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Epidemiology and Prevention |
Departments of 1 Genetics and Complex Diseases, 2 Biostatistics, and 3 Environmental Health, Harvard School of Public Health; 4 International Mesothelioma Program, Division of Thoracic Surgery, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts; and 5 Department of Community and Family Medicine, Section of Biostatistics and Epidemiology, Dartmouth Medical School, Lebanon, New Hampshire
Requests for reprints: Karl T. Kelsey, Department of Genetics and Complex Diseases, Harvard School of Public Health, 665 Huntington Avenue, Boston, MA 02115. Phone: 617-432-3313; Fax: 617-432-0107; E-mail: kelsey{at}hsph.harvard.edu.
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Introduction |
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In colorectal cancer, a CpG island methylator phenotype (CIMP) has been described and characterized by (i) a high degree of methylation of CpG island regions in both gene promoter and noncoding region contexts and (ii) an association with microsatellite instability due to the methylation-silencing of the mismatch repair gene MLH1 (2). A CIMP also has been described in brain tumors (3, 4), gastric cancer (5), liver cancer (6), T-cell acute lymphoblastic leukemia (7), pancreatic tumors (8), and ovarian carcinoma (9), among others (10). However, the methylated genes used to define this phenotype have been inconsistent and the number of cases of cancer studied is often small. Further, some reports have questioned the existence of this phenotype (11). Issa (10) has suggested examination of promoter methylation of MINT1, MINT2, MINT31, CDKN2A, and MLH1 in colon cancer to define the phenotype, but it remains unclear if this same set of loci should be applied to all tumor types to detect the presence or absence of this phenotype. Additionally, delineation of the existence or absence of this phenotype in tumors from other tissues (and any correlation of the phenotype with clinical features of the disease) will require large, population-based samples that are devoid of bias arising from nonrandom selection.
Specific gene promoter hypermethylation has been described in lung, oral, and bladder cancer and malignant mesothelioma, but little has been done to examine if a CIMP exists in these tumors. Understanding the profile of promoter hypermethylation of a number of common genes and loci across these diseases allows for a direct comparison of CIMP in these diseases. Further, this approach could determine if such tissue-specific profiles may be clinically useful as biomarkers of these diseases. Together, these tumors contribute significantly to cancer mortality around the world, and early detection is critical to successful treatment and positive outcomes. As technology allows for identification of malignant cells in body fluids such as peripheral blood, using biomarkers such as promoter methylation profiles may be valuable in pinpointing the tissue of origin of the tumor.
We have combined data from studies of non–small-cell lung cancer (NSCLC), head and neck squamous cell carcinoma (HNSCC), bladder cancer, and malignant mesothelioma to examine the profile of promoter hypermethylation of 19 loci, examining the distribution of promoter methylation across diseases and determining both the evidence for CIMP and the potential diagnostic use of these profiles. We have used a number of statistical approaches to examine the distribution of hypermethylation within and across these diseases, beginning first with an examination of CIMP using the previously defined markers of the phenotype in colon cancer. We then further examine the interdependence of promoter hypermethylation between gene loci. Finding marked correlation among loci and highly variable prevalence of promoter hypermethylation between different loci, we employ novel statistical approaches to examine whether the distribution of promoter hypermethylation can be described by clustering across diseases, by examining latent classes within diseases, and finally by using an item response theory approach to model the latent propensity for hypermethylation within each of these diseases.
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Materials and Methods |
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DNA extraction and sodium bisulfite modification. All tumor samples used were histologically confirmed by an independent pathologic review. The proportion of malignant cells in the available sections was also observed and the specimens containing a majority of tumor tissue were used in these analyses. For formalin-fixed paraffin-embedded tumors, three 20-µm sections were cut from each tumor sample and transferred into microcentrifuge tubes. The paraffin was dissolved with Histochoice Clearing Agent (Sigma-Aldrich, St. Louis, MO), and then washed twice with 100% ethanol and once with PBS. The samples were then incubated in SDS-lysis solution [50 mmol/L Tris-HCl (pH 8.1), 10 mmol/L EDTA, 1% SDS] with proteinase K (Qiagen, Valencia, CA) overnight at 55°C. De-crosslinking was done by adding NaCl (final concentration, 0.7 mol/L) and incubating at 65°C for 4 hours. DNA was recovered using the Wizard DNA clean-up kit (Promega, Madison, WI) according to the protocols of the manufacturer. Fresh tumor samples were extracted essentially identically, except the paraffin removal steps were not used. Sodium bisulfite modification of the DNA was done with the EZ DNA Methylation Kit (Zymo Research, Orange, CA) following the protocol of the manufacturer, with the addition of a 5-minute initial incubation at 95°C before addition of the denaturation reagent. The de-crosslinking incubation as well as the 95°C incubation ensures more complete melting of the DNA and thus more complete sodium bisulfite conversion, particularly for the formalin-fixed specimens.
