Aberrant methylation of CpG islands acquired in tumor cells in promoter regions is one method for loss of gene function. We determined the frequency of aberrant promoter methylation (referred to as methylation)of the genes retinoic acid receptor β-2(RARβ), tissue inhibitor of metalloproteinase 3(TIMP-3), p16INK4a, O6-methylguanine-DNA-methyltransferase(MGMT), death-associated protein kinase(DAPK), E-cadherin (ECAD), p14ARF, and glutathione S-transferase P1 (GSTP1) in 107 resected primary non-small cell lung cancers (NSCLCs) and in 104 corresponding nonmalignant lung tissues by methylation-specific PCR. Methylation in the tumor samples was detected in 40% for RARβ, 26%for TIMP-3, 25% for p16INK4a,21% for MGMT, 19% for DAPK, 18% for ECAD, 8% for p14ARF, and 7% for GSTP1, whereas it was not seen in the vast majority of the corresponding nonmalignant tissues. Moreover, p16INK4a methylation was correlated with loss of p16INK4a expression by immunohistochemistry. A total of 82% of the NSCLCs had methylation of at least one of these genes; 37%of the NSCLCs had one gene methylated, 22% of the NSCLCs had two genes methylated, 13% of the NSCLCs had three genes methylated, 8% of the NSCLCs had four genes methylated, and 2% of the NSCLCs had five genes methylated. Methylation of these genes was correlated with some clinicopathological characteristics of the patients. In comparing the methylation patterns of tumors and nonmalignant lung tissues from the same patients, there were many discordancies where the genes methylated in nonmalignant tissues were not methylated in the corresponding tumors. This suggests that the methylation was occurring as a preneoplastic change. We conclude that these findings confirm in a large sample that methylation is a frequent event in NSCLC, can also occur in smoking-damaged nonmalignant lung tissues, and may be the most common mechanism to inactivate cancer-related genes in NSCLC.

Alterations of the pattern of DNA methylation have been recognized as common changes in human cancers (1). These are thought to have important implications for abnormalities of gene expression,chromosome structure, timing of DNA replication, and chromatin organization (1, 2). Aberrant methylation of normally unmethylated CpG-rich areas, also known as CpG islands, which are located in or near the promoter region of many genes, has been associated with transcriptional inactivation of defined TSGs3in human cancer (3). Thus, aberrant methylation serves as an alternative to the genetic loss of a TSG function by deletion or mutation (4).

Aberrant promoter methylation (referred to as methylation) has been described for several genes in various malignant diseases including lung cancer (5, 6, 7, 8, 9, 10, 11, 12, 13). In lung cancer, methylation of the TSG p16INK4, the DNA repair gene MGMT, and the detoxification gene GSTP1 has been found in primary tumors (3, 7, 12, 14). Moreover,methylation of these genes and the apoptosis-associated gene DAPK has been described in serum DNA of NSCLC patients(15). Interestingly, p16INK4amethylation has also been observed in precursor lesions of lung carcinomas, which makes it a reasonable candidate biomarker for the early diagnosis of lung cancer (16). Methylation of TIMP-3 has been described in 4 of 21 (19%) NSCLCs(11), and TIMP-3 is thought to suppress primary tumor growth (17, 18). The RARβ-2 gene, RARβ, which may function as a TSG (19, 20),has been observed to be silenced by methylation in colon cancer and breast cancer (6, 9). ECAD, which plays a role in invasion suppression, has been found methylated in breast and prostate carcinomas (5). Esteller et al.(13) recently reported methylation of p14ARF in 28% of primary colorectal carcinomas and suggested that methylation-associated inactivation of p14ARF is independent of p16INK4amethylation and p53 mutational status.

Although several reports about methylation of various genes in lung cancer have been published, in most cases, the methylation status has been investigated for just a single gene or in a small number of samples (3, 7, 11, 12, 15, 21). Therefore, we decided to investigate methylation of multiple genes in a large sample collection of primary resected NSCLCs and their associated nonmalignant lung tissues, for which we also had clinical data and results about certain other molecular abnormalities. We determined the frequency of methylation of the eight genes RARβ, TIMP-3, p16INK4a, MGMT, DAPK, ECAD, p14ARF, and GSTP1 in 107 primary NSCLCs and 104 corresponding nonmalignant lung tissues by MSP. Methylation of these genes was shown to occur in confirmed promoter regions or in 5′ CpG islands in or near putative promoter regions. This analysis also provided us with the opportunity to determine whether the methylation status of the individual genes occurred independently of one another or with other molecular abnormalities. Finally, we wanted to know whether methylation of these genes was correlated with clinical features such as sex, age, smoking history, tumor stage, histology, and overall survival of the patients.

Tumor Samples.

Primary tumor samples (n = 107) and corresponding nonmalignant lung tissues (n = 104) were obtained from NSCLC patients who had been treated with curative resectional surgery in The Prince Charles Hospital (Brisbane,Australia) between June 1990 and March 1993. This cohort of patients had been investigated previously for various genetic abnormalities(22, 23, 24, 25, 26, 27, 28). There were 76 males and 31 females (age, 28–81 years; mean age at diagnosis, 61 years). Sixty-one patients had stage I disease, 21 patients had stage II disease, 24 patients had stage IIIA disease, and 1 patient had stage IIIB disease. Histological subtypes included 45 adenocarcinomas, 43 squamous cell carcinomas, 11 adenosquamous carcinomas, 4 large cell carcinomas, 3 atypical carcinoids, and 1 typical carcinoid. Ninety-eight patients were smokers(mean pack-years, 31), and the rest of patients were never smokers or nonsmokers. Survival data of 5 or more years were available on most patients.

