[Skip to Content]
Access to paid content on this site is currently suspended due to excessive activity being detected from your IP address 54.163.92.62. Please contact the publisher to request reinstatement.
[Skip to Content Landing]
Download PDF
Figure 1.
Messenger RNA expression (relative to normal mucosa) vs promoter methylation (based on the mean methylation index [MtI]) for the cyclin A1 gene (n = 42).

Messenger RNA expression (relative to normal mucosa) vs promoter methylation (based on the mean methylation index [MtI]) for the cyclin A1 gene (n = 42).

Figure 2.
Messenger RNA expression (relative to normal mucosa) vs promoter methylation (based on the mean methylation index [MtI]) for the MGMT gene (n = 43).

Messenger RNA expression (relative to normal mucosa) vs promoter methylation (based on the mean methylation index [MtI]) for the MGMT gene (n = 43).

Figure 3.
Messenger RNA expression (relative to normal mucosa) vs promoter methylation (based on the mean methylation index [MtI]) for the RARB gene (n = 45).

Messenger RNA expression (relative to normal mucosa) vs promoter methylation (based on the mean methylation index [MtI]) for the RARB gene (n = 45).

Figure 4.
Messenger RNA expression (relative to normal mucosa) vs promoter methylation (based on the mean methylation index [MtI]) for the E-cadherin gene (n = 39).

Messenger RNA expression (relative to normal mucosa) vs promoter methylation (based on the mean methylation index [MtI]) for the E-cadherin gene (n = 39).

Figure 5.
Messenger RNA expression (relative to normal mucosa) vs promoter methylation (based on the mean methylation index [MtI]) for the P16 gene (n = 45).

Messenger RNA expression (relative to normal mucosa) vs promoter methylation (based on the mean methylation index [MtI]) for the P16 gene (n = 45).

