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DNA methylation in tumor and matched normal tissues from non-small cell lung cancer patients


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Cancer Epidemiol Biomarkers Prev. Author manuscript; available in PMC 2009 December 28.
Published in final edited form as: Cancer Epidemiol Biomarkers Prev. 2008 March ; 17(3): 645–654. doi:10.1158/1055-9965.EPI-07-2518.

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DNA Methylation in Tumor and Matched Normal Tissues from NonSmall Cell Lung Cancer Patients
Qinghua Feng1, Stephen E. Hawes3, Joshua E. Stern1, Linda Wiens2, Hiep Lu1, Zhao Ming Dong4, C. Diana Jordan1, Nancy B. Kiviat1, and Hubert Vesselle2 1Department of Pathology, School of Medicine, University of Washington, Seattle, Washington
2Department 3Department

of Radiology, School of Medicine, University of Washington, Seattle, Washington

of Epidemiology, School of Public Health and Community Medicine, University of Washington, Seattle, Washington
4Veterans

Affairs Puget Sound Health Care System, Seattle, Washington

Abstract
We used MethyLight assays to analyze DNA methylation status of 27 genes on 49 paired cancerous and noncancerous tissue samples from non-small cell lung cancer (NSCLC) patients who underwent surgical resection. Seven genes (RARB, BVES, CDKN2A, KCNH5, RASSF1, CDH13, and RUNX) were found to be methylated significantly more frequently in tumor tissues than in noncancerous tissues. Only methylation of CCND2 and APC was frequently detected in both cancerous and noncancerous tissues, supporting the hypothesis that the methylation of these two genes is a preneoplastic change and may be associated with tobacco smoking exposure. Methylation of any one of eight genes (RASSF1, DAPK1, BVES, CDH13, MGMT, KCNH5, RARB, or CDH1) was present in 80% of NSCLC tissues but only in 14% of noncancerous tissues. Detection of methylation of these genes in blood might have utility in monitoring and detecting tumor recurrence in early-stage NSCLC after curative surgical resection.

Introduction
Primary lung cancer remains the leading cause of cancer death in the United States with an estimated 213,380 new patients diagnosed and 160,390 deaths expected in 2007 (1). Non-small cell lung cancer (NSCLC) accounts for 80% of these new cases and includes the following histologic types: adenocarcinoma, squamous cell carcinoma, large cell carcinoma, and mixed histologies. Epigenetic inheritance refers to changes in gene function that occur independent of changes in nucleotide sequence. The most well-studied epigenetic change is DNA methylation, which is the addition of a methyl group to cytosines preceding guanidines (also called CpG dinucleotides). Methylation of CpG clusters (also called CpG islands) in the promoter region of the gene leads to gene silencing. It has been proposed that DNA methylation of promoter regions of tumor suppressor genes plays as important a role as genetic mutations in tumor development (2,3). Multiple studies have assessed the epigenetic changes present in NSCLC (4,5), reporting a wide range of hypermethylation frequencies. Some studies have compared

Requests for reprints: Hubert Vesselle, Department of Radiology, School of Medicine, University of Washington, Box 357115, Seattle, WA 98195-7115. Phone: 206-221-4309; Fax: 206-543-6317. vesselle@u.washington.edu. Informed consent was obtained according to procedures approved by the Human Subjects Committee of the University of Washington.

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the methylation changes of NSCLC with that of matching noncancerous lung tissue from the same patient (6-17). Most of these studies relied on a qualitative methylation-specific PCR (MSP) methodology that is subjective, relying on the detection of a band after electrophoresis, and does not distinguish between low-level and high-level methylation. Only four studies have used a quantitative approach to investigate gene methylation frequencies in matched tissue pairs (8,9,18,19). Comparing methylation changes in NSCLC with those found in matched noncancerous lung is particularly important for determining potential links between DNA methylation changes and smoking, because, in any given patient, both tissue types have sustained the same exposure to tobacco smoke and other environmental factors. Further, to identify a panel of DNA methylation markers for detection of tumor recurrence, it is necessary to identify genes that are only methylated in tumor tissues but not in matched normal tissues. In this study, we determined the DNA methylation status of 27 genes using quantitative MethyLight assays in tumor and matching noncancerous lung samples from 49 NSCLC patients who underwent complete surgical resection of their tumors (Table 1). We further identified a panel of genes that differentiates tumor tissues from noncancerous tissues. Such a panel of genes could potentially be used to detect tumor recurrence early after surgery by detecting tumor-associated circulating methylated DNA in plasma.

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Pathology

Materials and Methods
Inclusion and Exclusion Criteria All subjects in the present methylation study are a subset of patients included in a larger prospective study of fluorodeoxyglucose positron emission tomography imaging in NSCLC conducted under the University of Washington Human Subjects Division approval. All patients were clinically diagnosed with NSCLC and had been referred to the thoracic surgery clinics at the University of Washington Medical Center or the Veterans Affairs Puget Sound Health Care System between February 1998 and August 2004. If histology of the lesion was subsequently confirmed to be other than NSCLC or if histology could not be confirmed, the patient was excluded from the study. Patients with a history of prior cancer were allowed to participate in the study if they had been disease free for at least 5 years. All patients had to be able to tolerate surgical resection if determined to be resectable. Patients who underwent wedge resection or segmentectomy and were later found to have local recurrence were excluded from the study due to the noncurative nature of this resection. Patients were also excluded if they had a history of type I diabetes (for the fluorodeoxyglucose imaging portion of the study), an inability to give informed consent, weighed over 350 pounds, or were either pregnant or breastfeeding. For imaging reasons, the primary NSCLC lesion had to be >1 cm on the mediastinal windows of the chest computed tomographic scan. Based on these criteria, 208 patients were enrolled into the imaging trial, the results of which have been recently reported in the literature (20), and followed the standard NSCLC care algorithm described previously (21). Among the 208 participants, a subgroup of 103 patients, plus 1 additional patient not reported previously, was surgically staged and underwent primary resection without any neoadjuvant or adjuvant therapy. Forty-nine of these resected patients had both NSCLC tissue blocks and matching noncancerous lung tissue collected at the time of surgery and constituted the study population.