MSP.

DNA was extracted as described previously (22), and bisulfite modification of genomic DNA was performed as reported by Herman et al.(29). Briefly, 1 μg of genomic DNA was denatured with NaOH (final concentration, 0.2 m), and 10 mm hydroquinone(Sigma) and 3 m sodium-bisulfite (Sigma) were added and incubated at 50°C for 16 h. Afterward, modified DNA was purified using Wizard DNA purification resin (Promega) followed by ethanol precipitation. Treatment of genomic DNA with sodium bisulfite converts unmethylated cytosines (but not methylated cytosines) to uracil, which are then converted to thymidine during the subsequent PCR step, giving sequence differences between methylated and unmethylated DNA. PCR primers that distinguish between these methylated and unmethylated DNA sequences were then used. Primer sequences of all genes for both the methylated and the unmethylated form, annealing temperatures, and the expected PCR product sizes are summarized in Table 1. The PCR mixture contained 10× PCR buffer (Qiagen), deoxynucleotide triphosphates (1.25 mm), primers (final concentration, 0.6 μm each per reaction), 1 unit of HotStarTaq (Qiagen), and bisulfite-modified DNA (∼150 ng). Amplification was carried out in a 9700 Perkin-Elmer Thermal Cycler. DNA from peripheral blood lymphocytes of healthy individuals and water blanks were used as a negative control for methylated genes. DNA from peripheral blood lymphocytes treated with SssI methyltransferase (New England Biolabs) was used as a positive control for methylated alleles. Fifteen μl of each PCR reaction were loaded onto a 2% agarose gel and visualized under UV illumination. The PCR for all samples demonstrating methylation for the individual genes was repeated to confirm these results.

Other Molecular Markers.

Data on immunohistochemistry of p16INK4a, RB, and p53 in 102 samples have been described by Geradts et al.(28). LOH analysis at 1p(MYCL), 3p21 (D3S1029), 3p25.3–26.2 (D3S1038), 5q(APC and MCC), 8p (LPL), 9p(IFNA and D9S126), 11p (H-ras, INS, RRM1, FSHB, and CAT), 13q(RB and D13S260), 17q (NF1, NM23-H1,D17S40, D17S21, and D17S4), and 18q (DCC) has also been described previously by Fong et al.(22, 23, 24, 25, 26). Other available molecular markers from previous studies included K-ras codon 12 mutations and p53 exon 5–8 mutations (25).

Statistics.

Statistical analysis was performed using the χ2and Fisher’s exact test for differences between groups and t tests between means. Overall survival was calculated using Kaplan-Meier log-rank testing. To determine the overall rate of methylation in individual samples, we used the MI. The MI is defined as a fraction representing the number of genes methylated/the number of genes tested. A previously designed Microsoft Visual Basic Program was used for color formatting and visualization of our data(30).

Frequency of Methylation in Primary NSCLCs and Their Corresponding Nonmalignant Lung Tissues.

We determined the frequency of methylation of RARβ, TIMP-3, p16INK4a, MGMT, DAPK, ECAD, p14ARF, and GSTP1 in 107 resected NSCLCs and in 104 corresponding nonmalignant lung tissues by MSP (Fig. 1; Table 2). In the corresponding nonmalignant lung tissues, methylation of p16INK4a, MGMT, ECAD, and GSTP1 was not detected, but it was seen at low frequencies for RARβ (14%), TIMP-3 (8%), DAPK(6%), and p14ARF (5%; Table 2). The bands that were seen for the methylated form in nonmalignant tissues, especially for RARβ, were faint. The detailed results of methylation for each gene in all tumors compared with the corresponding nonmalignant lung tissues are shown in Fig. 2. The unmethylated form of all genes was detected in 100% of samples in both tumors and nonmalignant tissues. Because the tumor specimens represented macroscopically isolated samples that contained both tumor and nonmalignant tissue, this was expected.

We found that at least one of these eight genes had methylation in 82%of the tumors; 37% of the tumors had only one gene methylated, 22% of the tumors had two genes methylated, 13% of the tumors had three genes methylated, 8% of the tumors had four genes methylated, and 2% of the tumors had five genes methylated, giving MIs of 0 in 19 tumors, 0.1 in 39 tumors, 0.3 in 24 tumors, 0.4 in 14 tumors, 0.5 in 9 tumors, and 0.6 in 2 tumors, respectively. A statistically significant correlation was found for the methylation status between RARβ and MGMT (P = 0.0005), whereas the methylation status of the other genes was independent when compared with each other.

Clinicopathological Correlations.

