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Clinical Cancer Research Vol. 11, 4400-4405, June 15, 2005
© 2005 American Association for Cancer Research

Imaging, Diagnosis, Prognosis

Methylation Profiling of Archived Non–Small Cell Lung Cancer: A Promising Prognostic System

A. Mazin Safar1,2, Horace Spencer, III2, Xiaobo Su1,2, Maureen Coffey1, Craig A. Cooney2, Luke D. Ratnasinghe1,3, Laura F. Hutchins1,2 and Chun-Yang Fan1,2

Authors' Affiliations: 1 Central Arkansas Veterans Healthcare System, 2 University of Arkansas for Medical Sciences, Little Rock, Arkansas and 3 National Center for Toxicological Research, Food and Drug Administration, Jefferson, Arkansas

Requests for reprints: A. Mazin Safar, Division of Hematology and Oncology, University of Arkansas for Medical Sciences, 4301 West Markham, Slot 508, Little Rock, AR 72205. Phone: 501-257-1000 ext. 55924; Fax: 501-257-4942; E-mail: safarahmedm{at}uams.edu.


   Abstract
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 Abstract
Materials and Methods
 Results
 Discussion
 References
 
Purpose: Enhanced prognostication power is becoming more desirable in clinical oncology. In this study, we explored the prognostic potential of multigene hypermethylation profiling in non–small-cell lung cancer.

Experimental Design: We evaluated a panel of eight genes (p16, APC, ATM, hMLH1, MGMT, DAPK, ECAD, and RASSF1A) using methylation-specific PCR in 105 archived specimens of non–small-cell lung cancer representing all stages of the illness. We analyzed the effect of gene methylation status on outcome individually in a cumulative manner and in a combinatorial approach using recursive partitioning to identify methylation profiles, which affect overall survival.

Results: In this data set, tumors harboring promoter hypermethylation at two or more genes exhibit similar survival trends to others in the cohort. Using recursive partitioning, three genes (APC, ATM, and RASSF1A) emerged as determinants of prognostic groups. This designation retained its statistical significance even when disease stage and age were entered into a multivariate analysis. Using this approach, patients whose tumors were hypermethylated at APC and those hypermethylated at only ATM (not also at APC or RASSF1A) enjoyed substantially longer 1- and 2-year survival than patients in the remaining groups. In 32 adjacent histologically normal lung tissue specimens, we detected similar methylation abnormalities.

Conclusion: Assessment of promoter hypermethylation aberrations may facilitate prognostic profiling of lung tumors, but validation in independent data sets is needed to verify these profiles. This system uses material that is abundantly available with linked outcome data and can be used to generate reliable epigenetic determinants.

Key Words: Epigenetics • paraffin-embedded • biomarker • ATM

During tumorigenesis, aberrant promoter hypermethylation is a common mechanism for silencing tumor suppressor genes. This mechanism is noted in almost all solid tumors (1), including non–small-cell lung cancer (NSCLC; refs. 2–4). Because of its high prevalence and mechanistic contributions to tumorigenesis (1), epigenetic abnormality is a prime candidate for integration into clinical cancer medicine. The availability of sensitive molecular tools to examine the methylation status of specific genes (5) enhances the clinical potential of this biomarker.

The effect of methylation abnormalities on prognosis has been investigated in lung cancer (6–10). The majority of studies addressed the question by examining the contribution of individual genes on outcome and produced variable results.

In this study, we examined 105 NSCLC specimens (all stages) for a prognostic effect of promoter hypermethylation using a multigene approach. We used methylation-specific PCR to determine the status of a panel of eight genes (p16, APC, ATM, hMLH1, MGMT, DAPK, ECAD, and RASSF1A), and examined the possibility of methylation "signature" affecting overall survival.

We propose that NSCLCs encompass different biological entities with variable clinical outcomes, and that evaluating a constellation of hypermethylated genes can potentially identify these subsets. With confirmatory studies in lung and other cancers, methylation signatures may emerge as a tool for improved prognostication in the clinic, and possibly for biological classification of tumors.


   Materials and Methods
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Results
 Discussion
 References
 
Study cohort. Patients eligible for this study were newly diagnosed with stage I to IV NSCLC. Diagnoses occurred between January 1, 1999 and July 31, 2003 at the Central Arkansas Veterans Healthcare System in Little Rock, Arkansas. To be included in the cohort, an eligible patient must have a confirmed diagnosis of NSCLC and a sufficient amount of archived tumor material to allow for DNA extraction (tissue preserved in sectioned blocks; >50% of cells are malignant). Cases of small-cell lung cancers, mixed histology, metastatic tumors to the lung, and indeterminate clinical stage were excluded. Demographic and clinical information, including survival information, was obtained from the computerized tumor registry at the Central Arkansas Veterans Healthcare System. Approval of this study was obtained from the Central Arkansas Veterans Healthcare System Institutional Review Board.

Gene selection. A total of eight genes were selected for methylation-specific PCR–based examination of methylation abnormalities. The gene profile is composed of p16, APC, ATM, hMLH1, MGMT, DAP kinase (DAPK), E cadherin (ECAD), and RASSF1A. The panel included genes reported as targets for epigenetic silencing in lung cancer (2, 4, 11). ATM was included in this panel given the recent reports of hypermethylation in head and neck cancers (12), a tobacco-related malignancy. MGMT and hMLH1 are involved in DNA repair, the inactivation of which results in increased mutagenecity. RASSF1A and DAPK are genes involved in apoptosis whereas APC and p16 affect cell cycle and are frequently inactivated in human cancers (13).

