate a near-significant hypermethylation of the runt-related transcription factor 3 (RUNX3) gene (P = 0.05). These results suggest that the overall promoter hypermethylation status at diagnosis could predict survival and that NB stages can be associated with methylation changes at specific genes.

After establishing a significant association between promoter hypermethylation and NB, we then aimed to identify particular methylation events. Comparison of methylation values of high-risk patients who died of disease, and of high-risk patients who survived, revealed 70 genes that significantly differentiate these two groups (Figure 1C and Table 2). Interestingly, only specific CpG sites within promoter regions were methylated (compare total probes vs. significative probes that were methylated on Table 2 and see the scheme on Additional file 1). This result revealed the importance of sufficient CpG probes at promoter regions to capture the dynamics of methylation changes. Sixteen genes with high impact on cancer biology were selected for validation. The methylated probes and P values as well as the methylation level of the selected hypermethylated genes are shown in Table 3. Detailed information of these genes’ promoter methylation sites in each patient is described in Additional file 2. These genes regulate the following: maturation and maintenance of the overall structure of the nervous system (neuronatin (NNAT)); cell cycle progression (cyclin D1 (CCND1), janus kinase 2 (JAK2), retinoblastoma 1 (RB1), leucine-rich repeat C4 protein (LRRC4), and tumor protein P73 (TP73)); cell growth and differentiation (dual specificity phosphatase 2 (DUSP2), paired box 8 (PAX8), and hepsin (HPN)); tumorigenesis (melanoma antigen family A, 2 (MAGEA2), RUNX3, cathepsin Z (CTSZ), teratocarcinoma-derived growth factor 1 (TDGF1), and tetraspanin 32 (TSPAN32)); apoptosis (JAK2 and esophageal cancer-related gene 4 protein (ECRG4)) and DNA repair mechanisms (MGMT).
Table 2