Table. Promoter Methylation (Based on the Mean Methylation Index [MtI]) and Messenger RNA (mRNA) Expression Data for 37 Tumor and 8 Normal Control Samplesa
Table. Promoter Methylation (Based on the Mean Methylation Index [MtI]) and Messenger RNA (mRNA) Expression Data for 37 Tumor and 8 Normal Control Samplesa
1.
Shaw  R The epigenetics of oral cancer. Int J Oral Maxillofac Surg 2006;35 (2) 101- 108
PubMed
2.
Ha  PKCalifano  JA Promoter methylation and inactivation of tumour-suppressor genes in oral squamous-cell carcinoma. Lancet Oncol 2006;7 (1) 77- 82
PubMed
3.
Singer-Sam  JLeBon  JMTanguay  RLRiggs  AD A quantitative HpaII-PCR assay to measure methylation of DNA from a small number of cells. Nucleic Acids Res 1990;18 (3) 687
PubMed
4.
Hatada  IHayashizaki  YHirotsune  SKomatsubara  HMukai  T A genomic scanning method for higher organisms using restriction sites as landmarks. Proc Natl Acad Sci U S A 1991;88 (21) 9523- 9527
PubMed
5.
Herman  JGGraff  JRMyöhänen  SNelkin  BDBaylin  SB Methylation-specific PCR: a novel PCR assay for methylation status of CpG islands. Proc Natl Acad Sci U S A 1996;93 (18) 9821- 9826
PubMed
6.
Adorján  PDistler  JLipscher  E  et al.  Tumour class prediction and discovery by microarray-based DNA methylation analysis. Nucleic Acids Res 2002;30 (5) e21http://nar.oxfordjournals.org/cgi/content/full/30/5/e21. Accessed December 28, 2007
PubMed
7.
Eads  CADanenberg  KDKawakami  K  et al.  MethyLight: a high-throughput assay to measure DNA methylation. Nucleic Acids Res 2000;28 (8) e32http://nar.oxfordjournals.org/cgi/content/full/28/8/e32. Accessed December 28, 2007
PubMed
8.
Gonzalgo  MLJones  PA Rapid quantification of methylation differences at specific sites using methylation-sensitive single nucleotide primer extension (MS-SNuPE). Nucleic Acids Res 1997;25 (12) 2529- 2531
PubMed
9.
Frommer  M McDonald  LEMillar  DS  et al.  A genomic sequencing protocol that yields a positive display of 5-methylcytosine residues in individual DNA strands. Proc Natl Acad Sci U S A 1992;89 (5) 1827- 1831
PubMed
10.
Xiong  ZLaird  PW COBRA: a sensitive and quantitative DNA methylation assay. Nucleic Acids Res 1997;25 (12) 2532- 2534
PubMed
11.
Colella  SShen  LBaggerly  KAIssa  JPKrahe  R Sensitive and quantitative universal pyrosequencing methylation analysis of CpG sites. Biotechniques 2003;35 (1) 146- 150
PubMed
12.
Tost  J Dunker  JGut  IG Analysis and quantification of multiple methylation variable positions in CpG islands by pyrosequencing. Biotechniques 2003;35 (1) 152- 156
PubMed
13.
Tokumaru  YYamashita  KOsada  M  et al.  Inverse correlation between cyclin A1 hypermethylation and p53 mutation in head and neck cancer identified by reversal of epigenetic silencing. Cancer Res 2004;64 (17) 5982- 5987
PubMed
14.
Bolt  JVo  QNKim  WJ  et al.  The ATM/p53 pathway is commonly targeted for inactivation in squamous cell carcinoma of the head and neck (SCCHN) by multiple molecular mechanisms. Oral Oncol 2005;41 (10) 1013- 1020
PubMed
15.
Deng  GChen  AHong  JChae  HSKim  YS Methylation of CpG in a small region of the hMLH1 promoter invariably correlates with the absence of gene expression. Cancer Res 1999;59 (9) 2029- 2033
PubMed
16.
Zuo  CAi  LRatliff  P  et al.  O6-methylguanine-DNA methyltransferase gene: epigenetic silencing and prognostic value in head and neck squamous cell carcinoma. Cancer Epidemiol Biomarkers Prev 2004;13 (6) 967- 975
PubMed
17.
Youssef  EMIssa  JPLotan  R Regulation of RARβ1 expression in head and neck cancer cells by cell density–dependent chromatin remodeling. Cancer Biol Ther 2004;3 (10) 1002- 1006
PubMed
18.
Youssef  EMLotan  DIssa  JP  et al.  Hypermethylation of the retinoic acid receptor–β2 gene in head and neck carcinogenesis. Clin Cancer Res 2004;10 (5) 1733- 1742
PubMed
19.
Yeh  KTShih  MCLin  TH  et al.  The correlation between CpG methylation on promoter and protein expression of E-cadherin in oral squamous cell carcinoma. Anticancer Res 2002;22 (6C) ((6C)) 3971- 3975
PubMed
20.
Xi  SDyer  KFKimak  M  et al.  Decreased STAT1 expression by promoter methylation in squamous cell carcinogenesis. J Natl Cancer Inst 2006;98 (3) 181- 189
PubMed
21.
Clément  GBraunschweig  RPasquier  NBosman  FTBenhattar  J Methylation of APC, TIMP3, and TERT: a new predictive marker to distinguish Barrett's oesophagus patients at risk for malignant transformation. J Pathol 2006;208 (1) 100- 107
PubMed
22.
Shaw  RJLiloglou  TRogers  SN  et al.  Promoter methylation of P16, RARβ, E-cadherin, cyclin A1 and cytoglobin in oral cancer: quantitative evaluation using pyrosequencing. Br J Cancer 2006;94 (4) 561- 568
PubMed
23.
Livak  KJSchmittgen  TD Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT method. Methods 2001;25 (4) 402- 408
PubMed
24.
Shaw  RJAkufo-Tetteh  EKRisk  JMField  JKLiloglou  T Methylation enrichment pyrosequencing: combining the specificity of MSP with validation by pyrosequencing. Nucleic Acids Res 2006;34 (11) e78
PubMed10.1093/nar/gkl424
25.
Maruya  SIssa  JPWeber  RS  et al.  Differential methylation status of tumor-associated genes in head and neck squamous carcinoma: incidence and potential implications. Clin Cancer Res 2004;10 (11) 3825- 3830
PubMed
26.
Ogi  KToyota  MOhe-Toyota  M  et al.  Aberrant methylation of multiple genes and clinicopathological features in oral squamous cell carcinoma. Clin Cancer Res 2002;8 (10) 3164- 3171
PubMed
27.
Williams  MD Chakravarti  NKies  MS  et al.  Implications of methylation patterns of cancer genes in salivary gland tumors. Clin Cancer Res 2006;12 (24) 7353- 7358
PubMed
28.
Kelavkar  UPHarya  NSHutzley  J  et al.  DNA methylation paradigm shift: 15-lipoxygenase-1 upregulation in prostatic intraepithelial neoplasia and prostate cancer by atypical promoter hypermethylation. Prostaglandins Other Lipid Mediat 2007;82 (1-4) 185- 197
PubMed
Original Article
March 01, 2008

The Role of Pyrosequencing in Head and Neck Cancer EpigeneticsCorrelation of Quantitative Methylation Data With Gene Expression

Author Affiliations

Author Affiliations: Molecular Genetics & Oncology Group, School of Dental Sciences (Drs Shaw, Hall, Field, and Risk), and Cancer Research Centre, Roy Castle Lung Cancer Research Programme (Drs Liloglou and Field), University of Liverpool, and Regional Maxillofacial Unit, University Hospital Aintree (Dr Shaw and Mr Lowe), Liverpool, and Department of Oral Pathology, Turner Dental School, Manchester (Dr Sloan), England.