All biopsy and resection specimens were reviewed by the Department of Pathology of the University of Washington Medical Center or the Veterans Affairs Puget Sound Health Care System to (a) verify non-small cell histology of the lung cancer samples and (b) verify the noncancerous status of the tissue block selected as a matching noncancerous normal lung for analysis. The noncancerous lung sample was acquired at the time of evaluation of the

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lobectomy or pneumonectomy specimen by routine pathology procedures and selected from a location away from the primary tumor.

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DNA Isolation from Paraffin Blocks Six 20-μm sections were cut from each block and deparaffined by xylene extraction. The resulting tissue pellets were digested with proteinase K at 48°C overnight. The genomic DNA was isolated by phenol/chloroform extraction and ethanol precipitation. Finally, the DNA was purified using QIAamp DNA mini-column according to the manufacturer's protocol (Qiagen). Sodium Bisulfite Conversion Unmethylated human sperm DNA and in vitro fully methylated DNA were converted with clinical samples as described before (22). Briefly, ?1 μg DNA was modified by 5 mol/L sodium bisulfite, desulfonated with NaOH, then purified and resuspended in 80 μL EB buffer (10 mmol/L Tris-HCl, pH 8.0). DNA Methylation (MethyLight) Analysis For each gene, the primers and probe were designed specifically for bisulfite-converted fully methylated DNA (Appendix 1). Amplification of bisulfite converted ACTB was used to normalize for input DNA. Samples that were negative for ACTB were excluded in the methylation analysis. A plasmid containing bisulfite converted ACTB gene of known concentration was diluted and used as the standard curve for quantification. The assay for a given set of samples was considered valid only if the converted unmethylated human sperm DNA was not amplified, whereas the converted methylated DNA was amplified. The percentage methylated reference (PMR) for each locus was calculated by dividing the GENE/ reference ratio of a sample by the GENE/reference ratio of methylated DNA and multiplying by 100 (23). Twenty-seven genes, plus the control gene ACTB, were analyzed in this study. Statistical Methods In this study, we dichotomized the semiquantitative MethyLight data in two different ways: the first classifying a specific gene positive for any hypermethylation and the second positive for high levels of hypermethylation. Earlier studies have reported that the 4% cutoff best discriminates between normal and premalignant/malignant tissues, and PMR ≥ 4% was associated with loss of expression (24,25). However, other researchers have evaluated three levels of methylation based on PMR values, assigning PMR = 0 as no methylation, and using two levels of methylation (low and high) based on PMR values above and below the median value for a given gene (26). In our unpublished data, we confirmed the presence of low levels of methylation when PMR values were positive but <4%, but the significance of this level of methylation is not clear. Because of the uncertainty regarding an appropriate cutoff for PMR values, we have presented our data in two ways: (a) a specific gene was considered positive for any hypermethylation if the PMR > 0% and (b) a specific gene was considered positive for high levels of hypermethylation if the PMR ≥ 4% in the sample. When multiple cancerous and/ or noncancerous tissue samples were available, we randomly chose one of the cancerous and/ or noncancerous blocks to be used in this analysis. McNemar's and Exact McNemar's tests for paired data were used to compare the frequency of gene-specific hypermethylation between NSCLC tissues and matched noncancerous tissue. A two-sided 0.05 test level determined statistical significance for all analyses. Gene sensitivity was defined as the proportion of cancerous tissue samples, which were methylated at a given level (PMR > 0% or PMR > 4%), whereas gene specificity was defined as the proportion of noncancerous tissue samples that were not methylated at that level. A gene panel consisting of methylated (at any level) genes that best distinguished cancerous from noncancerous tissue was created by first identifying genes that were 100% specific for cancerous tissue (not detected in the noncancerous tissue).

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These genes were ranked and added in a stepwise manner by increasing sensitivity, with the most sensitive, 100% specific gene first. Additional genes were subsequently added in a stepwise manner, by decreasing specificity, if the gene increased sensitivity by at least 4% (2 cases) and the increase in sensitivity exceeded the decrease in specificity. This process was continued until all the genes that met the above criteria were included. This same panel was then evaluated in terms of sensitivity and specificity for methylation at high levels (PMR ≥ 4%). Gene panel sensitivity was defined as the proportion of cancerous tissue samples in which gene methylation was detected in one or more of the genes in the panel, whereas gene panel specificity was defined as the proportion of noncancerous tissue samples in which gene methylation was not detected in any of the genes in the panel. Genes were also classified into nonmutually exclusive pathways according to their function, including apoptosis (DAPK1, RUNX3, TMS1, PTEN, and SOCS3), cell adhesion/invasion/metastasis (OPCML, CDH13, BVES, APC, CDH1, IGSF4, KCNH5, and PCSK6), cell cycle control (CCND2, RASSF1, APC, FHIT, CDKN2A, CDKN2B, P14, and PTEN), cell proliferation/differentiation (RARB, IGFBP3, KCNH5, KCNH8, SOCS3, and PTGS2), and DNA repair/detoxification (FHIT, MLH1, MGMT, FANCF, and GSTP1). All analyses were conducted using SAS version 9.1 (SAS Institute) and STATA version 8.2 (Stata).

