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Clinical Cancer Research Vol. 8, 3164-3171, October 2002
© 2002 American Association for Cancer Research

Molecular Oncology, Markers, Clinical Correlates

Aberrant Methylation of Multiple Genes and Clinicopathological Features in Oral Squamous Cell Carcinoma1

Kazuhiro Ogi, Minoru Toyota, Mutsumi Ohe-Toyota, Nobuyuki Tanaka, Makoto Noguchi, Tomoko Sonoda, Geniku Kohama and Takashi Tokino2

Department of Molecular Biology, Cancer Research Institute [K. O., M. T., M. O-T., T. T.], Department of Oral Surgery [K. O., N. T., M. N., G. K.], and Department of Public Health [T. S.], Sapporo Medical University School of Medicine


   ABSTRACT
Top
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The purpose of this study was to examine the methylation profile of various oral squamous cell carcinomas and to correlate the methylation of particular chromosomal loci with the clinicopathological features of the tumors.

A semiquantitative analysis of the methylation status of 12 loci in 96 primary tumors and 13 cell lines was carried out. Methylation frequency was calculated as the percentage of methylated alleles detected by bisulfate-PCR.

Of the 12 loci examined, 9 (p16INK4A, p15INK4B, p14ARF, DCC, DAP kinase, MINT1, MINT2, MINT27, and MINT31) exhibited aberrant methylation at various frequencies, whereas 3 (hMLH1, HRK, and CACNA1G) showed no methylation. Dense methylation of the 5' CpG island of DAP kinase and MINT1 was well correlated with loss of gene expression. In addition, methylation of DCC was correlated with bone invasion by gingival tumors (P = 0.036), with aggressive invasiveness of tumors of the tongue (P = 0.046), and with reduced survival (P = 0.050). Methylation of MINT1 and MINT31 also correlated with poor prognoses (P = 0.058 and 0.041), whereas methylation of p14ARF correlated with a good prognosis (P = 0.021). Cox regression analysis showed methylation of MINT31 to be an independent predictor of outcome (hazard ratio, 3.79; 95% confidence interval, 1.58–9.10) and to be associated with the T4 disease group (hazard ratio, 5.71; 95% confidence interval, 1.25–26.07).

Analysis of DNA methylation is a useful approach to evaluation of the biological characteristics of oral cancers and may be a useful diagnostic indicator of patient prognosis.


   INTRODUCTION
Top
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
With etiologies predominantly involving the use of tobacco and consumption of alcohol, OSCCs3 are among the most frequently observed neoplasias (1 , 2) . Recent studies indicate that mutation of the p53 tumor suppressor gene occurs in about 50% of OSCCs (3 , 4) and that multiple genetic alterations, including loss of heterozygosity of chromosomes 3p, 8p, 9p, 9q, and11q, accumulate during their progression (4, 5, 6, 7) . Still, the precise molecular mechanisms involved in the development and progression of OSCCs remain unclear.

In addition to genetic alterations, DNA methylation of 5' CpG islands has been shown to be a major cause of inactivation of tumor suppressor genes (8, 9, 10) . Normally, DNA methylation is involved in gene imprinting, X chromosome inactivation, and inactivation of exogenous pathogens. The aberrant DNA-methylation seen in neoplasias usually occurs at CpG islands, which are regions of DNA rich in CpG dinucleotides. To date, genes involved in cell cycle regulation, DNA repair, apoptosis, and angiogenesis have all been shown to be inactivated by CpG island methylation in various human neoplasias (8, 9, 10) .

DNA methylation is now known to be involved in the development and progression of head and neck cancers, including OSCCs (11, 12, 13, 14) . However, no global profile of CpG island methylation in OSCCs has yet emerged, because earlier studies used only a limited number of samples in which only a few genes were analyzed (11 , 12 , 14) . Consequently, the target genes inactivated by DNA methylation in OSCCs remain largely unknown. To explore the role of DNA methylation in OSCC tumorigenesis, we used a semiquantitative methylation assay to examine 12 chromosomal loci in a group of primary SCCs of the tongue, oral floor, and lower gingiva, and in a panel of OSCC cell lines. We also examined the correlation between methylation of each locus and the clinicopathological features of the tumors. Our findings indicate that multiple genes are aberrantly methylated in OSCCs and that such methylation can be a useful molecular marker predictive of patient prognosis.