Arch Otolaryngol Head Neck Surg. 2008;134(3):251-256. doi:10.1001/archoto.2007.50
Abstract

Objective  To evaluate promoter methylation quantitation using recently described pyrosequencing techniques by correlation with messenger RNA (mRNA) expression.

Design  DNA was extracted from tissue samples and was subjected to bisulphite conversion. Quantitative methylation data for multiple CpG sites in each of 9 gene promoters were obtained for tumors using pyrosequencing. RNA was extracted and converted to complementary DNA, and this formed the template for relative quantitation assays of the expression of each gene by real-time reverse transcription–polymerase chain reaction.

Setting  Academic research.

Patients  Thirty-seven patients with head and neck squamous cell carcinoma.

Main Outcome Measures  The genes studied were P16 (OMIM 600160), cyclin A1 (OMIM 604036), RARB (OMIM 180220), E-cadherin (OMIM 192090), MGMT (OMIM 156569), STAT1 (OMIM 600555), ATM (OMIM 607585), hMLH1 (OMIM 120436), and TIMP3 (OMIM 188826). Immunohistochemistry was also performed for p16.

Results  STAT1, TIMP3, ATM, and hMLH1 promoters were essentially unmethylated in all cases. The data for cyclin A1 (Spearman rank correlation, ρ = −0.53; P < .001), MGMT (ρ = −0.53, P < .001), and RARB (ρ = −0.34, P =.02) showed the expected negative correlation between levels of methylation and mRNA expression. The data relating to E-cadherin were inconclusive. Surprisingly, P16 expression was statistically significantly greater in those cases with higher levels of methylation (ρ = 0.57, P < .001), a finding at odds with assumptions usually made in the literature relating gene promoter methylation to reduced gene expression. The results from p16 immunohistochemistry were in keeping with the mRNA data, but the number of positive staining samples proved too few for statistical analysis.

Conclusions  These data present a novel perspective on head and neck cancer epigenetics and reveal new and some unexpected associations and findings. The advantages of pyrosequencing over nonquantitative techniques are discussed in analyses of this nature.

Although the role of gene promoter methylation in head and neck cancer is not in doubt,1,2 the methods available to detect and to quantify methylation are numerous, and opinions of the merits of each differ. The methods described might best be divided into those using and those not using bisulphite conversion. In the case of the former methods, methylation-sensitive and methylation-insensitive restriction enzymes3 are used to digest DNA followed by Southern blot hybridization, and derivations of this technique use restriction landmark genomic scanning4 to analyze the digested product. In the case of the latter methods, a landmark article describing bisulphite conversion was published by Herman et al5 in 1996. The DNA sample is treated using bisulphite, which specifically converts all methylated cytosine residues to uracil. Uracil is read by subsequent polymerase chain reactions (PCRs) as thymidine; hence, this method transfers a positive or negative methylation status to a C/T polymorphism, which is subsequently more readily amenable to measurement by several available techniques of DNA assay. Methylation-specific PCR (MSP), also described by Herman et al,5 uses 2 sets of PCR primers complementary to “methylated” or “un methylated” sequences, the former to select for cytosine in the target sequence and the latter for thymidine. This method is cheap, rapid, and highly sensitive compared with predecessors and provided the basis of most methylation reports in the oncology literature for the subsequent decade.

As technology has progressed and the limitations of MSP have become apparent,6 the resultant demand for other techniques has been met by numerous alternatives. Notable oversensitivity, with false-positive methylation results, can be the outcome of inadequate completeness of bisulphite treatment (inherent PCR specificity limitations) or the use of high numbers of PCR cycles. In addition, MSP usually gives results for 2 to 4 CpG sites in a gene promoter, and assumptions are generally made about comethylation of neighboring sequences. Some MSP modifications have been described, including the use of nested primers to control for bisulphite conversion or the addition of fluorescent dyes7 or radioactive nucleotides8 to primers to aid quantitative evaluation.

Bisulphite treatment followed by non–methylation-specific PCR and subsequent C/T assay has been a promising avenue. Although conventional sequencing techniques9 and restriction enzyme approaches (COBRA [combined bisulfite restriction analysis]10) have been described, they have proved complex and cumbersome for large-scale analysis. The most recently described technique is pyrosequencing.11,12 Pyrosequencing offers a semiquantitative, high-throughput, and reliable method with built-in internal control for adequacy of bisulphite treatment. Following PCR of bisulphite-converted DNA using primers that anneal to CpG-free regions within CpG islands, the target CpGs are evaluated by stepwise addition of bases to a cytosine-free pyrosequencing primer and by analysis of the resulting pyrogram. Guanine is incorporated during pyrosequencing if the template CpG was methylated, whereas adenine is incorporated if the template CpG was unmethylated. Therefore, the proportion of G/A incorporated is stoichiometrically proportional to the degree of methylation at that CpG site in the template DNA. The analysis of a non-CpG cytosine during pyrosequencing provides the internal control of the completeness of bisulphite treatment that critically underpins all of the analysis but is absent from many other techniques. In effect, pyrosequencing offers a high-throughput method of bisulphite sequencing that offers many logistic advantages.