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The mean age of the 49 NSCLC patients was 64.3 years. Approximately half were female (47%) and most were Caucasian (86%). All but one patient reported a history of smoking, with 65% reporting at least 40 lifetime pack-years of smoking. The majority of these NSCLC cases had surgical stage I (43%) or II (35%) disease and nearly half (49%) had tumors less than 3 cm. By histology, 41% of tumors were adenocarcinomas, 29% were squamous cell carcinomas, 16% were large cell carcinomas, and 6% were bronchioloalveolar carcinomas. This subset of patients (n = 49) was similar with regards to age, gender, tumor size, race, lifetime pack-years, differentiation, and histology when compared with the entire series (n = 208; data not shown). However, this study has a somewhat larger proportion of lower surgical stages (P = 0.004) compared with the larger group. The absence of late-stage patients in the present study is explained by the fact that most stage III and IV NSCLCs do not undergo a primary surgical resection but are treated with chemotherapy. Hence, matching noncancerous lung samples would not be available for these patients. Of the 27 genes tested, five genes (FHIT, P14, SOCS3, PTGS2, and GSTP1) were never methylated and four genes (MLH1, FANCF, CDKN2B, and PTEN) were rarely methylated (≤3%) in cancerous or noncancerous tissue (Table 2). Of the four genes that were rarely methylated in patients with NSCLC, MLH1, CDKN2B, and PTEN were each detected in the cancerous tissue of a single patient, whereas FANCF was detected in the noncancerous tissue of a single patient. The remaining 18 genes had higher frequencies of methylation in NSCLC tissues compared with their matched noncancerous tissues, and methylation of 10 of these genes (RARB, BVES, CDH1, CDKN2A, KCNH5, RASSF1, CDH13, IGSF4, DAPK1, and RUNX) was detected significantly more often in NSCLC tissues. Similarly, seven genes were significantly more often methylated at high levels (PMR ≥ 4%) in cancerous compared with noncancerous tissue, including BVES, KCNH5, RASSF1, CDH13, and RUNX, which had high levels of methylation in cancerous but never noncancerous tissue, and RARB and CDKN2A, which were often methylated at high levels in cancerous tissue but infrequently methylated at high levels in noncancerous tissue. Methylation of at least one of the 27 genes tested was present in 45 (92%) of 49 cancerous tissues and 26 (53%) of 49 adjacent normal tissues (P < 0.001; data not shown). Similarly, high levels of methylation (PMR > 4%) of at least one gene were present in 76% of cancerous tissues but only 39% of normal tissues (P < 0.001). Methylation of multiple genes was common,
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especially in cancerous tissue; methylation of two or more genes was detected in 76% of lung cancer tissue compared with 24% of normal tissue (P < 0.001). High-level methylation of multiple genes was detected in 55% of cancerous lung tissues but only 4% of adjacent normal tissues (P < 0.001). We constructed a panel of genes, which was able to sensitively and specifically distinguish cancerous from noncancerous tissue in NSCLC patients. Two genes (RASSF1 and DAPK1) were 100% specific and together identified 39% of NSCLC tissues (Table 3). The presence of methylation of at least one of the eight genes (RASSF1, DAPK1, BVES, CDH13, MGMT, KCNH5, RARB, and CDH1) identified 80% of NSCLC tissues and only occurred in 14% of the surrounding histologically noncancerous lung tissues of the same individuals. High levels of methylation of six genes (RASSF1, DAPK1, BVES, CDH13, MGMT, or KCNH5) identified 53% of cancerous tissues and were 100% specific. High levels of methylation of at least one of eight genes (adding RARB and CDH1) occurred in 63% of NSCLC cancerous tissues and only occurred in 6% of the surrounding histologically noncancerous lung tissues of the same individuals. To assess the importance of inactivation of biological pathways through methylation in NSCLC, genes were categorized into nonmutually exclusive functional pathways, including apoptosis, cell adhesion/invasion/metastasis, cell cycle control, cell proliferation/ differentiation, and DNA repair/detoxification (see methods for specific genes). In four of the five pathways assessed, any methylation and high levels of methylation (PMR > 4) were significantly more frequent (P < 0.05) in cancerous lung compared with normal lung tissue (Table 4), whereas for DNA repair/detoxification, methylation was infrequent (≤10%) and not significantly different in cancerous and normal lung. Methylation of genes associated with apoptosis was observed in 29% of cancerous lung tissue but was not observed in normal tissue. Similarly, methylation of genes associated with cell proliferation/differentiation was common (57%) in cancerous lung tissue but rare (12%) in normal tissue, especially at high levels (4%). Methylation of genes associated with cell adhesion/invasion/metastasis and cell cycle control was present in ?50% of normal tissue and the majority (78%) of cancer tissues. Because APC, associated with cell adhesion/invasion/metastasis and cell cycle control, and CCND2, important in cell cycle control, were frequently methylated in both cancerous and noncancerous tissue, we reassessed these pathways excluding these two genes. In this analysis, methylation of cell adhesion/invasion/metastasis genes was present in the majority (57%) of lung tumor tissue but was less commonly detected (16%) in normal tissue. Likewise, methylation of cell cycle control genes (excluding APC and CCND2) was present in nearly half (47%) of cancerous tissue and always at high levels but was almost never present (4%) in normal tissue. Methylation of multiple genes in cancerous tissue was common, occurring in 28 (72%) of 39 cases that were identified by the gene panel (Fig. 1). For example, case 4 had high levels of methylation of RASSF1, BVES, KCHN5, and RARB and lower levels of methylation of CDH13 in the same cancerous tissue sample. Similarly, case 7 had high levels of methylation of the same five genes. In contrast, some genes, such as RASSF1 and DAPK1, were rarely methylated in the same cancerous tissue sample. Methylation of multiple genes was present in noncancerous tissue samples in only two samples (cases 3 and 48), and in one case (case 48), low-level methylation of a number of genes in the panel was present in noncancerous tissue but was not detected in cancerous tissue. CCND2 and APC were frequently methylated, often at high levels, in both cancerous and noncancerous lung tissues of smokers with NSCLC, suggesting that hypermethylation of these genes may be associated with some environmental factors, such as chronic smoking (Fig. 2). Methylation of neither CCND2 (P = 0.07) nor APC (P = 0.3) was strongly correlated in paired cancerous and noncancerous tissues. Only one other gene (OPCML) had a high level of

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methylation detected in noncancerous but not cancerous tissue from any NSCLC patient (case 14). Most genes were never or rarely methylated in noncancerous tissue in the absence of methylation in the paired cancerous tissue.