   MATERIALS AND METHODS
Top
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Specimens and Cell Lines.
Ninety-six tumor specimens were obtained from patients diagnosed with SCC of the tongue, lower gingiva, and oral floor between 1985 and 1994 at the Department of Oral Surgery, Sapporo Medical University Hospital. In accordance with our institutional guidelines, all patients gave informed consent before the collection of specimens. Tumor staging was done according to the UICC classification (15) . Specimens were obtained from biopsy or surgical resection and stored at -80°C. In addition, 13 OSCC cell lines (HSC-2, HSC-3, HSC-4, Ca9–22, OSC-20, SAS, OSC-30, OSC-19, OSC-70, KOSC-3, Ho-1-U-1, Ho-1-N-1, and SCC4) were obtained from the Japanese Collection of Research Bioresources (Tokyo, Japan) and cultured in RPMI 1640 supplemented with 10% fetal bovine serum. Genomic DNA was extracted from the specimens and cell lines using the conventional phenol/chloroform method; total RNA was extracted using Isogen (Nippon Gene, Tokyo, Japan). For reexpression studies, cell lines were treated with 1 µM 5-aza-dC for 72 h, after which the cells were harvested and the RNA extracted. The mode of invasion was determined as described previously (16) .

Bisulfite Treatment.
DNA from primary tumors and cell lines was treated with sodium bisulfite as described previously (17) . Briefly, 2 µg of genomic DNA was resuspended in 50 µl of water and then denatured in 2 M NaOH for 10 min at 37°C. The denatured DNA was diluted in 550 µl of freshly prepared solution containing 10 mM hydroquinone (Sigma) and 3 M sodium bisulfite (Sigma). The resultant solution was covered with mineral oil and incubated for 16 h at 50°C. After incubation, the samples were desalted using a Wizard DNA Clean-Up System (Promega) and treated with 3 M NaOH for 5 min at room temperature. Sixty-six microliters of NH4OAc and 2 volumes of 100% ethanol were then added, and the DNA was precipitated for at least 1–2 h at -80°C. After precipitation, the pellets were washed with 70% ethanol, dried, resuspended in 20 µl of water, and stored at -20°C.

Bisulfite-PCR Methylation Analysis.
Bisulfite-PCR was performed as described previously (18) . Briefly, 2-µl aliquots were amplified in a 50-µl reaction mixture consisting of 67 mM Tris-HCl (pH 8.8), 16.6 mM (NH4)2SO4, 6.7 mM MgCl2, 10 mM ß-mercaptoethanol, 2.5 mM each dNTP mixture, 0.5 µM each primer, and 1 unit of Taq polymerase (TaKaRa, Tokyo, Japan). After PCR, samples (20–40 µl) of the products were digested with restriction enzymes that recognize CpG sites retained after bisulfite treatment because of methylation. After digestion, the DNA was precipitated with ethanol, electrophoresed in 3% agarose gels, and stained with ethidium bromide. The intensity of the methylated alleles was quantitated by densitometry using a Lane and Spot Analyzer 6.0 (Atto, Tokyo, Japan). The methylation density of each sample was calculated as a ratio of the digested band to the density of all of the bands in a given lane. Primer sequences, PCR parameters, and the restriction enzymes used are listed in Table 1Citation .


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  		Table 1 Genes selected for methylation analysis

 
RT-PCR.
Reverse transcription was performed using Superscript II reverse transcriptase (Life Technologies, Inc.). Five-microgram samples of total RNA were transcribed in a 40-µl volume according to the manufacturer’s instructions, after which 1 µl of synthesized cDNA was used for the PCR reaction. After a hot start, the cycling parameters consisted of 3 cycles of 94°C for 30 s, 60°C for 30 s, and 72°C for 30 s; 4 cycles of 94°C for 30 s, 58°C for 30 s, and 72°C 30 s; 5 cycles of 94°C for 30 s, 56°C for 30 s, and 72°C for 30 s; and finally 23 cycles of 94°C for 30 s, 54°C for 30 s, and 72°C for 30 s. The primers used were: DAPKRT-F, 5'-AACGTGAACATCAAG AACCGAGAA-3'; DAPKRT-R, 5'-CTGGCTCCCATCAGACAGAGATAC-3'; MINT1RT-F, 5'-AGCACTTACTGCGGCTACCTCTGCTC-3'; and MINT1RT-R, 5'-AACGATTTCGGGGACGGCACAC-3'. The integrity of the RNA was evaluated by amplification of human GAPDH mRNA using primers GAPDHRT-F, 5'-CAGCCGAGCCACATCG-3' and 5'-TGAGGCTGTTGTCATACTTCTC-3'.