The objective of this study was to evaluate promoter methylation quantitation using pyrosequencing in head and neck cancer by correlation with expression. The literature is now burdened with studies investigating methylation in isolation. The importance of methylation to gene expression, or to overall biological behavior of the tumor, is often overlooked. We aimed to address this problem using techniques we regard as gold standard.

Although various methods exist, relative quantification of messenger RNA (mRNA) by real-time reverse transcription–PCR using fluorescent probes has become a widely accepted and valuable technique. A panel of 9 genes was formulated, and appropriate primers were designed to amplify CpG islands within their promoter regions. These genes were selected as representative of different cellular functions and as promising candidates for silencing in head and neck squamous cell carcinoma by promoter methylation based on published literature. P16 is a commonly studied gene in this context and is involved in cell cycle checkpoint control. The cyclin A1 promoter has been shown to be frequently methylated in head and neck cancer using a pharmacologic unmasking array approach13 and is involved in regulation of the cell cycle. ATM,14hMLH1,15 and MGMT,16 all of which have been suggested to be regulated by epigenetic means in cancer at this site, have functions in DNA mismatch repair. The literature describes methylation of the cell differentiation regulation gene RARB17,18 and of the cell adhesion molecule E-cadherin19 in head and neck cancer. Recently, methylation in the promoters of the cell-signaling molecule STAT120 and of the matrix metalloproteinase inhibitor TIMP321 has been suggested to be important in these cancers. By comparing paired pyrosequencing methylation assay (PMA) and expression data for the genes studied, further evaluation of the merits (or otherwise) of pyrosequencing is expected to shed more light on the contribution of methylation to carcinogenesis in head and neck cancer.

METHODS
DESIGN
Sample Preparation

DNA was extracted from 2-mm3 snap-frozen tissue samples using a tissue kit (DNeasy; Qiagen, Studio City, California). Thirty-seven tumor and 8 “normal” tissue samples from beyond the edge of the surgical resection were prepared for methylation analysis. Bisulphite conversion of 2 μg of each sample (for use in methylation assays) was undertaken using a kit (EZ DNA Methylation; Zymo Research, Orange, California), and the converted DNA was eluted in 50 μL of 0.1 × TE buffer (10mM Tris hydrochloride, 1mM EDTA, pH 8.0).

RNA was prepared from additional 2-mm3 specimens of the 37 tumors and 8 normal tissues using a tissue kit (RNeasy; Qiagen). Adequacy of RNA quality and concentration was confirmed (RNA Labchip with 2100 Bioanalyzer; Agilent Technologies Ltd, Santa Clara, California). Complementary DNA (cDNA) was prepared from 1 to 2 μg of RNA using 2-step reverse transcriptase (RETROscript; Ambion, Austin, Texas). The quality of resultant cDNA and the absence of contamination were confirmed by PCR using β-actin primers.

Quantitative PMA

Pyrosequencing methylation assays were performed as previously described.11,12 Briefly, hot-start PCR was carried out using 3 μL of bisulphite-treated DNA template in each reaction. Primer sequences, PCR conditions, and pyrosequencing primer sequences are available on request from the authors. Confirmation of PCR product quality and of freedom from contamination was established on 2% agarose gels using ethidium bromide staining. Pyrosequencing was performed (PSQ96MA System; Biotage, Uppsala, Sweden) according to the manufacturer's protocol using single-strand binding protein (PyroGold reagents; Biotage Ltd, Uppsala, Sweden). A mean methylation index (MtI) was calculated from the mean of the methylation percentages for the CpG sites evaluated as previously described.22 Some PMA results were compared with those of a previously described semiquantitative technique, namely, real-time methylation-specific PCR7 (RTMSP). Eighteen bisulphite-converted DNA samples with a spectrum of P16 promoter methylation were also subjected to RTMSP using techniques previously described7 and a thermal cycler (model 7500; Applied Biosystems, Foster City, California). Sequences for methylation-specific primers and probe are available on request from the authors.