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Discussion
In this study, we compared methylation changes in paired tumor and nonmalignant lung tissue blocks using quantitative MethyLight assays from 49 NSCLC patients who underwent surgical resection. We found that methylation of 10 genes was significantly more frequent in tumor than in matched noncancerous tissues. As few genes, except for CCND2 and APC, were methylated in noncancerous tissues of NSCLC patients, methylation of most genes studied is associated with the molecular pathogenesis of lung cancer but is not linked to smoking exposure. We classified genes according to known function and pathway and noted that genes associated with apoptosis and cell proliferation/differentiation as well as cell adhesion/ invasion/metastasis and cell cycle control genes, with the exception of CCND2 and APC, were all commonly methylated, and at higher levels, in cancerous lung tissue but were infrequently methylated in normal lung tissue. Finally, we derived a panel of eight genes that identified 80% of NSCLC tissues but only 14% noncancerous lung tissues. Our study is unique in several aspects: we analyzed more genes on paired samples than most other studies; we showed feasibility of using paraffin tissue blocks for methylation analysis, whereas most previous studies have used frozen tissues; we used quantitative MethyLight assays to detect DNA methylation, whereas most other studies used qualitative MSP; and our study is the first one to show that methylation of BVES is a frequent event in NSCLC. We analyzed the methylation of the largest number (27) of genes on paired tumor and noncancerous tissues. Only a handful of other studies (8,13-15,17) have assessed five or more genes with 30 or more paired cases of NSCLC. Several studies have reported findings similar to ours. Toyooka et al. analyzed methylation of seven genes using MSP on 84 cancerous and noncancerous tissue pairs from NSCLC patients and reported methylation of five genes (APC, RASSF1, CDH13, p16, and RARB) was significantly more frequent in cancerous compared with noncancerous tissues. When methylation was detected in noncancerous tissues, methylation was also present in the corresponding tumor tissues (14). Yanagawa et al. analyzed eight genes using MSP on 75 pairs of tissue from NSCLC patients and found that methylation of five genes (DAPK1, CDH1, CDKN2A, RASSF1A, and RUNX3) was significantly more frequent in cancerous compared with noncancerous lung tissue (13). As in our study, methylation of APC was detected in both noncancerous (48%) and cancerous (37%) tissue. Zochbauer-Muller et al. analyzed methylation of eight genes using MSP on 104 pairs of NSCLC tissues. Methylation of at least one gene was detected in 82% of tumors and in 29% of the noncancerous tissues. Most genes were rarely methylated in the noncancerous tissues (15). RARB was methylated in 14% of the noncancerous tissues, a rate somewhat higher than we found (8%); however, methylation of this gene occurred only at low levels. Further, they showed that most of the methylation detected in the noncancerous tissues was not detected in the matched tumor tissues, suggesting that these methylation changes were preneoplastic. Shivapurkar et al. analyzed methylation of 11 genes using quantitative MSP on 40 pairs of NSCLC tissues. Methylation of at least one gene was present in 98% of the tumors. APC was frequently methylated in noncancerous lung tissues but never in peripheral blood mononuclear cells from cancer-free patients (8). Methylation of nine genes was significantly more frequent in cancer than in noncancerous tissues. However, in that study, individual gene methylation quantification level cutoffs to differentiate cancerous from noncancerous tissues were optimized post hoc, resulting in an overestimate of the importance of specific genes and making comparisons to the present study infeasible.

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Contrary to the present study, two prior studies have reported similar frequencies of methylation in cancerous and noncancerous tissue samples. Kim et al. analyzed methylation of five genes using MSP on 72 pairs of adenocarcinoma tissues (17). High frequencies of methylation were observed for both tumor and noncancerous tissues, with methylation of at least one gene being found in 88% of cancer tissues and 79% of noncancerous tissues. Similarly, Safar et al. analyzed methylation of eight genes using MSP on 32 pairs of NSCLC tissues, and high frequencies of methylation in 6 genes were observed in cancerous as well as noncancerous tissues (7). The discrepancy is likely due to the shortcomings of the qualitative MSP method these researchers used for methylation detection. The methylation frequencies of most genes we assessed in NSCLC tissues generally fell within the wide range of frequencies reported in the literature, although most studies used qualitative MSP. However, our findings with regard to methylation of CCND2, FHIT, and TMS1 differed from that reported in other populations. In our study, 43% of the cancerous NSCLC tissues were methylated for CCND2, a rate similar to Virmani et al.'s study (40%; ref. 27). However, whereas Virmani et al. reported that CCND2 was 100% specific for cancerous tissue, we detected CCND2 in 24% of noncancerous tissues. TMS1 was not frequently methylated in cancerous tissue in our study (8%), but it is frequently methylated in Virmani et al.'s report (40%). Although methylation of FHIT has been shown to be associated with smoking and occurs early during tumor development (28-30), we did not detect any methylation of FHIT in our study. The gene-specific discrepancies we observed are likely due to the different methylation detection assays used in different studies. Although the same CpG island is investigated, the somewhat different primers and probes used in various methylation analyses indicate that different sequence regions are investigated. For example, the primers and probe sequences we used for FHIT is located in a different region from other primers and probes used in the literature (30,31). Even when the same primers are used, the specific PCR conditions may affect the MSP results. For example, several studies analyzed CDKN2A methylation using the same primers. However, different methylation frequencies were detected because different annealing temperatures and master mixes were used in these studies (10,12,13,32). Because of the incorporation of sequence-specific probes in quantitative MSP assays, these assays tend to have improved specificities due to elimination of nonspecific products detection and identification of only fully methylated sequences. This emphasizes the importance of standardization of methylation analysis protocols, so that meaningful comparisons can be made among different studies. Finally, because little is known of the origin of cancer-specific DNA methylation changes, the discrepancies observed might be due to the differences in patient population and tumor heterogeneity (14). Given the central role smoking plays in lung cancer development and that DNA methylation is an early event in tumorigenesis, it is somewhat surprising that we did not detect frequent methylation of most genes we analyzed in the adjacent noncancerous lung tissues in NSCLC patients. Several studies have looked at the effect of smoking on DNA methylation (14, 33-35). Some studies have shown no correlation between methylation of CDKN2A, DAPK1, GSTP1, and APC and smoking history (19,34,36,37), whereas Kim et al. showed that increased CDKN2A methylation with increased pack-years and duration of smoking (34). Methylation of RASSF1A has also been associated with exposure to smoke in lung cancer (14,38,39), and methylation of FHIT has been shown to be an early event in smoking-caused squamous cell lung cancer (29,30). It is possible that the DNA methylation of most of the genes we analyzed (except for APC and CCND2) is a cancer-specific change only present in tumor tissues but not in preneoplastic tissues. However, it is also possible that these genes are only partially methylated in preneoplastic tissues and hence could not be detected by MethyLight assays. Only detailed bisulfite sequencing analysis would be able to detect these changes.
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In the current study, we detected methylation of CCND2 and APC at high frequencies in matched noncancerous lung samples, suggesting that methylation of these genes in both cancerous and noncancerous tissue was associated with a common environmental exposure, such as tobacco smoke. Several studies have reported the methylation of APC in noncancerous lung tissues. Two studies used conventional MSP to show that APC was methylated in 30% to 50% tumors and also methylated in 14% and 48%, respectively, noncancerous tissues (13, 14). Three studies detected APC methylation in ?90% tumors using quantitative MSP assays and 58% to 88% in noncancerous tissues (8,9,19). Although we detected methylation of CCND2 in 25% of noncancerous tissues, Virmani et al. did not detect any methylation in adjacent normal lung tissues (27). Unfortunately, in the present study, we did not assess gene hypermethylation in nonsmokers with NSCLC or in smokers without NSCLC, so we were unable to directly assess the relationship between smoking, NSCLC, and gene methylation. Our data indicate that NSCLC-specific DNA methylation changes of many genes are rare in noncancerous lung tissues from NSCLC patients. Several pathways appear to be important for tumorigenesis of NSCLC, including apoptosis, cell adhesion/invasion/metastasis, cell cycle control, and cell proliferation/differentiation. This has important implications with regards to (a) understanding the role of environmental factors (that is, tobacco smoke) in the development of hypermethylation changes in NSCLC and (b) using a DNA hypermethylation panel to detect tumor recurrence after surgical resection of NSCLC. The fact that we were able to identify a panel of eight hypermethylated genes that were present in 80% of the NSCLC tissues but only present in 14% of the noncancerous lung tissue suggests that a methylated gene panel from sputum or blood may have potential in detecting occult NSCLC in high-risk individuals, such as smokers, as well in predicting tumor recurrence in postoperative NSCLC patients (40-42).