Statistical Methods.
All statistical analyses were performed using SPSS 10.0J for Windows. The association between DNA-methylation and the clinicopathological features of OSCCs (sex, age, UICC stage, T-category, G-category) were analyzed using {chi}2 tests and Fisher’s exact tests for bone invasion. P < 0.05 was considered significant. Survival curves taking into consideration DNA methylation and clinicopathological factors were inspected graphically using the Kaplan-Meier method. The censored time was defined as 60 months. Differences between curves were evaluated using log rank tests. The factors determined to be statistically significant (P < 0.05) in the log rank test were subsequently included in a Cox proportional hazards model to test the significance of DNA methylation as a predictor of survival time. The model was simplified using stepwise methods that removed variables with Ps > 0.1, and the final model was regarded as the best.


   RESULTS
Top
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Specimens and Genes Analyzed.
We initially assessed the methylation status of multiple CpG islands in 64 primary SCCs of the tongue, 16 of the mandibular gingiva, and 16 of the oral floor, and in 13 OSCC cell lines. Examined were eight genes known to be methylated in various tumors (p16INK4A, p15INK4B, p14ARF, hMLH1, DAP kinase, CACNA1G, HRK, and DCC; Refs. 19, 20, 21, 22, 23, 24, 25 ), and four CpG islands (MINT1, MINT2, MINT27, and MINT31) identified using the methylated CpG island amplification technique (25 , 26) . Bisulfite-PCR using primers designed to amplify the region around the transcription start site was applied, and the relative levels of CpG methylation were calculated as the percentage of methylated alleles (Fig. 1, A and B)Citation . Signals above 5% were considered to indicate the presence of methylation; signals below 5% were not reproducible.



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  		Fig. 1. Analysis of the methylation of multiple CpG islands in OSCC cell lines (A) and primary OSCCs (B) using bisulfite-PCR. PCR products were digested with restriction enzymes that cleave CpG sites retained after bisulfite treatment because of methylation. M, methylated alleles. The cell lines or specimens examined are listed above each lane; percentages of methylated alleles are indicated below. The genes analyzed are indicated below the gels.

 
Methylation of Multiple CpG Islands in OSCC.
Of the 12 chromosomal loci examined, 9 showed methylation at frequencies ranging from 6–100%, whereas the remaining 3 (hMLH1, HRK, and CACNA1G) were unmethylated (Fig. 1Citation , Table 2Citation ). Cell lines tended to be more heavily methylated than primary tumors (average number of methylated loci, 4.7 ± 1.6 versus 1.1 ± 1.0, P = 0.001). On the other hand, none of these loci were methylated in samples of normal tongue tissue (data not shown). We found that 67 of 96 (70%) cases of primary OSCC exhibited aberrant promoter methylation in at least one of the genes analyzed; 21 of 96 (22%) cases showed methylation of three or more loci; and 46 of 96 (48%) cases showed methylation of one or two loci. The frequency of methylation of each locus varied significantly depending on the location of the tumors (Fig. 2)Citation : p15INK4B, MINT2, and MINT31 were most frequently MINTs of the tongue; DCC and MINT1 were more frequently MINTs of the lower gingiva than the tongue; and MINT27 was more frequently MINTs of both the lower gingiva and oral floor than the tongue.


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  		Table 2 Methylation of multiple genes in oral cancer and normal tongue tissue

 


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  		Fig. 2. Frequency (%) of methylation of the indicated genes in OSCCs derived from the indicated locations within the oral cavity. *, P < 0.05.