REAL-TIME PCR mRNA EXPRESSION ASSAY

Real-time PCR expression assays were performed for genes showing statistically significant promoter hypermethylation in the tumor samples. Commercially available intron-spanning probes (FAM/MGB TaqMan) were analyzed and compared with the TATA box-binding protein endogenous control “housekeeping gene” (TBP) using the thermal cycler. Each reaction was performed in duplicate, and the mean result was used. Relative quantification was by the 2−ΔΔCT method.23 The values for the tumor-derived samples were subsequently equated to an approximation for normal tissue by equating to the mean of the values for the 8 normal marginal samples. A nontemplate control assay was performed simultaneously in every PCR reaction to exclude contamination.

IMMUNOHISTOCHEMISTRY FOR p16 EXPRESSION

Thirty-eight fixed-tissue specimens corresponding to the fresh tissue analyzed as already described were available and deemed suitable for immunohistochemistry (IHC). Sections were deparaffinized in xylene followed by dehydration in graded alcohol. Epitope retrieval was performed for 20 minutes using microwave retrieval solution. Following cooling and rinsing with wash buffer, sections were quenched of endogenous peroxidase by peroxidase block for 5 minutes and were rinsed again in wash buffer. Mouse antihuman p16 antibody (CINTEC histology kit; MTM Laboratories, Heidelberg, Germany) was applied to one slide and negative control to another slide from the same block, which were then incubated at room temperature for 30 minutes. Visualization reagent, consisting of secondary goat antimouse immunoglobulin and horseradish peroxidase molecules linked to a polymer backbone, was applied and incubated for an additional 30 minutes at room temperature. Localization of antigen was visualized using chromogen (DAB; DAKO, Carpinteria, California) for 10 minutes, and sections were then counterstained with hematoxylin, dehydrated in alcohol, cleared in xylene, and mounted. All sections were evaluated by 2 of us (G.L.H. and P.S.).

STATISTICAL ANALYSIS

The paired methylation and expression data were tabulated and analyzed using commercially available software (SPSS, version 15; SPSS Inc, Chicago, Illinois). Correlations between the 2 methylation assays and expression with methylation values were performed using Spearman rank correlation (ρ) test.

PATIENTS

The cohort for this study comprised 37 patients with squamous cell carcinoma of the oral cavity (30 cases [12 oral tongue, 6 floor of mouth, 5 maxilla, 3 buccal, 3 mandibular alveolus, and 1 retromolar]) or oropharynx (7 cases [4 tonsil, 2 base of tongue, and 1 soft palate]). Selection criteria specified an intent to treat surgically and the absence of previous malignant neoplasms and curative treatments. Tumor samples were immediately snap-frozen in liquid nitrogen at the time of surgery. For comparison with each tumor, additional epithelial samples were obtained from beyond the surgical margin as best representative of normal tissue. These normal samples were subsequently used only if histopathologic staging confirmed mucosal margins free of malignancy exceeding 5 mm. Each sample was coded and subsequently stored at −85°C for a maximum period of 22 months. All patients provided informed consent for use of their tissue for research, and ethical approval was obtained as per local protocols.

RESULTS
METHYLATION DATA

Methylation analysis was undertaken on 37 tumor and 8 normal control samples and was obtained for gene promoters in more than 87% of specimens (Table). The promoters of the hMLH1, ATM, and STAT1 genes were unmethylated in all tumor and normal control samples (MtI, < 0.05), while the TIMP3 promoter was methylated in only 2 tumors. No further data will be presented for these genes. Promoter methylation (MtI, > 0.05) was observed for the remaining genes (P16 [26%], cyclin A1 [43%], MGMT [31%], RARB [75%], and E-cadherin [38%]).

Methylation data for 18 tumor samples were compared relative to PMA vs RTMSP. All of the PMA results exceeding an MtI of 0.05 reached threshold using RTMSP at between 31 and 35 PCR cycles. An additional 3 specimens that had an MtI of less than 0.05 using PMA reached threshold after 41, 43, and 46 cycles, demonstrating the predictable increased sensitivity offered by RTMSP. All 18 paired results for PMA and RTMSP were ranked and correlated with a Spearman ρ value of 0.72 (P < .001).

mRNA EXPRESSION DATA

Messenger RNA expression data were obtained for all 5 genes (and TBP endogenous control) that showed statistically significant promoter methylation (Table). Overall, cyclin A1 and P16 showed much greater expression in tumors than in normal controls, whereas RARB, E-cadherin, and MGMT were expressed at similar orders of magnitude in tumors and in normal controls. Partly as a consequence, the ranges observed between the lowest and highest expression data points were much greater in cyclin A1 and P16 (103-104 fold) than in the other 3 genes (40-50 fold).