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Acknowledgments
Grant support: NIH/National Cancer Institute grants CA115559, CA107264, and CA80907.

Appendix A. MethyLight Primers and Probes

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Gene ACTB

Forward primer, probe, and reverse primer Forward: TGGTGATGGAGGAGGTTTAGTAAGT Probe: 6FAM-ACCACCACCCAACACACAATAACAAACACA-TAMRA Reverse: AACCAATAAAACCTACTCCTCCCTTAA

DAPK1

Forward: GGAGAGGGTGGTTACGGTGTT Probe: 6FAM-CAACCTACGACAACGATAA-MGB Reverse: ACCCTCGCCCAAACGATAC

RARB

Forward: TGAGGATTGGGATGTCGAGAA Probe: 6FAM-CTACTCGAATCGCTCG-MGB Reverse: CCCGACGATACCCAAACAAA

CCND2

Forward: CGTGTTAGGGTCGATCGTGTT Probe: 6FAM-ACTACGATAAAATCGCCG-MGB Reverse: CTCGCCAAACTTTCTCCCTAAA

RASSF1

Forward: TAGGTTTTTATTGCGCGGTTTT Probe: 6FAM-CGCGAACCGAACGAA-MGB Reverse: TACTTCGCTAACTTTAAACGCTAACAA

OPCML

Forward: CGGTTAGGGATGGAGTTGTTG Probe: 6FAM-CCGAACCGCCGAAA-MGB Reverse: CGCCTTCCTCCCGAAAAC

CDH13

Forward: GATTTTTGGGTTCGGAATGATTT Probe: 6FAM-TTTTCGTCGTCGCGATC-MGB Reverse: ATCGCCCGACACGAACAA

BVES

Forward: GGACGGAGTGGGCGATATC Probe: 6FAM-CCTACGTACAACCGAACG-MGB Reverse: CCTCGAACCGCGCAAA

APC

Forward: TTATATGTCGGTTACGTGCGTTTATAT Probe: 6FAM-CCCGTCGAAAACCCGCCGATTA-TAMRA Reverse: GAACCAAAACGCTCCCCAT

CDH1

Forward: AATTTTAGGTTAGAGGGTTATCGCGT Probe: 6FAM-CGCCCACCCGACCTCGCAT-TAMRA Reverse: TCCCCAAAACGAAACTAACGAC

FHIT

Forward: CGCGCGTTAGGTTATTATTTCG Probe: 6FAM-TCGGCGTCGTTTAC-MGB Reverse: ACCCTAAAACCCTCGTAAAACGA

MLH1

Forward: CGTTATATATCGTTCGTAGTATTCGTGTT Probe: 6FAM-CAAACGCCACTACGAAA-MGB Reverse: ACCTAATCTATCGCCGCCTCAT

IGSF4

Forward: AGGGAGCGAGGTTTTTCGA Probe: 6FAM-CGAACCCAACCCGAC-MGB Reverse: ACGAAATCCGAACAAACCAATC

MGMT

Forward: GCGTTTCGACGTTCGTAGGT Probe: 6FAM-CGATACGCACCGCGAA-MGB

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Gene

Forward primer, probe, and reverse primer Reverse: ACCCAAACACTCACCAAATCG

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CDKN2A

Forward: TGGAGTTTTCGGTTGATTGGTT Probe: 6FAM-ACCCGACCCCGAACCGCG-TAMRA Reverse: AACAACGCCCGCACCTCCT

RUNX

Forward: CGAGGTTTCGTTGGTTCGA Probe: 6FAM-CCGCACGCGAAAC-MGB Reverse: GCCGCGACCCAAACAA