Methylation-specific PCR. We have specifically chosen to use traditional methylation-specific PCR for the analysis of promoter hypermethylation in these studies, as we have done matched analysis between fresh-frozen and formalin-fixed paraffin-embedded samples and found the greatest concordance (>95%) for methylation detection using this method. We have also previously examined potential biases in the sensitivity of using this assay against the relative quantitative TaqMan-based methods (13) and found no evidence for potential bias based on tumor quantity in the samples analyzed. Finally, this method allows for detection of a large number of genes from the limited DNA samples available on many of these tumors, whereas the quantitative assays require larger DNA quantities for the multiple amplifications of specific genes and reference genes.
Sodium bisulfite–modified DNA was used as the template for methylation-specific PCR as previously described (16) using primers specific for the methylated promoters of CDKN2A (16), RASSF1A (17), APC (18), PYCARD (19), LAMC2 (20), SFRP1, SFRP2, SFRP4, SFRP5 (21), GSTP1, MGMT, DAPK, RARB, CDH1 (22), CDH13 (23), MLH1, MINT1, MINT31 (24), and MINT32 (25). All methylation-specific PCRs were optimized to detect >5% methylated substrate in each sample. To control for the presence of modified DNA, primers specific to a modified region of the ACTB genes containing no CpG sites were used (26). Modified circulating blood lymphocyte DNA (obtained from a control subject) and the same lymphocyte DNA completely methylated using SssI DNA methylase and modified by treatment with sodium bisulfite were used as the negative and positive controls, respectively, in each run.
Statistical analysis. Prevalence of the promoter hypermethylation of each of the examined loci was tabulated by disease and a 
2 test was done to examine the difference in the prevalence of methylation across the disease classes. Because there were numerous zero counts in the cross-classification of disease by hypermethylation, we also did a permutation test of association using the 2 statistic.
Methylation index (27) was calculated by taking the sum of gene promoter hypermethylation events in each tumor divided by the number of genes examined. The distribution of the methylation index was plotted and the difference in the distribution across disease classes was examined using a quasi-likelihood approach, with a log link function and binomial variance as is appropriate for bounded discrete outcomes such as the methylation index. To examine the possible association between methylation index and MINT loci methylation, we again used the quasi-likelihood approach described above, modeling methylation index as the outcome with the sum of the number of MINT loci methylation events as the predictor. We also fit a more general model with separate MINT effects; however, a score test for the equivalence of the effects produced an insignificant P value, suggesting that the sum of the MINT loci is an adequate summary predictor of hypermethylation.
To examine associations between loci, we employed both descriptive and inferential approaches. We visually examined the Pearson correlation coefficients between loci and, using the hclust function in R (28), did hierarchical clustering using 1 minus the absolute value of the correlation coefficient as a distance metric. We then used several latent variable inferential techniques appropriate for binary data: latent class analysis and item response theory models (29). The latent class model is characterized by
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k = P(U = k), must also be estimated, and may be assumed to be uniform across all subjects or, via the regression approach of (30), to vary among different predefined populations (e.g., tumor types).
We fit models with K = 2 to 6 classes, assuming either uniform prevalence k across all four tumor types or tumor-specific prevalences. Item response theory models, also known as latent trait models, can be thought of as a limiting case of latent class models when the number of latent classes is large (31). The item response theory model is characterized by the following logistic model:
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We used two methods to predict tumor type from methylation events: Classification and Regression Trees (CART; ref. 32), implemented in R (28) through the tree package (33) using the default splitting-rule parameters, and polytomous (multinomial) logistic regression (34), implemented in R (28). Both methods were used to model the probability that a random categorical variable Y takes on a particular value c, where c is one of a finite number of known categories (c = 1,..., C), conditional on an observed vector of values x associated with Y. CART is a nonparametric method that partitions the domain of x into regions R using successive binary splits; over each region R, the probability 
c(R) of response Y = c conditional on x 
R is calculated as
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Polytomous regression is a parametric method that models the conditional probabilities
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Results |
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2 test statistic to assess if the prevalence of gene promoter hypermethylation across all loci differed by tissue (Table 1). This analysis showed a statistically significant permutation test P value of <0.001 indicating a significant association between tumor type and locus-specific prevalence of promoter hypermethylation.