CORRELATION BETWEEN METHYLATION AND mRNA EXPRESSION DATA

Quantitative promoter methylation and mRNA expression data for all samples, including the 8 normal controls, were plotted on graphs. The cyclin A1, MGMT, and RARB genes showed negative correlations between levels of methylation and mRNA expression (ie, the higher the MtI, the lower the expression) as shown by negative ρ values for Spearman rank correlations (Figures 1, 2, and 3 and Table); these 3 negative correlations were statistically significant (P < .001, P < .001, and P =.02, respectively), and it was noted that an MtI of greater than 0.20 to 0.25 seemed necessary to statistically significantly reduce mRNA expression of each gene. Data from the E-cadherin gene showed no trend between the 2 variables (Figure 4). Surprisingly, the data for P16 showed a statistically significant positive correlation between the 2 data sets (ie, a greater degree of methylation correlated with a higher level of mRNA expression) (Figure 5 and Table).

IHC AND CORRELATIONS WITH METHYLATION AND mRNA EXPRESSION DATA FOR p16

Immunohistochemistry staining for p16 was graded absent (32 samples), weak (1 sample), moderate (2 samples), or strong (3 samples). Because of the small number of samples with statistically significant positive staining, subsequent statistical analysis proved difficult. In correlating with mRNA expression, it was notable that specimens with the highest mRNA values had strong or moderate staining; however, there was also a wide spectrum of mRNA expression values with no identifiable staining with IHC (Spearman ρ mRNA expression vs IHC, 0.37; P = .02). With regard to correlations between methylation and IHC results, 5 of 6 samples with positive staining had zero methylation (MtI, < 0.001); however, the remaining sample (with strong staining) had an MtI of 0.27 (Spearman ρ MtI vs IHC, −0.13; P = .45).

COMMENT

In this article, we describe the application of PMA, a new technique of quantitative methylation analysis that allows correlation with quantitative expression data. This technique seems to give results that are broadly comparable to those of a previously described semiquantitative technique. The strengths of PMA lie in providing quantitative methylation data11; however, there is an MtI sensitivity threshold of approximately 0.05 (5% methylation).22 In applications requiring greater sensitivity, assays such as MSP, or derivations of MSP such as RTMSP or methylation enrichment pyrosequencing,24 have greater usefulness.

Four of 9 genes identified from the literature as being methylated in head and neck cancer were effectively unmethylated in our series. However, as might be expected, 3 of 5 of the remaining genes (RARB, cyclin A1, and MGMT) showed strong and statistically significant negative correlations between the amount of promoter methylation and mRNA expression. The data for E-cadherin showed no statistically significant correlation but, remarkably, P16 expression positively correlated with promoter methylation, opposite to the relationship that has previously been assumed. These data present a novel perspective on head and neck cancer epigenetics and reveal new and some unexpected associations and findings. The advantages of pyrosequencing over nonquantitative techniques are apparent in analyses of this nature.

There was a small number of cases (2-6 samples) in which methylation data could not be obtained. We speculate that these failed because of excess DNA fragmentation during bisulphite conversion or because there were deletions or mutations at critical sites on the promoter. This reinforces our impression that methylation studies can be demanding of DNA in quality and in quantity. The effects of bisulphite conversion and the large amounts of PCR product required for successful pyrosequencing reactions lie behind this; this might be seen as a disadvantage of pyrosequencing, particularly if fixed specimens must be used as source material. All failures were repeated but with disappointing results. However, the real-time expression data were 100% complete, possibly relating to the high quality of tissue and RNA obtained, the meticulous preparation of the samples, and the use of fully optimized and validated commercial probes.

The finding that 4 of the genes selected from the literature were unmethylated in our series requires some explanation. In some cases, the previous sequences examined are unavailable; therefore, it may be that a different CpG island has been chosen or that the levels of CpG methylation vary within a single island. Another possibility is that the methylation results in the reports overestimated the number of tumors showing methylation because of limitations of the techniques used24 (eg, because of false-positive results due to incomplete bisulphite conversion).

The tissues used were not microdissected specimens and the tumor purity was unknown, although previous findings indicate that most of the tumor specimens are greater than 75% tumor cells (J.M.R. and J.K.F., unpublished data, 2003). It would be informative to repeat the study after microdissection and to compare the results. However, the amounts of DNA resulting from microdissection can be very low, and the total available after bisulphite conversion would not have been sufficient to study 9 gene promoters. In several genes, a minimum threshold MtI of approximately 0.25 (ie, 25% of the copies of the gene methylated) was required to statistically significantly downregulate expression. To what extent this threshold might relate to partial, monoallelic, or biallelic promoter methylation is not clear. These data seem to suggest that the levels of methylation required to suppress expression are high and that the percentage of tumors displaying this is lower than the usually quoted figures for methylation, particularly using MSP.