We analyzed the methylation changes in the tumors and the clinical data obtained from these patients (Table 3). Overall, we found no correlation between the MI (overall fraction of genes methylated) and any of the clinicopathological characteristics of the patients. A significantly longer overall survival was found for patients whose tumors showed methylation of ECAD (P = 0.005, Kaplan-Meier log-rank test). This result was seen particularly in stage I disease. We found no association between methylation of RARβ, TIMP-3, p16INK4a, MGMT, DAPK, p14ARF, or GSTP1 and survival, regardless of whether we analyzed all stages combined or performed a separate analysis for stage I, II, and III disease. Lymph node involvement is a well-established prognostic indicator for resected NSCLC, and we found lymph nodes were involved with the tumor in 41% of samples with any gene methylated, but in only 11% of samples in which no genes showed methylation (P = 0.012). To summarize our findings regarding other clinical parameters, methylation of TIMP-3 was detected more frequently in women than in men, DAPK methylation and p16INK4a methylation were more frequent in men than in women, and p16INK4a methylation was more frequent in squamous carcinomas than in adenocarcinomas and was seen only in smokers. When making multiple comparisons of clinical data with multiple biomarkers like the methylated genes, such as was done in this study, caution must be used with conservative statistical corrections(such as the Bonferroni, Tukey, and Newman-Kauls post tests)before deciding that significant correlations exist. These also need to be confirmed in other data sets and larger series. Thus, we feel the most conservative approach is to present the data in tabular form for future reference without drawing any statistical conclusions of significance (Table 3).

Immunohistochemistry and Molecular Correlations.

These tumor samples had been scored previously for immunohistochemical staining for p16INK4a, RB, and p53(28). As expected, we found a significant correlation between p16INK4a methylation and loss of p16INK4a tumor staining (P = 0.009), whereas an inverse correlation (which was also expected)was found between loss of RB protein expression and p16INK4a methylation (P = 0.009). No correlation was seen between methylation of any gene and p53 immunohistochemical or mutational (exons 5–8) status. Data about LOH on different chromosomal regions (1p, 3p21,3p25.3–26.2, 5q, 8p, 9p, 11p, 13q, 17q, and 18q) were compared with the results about methylation of the various genes. Given the large number of comparisons, no significant association was seen for LOH on other chromosomal regions and methylation of the investigated genes. Specifically, no association was detected between LOH on 9p21 and p16INK4a methylation.

This report describes the frequency of methylation of the genes RARβ, TIMP-3, p16INK4a, MGMT, DAPK, ECAD, p14ARF, and GSTP1 as well as the correlation of these methylation changes with clinicopathological characteristics and molecular abnormalities in a large number of patients. Methylation of RARβ, TIMP-3, p16INK4a, MGMT, DAPK, and GSTP1 had been described previously in lung cancer cell lines or small numbers of primary lung tumors, and our results are similar to the previously reported data (3, 7, 11, 12, 14, 15, 21). However, methylation of ECAD and p14ARF had not been reported in lung cancer. We also investigated methylation in the corresponding nonmalignant lung tissues. Several studies reported the lack of methylation in nonmalignant tissues and described methylation as a tumor-restricted event. However, we found methylation of RARβ, TIMP-3, DAPK, and p14ARF in some of the matched nonmalignant lung tissues. We cannot exclude that this may be due in some cases to contamination with adjacent malignant cells. However, from many previous studies, we have learned that preneoplastic/preinvasive and even histologically normal smoking-damaged epithelium has suffered genetic changes(31, 32, 33). Thus, a possible explanation for detecting methylated alleles in the nonmalignant lung samples is that they represent premalignant changes. To begin to address this issue, we compared the specific genes methylated in the tumors and nonmalignant tissues from the same patient (Fig. 2; Table 4). In the majority of cases, the genes methylated in the nonmalignant tissues were not those methylated in the corresponding tumors from the same patients. This would indicate that the methylation changes in these cases do not represent tumor contamination but are more likely premalignant changes. This was especially true in the case of RARβ methylation, which we found relatively frequently but with weak signals in the corresponding nonmalignant lung tissues. RARβ mRNA and protein expression have been lost in some smoking-damaged lungs (34, 35, 36). These findings are also in agreement with the results reported by Cote et al.(6) and Bovenzi et al.(9), who described RARβ methylation in a few samples of corresponding normal colon and breast tissues associated with cancers. The studies by Belinsky et al.(16), who reported the appearance of p16INK4a methylation in hyperplasias associated with squamous lung cancers, and Esteller et al.(13), who reported a similar rate of p14ARF methylation in colorectal adenomas and colorectal carcinomas, are other examples where methylation may be an early event in cancer development. However, we found no examples of p16INK4a methylation and only a few examples of p14ARF methylation in nonmalignant lung samples from lung cancer patients. It has been described that aging is associated with methylation of certain genes (4). Therefore, the aging mechanism could also be a possible explanation for detecting methylation in nonmalignant lung tissues.

Overall, at least one gene was methylated in 82% of the NSCLCs. To identify a subset of tumors that have concordant methylation of multiple loci and may therefore lead to the simultaneous inactivation of multiple genes, we determined the MI of the individual samples. In our study, 2 tumors had a MI of 0.6, 9 tumors had a MI of 0.5, 14 tumors had a MI of 0.4, 24 tumors had a MI of 0.3, 39 tumors had a MI of 0.1, and 19 tumors had a MI of 0, whereas in the corresponding nonmalignant lung tissues, only 4 had a MI of 0.3, 26 had a MI of 0.1,and 74 had a MI of 0. These results indicate there is a subset of lung cancers with widespread acquired methylation. Tumors with a high MI may have a distinct pathogenesis from tumors with a low MI. Patients whose tumors have several genes inactivated by methylation are appropriate candidates for clinical trials of drugs blocking methylation. In this case, if preclinical data showed that reactivation of expression of genes extinguished by methylation led to inhibition of tumor cell growth or induction of apoptosis, there would appear to be a strong rationale for testing such drugs in patients.