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Discussion |
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In these four tumor types, there was a strikingly different prevalence, by disease, in the promoter hypermethylation of specific tumor suppressor genes. This was not unexpected as previous examinations have alluded to such a difference (38). Importantly, our work examined hypermethylation of 16 candidate genes in a consistent population of tumors, allowing us to more rigorously examine and test this hypothesis. A simple examination of the difference in the prevalence of methylation across diseases, presented in Table 1, shows a highly statistically significant difference. This suggests that there is a disease-specific selection of the candidate genes being silenced epigenetically, and that these events do not appear in a random fashion. For example, RARB was silenced in 63% of NSCLC and 30% of HNSCC, but only in 15% and 10% of bladder cancers and MPM, respectively. These data suggest that this pathway is particularly targeted for silencing in certain diseases, but not in others, or that the mode of inactivation (e.g., promoter hypermethylation versus gene deletion) occurs differentially by disease. The particular factors responsible for selection remains unclear; it may be the innate differences in the target tissue or the differences in the type, dose, or duration of carcinogen exposure at the different sites, selecting for differential gene inactivation. Carcinogen-specific selection of individual pathways for inactivation is consistent with our prior work (39) and with a recent work from Toyooka et al. (40) studying methylation and mutation in pathways in lung cancer. It should be remembered, of course, that there are some genes of which the silencing is common across tumor types. CDKN2A, encoding the P16INK4A protein, shows promoter hypermethylation relatively consistently across these diseases, suggesting that epigenetic inactivation of this pathway (the Rb cell cycle checkpoint pathway) is common across tumor types.
Although the profiles of the promoter hypermethylation seem to be distinct across these tumor types, they are not definitive enough to be able to correctly or consistently classify tumors. Examination of a variety of measures, including gene expression in the form of microarrays, protein expression, and now even miRNA expression profiles, have been suggested as methods for classifying tumors, although none of these techniques, as yet, have shown prospective clinical use, even with relatively large data sets. Promoter hypermethylation profiles have also been suggested as an alternative to provide this kind of classification. This approach has been touted as favorable because it is based on DNA (as compared with RNA or protein, which are more variable and unstable) and can be done even on limited samples with use of PCR amplification. To explore this, we used two approaches to classify tumors based on promoter methylation profiles: the CART approach and a multinomial logistic regression model. With cross-validation analyses to examine misclassification using these schemes, we saw poor performance of both techniques, with misclassification rates between 28% and 34%. Additional methods, such as neural network approaches, may provide better classification. However, we have attempted other model-free approaches, such as k-nearest neighbor (data not shown; ref. 32), and have observed no improvement in error rates above those with the classification approaches described. Although these results suggest that the multinomial logistic approach has less classification error, the results are more difficult to interpret. The CART approach, which has a slightly greater misclassification rate, has a relatively straightforward decision tree–like interpretation; therefore, these approaches are not easily applicable to the clinical setting, where such classification may be useful. Examination of additional loci may also aid in creating more sensitive, powerful, and specific profiles to allow for more efficient classification, particularly if very tissue-specific loci could be identified. Although these results suggest that general classification is error-prone, it may be more valuable to focus on particular classifications that are most clinically useful or that pose the greatest problems for diagnostic pathology, such as distinguishing between MPM and lung adenocarcinoma.
One goal of this study was to examine whether CIMP (2, 41), as has been described most conclusively in colorectal cancer, is a phenotype that may characterize other solid tumors. One oft-encountered problem with defining this phenotype is the lack of a specific definition by which to judge its presence or absence. Issa (10) has suggested that examination of the hypermethylation of the MINT loci, as well as specific gene promoters, such as CDKN2A and MLH1, can be used in this determination. Therefore, we included these loci and genes in our study design and examined the relationship of MINT hypermethylation with the extent of methylation, as measured by the methylation index, in these tumors. In bladder cancer, there is an identifiable group of tumors (see Fig. 2) that show a greater proportion of gene promoter hypermethylation, whereas at the same time there is a group showing a complete lack of hypermethylation. Using our quasi-likelihood approach, we have examined the association between MINT loci hypermethylation and overall tumor suppressor gene hypermethylation (as measured by methylation index) in these tumors. We observed a significant association between MINT loci hypermethylation and methylation index in bladder cancer, NSCLC, and HNSCC, suggesting that MINT loci hypermethylation may be an appropriate marker for a generally higher degree of gene hypermethylation in these tumors. In MPM, there was no significant association between MINT loci hypermethylation and methylation index, although assessing this relationship was limited by the small number and generally lower methylation index of these tumors compared with the other tumor types, as shown in Fig. 1. This would not suggest, though, that a form of methylator phenotype should be ruled out in this disease, as we observed a number of tumors with a large number of hypermethylated loci. It will require larger series of these tumors, similar to what we have used for the other diseases, to be able to better define this phenotype in this disease.