The mRNA data for P16 were particularly surprising. P16 is widely studied in the field of epigenetics, and it is an oft-repeated assumption, although seemingly rarely tested, that promoter methylation silences the gene.25,26 In attempting to explore this correlation by the additional use of IHC for the same samples, we aimed to validate this finding. However, the results seemed inconclusive, although correlations have previously been attempted between methylation and protein expression in head and neck tumors, with similarly disappointing results.27 There seems to be some precedent in the recent literature that promoter methylation may paradoxically be associated with gene upregulation, a finding described by the authors as a “paradigm shift.”28 Methylation may be initiated in a reaction to overexpression of the gene (ie, as a controlling arm of a negative feedback loop). It is also possible that, for some reason, the CpG island studied has the opposite downstream effect on histones than might normally be expected, although the CpGs studied herein include those originally described by Herman et al.5 This remains an intriguing finding that may be worthy of further investigation and validation in other tumor series.

As the various platforms available for molecular assays develop, it becomes possible to gain more accurate and comprehensive data about the aberrations found in cancer. For methylation assays, we believe that the benefits of pyrosequencing are notable in this regard. It is anticipated that our understanding of the functional significance of promoter methylation, as well as the validation of epigenetic biomarkers in head and neck cancer, might be facilitated by such new technologies.

Back to top
Article Information

Correspondence: Richard J. Shaw, MD, FDS, FRCS, Regional Maxillofacial Unit, University Hospital Aintree, Longmoor Lane, Liverpool L9 7AL, England (richard.shaw@liverpool.ac.uk).

Submitted for Publication: August 21, 2006; final revision received April 23, 2007; accepted June 13, 2007.

Author Contributions: Drs Shaw, Field, and Risk had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Shaw, Hall, Field, and Risk. Acquisition of data: Shaw, Hall, and Liloglou. Analysis and interpretation of data: Shaw, Lowe, Liloglou, Sloan, and Risk. Drafting of the manuscript: Shaw, Lowe, and Risk. Critical revision of the manuscript for important intellectual content: Shaw, Liloglou, Field, Sloan, and Risk. Statistical analysis: Shaw and Lowe. Obtained funding: Shaw and Liloglou. Administrative, technical, and material support: Shaw, Hall, and Field. Study supervision: Shaw, Liloglou, Field, and Risk.

Financial Disclosure: None reported.

Funding/Support: The study was supported by a Royal College of Surgeons of England Research Fellowship (Dr Shaw), by the Roy Castle Foundation (Dr Liloglou), and by the British Association of Oral and Maxillofacial Surgeons.

Previous Presentation: This study was presented at the American Head and Neck Society 2006 Annual Meeting & Research Workshop on the Biology, Prevention, and Treatment of Head & Neck Cancer; August 19, 2006; Chicago, Illinois.