Another aim of this study was to investigate whether methylation of RARβ, TIMP-3, p16INK4a, MGMT, DAPK, ECAD, p14ARF, and GSTP1 in NSCLC is associated with clinicopathological parameters, particularly survival, of these patients. With the exception of ECAD, the presence of methylation of these genes or a group of genes was not associated with different survival. The longer survival associated with ECADmethylation will need to be confirmed by other series. However, a possible explanation for this surprising finding might be the fact that methylation of ECAD seems to be dynamic and heterogenous as described by Graff et al.(37). In the Graff et al.(37) study, breast cancers under conditions favoring invasion (with loss of adhesion) exhibited densely methylated ECAD promoter with reduced ECAD expression,whereas their survival and growth under conditions similar to metastatic deposits (spheroids) requiring cell adhesion actually showed loss of ECAD methylation with reestablished ECAD expression. It will be of interest to see whether similar findings occur in lung cancer primary lesions and metastatic deposits. Although it was not correlated with survival, the presence of finding any gene methylated was correlated with lymph node positivity. Methylation of TIMP-3 was seen more frequently in women, whereas methylation of DAPK and p16INK4a was more common in men. The reason for this gender difference is unknown. Methylation of p16INK4a was more frequent in squamous cell carcinomas than in adenocarcinomas. These results demonstrate that methylation of certain genes may be associated with some clinicopathological characteristics of these patients. These clinical correlations need to be confirmed in other independent studies.

For p16INK4a and RB, which are involved in the p16INK4a/cyclin D1/cyclin-dependent protein kinase 4/RB pathway, data about protein expression from immunohistochemistry studies were available. We found a statistically significant correlation between loss of p16INK4aexpression and methylation of p16INK4a and, as expected, an inverse correlation between loss of RBexpression and p16INK4a methylation. These findings confirm previous data showing that methylation is a mechanism for gene silencing of p16INK4a. This finding is also in agreement with finding either p16INK4a or RB mutations or loss of protein expression, but not both, in the same tumor (28, 38). Esteller et al.(13) reported that methylation-associated inactivation of p14ARF is independent of p16INK4amethylation and p53 mutational status in colon carcinomas. In agreement with their study, we also found methylation-associated inactivation of p14ARF to be independent of p16INK4a methylation and p53 mutational status.

In conclusion, our study about methylation of RARβ, TIMP-3, p16INK4a, MGMT, DAPK, ECAD, p14ARF, and GSTP1 in primary resected NSCLCs stresses the high frequency of methylation in a large collection of samples and demonstrates that methylation may be the most common mechanism to inactivate cancer-related genes in NSCLC. Why certain genes are targeted for methylation and the enzymes involved in this methylation merit special attention, and the answers should be of translational value. In the meantime, the detection of methylated genes is an attractive biomarker for testing the early detection of lung cancer and for monitoring chemoprevention efforts as proposed by Belinsky et al.(16). In these studies, it will be necessary to show in prospective clinical trials that persons at high risk for developing lung cancer (such as smokers with a heavy smoking history who go on to develop lung cancer) have certain key genes methylated in the nonmalignant tissues (e.g., sputum or bronchial brushes and washings) before the cancer becomes clinically evident.

Fig. 1.

Methylation analysis of eight genes in primary NSCLCs and their corresponding nonmalignant lung tissues by MSP. The gene studied is given at the left of each panel. TU,tumor; NL, nonmalignant lung tissue; Lane U, amplified product with primers recognizing unmethylated sequence; Lane M, amplified product with primers recognizing methylated sequence. Peripheral blood lymphocytes(L) were used as a negative control. In vitro methylated DNA (IVD) was used as a positive control for methylation. H2O, water blanks. The PCR product sizes of all of the genes are summarized in Table 1.

Fig. 1.

Methylation analysis of eight genes in primary NSCLCs and their corresponding nonmalignant lung tissues by MSP. The gene studied is given at the left of each panel. TU,tumor; NL, nonmalignant lung tissue; Lane U, amplified product with primers recognizing unmethylated sequence; Lane M, amplified product with primers recognizing methylated sequence. Peripheral blood lymphocytes(L) were used as a negative control. In vitro methylated DNA (IVD) was used as a positive control for methylation. H2O, water blanks. The PCR product sizes of all of the genes are summarized in Table 1.

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Fig. 2.

Summary of methylation of RARβ, TIMP-3, p16INK4a, MGMT, DAPK, ECAD, p14ARF, and GSTP1 in resected primary NSCLCs (left) and corresponding nonmalignant lung tissues (right). Green boxesrepresent samples that are not methylated; blue boxesrepresent samples that are methylated.

Fig. 2.

Summary of methylation of RARβ, TIMP-3, p16INK4a, MGMT, DAPK, ECAD, p14ARF, and GSTP1 in resected primary NSCLCs (left) and corresponding nonmalignant lung tissues (right). Green boxesrepresent samples that are not methylated; blue boxesrepresent samples that are methylated.

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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 Grants J1658-MED and J1860-MED from the Austrian Science Foundation, Lung Cancer SPORE (Special Program of Research Excellence) P50 CA70907, and The G. Harold and Leila Y. Mathers Charitable Foundation.

3

The abbreviations used are: TSG, tumor suppressor gene; NSCLC, non-small lung cancer; RAR, retinoic acid receptor; TIMP, tissue inhibitor of metalloproteinase; MGMT, O6-methylguanine-DNA-methyltransferase;DAPK, death-associated protein kinase; ECAD, E-cadherin; GSTP1,glutathione S-transferase P1; RB, retinoblastoma; LOH, loss of heterozygosity; MI, methylation index; MSP, methylation-specific PCR.