 
To determine whether the observed aberrant methylation was associated with gene silencing, we examined the expression of DAP kinase in the 13 OSCC cell lines. Of the seven lines that showed dense methylation of the DAP kinase CpG island (Fig. 1A)Citation , none showed expression of DAP kinase (Fig. 3A)Citation . By contrast, the six remaining cell lines, which showed only partial methylation or no methylation at all, expressed DAP kinase at readily detectable levels. Treating cells with 5-aza-dC, a methyltransferase inhibitor, restored gene expression, indicating aberrant methylation of DAP kinase played a key role in silencing the gene (Fig. 3B)Citation . We also examined transcription of the region close to the MINT1 CpG island, which was identified by Blast analysis of the human EST database (GenBank accession no. XM_068374). Expression of MINT1 was detected in two cell lines that showed only low levels of methylation, but was not detected in five densely methylated cell lines (Fig. 3C)Citation .



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  		Fig. 3. Expression of DAP kinase and MINT1 in OSCC cell lines. A, total RNA from the indicated cell lines was reverse transcribed (RT+) and amplified by PCR. Each PCR reaction is accompanied by a negative control (RT-); amplification of GAPDH served as a positive control. Methylation density determined by bisulfite-PCR is shown below. B, expression of DAP kinase after treatment with 5-aza-dC. RNA was extracted from cell lines before (No treat) or after (Aza-dC) incubating cells for 3 days with 1 µM 5-aza-dC, after which RT-PCR was carried out. C, expression analysis of MINT1 in OSCC cell lines with various levels of methylation. Methylation density determined by bisulfite-PCR is shown below.

 
Clinical Features of OSCCs, with or without Methylation.
We next examined the correlation between the clinicopathological features of OSCCs and methylation of CpG islands (Table 3)Citation . Methylation of MINT1 correlated with UICC stage (P = 0.048) and T-category (P = 0.023), methylation of DCC correlated with invasion of the bone of the lower gingival (P = 0.036), and methylation of p16INK4A correlated with younger age (P = 0.043) and T-category (P = 0.038). Survival curves based on UICC stage, T-category, MINT1, MINT31, p14ARF, and DCC are shown in Fig. 4Citation . Methylation of MINT31 and DCC led to significantly shorter survival times (P = 0.041 and P = 0.050), whereas methylation of p14ARF led to significantly longer survival times (P = 0.021). Methylation of MINT1 also led to shorter survival times, but the effect was not significant (P = 0.58). Finally, UICC stage, T classification, MINT31, p14ARF, and DCC were included in a Cox proportional hazards model, and T-category, MINT31 and p14ARF were identified as predictive factors of survival (Table 4)Citation . The hazard ratio for methylation of MINT31 was predictive of a poor prognosis [hazard ratio, 3.79; 95%CI, 1.58–9.10] and association with the T4 disease group (hazard ratio, 5.71; 95%CI, 1.25–26.07). Conversely, methylation of p14ARF was predictive of a positive prognosis (hazard ratio, 0.30; 95%CI, 0.08–1.07).


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  		Table 3 Association between these genes analyzed and clinical factors in oral cancer

 


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  		Fig. 4. Kaplan-Meier survival curves showing the survival rates among 96 patients, taking into consideration the T-category, UICC stage, and methylation status of the indicated loci.

 

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  		Table 4 Hazard ratios for factors associated with survival time in oral cancer

 

   DISCUSSION
Top
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
Accumulating evidence suggests that DNA methylation plays a key role in the gene silencing that occurs in cancer (8, 9, 10) , including OSCCs and other head and neck cancers (11, 12, 13, 14) . Using primary tumor specimens and OSCC cell lines, we determined that multiple CpG islands were methylated at various frequencies in 9 of 12 chromosomal loci, and that the frequency of methylation varied depending on the location of tumor (Fig. 2)Citation . For instance, methylation of DCC, MINT1, and MINT27 was detected most often in tumors of the oral floor and lower gingiva, whereas methylation of p15INK4B, MINT2, and MINT31 was most often detected in tumors of the tongue. These differences likely reflect the fact that although these tumors share various histological features, their etiologies and associated risk factors differ (2) .

We observed a remarkable association between aberrant methylation of DCC, MINT1, MINT31, and p14ARF and the clinicopathological features of the tumors. For instance, methylation of DCC correlates with invasion of the bone of the lower gingiva and reduced survival. DCC was first identified as a tumor suppressor gene frequently deleted in colorectal cancer (27) , but its expression is now known to be repressed in carcinomas of the colon, stomach, pancreas, esophagus, prostate, bladder, and breast, and in gliomas (28 , 29) . In colorectal cancer, at least, expression of DCC is a strong predictive factor for survival (30) . Perhaps because DCC is involved in cellular attachment, its loss through aberrant methylation facilitates metastasis and invasion of the surrounding tissue by a tumor.