FANCF

Forward: TTTTTATTGGTTGTGTAGTCGTCGTT Probe: 6FAM-CCATTCGCACGACTC-MGB Reverse: CATCCATCGACGCTTTAATCG

TMS1

Forward: CGTTATTTTGGATGCGTTGGA Probe: 6FAM-ACTCCTCGACGATCA-MGB Reverse: ACGACACCGACAACAACTTCAA

IGFBP3

Forward: GGGCGTGTTTTGGGTTATTTC Probe: 6FAM-CGAACGCGCCGACC-MGB Reverse: CGCAATACTCGCATCTAAACGA

CDKN2B

Forward: TCGTTTTTTTGCGGTTTGG Probe: 6FAM-AACCCGAAACTAACGACCGA-MGB Reverse: CGCGCCCGCTAATTCTTAA

KCNH5

Forward: ATTGTGTTTCGCGGAGTTTGT Probe: 6FAM-CCGCACACGAAACT-MGB Reverse: GCCGCTACCCAAACTATAACGA

KCNH8

Forward: CGTCGCGCGGTGTTTC Probe: 6FAM-CTCTTTCCGAACGAACG-MGB Reverse: GCCCCGAACCACTACGAA

PCSK6

Forward: TCGGGAGGTTCGAAACGTT Probe: 6FAM-CCAAAAATCGACGAAAAA-MGB Reverse: ACACCGCGTCCCGAAAA

PTEN

Forward: TTAAGCGTTAGTTTCGATAGCGTTT Probe: 6FAM-TCGAACCAACCTCCCGAA-MGB Reverse: CTACGCCGCGACGAAAA

P14ARF

Forward: CGCGGCGGGTCGTA Probe: 6FAM-CGACGAAAACATAATACGCA-MGB Reverse: CGACTACCGAACTCGACCCTAA

SOCS3

Forward: TGTGGATTTTACGGTCGTTAATATTT Probe: 6FAM-AACCCGCGCTACGC-MGB Reverse: AACGACGCGACGACGAAA

PTGS2

Forward: CGAAAAGGCGGAAAGAAATAGTT Probe: 6FAM-ATTAATCGCTAACCGAAAAA-MGB Reverse: TCCTAACGCTCACTACAAATCGTATAA

GSTP1

Forward: TCGGAGGTCGCGAGGTT Probe: 6FAM-CTACGACGACGAAACT-MGB

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Gene

Forward primer, probe, and reverse primer Reverse: CGCGCGTACTCACTAATAACGA

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References
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20. Vesselle H, Freeman JD, Wiens L, et al. Fluorodeoxyglucose uptake of primary non-small cell lung cancer at positron emission tomography: new contrary data on prognostic role. Clin Cancer Res 2007;13:3255–63. [PubMed: 17545531] 21. Vesselle H, Turcotte E, Wiens L, et al. Relationship between non-small cell lung cancer fluorodeoxyglucose uptake at positron emission tomography and surgical stage with relevance to patient prognosis. Clin Cancer Res 2004;10:4709–16. [PubMed: 15269143] 22. Weisenberger DJ, Campan M, Long TI, et al. Analysis of repetitive element DNA methylation by MethyLight. Nucleic Acids Res 2005;33:6823–36. [PubMed: 16326863] 23. Eads CA, Lord RV, Wickramasinghe K, et al. Epigenetic patterns in the progression of esophageal adenocarcinoma. Cancer Res 2001;61:3410–8. [PubMed: 11309301] 24. Eads CA, Lord RV, Kurumboor SK, et al. Fields of aberrant CpG island hypermethylation in Barrett's esophagus and associated adenocarcinoma. Cancer Res 2000;60:5021–6. [PubMed: 11016622] 25. Ogino S, Kawasaki T, Brahmandam M, et al. Precision and performance characteristics of bisulfite conversion and real-time PCR (MethyLight) for quantitative DNA methylation analysis. J Mol Diagn 2006;8:209–17. [PubMed: 16645207] 26. Tsou JA, Shen LY, Siegmund KD, et al. Distinct DNA methylation profiles in malignant mesothelioma, lung adenocarcinoma, and non-tumor lung. Lung Cancer 2005;47:193–204. [PubMed: 15639718] 27. Virmani A, Rathi A, Heda S, et al. Aberrant methylation of the cyclin D2 promoter in primary small cell, nonsmall cell lung and breast cancers. Int J Cancer 2003;107:341–5. [PubMed: 14506731] 28. Zochbauer-Muller S, Fong KM, Maitra A, et al. 5′ CpG island methylation of the FHIT gene is correlated with loss of gene expression in lung and breast cancer. Cancer Res 2001;61:3581–3585. [PubMed: 11325823] 29. Kim JS, Kim H, Shim YM, Han J, Park J, Kim DH. Aberrant methylation of the FHIT gene in chronic smokers with early stage squamous cell carcinoma of the lung. Carcinogenesis. 2004 30. de Fraipont F, Moro-Sibilot D, Michelland S, Brambilla E, Brambilla C, Favrot MC. Promoter methylation of genes in bronchial lavages: a marker for early diagnosis of primary and relapsing nonsmall cell lung cancer? Lung Cancer 2005;50:199–209. [PubMed: 16043258] 31. Tomizawa Y, Iijima H, Nomoto T, et al. Clinicopathological significance of aberrant methylation of RARbeta2 at 3p24, RASSF1A at 3p21.3, and FHIT at 3p14.2 in patients with non-small cell lung cancer. Lung Cancer 2004;46:305–12. [PubMed: 15541815] 32. Olaussen KA, Soria JC, Park YW, et al. Assessing abnormal gene promoter methylation in paraffinembedded sputum from patients with NSCLC. Eur J Cancer 2005;41:2112–9. [PubMed: 16129599] 33. Belinsky SA, Nikula KJ, Palmisano WA, et al. Aberrant methylation of p16(INK4a) is an early event in lung cancer and a potential biomarker for early diagnosis. Proc Natl Acad Sci U S A 1998;95:11891–6. [PubMed: 9751761] 34. Kim DH, Nelson HH, Wiencke JK, et al. p16(INK4a) and histology-specific methylation of CpG islands by exposure to tobacco smoke in non-small cell lung cancer. Cancer Res 2001;61:3419–24. [PubMed: 11309302] 35. Kersting M, Friedl C, Kraus A, Behn M, Pankow W, Schuermann M. Differential frequencies of p16 (INK4a) promoter hypermethylation, p53 mutation, and K-ras mutation in exfoliative material mark the development of lung cancer in symptomatic chronic smokers. J Clin Oncol 2000;18:3221–9. [PubMed: 10986054] 36. Sanchez-Cespedes M, Ahrendt SA, Piantadosi S, et al. Chromosomal alterations in lung adenocarcinoma from smokers and nonsmokers. Cancer Res 2001;61:1309–13. [PubMed: 11245426] 37. Kim DH, Nelson HH, Wiencke JK, et al. Promoter methylation of DAP-kinase: association with advanced stage in non-small cell lung cancer. Oncogene 2001;20:1765–70. [PubMed: 11313923] 38. Kim DH, Kim JS, Ji YI, et al. Hypermethylation of RASSF1A promoter is associated with the age at starting smoking and a poor prognosis in primary non-small cell lung cancer. Cancer Res 2003;63:3743–6. [PubMed: 12839968] 39. Toyooka S, Suzuki M, Tsuda T, et al. Dose effect of smoking on aberrant methylation in non-small cell lung cancers. Int J Cancer 2004;110:462–4. [PubMed: 15095316]