Table 1

Summary of primer sequences,aannealing temperatures, and PCR product sizes used for MSP

GeneForward primer (5′→3′)Reverse primer (5′→3′)Annealing temperature (°C)Product size (bp)
RARβ M: TCGAGAACGCGAGCGATTCGb M: GACCAATCCAACCGAAACGA 59 146 
 U: TTGAGAATGTGAGTGATTTGA U: AACCAATCCAACCAAAACAA 59 146 
TIMP-3 M: CGTTTCGTTATTTTTTGTTTTCGGTTTC M: CCGAAAACCCCGCCTCG 59 116 
 U: TTTTGTTTTGTTATTTTTTGTTTTTGGTTTT U: CCCCCAAAAACCCCACCTCA 59 122 
p16              INK4a M: TTATTAGAGGGTGGGGCGGATCGC M: GACCCCGAACCGCGACCGTAA 65 150 
 U: TTATTAGAGGGTGGGGTGGATTGT U: CAACCCCAAACCACAACCATAA 65 151 
MGMT M: TTTCGACGTTCGTAGGTTTTCGC M: GCACTCTTCCGAAAACGAAACG 66 81 
 U: TTTGTGTTTTGATGTTTGTAGGTTTTTGT U: AACTCCACACTCTTCCAAAAACAAAACA 66 93 
DAPK M: GGATAGTCGGATCGAGTTAACGTC M: CCCTCCCAAACGCCGA 64 98 
 U: GGAGGATAGTTGGATTGAGTTAATGTT U: CAAATCCCTCCCAAACACCAA 64 106 
ECAD M: TTAGGTTAGAGGGTTATCGCGT M: TAACTAAAAATTCACCTACCGAC 57 115 
 U: TAATTTTAGGTTAGAGGGTTATTGT U: CACAACCAATCAACAACACA 57 97 
p14              ARF M: GTGTTAAAGGGCGGCGTAGC M: AAAACCCTCACTCGCGACGA 64 122 
 U: TTTTTGGTGTTAAAGGGTGGTGTAGT U: CACAAAAACCCTCACTCACAACAA 64 132 
GSTP1 M: TTCGGGGTGTAGCGGTCGTC M: GCCCCAATACTAAATCACGACG 55 91 
 U: GATGTTTGGGGTGTAGTGGTTGTT U: CCACCCCAATACTAAATCACAACA 55 97 
GeneForward primer (5′→3′)Reverse primer (5′→3′)Annealing temperature (°C)Product size (bp)
RARβ M: TCGAGAACGCGAGCGATTCGb M: GACCAATCCAACCGAAACGA 59 146 
 U: TTGAGAATGTGAGTGATTTGA U: AACCAATCCAACCAAAACAA 59 146 
TIMP-3 M: CGTTTCGTTATTTTTTGTTTTCGGTTTC M: CCGAAAACCCCGCCTCG 59 116 
 U: TTTTGTTTTGTTATTTTTTGTTTTTGGTTTT U: CCCCCAAAAACCCCACCTCA 59 122 
p16              INK4a M: TTATTAGAGGGTGGGGCGGATCGC M: GACCCCGAACCGCGACCGTAA 65 150 
 U: TTATTAGAGGGTGGGGTGGATTGT U: CAACCCCAAACCACAACCATAA 65 151 
MGMT M: TTTCGACGTTCGTAGGTTTTCGC M: GCACTCTTCCGAAAACGAAACG 66 81 
 U: TTTGTGTTTTGATGTTTGTAGGTTTTTGT U: AACTCCACACTCTTCCAAAAACAAAACA 66 93 
DAPK M: GGATAGTCGGATCGAGTTAACGTC M: CCCTCCCAAACGCCGA 64 98 
 U: GGAGGATAGTTGGATTGAGTTAATGTT U: CAAATCCCTCCCAAACACCAA 64 106 
ECAD M: TTAGGTTAGAGGGTTATCGCGT M: TAACTAAAAATTCACCTACCGAC 57 115 
 U: TAATTTTAGGTTAGAGGGTTATTGT U: CACAACCAATCAACAACACA 57 97 
p14              ARF M: GTGTTAAAGGGCGGCGTAGC M: AAAACCCTCACTCGCGACGA 64 122 
 U: TTTTTGGTGTTAAAGGGTGGTGTAGT U: CACAAAAACCCTCACTCACAACAA 64 132 
GSTP1 M: TTCGGGGTGTAGCGGTCGTC M: GCCCCAATACTAAATCACGACG 55 91 
 U: GATGTTTGGGGTGTAGTGGTTGTT U: CCACCCCAATACTAAATCACAACA 55 97 
a

References for primer sequences: Refs. 6, 7, 8, 11, 12, 13, and 29.

b

M, methylated-specific primers; U,unmethylated-specific primers.