Seventy significantly hypermethylated genes in high-risk NB patients

Genes

Total probes

Sig. probes

ID. Sig. probes

P values

ANKRD30A

2

1

cg18493238

0.044554738

AYTL2

2

1

cg05365670

0.035713814

C14orf162

2

1

cg14912575

0.005363655

C1orf177

2

1

cg20903926

0.012915253

CCND1

18

2

cg11802013, cg07339327

0.0341943541, 0.047611226

CD164L2

2

1

cg15166089

0.021147357

CD3Z

1

1

cg09554443

0.018393493

CDH20

1

1

cg18509435

0.047064214

CGB1

2

1

cg17164520

0.020905647

CNNM1

2

1

cg15430659

0.020905647

CPZ

2

1

cg03292388

0.022686711

CRYAA

2

1

cg19378039

0.004448711

CSH1

2

1

cg11880211

0.035724213

CSMD2

2

1

cg05382123

0.035713814

CTSZ

7

1

cg01663968

0.037116693

DUSP2

2

1

cg01148741

0.012020747

ECRG4

2

1

cg10885338

0.004398444

ENTPD3

2

1

cg17200465

0.022686711

FLJ21736

2

1

cg09407859

0.039315449

FLJ30313

2

1

cg11682508

0.035713814

FLJ39822

2

1

cg04143809

0.047611226

FUT6

2

1

cg05444024

0.03549584

GBGT1

2

1

cg01169778

0.037116693

GDPD2

2

1

cg08254263

0.034194354

GGT6

2

1

cg22628873

0.010030882

GNGT2

2

2

cg17839611, cg24456340

0.0135039944, 0.0183934926

GPR1

1

1

cg19132372

0.047064214

GPR55

2

2

cg20287234, cg13531460

0.005474794, 0.0343533092

HIST1H1A

2

1

cg14652095

0.021147357

HOXB1

2

1

cg17233506

0.020905647

HPN

2

1

cg24715735

0.022686711

JAK2

1

1

cg20394284

0.035713814

KCNQ1

23

1

cg01734338

0.034194354

KRT5

2

2

cg23645091, cg04254916

0.0067809343, 0.0072736595

KRTHB3

2

1

cg19258973

0.012020747

KRTHB5

2

1

cg06132342

0.000549806

KRTHB6

2

2

cg04123507, cg16911220

0.000926591, 0.0053636552

LAT1-3TM

1

1

cg19889584

0.013503994

LOC161931

2

1

cg17561452

0.044554738

LRRC4

2

1

cg21129531

0.013503994

LY6D

2

1

cg05800321

0.032446014

MAGEA2

2

1

cg01743008

0.034353309

MGC35169

2

1

cg16384137

0.021877651

MGMT

26

1

cg02330106

0.031115525

MKNK2

2

1

cg21030400

0.031115525

MSH4

2

1

cg22035229

0.044554738

MUC5B

2

1

cg22407504

0.030056033

NNAT

7

3

cg22510412, cg12862537, cg21588305

0.0182304654, 0.0211473571, 0.0324460138

PADI3

2

1

cg01459162

0.010896145

PAX8

2

1

cg25042226

0.007819456

PECAM1

2

1

cg03886110

0.021147357

PIP5K2A

2

1

cg26368842

0.047611226

PLA2G12B

2

1

cg21820890

0.015211786

POLR3H

1

1

cg02738086

0.044554738

PSCD4

2

1

cg05259765

0.012683212

PTGER1

2

1

cg10468702

0.022686711

PTGIR

2

1

cg11822964

0.035713814

RB1

21

1

cg07880715

0.047611226

RUNX3

19

2

cg04757093, cg25178645

0.0004487598, 0.0390263555

SCNN1A

2

1

cg18738906

0.044000585

SMPX

2

1

cg05856884

0.035713814

SPO11

2

1

cg13888886

0.001023813

TBX4

2

1

cg03866607

0.006780934

TDGF1

2

1

cg10242476

0.037116693

THEG

1

1

cg27227797

0.030056033

TP73

12

3

cg03846767, cg26208930, cg25115460

0.0038887033, 0.0324460138, 0.0470642136

TPM1

2

1

cg00520135

0.007819456

TRIP10

2

1

cg02085507

0.037116693

TSPAN32

2

1

cg00041575

0.007819456

ZNF80

2

1

cg13334054

0.012599325

Table 3

Hypermethylated genes selected for pyrosequencing validation

Genes

Sign. probes

Total probes

P values

Gene function

NNAT

3

7

0.018, 0.021, 0.032

Involved in the maturation or maintenance of the overall structure of the nervous system

TP73

3

12

0.004, 0.032, 0.047

Participates in the apoptotic response to DNA damage

CCND1

2

18

0.034, 0.048

Essential for the control of the cell cycle at the G1/S (start) transition

RUNX3

2

19

0.0004, 0.039

Tumor suppressor gene

CTSZ

1

7

0.037

May be involved in tumorigenesis and metastasis

DUSP2

1

2

0.012

Regulates cellular proliferation and differentiation

HPN

1

2

0.023

Plays an essential role in cell growth and maintenance of cell morphology

JAK2

1

1

0.036

Involved in cell cycle progression, apoptosis, mitotic recombination, genetic instability, and histone modifications

LRRC4

1

2

0.014

Significantly downregulated in primary brain tumors. The exact function of the protein encoded is unknown

MAGEA2

1

2

0.034

May play a role in embryonal development and tumor transformation or aspects of tumor progression

MGMT

1

26

0.031

Involved in DNA repair mechanisms

PAX8

1

2

0.008

Transcription factor. Mutations in this gene are associated with carcinogenesis

ECRG4

1

2

0.004

Antiapoptotic gene

RB1

1

21

0.048

Negative regulator of the cell cycle

TDGF1

1

2

0.037

Plays an essential role in embryonic development and tumor growth

TSPAN32

1

2

0.008

Is one of several tumor-suppressing subtransferable fragments located in the imprinted gene domain of chromosome 11p15.5

Validation of the prognostic power of DNA methylation biomarkers by bisulfite pyrosequencing

To validate the specific CpG methylated sites, we performed sequencing analysis of the 16 promoter genes. Primers for bisulfite pyrosequencing were carefully designed flanking the methylated CpG sites detected in the array. Validation of results was carried out with an independent cohort of 83 NB including 12 L1, 21 L2, 42 M, and 8 MS stages. Clinical and biological data from the validation cohort is summarized in Table 4.
Table 4