References
1.
Shaw  R The epigenetics of oral cancer. Int J Oral Maxillofac Surg 2006;35 (2) 101- 108
PubMed
2.
Ha  PKCalifano  JA Promoter methylation and inactivation of tumour-suppressor genes in oral squamous-cell carcinoma. Lancet Oncol 2006;7 (1) 77- 82
PubMed
3.
Singer-Sam  JLeBon  JMTanguay  RLRiggs  AD A quantitative HpaII-PCR assay to measure methylation of DNA from a small number of cells. Nucleic Acids Res 1990;18 (3) 687
PubMed
4.
Hatada  IHayashizaki  YHirotsune  SKomatsubara  HMukai  T A genomic scanning method for higher organisms using restriction sites as landmarks. Proc Natl Acad Sci U S A 1991;88 (21) 9523- 9527
PubMed
5.
Herman  JGGraff  JRMyöhänen  SNelkin  BDBaylin  SB Methylation-specific PCR: a novel PCR assay for methylation status of CpG islands. Proc Natl Acad Sci U S A 1996;93 (18) 9821- 9826
PubMed
6.
Adorján  PDistler  JLipscher  E  et al.  Tumour class prediction and discovery by microarray-based DNA methylation analysis. Nucleic Acids Res 2002;30 (5) e21http://nar.oxfordjournals.org/cgi/content/full/30/5/e21. Accessed December 28, 2007
PubMed
7.
Eads  CADanenberg  KDKawakami  K  et al.  MethyLight: a high-throughput assay to measure DNA methylation. Nucleic Acids Res 2000;28 (8) e32http://nar.oxfordjournals.org/cgi/content/full/28/8/e32. Accessed December 28, 2007
PubMed
8.
Gonzalgo  MLJones  PA Rapid quantification of methylation differences at specific sites using methylation-sensitive single nucleotide primer extension (MS-SNuPE). Nucleic Acids Res 1997;25 (12) 2529- 2531
PubMed
9.
Frommer  M McDonald  LEMillar  DS  et al.  A genomic sequencing protocol that yields a positive display of 5-methylcytosine residues in individual DNA strands. Proc Natl Acad Sci U S A 1992;89 (5) 1827- 1831
PubMed
10.
Xiong  ZLaird  PW COBRA: a sensitive and quantitative DNA methylation assay. Nucleic Acids Res 1997;25 (12) 2532- 2534
PubMed
11.
Colella  SShen  LBaggerly  KAIssa  JPKrahe  R Sensitive and quantitative universal pyrosequencing methylation analysis of CpG sites. Biotechniques 2003;35 (1) 146- 150
PubMed
12.
Tost  J Dunker  JGut  IG Analysis and quantification of multiple methylation variable positions in CpG islands by pyrosequencing. Biotechniques 2003;35 (1) 152- 156
PubMed
13.
Tokumaru  YYamashita  KOsada  M  et al.  Inverse correlation between cyclin A1 hypermethylation and p53 mutation in head and neck cancer identified by reversal of epigenetic silencing. Cancer Res 2004;64 (17) 5982- 5987
PubMed
14.
Bolt  JVo  QNKim  WJ  et al.  The ATM/p53 pathway is commonly targeted for inactivation in squamous cell carcinoma of the head and neck (SCCHN) by multiple molecular mechanisms. Oral Oncol 2005;41 (10) 1013- 1020
PubMed
15.
Deng  GChen  AHong  JChae  HSKim  YS Methylation of CpG in a small region of the hMLH1 promoter invariably correlates with the absence of gene expression. Cancer Res 1999;59 (9) 2029- 2033
PubMed
16.
Zuo  CAi  LRatliff  P  et al.  O6-methylguanine-DNA methyltransferase gene: epigenetic silencing and prognostic value in head and neck squamous cell carcinoma. Cancer Epidemiol Biomarkers Prev 2004;13 (6) 967- 975
PubMed
17.
Youssef  EMIssa  JPLotan  R Regulation of RARβ1 expression in head and neck cancer cells by cell density–dependent chromatin remodeling. Cancer Biol Ther 2004;3 (10) 1002- 1006
PubMed
18.
Youssef  EMLotan  DIssa  JP  et al.  Hypermethylation of the retinoic acid receptor–β2 gene in head and neck carcinogenesis. Clin Cancer Res 2004;10 (5) 1733- 1742
PubMed
19.
Yeh  KTShih  MCLin  TH  et al.  The correlation between CpG methylation on promoter and protein expression of E-cadherin in oral squamous cell carcinoma. Anticancer Res 2002;22 (6C) ((6C)) 3971- 3975
PubMed
20.
Xi  SDyer  KFKimak  M  et al.  Decreased STAT1 expression by promoter methylation in squamous cell carcinogenesis. J Natl Cancer Inst 2006;98 (3) 181- 189
PubMed
21.
Clément  GBraunschweig  RPasquier  NBosman  FTBenhattar  J Methylation of APC, TIMP3, and TERT: a new predictive marker to distinguish Barrett's oesophagus patients at risk for malignant transformation. J Pathol 2006;208 (1) 100- 107
PubMed
22.
Shaw  RJLiloglou  TRogers  SN  et al.  Promoter methylation of P16, RARβ, E-cadherin, cyclin A1 and cytoglobin in oral cancer: quantitative evaluation using pyrosequencing. Br J Cancer 2006;94 (4) 561- 568
PubMed
23.
Livak  KJSchmittgen  TD Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT method. Methods 2001;25 (4) 402- 408
PubMed
24.
Shaw  RJAkufo-Tetteh  EKRisk  JMField  JKLiloglou  T Methylation enrichment pyrosequencing: combining the specificity of MSP with validation by pyrosequencing. Nucleic Acids Res 2006;34 (11) e78
PubMed10.1093/nar/gkl424
25.
Maruya  SIssa  JPWeber  RS  et al.  Differential methylation status of tumor-associated genes in head and neck squamous carcinoma: incidence and potential implications. Clin Cancer Res 2004;10 (11) 3825- 3830
PubMed
26.
Ogi  KToyota  MOhe-Toyota  M  et al.  Aberrant methylation of multiple genes and clinicopathological features in oral squamous cell carcinoma. Clin Cancer Res 2002;8 (10) 3164- 3171
PubMed
27.
Williams  MD Chakravarti  NKies  MS  et al.  Implications of methylation patterns of cancer genes in salivary gland tumors. Clin Cancer Res 2006;12 (24) 7353- 7358
PubMed
28.
Kelavkar  UPHarya  NSHutzley  J  et al.  DNA methylation paradigm shift: 15-lipoxygenase-1 upregulation in prostatic intraepithelial neoplasia and prostate cancer by atypical promoter hypermethylation. Prostaglandins Other Lipid Mediat 2007;82 (1-4) 185- 197
PubMed
×