Table 2

Frequency of methylation in NSCLCs and corresponding nonmalignant lung tissues from the same patients

GeneFrequency of methylation
Tumor (%) (N = 107)Nonmalignant tissue (%) (N = 104)
RARβ 43 (40) 15 (14) 
TIMP-3 28 (26) 8 (8) 
p16              INK4a 27 (25) 0 (0) 
MGMT 22 (21) 0 (0) 
DAPK 20 (19) 6 (6) 
ECAD 19 (18) 0 (0) 
p14              ARF 9 (8) 5 (5) 
GSTP1 7 (7) 0 (0) 
GeneFrequency of methylation
Tumor (%) (N = 107)Nonmalignant tissue (%) (N = 104)
RARβ 43 (40) 15 (14) 
TIMP-3 28 (26) 8 (8) 
p16              INK4a 27 (25) 0 (0) 
MGMT 22 (21) 0 (0) 
DAPK 20 (19) 6 (6) 
ECAD 19 (18) 0 (0) 
p14              ARF 9 (8) 5 (5) 
GSTP1 7 (7) 0 (0) 
Table 3

Correlation between clinical data and methylation of the different genes

Gene
RARβTIMP-3p16              INK4aMGMTDAPKECADp14              ARFGSTPAny gene
A.% methylated
Sex          
Female 45 42 13 16 23 81 
(n = 31)  P = 0.018        
Male 38 20 30 22 25 16 83 
(n = 76)   P = 0.048  P = 0.005     
Age (yrs)          
<65 46 29 25 21 16 22 86 
(n = 63)          
>65 32 23 25 20 23 11 77 
(n = 44)          
Smoking          
Noa 33 33 11 22 22 22 67 
(n = 9)          
Yesb 41 26 28 21 18 17 84 
(n = 98)          
TNM stagec          
Stage I 43 21 21 21 21 21 11 82 
(n = 61)          
Stage II 33 29 29 19 19 19 24 95 
(n = 21)        (P = 0.005)  
Stage III 40 36 32 20 12 72 
(n = 25)          
Histology          
AC 49 24 13 27 16 16 11 82 
(n = 45)   (P = 0.02)       
SCC 35 23 37 19 21 19 81 
(n = 43)          
LCC 25 25 25 25 50 100 
(n = 4)          
ASC 27 36 36 27 18 82 
(n = 11)          
Carc 50 50 25 25 75 
(n = 4)          
B. Months survival (mean)         
Overall survival 53 45 38 47 46 68 44 32 48 
(all stages)      (P = 0.005)    
Stage I 61 56 32 61 49 75 47 NE 57 
      (P = 0.03)    
Stage II 47 40 44 35 42 39 22 27 34 
Stage III 34 28 24 19 17 41 NE 13 27 
C. Pack-years smoked (mean)         
Pack-years 37 39 49 44 42 47 52 40 43 
Gene
RARβTIMP-3p16              INK4aMGMTDAPKECADp14              ARFGSTPAny gene
A.% methylated
Sex          
Female 45 42 13 16 23 81 
(n = 31)  P = 0.018        
Male 38 20 30 22 25 16 83 
(n = 76)   P = 0.048  P = 0.005     
Age (yrs)          
<65 46 29 25 21 16 22 86 
(n = 63)          
>65 32 23 25 20 23 11 77 
(n = 44)          
Smoking          
Noa 33 33 11 22 22 22 67 
(n = 9)          
Yesb 41 26 28 21 18 17 84 
(n = 98)          
TNM stagec          
Stage I 43 21 21 21 21 21 11 82 
(n = 61)          
Stage II 33 29 29 19 19 19 24 95 
(n = 21)        (P = 0.005)  
Stage III 40 36 32 20 12 72 
(n = 25)          
Histology          
AC 49 24 13 27 16 16 11 82 
(n = 45)   (P = 0.02)       
SCC 35 23 37 19 21 19 81 
(n = 43)          
LCC 25 25 25 25 50 100 
(n = 4)          
ASC 27 36 36 27 18 82 
(n = 11)          
Carc 50 50 25 25 75 
(n = 4)          
B. Months survival (mean)         
Overall survival 53 45 38 47 46 68 44 32 48 
(all stages)      (P = 0.005)    
Stage I 61 56 32 61 49 75 47 NE 57 
      (P = 0.03)    
Stage II 47 40 44 35 42 39 22 27 34 
Stage III 34 28 24 19 17 41 NE 13 27 
C. Pack-years smoked (mean)         
Pack-years 37 39 49 44 42 47 52 40 43 

a Never/nonsmokers

b Smokers (mean pack-years, 31).

c TNM, tumor-node-metastasis; AC,adenocarcinoma; SCC, squamous cell carcinoma; LCC, large cell carcinoma; ASC, adenosquamous carcinomas; Carc, carcinoid; NE, not evaluable because all observations are censored. Ps are given under the comparisons.

Table 4

Comparison of methylation of specific genes between tumors and nonmalignant lung tissues from the same patients

Data from Fig. 2 comparing the methylation status of each gene in the tumor and corresponding nonmalignant lung tissues from the same patient. The +/+ group could represent contamination of the nonmalignant tissue with tumor DNA. However, the (+) nonmalignant tissue/(−) tumor group (n = 25) must represent distinct methylation events occurring only in the nonmalignant tissue of that patient.

Gene methylated in tumorGene methylated in nonmalignant lung tissue
+
165 174 
− 25 633 658 
Total 34 798 832 
Gene methylated in tumorGene methylated in nonmalignant lung tissue
+
165 174 
− 25 633 658 
Total 34 798 832 

We thank Luc Girard for analysis with the Visual Basic Program.