Clinical and biological characteristics of the NB cohort used for pyrosequencing validation

 

INRG staging system

Characteristics

L1

L2

M

MS

Total

Number of patients

12

21

42

8

83

Patients over 18 months at diagnostic

3

8

38

0

49

Sex

 Female

8

6

16

3

33

 Male

4

15

26

5

50

MYCN status

 Amplified (%)

0

8 (38%)

16 (38%)

1 (12.5%)

25 (30%)

 Not amplified (%)

12 (100%)

13 (62%)

23 (55%)

7 (87.5%)

55 (66%)

 Not determined (%)

0

0

3 (7%)

0

3 (4%)

Patients with relapse (%)

3 (25%)

5 (24%)

22 (52%)

0

30 (36%)

Dead (%)

0

6 (29%)

25 (60%)

0

31 (37%)

We performed a multivariate analysis of variance (MANOVA) statistical analysis in order to study the methylation variations of selected genes among the established subgroups (see below and “Methods” section) of NB patients. Overall, we found significantly higher gene promoter methylation rates in patients with the following characteristics: metastatic tumors (either stage M or MS) (Figure 2A), aged 18 months or older at first diagnosis (Figure 2B) and MYCN amplification (Figure 2C). The same applies to patients who relapsed or died (Figure 2D). In particular, methylated status of TDGF1 and PAX8 allowed us to differentiate all the clinical subgroups mentioned above (Figure 2). In addition, high methylation rates of RUNX3, ECRG4, CTSZ, and RB1 also distinguished all NB subgroups but not MYCN-amplified status (Figure 2). We also found highest methylation rates of LRRC4 and CCND1 in patients older than 18 months (Figure 2B). Furthermore, DUSP2, TP73, JAK2, MGMT, and HPN methylation rates were significantly higher in MYCN-amplified patients than in non-amplified patients (Figure 2C). These results demonstrate that these set of methylated gene promoters allow the discrimination of specific NB subgroups defined in Figure 2.
Figure 2

Difference in methylation status of indicated genes (% units) among clinical variables in NB patients. Global P value tests the hypothesis of equal methylation status in both groups. (A) Stages M - MS vs. L1-L2. (B) Patients younger than 18 months at diagnosis vs. patients older than 18 months at diagnosis. (C) MYCN-amplified patients vs. patients with no-MYCN amplification. (D) Patients with events (relapse/death) vs. patients without events.

To study the influence of the degree of the 16 validated genes’ promoter methylation on survival, we evaluated them as independent prognostic variables by the elastic net penalized Cox’s regression model. Higher gene promoter methylation rates of RB1, PAX8, and TDGF1 remained as independent predictors of overall survival (OS) after adjusting for known prognostic factors (Table 5A). On the other hand, RB1 and TDGF1 but not PAX8 predicted worse event-free survival (EFS) (Table 5B). Information of TDGF1 and RB1 promoter methylation sites in the validation cohort of patients is described in Additional file 3. These regressions also confirmed the well-established stage, MYCN status, and age as independent predictors, thus further supporting the consistency of this model. The penalized coefficient of each independent variable directly associates with survival. Thus, MYCN amplification and staging have the highest influence on survival followed by the degree of TDGF1 and RB1 promoter methylation. Age at diagnosis had a lower impact in EFS and none in OS.
Table 5

Cox elastic net results for (A) OS (B) and EFS

Variable

Penalized coefficient

A. OS

  MYCN amplification

0.7217

  Stage M

0.4871

   TDGF1

0.0133

   RB1

0.0045

   PAX8

0.0002

B. EFS

  MYCN amplification

0.455

  Stage MS

−0.34

  Stage M

0.569

  Age

0.002

   TDGF1

0.009

   RB1

0.005

Non-zero coefficients after elastic net penalization. Negative coefficients stand for variables lowering risk and positive coefficients for variables increasing risk.