Methylation of MINT1 correlates with UICC staging and reduced survival. MINT1 was first shown to be methylated in colorectal cancer (25) and is now known to be MINTs of the colon (26) , stomach (31) , and pancreas (32) , and in acute myelogenous leukemia (33) . The functional consequence of MINT1 methylation in OSCCs remains unclear, because no gene has yet been identified in the vicinity of MINT1. Nevertheless, our expression data for EST downstream of MINT1 showed a close correlation between MINT1 methylation and loss of expression.

Methylation of MINT31 also correlated with a poor prognosis. MINT31 is situated on chromosome 17q21, where frequent loss of heterozygosity is detected in a number of tumor types. Recently, a t-type calcium channel gene (CACNA1G) was identified in the region close to MINT31 and was shown to be inactivated by aberrant methylation (24) . Methylation of CACNA1G and MINT31 are not perfectly correlated, however, and MINT31 appears to be located in a region involved in the transcriptional regulation of other genes (24) . In fact, about 2 kb upstream of the CACNA1G transcription start site are several ESTs, the expression of which can be silenced by MINT31 methylation.4 It is anticipated that further study will enable identification of the genes controlled by both the MINT1 and MINT31 CpG islands.

Surprisingly, methylation of p14ARF correlated with a good prognosis. Its product, an alternative transcript of p16INK4A, interacts with MDM2 and neutralizes MDM2-mediated p53 degradation (34) . Although decreased expression of p14ARF correlates with a poor prognosis in breast cancer (35) , its down-regulation is more frequently associated with less advanced tumors (36 , 37) . This discrepancy could reflect differences in tumor type or the methods used to detect the aberrations. Because our samples were not microdissected, we could not assess homozygous deletion of p14ARF in this study. Further analysis using a larger number of samples will be needed to determine whether loss of p14ARF is predictive of prognosis.

We found that the frequency of CpG island methylation was significantly higher in cell lines than in primary tumors. Similar excessive methylation has also been reported in other cancer cell lines (32 , 38) and could reflect the fact that it is the more heavily methylated tumor cells that tend become cell lines. Alternatively, tumors with heavily methylated loci may accumulate during the long period of cell culture needed to establish a cell line.

We detected no methylation of CACNA1G, HRK, or hMLH1. Although these genes are frequently methylated in other tumor types (21 , 24, 25, 26) , they are apparently not targets for methylation in OSCC. Similar organ-specific methylation patterns have been observed for p15INK4B, hMLH1, and BRCA1 (39) . We are currently trying to use methylated CpG island amplification coupled with representational difference analysis to identify the genes specifically methylated in OSCCs. Analysis of the methylation of the CpG islands identified with this protocol should provide us with detailed information about the epigenetic changes that occur during the development and progression of OSCCs.

In summary, we have determined the methylation status of 12 chromosomal loci in a group of primary OSCCs and cell lines. Our results suggest that aberrant methylation may be a useful molecular marker with which to predict the characteristics of the disease as well as patient prognosis.


   ACKNOWLEDGMENTS
 
We thank Dr. Tetsuyo Odajima for assistance in preparing the frozen tissue samples for our study and Dr. William F. Goldman for editing the manuscript.


   FOOTNOTES
 
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1 Supported by Grant-in-Aid for Scientific Research on Priority Areas from the Ministry of Education, Culture, Sports, Science, and Technology (to M. T. and T. T.). Back

2 To whom requests for reprints should be addressed, at Department of Molecular Biology, Cancer Research Institute, Sapporo Medical University South 1, West 17, Chuo-ku, Sapporo 060-8556, Japan. Phone: 81-11-611-2111, ext. 2386; Fax: 81-11-618-3313; E-mail: tokino{at}sapmed.ac.jp Back

3 The abbreviations used are: OSCC, oral squamous cell carcinoma; 5-aza-dC, 5-aza-deoxycytidine; MINT, methylated in tumor; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; UICC, International Union Against Cancer; CI, confidence interval; EST, expressed sequence tag. Back

4 M. Toyota, K. Ogi, and T. Tokino, unpublished observation. Back

Received 1/28/02; revised 6/17/02; accepted 6/18/02.


   REFERENCES
Top
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

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