1
Antequera F., Boyes J., Bird A. High levels of de novo methylation and altered chromatin structure at CpG islands in cell lines.
Cell
,
62
:
503
-514,  
1990
.
2
Keshet I., Lieman-Hurwitz J., Cedar H. DNA methylation affects the formation of active chromatin.
Cell
,
44
:
535
-543,  
1986
.
3
Merlo A., Herman J. G., Mao L., Lee D. J., Gabrielson E., Burger P. C., Baylin S. B., Sidransky D. 5′ CpG island methylation is associated with transcriptional silencing of the tumour suppressor p16/CDKN2/MTS1 in human cancers.
Nat. Med.
,
1
:
686
-692,  
1995
.
4
Baylin S. B., Herman J. G., Graff J. R., Vertino P. M., Issa J. P. Alterations in DNA methylation: a fundamental aspect of neoplasia.
Adv. Cancer Res.
,
72
:
141
-196,  
1998
.
5
Graff J. R., Herman J. G., Lapidus R. G., Chopra H., Xu R., Jarrard D. F., Isaacs W. B., Pitha P. M., Davidson N. E., Baylin S. B. E-cadherin expression is silenced by DNA hypermethylation in human breast and prostate carcinomas.
Cancer Res.
,
55
:
5195
-5199,  
1995
.
6
Cote S., Sinnett D., Momparler R. L. Demethylation by 5-aza-2′-deoxycytidine of specific 5-methylcytosine sites in the promoter region of the retinoic acid receptor β gene in human colon carcinoma cells.
Anticancer Drugs
,
9
:
743
-750,  
1998
.
7
Esteller M., Corn P. G., Urena J. M., Gabrielson E., Baylin S. B., Herman J. G. Inactivation of glutathione S-transferase P1 gene by promoter hypermethylation in human neoplasia.
Cancer Res.
,
58
:
4515
-4518,  
1998
.
8
Katzenellenbogen R. A., Baylin S. B., Herman J. G. Hypermethylation of the DAP-kinase CpG island is a common alteration in B-cell malignancies.
Blood
,
93
:
4347
-4353,  
1999
.
9
Bovenzi V., Le N. L., Cote S., Sinnett D., Momparler L. F., Momparler R. L. DNA methylation of retinoic acid receptor β in breast cancer and possible therapeutic role of 5-aza-2′-deoxycytidine.
Anticancer Drugs
,
10
:
471
-476,  
1999
.
10
Melki J. R., Vincent P. C., Clark S. J. Concurrent DNA hypermethylation of multiple genes in acute myeloid leukemia.
Cancer Res.
,
59
:
3730
-3740,  
1999
.
11
Bachman K. E., Herman J. G., Corn P. G., Merlo A., Costello J. F., Cavenee W. K., Baylin S. B., Graff J. R. Methylation-associated silencing of the tissue inhibitor of metalloproteinase-3 gene suggests a suppressor role in kidney, brain, and other human cancers.
Cancer Res.
,
59
:
798
-802,  
1999
.
12
Esteller M., Hamilton S. R., Burger P. C., Baylin S. B., Herman J. G. Inactivation of the DNA repair gene O6-methylguanine-DNA methyltransferase by promoter hypermethylation is a common event in primary human neoplasia.
Cancer Res.
,
59
:
793
-797,  
1999
.
13
Esteller M., Tortola S., Toyota M., Capella G., Peinado M. A., Baylin S. B., Herman J. G. Hypermethylation-associated inactivation of p14ARF is independent of p16INK4a methylation and p53 mutational status.
Cancer Res.
,
60
:
129
-133,  
2000
.
14
Kashiwabara K., Oyama T., Sano T., Fukuda T., Nakajima T. Correlation between methylation status of the p16/CDKN2 gene and the expression of p16 and Rb proteins in primary non-small cell lung cancers.
Int. J. Cancer
,
79
:
215
-220,  
1998
.
15
Esteller M., Sanchez-Cespedes M., Rosell R., Sidransky D., Baylin S. B., Herman J. G. Detection of aberrant promoter hypermethylation of tumor suppressor genes in serum DNA from non-small cell lung cancer patients.
Cancer Res.
,
59
:
67
-70,  
1999
.
16
Belinsky S. A., Nikula K. J., Palmisano W. A., Michels R., Saccomanno G., Gabrielson E., Baylin S. B., Herman J. G. Aberrant methylation of p16INK4a is an early event in lung cancer and a potential biomarker for early diagnosis.
Proc. Natl. Acad. Sci. USA
,
95
:
11891
-11896,  
1998
.
17
Bian J., Wang Y., Smith M. R., Kim H., Jacobs C., Jackman J., Kung H. F., Colburn N. H., Sun Y. Suppression of in vivo tumor growth and induction of suspension cell death by tissue inhibitor of metalloproteinases (TIMP)-3.
Carcinogenesis (Lond.)
,
17
:
1805
-1811,  
1996
.
18
Anand-Apte B., Bao L., Smith R., Iwata K., Olsen B. R., Zetter B., Apte S. S. A review of tissue inhibitor of metalloproteinases-3 (TIMP-3) and experimental analysis of its effect on primary tumor growth.
Biochem. Cell Biol.
,
74
:
853
-862,  
1996
.
19
Houle B., Rochette-Egly C., Bradley W. E. Tumor-suppressive effect of the retinoic acid receptor β in human epidermoid lung cancer cells.
Proc. Natl. Acad. Sci. USA
,
90
:
985
-989,  
1993
.
20