RB1 expression in NB correlates with survival

Taking into account that RB1 and TDGF1 promoter methylation is one of the mechanisms responsible for the downregulation of these genes in other tumors [21,22], we explored RB1 and TDGF1 expression in NB, the two only independent predictors of EFS and OS that we found in this study. For this purpose, we analyzed a new cohort of 251 NB patients using the R2: microarray analysis and visualization platform (http://r2.arnc.nl). The results of this analysis are displayed in Figure 3. The Kaplan-Meier plots show the significant association between low expression levels of RB1 and poorer outcome (Figure 3A). Based on these findings, promoter methylation could contribute to the transcriptional silencing of RB1 in NB. On the other hand, low expression levels of TDGF1 associates with better outcome in this patient cohort (Figure 3B). Since our results show that high methylation levels of TDGF1 negatively affect patient survival, the TDGF1 expression-Kaplan-Meier plots suggest that this particular epigenetic event on cg10242476 is not involved in TDGF1 downregulation but rather in its expression. In order to investigate the positive correlation between CpG methylation and TDGF1 expression, we explored the correlation between TDGF1 expression and the DNA methyltransferases DNMT1, DNMT3A, and DNMT3B. As shown in Additional file 4, we found a significant correlation between TDGF1 and DNMT1 and DNMT3B but not between TDGF1 and DNMT3A in the cohort of patients with MYCN amplification. These correlations further support that DNA methylation might as well be implicated in the upregulation of TDGF1 expression.
Figure 3

Results of the analysis of a new cohort of 251 NB patients using the R2. Expression graphs (on the right) and Kaplan-Meier plots (on the left) for RB1 (A) and TDGF1 (B) obtained using the R2: microarray analysis and visualization platform in a cohort of 251 NB patients.

Discussion

Most of the DNA methylation studies in NB are experimentally limited because they used previously selected candidate genes based on their implication in cancer development or tumor biology [16,23-25]. A recent report described the signature of hypomethylated regions within non-promoter regulatory sites [20], thus complementing our findings on hypermethylated promoter regions. These characteristic profiles may reflect specific chromatin remodeling events that could contribute to the generation of chromosomal instability. Another recent genome-wide study has proposed new epigenetic biomarkers of interest in NB patients [19,26]. However, authors only used cell lines in the selection phase of potential prognostic DNA methylation biomarkers. Although candidate genes were further validated in 89 primary tumor samples, this approach may have skipped many candidate biomarkers occurring on primary tumors due to the passage-dependent epigenetic changes on cell lines. Lau et al. applied a genome-wide screen of DNA methylation changes using NB primary tumors [26]. In their study, they pre-selected candidate genes which may resulted in the loss of novel epigenetic biomarkers. In here, we adopted a non-targeted approach based on genome-wide screen of DNA-methylation changes. Our results reveal 70 candidate genes that showed epigenetic changes within the high-risk group. We further validated 16 out of 70 candidate genes, leaving for future validation studies the remaining group.

The two patient cohorts used in this study included a representative distribution of all the INRG-based NB subgroups, being among the highest and well-characterized NB cohorts used for genome-wide epigenetic studies so far reported to our knowledge. Interestingly, the degree of methylation of the proposed biomarkers is able to distinguish between different subgroups of NB: patients who were older than 18 months at diagnosis from younger than 18 months at diagnosis, patients with MYCN-amplified tumors from MYCN non-amplified tumors, patients with metastatic tumors from localized tumors and relapsed or dead patients from relapse-free survivors. These findings support the idea that aberrant DNA methylation could be related to NB pathogenesis. Notably, only RB1 and TDGF1 remained as independent prognostic predictors of poorer OS and EFS. Therefore, our predictive epigenetic biomarkers constitute a new set of robust risk predictors of the disease.