Although the data in bladder cancer, HNSCC, and NSCLC suggest that MINT loci hypermethylation may be useful markers of a form of methylator phenotype, the relative similarity in the prevalence of CDKN2A hypermethylation and the general lack of MLH1 hypermethylation in these diseases further suggest that hypermethylation of these genes is not generally useful as a powerful delimiter of this phenotype, as has been suggested in colorectal cancer (10). The description of CIMP in colorectal cancer includes specific clinical phenotypes, such as a greater prevalence of a particular type of genomically unstable tumor, and tumor location, which is not observed in most solid tumors. Whereas the phenotype that we observed compared with that observed in colon cancer may have similarities, it also is clear that it has fundamental differences. This is not surprising given that the pathways for inactivation differ across tumors, the nature and type, duration, and intensity of carcinogen exposure differ, and underlying tissue susceptibility may also vary. Hence, whereas there may be differences in the proteins and genes responsible for epigenetic silencing thereby giving rise to what has been described as CIMP, it remains possible that the phenotype is a consequence of differences in clonal growth, carcinogen exposure, and tumor metabolism, rather than an underlying or induced difference in the propensity for methylation silencing. This may also explain the differences in the form of the phenotype between diseases. It will also be of interest to examine in these diseases and in others where CIMP has been reported if there are particular clinical correlates with a higher degree of methylation, such as those observed in colorectal cancer.
In our examination of the relationship of methylation index to MINT loci hypermethylation status, we also observed highly significant differences in the distribution of methylation across the tumor types. Our initial examination of methylation index suggests that NSCLC, for example, is characterized by a much larger methylation index than the other tumor types, a result consistent with previous reports comparing NSCLC and MPM (23). At the same time, we also noted the striking correlation between methylation of different genes, as well as the varied prevalence of promoter methylation at these loci, suggesting that simple counts of the number of genes methylated is not a statistically rigorous method to examine these differences. To overcome these challenges, we used novel statistical approaches that can more appropriately model this methylation data and can address the outstanding issue surrounding the presence of CIMP in these tumors and the propensity for methylation observed in some of these cancers.
Our results of this modeling suggested that methylation status at these gene promoters could not be most appropriately modeled into distinct classes or clusters, as would be expected if a true methylator phenotype existed. Instead, use of a latent trait method produces the most parsimonious model and suggests that there is an underlying propensity for hypermethylation, which may be driving the distribution of methylation across these diseases. Although beyond the scope of this examination, use of the item response theory modeling can also allow for an exploration of the factors, such as demographics and exposures that may be associated with this methylation propensity within each of these diseases, and can also be used to model clinical outcomes of the disease, most importantly patient survival. Thus, we believe that this type of modeling is critical to a better understanding of the biology driving promoter hypermethylation and may be able to provide clues to what specific factors are driving these epigenetic alterations in human cancer.
The difference in the propensity for methylation and the underlying biology driving this propensity may reflect an overall predisposition of specific tumors, or target tissues, to epigenetically silence tumor suppressor genes instead of undergoing genetic alterations. Indeed, to produce a malignant phenotype in the different tissues, distinct pathways are likely necessary to be inactivated. The difference between HNSCC and NSCLC is particularly surprising given the similar etiology of these diseases (both are highly associated with tobacco-smoke exposure), yet we see vastly different methylation indices and profiles in these diseases. This is consistent with our data relating exposures to specific gene hypermethylation; for example, CDKN2A is silenced at a similar prevalence in both NSCLC and HNSCC but shows a significant association with duration of tobacco smoking in NSCLC (42), but not in HNSCC (43). Therefore, the susceptibility of different tissues to exposure-related epigenetic alteration may be quite different and may depend on many factors, including the target cancer stem cell, the particular nature of the growth pathways active in different tissues, the type, duration, and intensity of carcinogen exposure, and additional underlying genetic or epigenetic susceptibility to either genotoxic insult or epigenetic inactivation.
It should be noted that our observation of these differences in the underlying propensity for hypermethylation across diseases may be biased by the set of gene promoters that we have examined herein. Using the same statistical approach, examination of a novel set of gene promoters within this population of tumors would help to clarify if the differences in methylation propensity are truly varied across tissue types. Additionally, replication of this work in an independent population of tumors would clarify if these results are generalizable to the population.
Our data represent a large body of information on the molecular and, particularly, the epigenetic profiles of four exposure-related tumor types. Although the profiles of promoter hypermethylation are distinct across these diseases, alone they are not sufficient for clinically useful classification of tumors. At the same time, our data strongly suggest that promoter hypermethylation of specific genes does not occur independently or randomly, but is instead the product of a specific and complex selection process driven, at least in part, by carcinogen exposures, innate susceptibility, and tissue-specific processes. Further work is needed to better characterize the etiology of this methylation phenotype as well as to determine if this phenotype has important prognostic or clinical use. Understanding the profiles of these epigenetic alterations may be critical for understanding the nature of the carcinogenic process.
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Acknowledgments |
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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.
Received 5/ 9/06. Revised 8/29/06. Accepted 9/11/06.
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