Berard J., Laboune F., Mukuna M., Masse S., Kothary R., Bradley W. E. Lung tumors in mice expressing an antisense RARβ2 transgene.
FASEB J.
,
10
:
1091
-1097,  
1996
.
21
Virmani A. K., Rathi A., Zöchbauer-Müller S., Sacchi N., Fukuyama Y., Bryant D., Maitra A., Heda S., Fong K. M., Thunnissen F., Minna J. D., Gazdar A. F. Promoter methylation and silencing of the retinoic acid receptor-β gene in lung carcinomas.
J. Natl. Cancer Inst. (Bethesda)
,
92
:
1303
-1307,  
2000
.
22
Fong K. M., Zimmerman P. V., Smith P. J. Correlation of loss of heterozygosity at 11p with tumour progression and survival in non-small cell lung cancer.
Genes Chromosomes Cancer
,
10
:
183
-189,  
1994
.
23
Fong K. M., Kida Y., Zimmerman P. V., Ikenaga M., Smith P. J. Loss of heterozygosity frequently affects chromosome 17q in non-small cell lung cancer.
Cancer Res.
,
55
:
4268
-4272,  
1995
.
24
Fong K. M., Zimmerman P. V., Smith P. J. Tumor progression and loss of heterozygosity at 5q and 18q in non-small cell lung cancer.
Cancer Res.
,
55
:
220
-223,  
1995
.
25
Fong K. M., Zimmerman P. V., Smith P. J. Microsatellite instability and other molecular abnormalities in non-small cell lung cancer.
Cancer Res.
,
55
:
28
-30,  
1995
.
26
Fong K. M., Kida Y., Zimmerman P. V., Smith P. J. MYCL genotypes and loss of heterozygosity in non-small-cell lung cancer.
Br. J. Cancer
,
74
:
1975
-1978,  
1996
.
27
Fong K. M., Biesterveld E. J., Virmani A., Wistuba I., Sekido Y., Bader S. A., Ahmadian M., Ong S. T., Rassool F. V., Zimmerman P. V., Giaccone G., Gazdar A. F., Minna J. D. FHIT and FRA3B 3p14.
2 allele loss are common in lung cancer and preneoplastic bronchial lesions and are associated with cancer-related FHIT cDNA splicing aberrations. Cancer Res.
,
57
:
2256
-2267,  
1997
.
28
Geradts J., Fong K. M., Zimmerman P. V., Maynard R., Minna J. D. Correlation of abnormal RB, p16ink4a, and p53 expression with 3p loss of heterozygosity, other genetic abnormalities, and clinical features in 103 primary non-small cell lung cancers.
Clin. Cancer Res.
,
5
:
791
-800,  
1999
.
29
Herman J. G., Graff J. R., Myohanen S., Nelkin B. D., Baylin S. B. Methylation-specific PCR: a novel PCR assay for methylation status of CpG islands.
Proc. Natl. Acad. Sci. USA
,
93
:
9821
-9826,  
1996
.
30
Girard L., Zöchbauer-Müller S., Virmani A. K., Gazdar A. F., Minna J. D. Genome-wide allelotyping of lung cancer identifies new regions of allelic loss, differences between small cell lung cancer and non-small cell lung cancer, and loci clustering.
Cancer Res.
,
60
:
4894
-4906,  
2000
.
31
Bennett W. P., Colby T. V., Travis W. D., Borkowski A., Jones R. T., Lane D. P., Metcalf R. A., Samet J. M., Takeshima Y., Gu J. R., et al p53 protein accumulates frequently in early bronchial neoplasia.
Cancer Res.
,
53
:
4817
-4822,  
1993
.
32
Westra W. H., Baas I. O., Hruban R. H., Askin F. B., Wilson K., Offerhaus G. J., Slebos R. J. K-ras oncogene activation in atypical alveolar hyperplasias of the human lung.
Cancer Res.
,
56
:
2224
-2228,  
1996
.
33
Wistuba I. I., Lam S., Behrens C., Virmani A. K., Fong K. M., LeRiche J., Samet J. M., Srivastava S., Minna J. D., Gazdar A. F. Molecular damage in the bronchial epithelium of current and former smokers.
J. Natl. Cancer Inst. (Bethesda)
,
89
:
1366
-1373,  
1997
.
34
Gebert J. F., Moghal N., Frangioni J. V., Sugarbaker D. J., Neel B. G. High frequency of retinoic acid receptor β abnormalities in human lung cancer.
Oncogene
,
6
:
1859
-1868,  
1991
.
35
Geradts J., Chen J. Y., Russell E. K., Yankaskas J. R., Nieves L., Minna J. D. Human lung cancer cell lines exhibit resistance to retinoic acid treatment.
Cell Growth Differ.
,
4
:
799
-809,  
1993
.
36
Xu X. C., Sozzi G., Lee J. S., Lee J. J., Pastorino U., Pilotti S., Kurie J. M., Hong W. K., Lotan R. Suppression of retinoic acid receptor β in non-small-cell lung cancer in vivo: implications for lung cancer development.
J. Natl. Cancer Inst. (Bethesda)
,
89
:
624
-629,  
1997
.
37
Graff J. R., Gabrielson E., Fujii H., Baylin S. B., Herman J. G. Methylation patterns of the E-cadherin 5′ CpG island are unstable and reflect the dynamic, heterogeneous loss of E-cadherin expression during metastatic progression.
J. Biol. Chem.
,
275
:
2727
-2732,  
2000
.
38
Sekido Y., Fong K. M., Minna J. D. Progress in understanding the molecular pathogenesis of human lung cancer.
Biochim. Biophys. Acta
,
1378
:
F21
-F59,  
1998
.