TDGF1 promoter is hypomethylated and highly expressed in human-induced pluripotent stem (iPS) and embryonic stem (ES) cells [27]. Using the same Illumina array, our results reveal a different CpG site not only localized downstream but also within the first exon (5′-UTR) of TDGF1 gene (cg10242476) that significantly predicts EFS and OS in NB patients. Importantly, TDGF1 has been shown to be regulated by the ES cell-related transcription factors Oct4/Nanog and to a lesser extent by the DNA methylation status of the promoter region [28]. TDGF1 has been found overexpressed in a variety of human tumors such as breast, colorectal, and gastric cancers, and high expression levels of this gene are associated with poorer prognosis in those tumors [29]. Using the microarray analysis and visualization platform R2 (http://r2.arnc.nl), we confirmed that high TDGF1 expression levels are also associated with poor prognosis in NB (Figure 3B). Following this reasoning, higher methylation of this CpG would correlate with higher TDGF1 expression and, therefore, with poorer prognosis. Interestingly, this CpG site is localized in the first exon of TDGF1 transcript variant 1 (NM_003212) and coincides with the first intron of TDGF1 transcript variant 2 (NM_001174136). One plausible explanation for this correlation is that intronic CpG methylation results in TDGF1 transcript variant 2 expression. In this regard, regulation of imprinted IGF2R expression is mediated by methylation of an intronic CpG island [30]. Taken all together, these results suggest that intronic CpG (cg10242476) methylation in TDGF1 transcript variant 2 positively regulates its expression in NB whereas CpG (cg27371741) hypomethylation in the first exon of TDGF1 transcript variant 1 regulates its expression in embryonic stem cells. The strong association between hypermethylation and poor prognosis reflects the consequences of epigenetic changes occurring in high-risk NB.

Loss of RB1 expression is associated with a higher grade of malignancies and seems to be a prognostic indicator in a variety of human tumors [31,32]. Hypermethylation of RB1 CpG island is a common epigenetic event associated with the development of malignant nervous system tumors [22]. A clear correlation between loss of RB1 expression and promoter hypermethylation was found in glioblastomas [32]. Although hypermethylation of RB1 promoter has previously been reported in NB, no association with patient survival was included in the study [33]. Moreover, the methylation-specific PCR approach used to detect RB1 promoter methylation does not allow to study the broad CpG sites analyzed with a genome-wide approach. We describe for the first time that the degree of RB1 promoter methylation associates with poorer prognosis in NB patients. In agreement with our findings, using the publicly available R2 platform, we found that low expression levels of RB1 associates with poorer outcome. These results suggest that RB1 promoter methylation could contribute to its silencing and enhance NB development and aggressiveness.

Conclusions

Biomarkers are playing an increasing role in the management of NB patients and, together with drug targets, represent the future analytical platforms for personalized clinical intervention. In this work, we demonstrate that high promoter methylation rates of TDGF1 and RB1 genes are independent predictive biomarkers of NB aggressiveness and disease progression. Our findings highlight the use of methylation profiling to identify risk-independent prognostic markers in NB and reinforce the connection between epigenetic events and NB biology. Taking into account that survival rates remain sadly low in high-risk NB patients, our epigenetic biomarkers are valuable tools for future patient stratification and treatment management.

Methods

Patients and samples

Tumor samples were resected from 131 children diagnosed with NB between years 1996 and 2010 in Spanish cooperative hospitals. Patients were included in different national and European studies (LNESG I and II, INES, EUNS, N-AR-99, N-II-92, and HR-NBL1) and carefully selected in order to have all NB subtypes represented (Table 1). Forty-eight NB tumor samples were used for genome-wide promoter methylation analysis, and an additional cohort of 83 tumor samples were used for validation. Staging and risk stratification was established according to International Neuroblastoma Risk Group (INRG) criteria [2]. However, very low- and low-risk patients were joined for the statistical analysis and were considered as low-risk group. Samples were centrally reviewed and classified according to the International Neuroblastoma Pathology Committee (INPC) system [5,34]. Biological studies included status of MYCN and 1p, both studied by FISH according to ENQUA guidelines [35,36]. Parents or guardians signed an informed consent statement for sample and data management. The study was approved by the Hospital La Fe Research Institute Ethical Committee.