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40. Belinsky SA, Grimes MJ, Casas E, et al. Predicting gene promoter methylation in non-small-cell lung cancer by evaluating sputum and serum. Br J Cancer 2007;96:1278–83. [PubMed: 17406356] 41. Belinsky SA, Klinge DM, Dekker JD, et al. Gene promoter methylation in plasma and sputum increases with lung cancer risk. Clin Cancer Res 2005;11:6505–11. [PubMed: 16166426] 42. Sato M, Shames DS, Gazdar AF, Minna JD. A translational view of the molecular pathogenesis of lung cancer. J Thorac Oncol 2007;2:327–43. [PubMed: 17409807]

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Figure 1.

Summary of methylation patterns of paired cancerous (left) and noncancerous (right) tissues in 49 NSCLC patients for the eight genes included in the panel that best distinguishes cancerous from noncancerous tissue. Each row represents one patient. Genes are shown in the order in which they were added to the panel, starting with the most sensitive gene that was 100% specific for NSCLC tissue, and additional genes were added if the gain in sensitivity was greater than the loss of specificity. Black shaded boxes, samples methylated at very high levels (PMR ≥ 10%); dark gray boxes, samples methylated at high levels (PMR ≥ 4%); light gray boxes, samples methylated at low levels (PMR < 4%); white boxes, samples not methylated.

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Cancer Epidemiol Biomarkers Prev. Author manuscript; available in PMC 2009 December 28.

Figure 2.

Summary of methylation patterns of paired cancerous (C) and noncancerous (NC) tissues in 49 NSCLC patients for 10 genes that were not included in the panel that distinguishes cancerous from noncancerous tissue. Genes methylated in fewer than two samples (n = 9) are not shown. Each row represents one patient. Black shaded boxes, samples methylated at very high levels (PMR ≥ 10%); dark gray boxes, samples methylated at high levels (PMR ≥ 4%); light gray boxes, samples methylated at low levels (PMR < 4%); white boxes, samples not methylated.

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Table 1

Baseline demographic and tumor characteristics of 49 NSCLC patients, n (%)

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Age ?<50 ?50-59 ?60-69 ?≥70 Female Race ?White ?African American ?Asian ?Hispanic Histologic stage ?I ?II ?III ?IV Tumor differentiation ?Well ?Moderate ?Poor Histologic type ?Adenocarcinoma ?Squamous ?Large cell ?Bronchoalveolar (adenocarcinoma) ?NSCLC, not otherwise specified/mixed Tumor size, cm (mean ± SD) ?<3 ?3-5 ?>5 Pack-years ?0 ?1-39 ?40+ ?Smoker but amount unknown 1 (2) 14 (29) 32 (65) 2 (4) 20 (41) 14 (29) 8 (16) 3 (6) 4 (8) 3.2 ± 1.5 24 (49) 20 (41) 5 (10) 3 (6) 16 (33) 30 (61) 21 (43) 17 (35) 11 (22) 0 (0) 42 (86) 3 (6) 3 (6) 1 (2) 3 (6) 11 (22) 23 (47) 12 (24) 23 (47)

Cancer Epidemiol Biomarkers Prev. Author manuscript; available in PMC 2009 December 28.

Table 2

Promoter hypermethylation in NSCLC tissue and matched noncancerous tissue (n = 49)
Feng et al.
Any methylation (PMR > 0%) High methylation (PMR ≥ 4%)

Gene

Cancerous tissue, n (%) 26 (53) 23 (47) 21 (43) 17 (35) 15 (31) 14 (29) 14 (29) 12 (24) 11 (22) 9 (18) 9 (18) 8 (16) 8 (16) 7 (14) 5 (10) 4 (8) 4 (8) 3 (6) 1 (2) 1 (2) 1 (2) 0 (0) 0 (0) 0 (0) 1 (2) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 1 (2) 0.18 0.13? 0.25? 1.00? 1.00? 1.00? 1.00? — — 1 (2) 0.10 3 (6) 0.16 0 (0) 0.008? 0 (0) 0.008? 2 (4) 0.035 2 (4) 5 (10) 8 (16) 6 (12) 3 (6) 2 (4) 2 (4) 1 (2) 0 (0) 1 (2) 1 (2) 0 (0) 0 (0) 0 (0) 3 (6) 0.058 5 (10) 1 (2) 0.004 8 (16) 0 (0) 0.001? 12 (24) 2 (4) 0.003 9 (18) 2 (4) 0.001 14 (29) 4 (8) 0.005 4 (8) 1 (2) <0.001 12 (24) 0 (0) 1 (2) 1 (2) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 2 (4) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 12 (24) 0.07 10 (20) 4 (8) 4 (8) <0.001 20 (41) 2 (4) 21 (43) 0.30 20 (41) 13 (27)