Genome-wide promoter methylation profiling

Genomic DNA was extracted from frozen tumor tissues by a standard proteinase K and phenol-chloroform extraction protocol. The quality and quantity of the extracted DNA was measured by A260 spectrophotometric absorbance. Genomic DNA bisulphite modification was carried out following the manufacturer’s instructions (Zymo Research). Promoter methylation analysis was performed using the Infinium HumanMethylation27 BeadChip (Illumina Inc., CA, USA) at the Spanish National Cancer Center (CEGEN-CNIO, Madrid, Spain). The Infinium HumanMethylation27 BeadChip allowed us to interrogate 27,578 highly informative CpG sites per sample, located within the proximal gene promoter regions of transcription. A file containing all CpG sites used in the Illumina array HumanMethylation27 BeadChip can be localized at http://support.illumina.com/array/array_kits/infinium_humanmethylation27_beadchip_kit/downloads.html. BeadStudio software (version 3, Illumina Inc, USA) [37,38] was used to analyze the data. For each CpG site, we calculated the beta-value (b-value), a quantitative measure of DNA methylation levels ranging from 0 for completely unmethylated to 1 for completely methylated cytosines.

Bisulfite pyrosequencing

Promoter methylation data from the array was validated by bisulfite pyrosequencing. Genomic DNA was bisulfite modified using EZ DNA Methylation Gold Kit™ (Zymo Research). A subsequent PCR amplification was performed using biotinylated primers designed with the PyroMark Assay Design 2.0 software, Qiagen (Table 6). The pyrosequencing and data analysis were performed in a PyroMark Q24 System version 2.0.6 (Qiagen) following the manufacturer’s instructions.
Table 6

Sequences of primers used for pyrosequencing validation

Genes

Amplification primers

Sequencing primers

Name

Sequence

CTSZ

Forward

GTTGGGGYGTAGGTGGGTAT

GTAGTTTTGGGGGGA

Reverse

[Btn]CACAAACATCAAAACTCACCCTAAATAT

DUSP2

Forward

TTGAGTGGTTTGGGATAGGTTAA

GGGATAGGTTAAAGGGT

Reverse

[Btn]AAAACRCAATCTAAACTAACCTAAAAACTT

CCND1

Forward

[Btn]GGGYGGTTGGGTTTGTGTATTT

CATATTTATCTTTTTATCTTCTACT

Reverse

ACCACCCTACCCTAATTCT

ECRG4

Forward

ATTTTGGGTAAGGAGGGTTAG

GGGTAAAAGGGTTGT

Reverse

[Btn]TACCATTTACCTCCTCTAAATTACCA

RUNX3

Forward

[Btn]GGTTTTGGGAATTAGAGTTTAAGG

AAAAAAAAATCAATTCCAACT

Reverse

ACTAACATAACCCCRAAATAATACATCCTA

TP73

Forward

AGTTAGTTGATAGAATTAAGGGAGATG

ATGGGAAAAGYGAAAATGTTAATAA

Reverse

[Btn]ATCTACACACRCCAAAAAACTAATATCCC

LCCR4

Forward

TGGAAAGAGGAGTTTTTAGTTTATTTAAG

AAATTTTAGGYGATGGTGAATTA

Reverse

[Btn]ATTACCTACCACAAAAACTTCATAATAT

MAGEA2

Forward

GATTTGYGTATTGGAGGTTAGAGGATA

GTAAGAYGTYGAGGGAGGATTGA

Reverse

[Btn]TAAAAAATCTTCCCCTACAAAATAATCCA

MGMT

Forward

TTTTTGGAGAGYGGTTGAGTTAGGT

AGGTTATYGGTGATTGTAGTT

Reverse

[Btn]CCAAACCAACAAAAACCCTATCA

NNAT

Forward

TGTAGGTTAGGGATTGGGGAGAA

TTAAAGTAAAATTTAAAAGTAAGT

Reverse

[Btn]TCCATCTTAACCCCCTTCCAA

TSPAN32

Forward

GAGGTTTATAAAGTTTTTTTTTGGAGG

GAGGTTTTAYGTGAGTGTGA

Reverse

[Btn]CACCCTTTAAAATATCCTATAACAACTT

HPN

Forward

ATGAAATAAAGATTTTTGGATTTGATGTAT

GTGAGTTTYGTTATTTTTTTTTTAT

Reverse

[Btn]TAAATAACTTCACCTATAAACCCTCAAAT

JAK2

Forward

[Btn]TTTTTTAGATAGTTATGGGATTGGTTTAT

AATAAAAACRACAAAACAACAAACA

Reverse

ACACTCCTTACCCTACTAAATTATATT

PAX 8

Forward

GTGATTTAGGAGGATTTAGAGAATTTTATT

ATTTTTTTGTATTTAGTTAGTTAA

Reverse

[Btn]CTCTCCTCCTTCTAAAATTTATTCC

TDGF1

Forward

ATTGGGGTTTGTTGTTGAAGAA

TTTATTTTTTTTTTAAATTGTTATT

Reverse

[Btn]AAACAACCAAAAAAAAACATTCATCTCC

RB1

Forward

[Btn]TTGGGGTTGGTTTATTTATTTAGTTTTG

TTACCCCTCCTCCCC

Reverse

AACRAAAAACCCTTACCCCTCCTC

Statistical analysis

The methylation status at each array probe was established by analyzing the distribution of b-values for all samples and CpG sites and transforming data into discrete values. The cutoff values for hyper- and hypomethylation were established using the minAS method [39] and considering the bimodal distribution of b-values. Differential methylation analysis was done at the single CpG, CpG island, and gene-centric levels. For the analysis of CpG island, individual probes were considered independent observation, whereas the gene-level analysis simply counted the percentage of CpG and CpG island associated to each gene that were declared significant. Differential methylation across the NB subgroups was determined by either a proportion test when several groups were compared or by Fisher’s exact test when only two subgroups were involved. Subgroups were established and compared based on clinical and biological parameters such as age at first diagnosis (younger vs. older than 18 months), MYCN status (MYCN-amplified vs. non-amplified tumors), stage (L1-L2 vs. M-MS), risk groups (low and intermediate vs. high risk), relapse, and death (patients with events (relapse/death) vs. patients without events). Nominal P values were corrected for multiple tests using the Benjamini and Hochberg FDR procedure [40].

Data distribution from pyrosequencing analysis was not bimodal and varies among genes; therefore, variables were analyzed as continuous. The relationship between the methylation status and NB risk factors was evaluated using a MANOVA test. A Cox elastic net analysis [41] was performed to evaluate the influence on survival of gene promoter methylation. This novel statistical analysis method is especially suited for analyzing data with many variables and few observations by performing variable selection. This is done by penalizing predictors’ coefficients towards zero according to their association with survival. Coefficients from variables with less influence on survival were more penalized, dropped to zero, and excluded from the predictive model.

For all the above-mentioned statistical tests, R software (version 3.0.2) and package glmnet (version 1.9-5) were used. For EFS analysis, time to event was defined as the time from diagnosis until the time of first occurrence of relapse, progression, or death. For OS, time to event was defined as time until death or until last contact if the patient was alive. P values <0.05 were considered statistically significant.

Declarations

Acknowledgements

This work was supported in part by FIS PS09/02323 and Prometeo 2013. Conselleria de Educació Cultura I Esports.

We thank Désirée Ramal for her help in the data management and all the Spanish collaborating hospitals who register their patients in neuroblastoma studies. We thank Ana Gonzalez-Neira (head of the Human Genotyping Unit at CEGEN-CNIO, Madrid, Spain) for her technical support.

Authors’ Affiliations

(1)
Pediatric Oncology Unit, Hospital Universitari i Politècnic La Fe
(2)
Chromatin and Disease Group, Cancer Epigenetics and Biology Programme (PEBC) Bellvitge Biomedical Research Institute (IDIBELL)
(3)
Biostatistics Unit, Instituto de Investigación Sanitaria La Fe
(4)
Genomics of Gene Expression Lab, Centro de Investigaciones Príncipe Felipe
(5)
Department of Pathology, Medical School, University of Valencia
(6)
Department of Pathology, Hospital Universitari i Politècnic La Fe
(7)
Laboratory of Cellular and Molecular Biology, Instituto de Investigación Sanitaria La Fe

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© Yañez et al.; licensee BioMed Central. 2015

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