Matched noncancerous tissue, n (%)

P*

Cancerous tissue, n (%)

APC

RARB

CCND2

BVES

CDH1

CDKN2A

KCNH5

RASSF1

CDH13

KCNH8

IGSF4

DAPK1

RUNX

OPCML

PCSK6

MGMT

TMS1

Cancer Epidemiol Biomarkers Prev. Author manuscript; available in PMC 2009 December 28.

IGFBP3

CDKN2B

MLH1

PTEN

FANCF

FHIT

Page 17

P14

NIH-PA Author Manuscript
Matched noncancerous tissue, n (%) P* 0.09 <0.001 0.11 0.001? 0.18 <0.001 0.004? 0.001? 0.008? 0.06? 0.50? 0.06? 0.008? 0.10 0.25? 0.50? 0.50? 1.00? — 1.00? 1.00? — — —

NIH-PA Author Manuscript

NIH-PA Author Manuscript

Gene

Any methylation (PMR > 0%)

High methylation (PMR ≥ 4%)

Feng et al.

Cancerous tissue, n (%) 0 (0) 0 (0) 0 (0) 0 (0) — 0 (0) 0 (0) 0 (0) — 0 (0) 0 (0) 0 (0) — 0 (0) 0 (0) — — —

Matched noncancerous tissue, n (%)

P*

Cancerous tissue, n (%)

SOCS3

PTGS2

GSTP1

*

McNemar's test.

Cancer Epidemiol Biomarkers Prev. Author manuscript; available in PMC 2009 December 28.

?

Exact McNemar's test.

NIH-PA Author Manuscript
Matched noncancerous tissue, n (%) P*

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Table 3

Panel of methylated genes sensitive and specific for cancerous NSCLC tissue in paired NSCLC cancerous and noncancerous tissue (n = 49)
Panel Any methylation (PMR > 0%) High methylation (PMR ≥ 4%)

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Cancerous tissue, n (%) RASSF1 +DAPK1 +BVES +CDH13 +MGMT +KCNH5 +RARB +CDH1 12 (24) 19 (39) 25 (51) 28 (57) 30 (61) 32 (65) 36 (73) 39 (80)

Matched noncancerous tissue, n (%) 0 (0) 0 (0) 1 (2) 1 (2) 1 (2) 2 (4) 5 (10) 7 (14)

Cancerous tissue, n (%) 12 (24) 16 (33) 20 (41) 23 (47) 24 (49) 26 (53) 31 (63) 31 (63)

Matched noncancerous tissue, n (%) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 2 (4) 3 (6)

NOTE: Panel of hypermethylated genes that best distinguish cancerous and noncancerous tissues from NSCLC patients. Genes are shown in the order in which they were added to the panel, starting with the most sensitive gene (RASSF1), which was 100% specific for NSCLC tissue. A two-gene panel of hypermethylation of RASSF1 and/or DAPK1 identified 39% of cancerous tissues but did not identify any noncancerous tissues. The three-gene panel (RASSF1, DAPK1, and/or BVES) identified 51% of cancerous tissues and only 2% of noncancerous tissues. This process was continued as long as adding genes resulted in at least a 4% gain in sensitivity with a corresponding loss of specificity that was less than the gain in sensitivity and resulted in a final eight-gene panel.

Cancer Epidemiol Biomarkers Prev. Author manuscript; available in PMC 2009 December 28.

Table 4

Hypermethylation, by gene function, in paired NSCLC cancerous and noncancerous tissue (n = 49)
Feng et al.
Any methylation (PMR > 0%) High methylation (PMR ≥ 4%)

Pathway

Cancerous tissue, n (%) 14 (29) 35 (71) 28 (57) 41 (84) 23 (47) 28 (57) 5 (10) 2 (4) 0.26 3 (6) 6 (12) <0.001 21 (43) 2 (4) <0.001 23 (47) 24 (49) <0.001 33 (67) 8 (16) <0.001 17 (35) 3 (6) 16 (33) 1 (2) 2 (4) 0 (0) 25 (51) 0.03 25 (51) 15 (31) 0 (0) <0.001 11 (22) 0 (0)

Matched noncancerous tissue, n (%)

P

Cancerous tissue, n (%)

Apoptosis*

Cell adhesion/invasion/metastasis?

Cell adhesion/invasion/metastasis?

Cell cycle control§

Cell cycle control‖

Cell proliferation/differentiation?

DNA repair/detoxification**

*

Apoptosis genes: DAPK1, RUNX3, TMS1, PTEN, and SOCS3.

?

Cell adhesion/invasion/metastasis genes: OPCML, CDH13, BVES, APC, CDH1, IGSF4, KCNH5, and PCSK6.

?

Cell adhesion/invasion/metastasis genes, excluding APC.

§

Cell cycle control genes: CCND2, RASSF1, APC, FHIT, CDKN2A, CDKN2B, P14, and PTEN.



Cell cycle control genes, excluding APC and CCND2.

? Cell proliferation/differentiation genes: RARB, IGFBP3, KCNH5, KCNH8, SOCS3, and PTGS2.

Cancer Epidemiol Biomarkers Prev. Author manuscript; available in PMC 2009 December 28.

**

DNA repair/detoxification genes: FHIT, MLH1, MGMT, FANCF, and GSTP1.

NIH-PA Author Manuscript
Matched noncancerous tissue, n (%) P 0.001 0.03 <0.001 <0.001 <0.001 <0.001 0.25

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