DNA extraction. Using the EX-WAX DNA Extraction Kit (Chemicon International, Temecula, CA) according to the protocols of the manufacturer, DNA of each patient sample was extracted from material from four deparaffinized, 10-µm-thick tissue sections. Human placental DNA (Sigma Chemical Co., St. Louis, MO) was used as a negative control and CpGenome universal methylated human DNA (Chemicon International) served as a positive control in which all CpG sites within the genomic DNA are chemically methylated.

Bisulfite modification of DNA for methylation-specific PCR. DNA samples from all identified NSCLC cases and negative and positive controls were used for methylation-specific PCR. Prior to methylation-specific PCR, we used CpGenome DNA modification kit (Chemicon International) to subject all DNA samples to bisulfite modification.

Methylation-specific PCR amplification and primers. Primer sequences and annealing temperatures have been listed elsewhere (7, 4, 12, 14). Amplification of the promoter region of each of the eight genes selected for this study was carried out in a Touchgene Gradient Thermal Cycler (Techne Inc., Burlington, NJ) for 40 cycles. Each 50-µL PCR mixture contained 2 µL of bisulfite-treated genomic DNA, deoxynucleotide triphosphates (200 µmol/L each; Roche Molecular Biochemicals, Indianapolis, IN), sense and antisense primers (50 pmol each), 2.5 mmol/L MgCl2, and 1.25 units Hotstar Taq in 1x PCR buffer. All reagents were supplied with the Qiagen Hotstar Taq Kit (Qiagen, Valencia, CA). Representative examples of methylation-specific PCR assay results are presented in Fig. 1.



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  		Fig. 1. Representative methylation-specific PCR assays for six samples in the selected genes for this study. M, reactions with the methylated primer sets; U, reactions with the unmethylated primers.

 
Statistical methods. Subgroup characteristics were compared using Pearson's {chi}2 tests for categorical data (in instances when expected cell sizes were small, Fisher's exact tests were used) and Wilcoxon rank sum tests for continuous data.

Overall survival was defined as the time from histologic diagnosis to last follow-up or death. The method of Kaplan and Meier was used to estimate survival distributions for the whole cohort and for subgroups identified by methylation status for each gene. Log-rank tests were used to compare these survival distributions. Additionally, Cox proportional hazard models were used to estimate hazards ratios (HR) and 95% confidence intervals (CI). Survival analyses were done using PROC LIFETEST and PROC PHREG in SAS version 9.1 (SAS Institute, Cary, NC).

In a separate analysis, we used recursive partitioning (15) to identify a panel of genes that may collectively affect overall survival. Briefly, a recursive partitioning model examines each variable in the model and, based on a splitting rule, determines the best dichotomization of the data. The splitting rule used in the analysis of overall survival is equivalent to a likelihood ratio test for two Poisson groups (16). The initial split is determined using all variables from the entire sample called the root node, and subsequent splits are made on subgroups called descendant nodes. Descendant nodes that can no longer be split are called terminal nodes. At the end of the partitioning process, the initial tree is pruned using cross-validation methods (16). Recursive partitioning modeling was implemented in SPLUS (Insightful Corp., Seattle, WA) using the rpart library (version 3.1-22, March 2002; ref. 16).


   Results
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Discussion
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Characteristics of patient cohort. One-hundred five patients constitute our analysis cohort. Within our patient cohort, the median age at diagnosis was 67 years (range: 45-86 years); 12% were African American and 88% were Caucasian; and 38% presented with stage I disease, 8% with stage II disease, 32% with stage III disease, and 22% with stage IV disease. Forty-seven percent of the cohort had surgical resection. Median follow-up of the cohort was 23 months, and median survival was 15 months (95% CI, 11.3-24.0).

Methylation status and survival. Figure 2 presents the methylation profile for each individual subject and each gene, stratified by disease stage. The overall prevalence of hypermethylation seems comparable to rates reported in the literature for the respective genes (2, 4, 11). Upon univariate analyses, no statistically significant association was detected between overall survival and hypermethylation of any individual gene in our panel (Table 1). Surprisingly, for all genes except RASSF1A, the presence of hypermethylation seems to indicate, if anything, a protective effect in our cohort, as shown by HR < 1.



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  		Fig. 2. Distribution of promoter methylation (shaded cells) for selected genes in all105 samples of NSCLC by clinical stage. Eachr ow represents one patient.

 

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  		Table 1. Association between methylation status of individual genes and overall survival using Cox proportional hazard models

 
To explore the potential cumulative effect of methylation, a methylation phenotype was defined as any patient with two or more of the selected genes exhibiting hypermethylation. In this cohort, 69% were deemed to have this phenotype, but no significant association with overall survival was noted (P = 0.438; HR, 0.81; 95% CI; 0.48-1.37). To illustrate this, Fig. 3A depicts survival curves based on the number of methylated genes observed.



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  		Fig. 3. Kaplan-Meier survival estimates of patients with hypermethylated genes. A, the curves reflect groups with cumulative degree of methylated genes. B, results reflecting the groups identified by recursive partitioning, which examines each gene methylation status and, based on a splitting rule, determines the best dichotomization of the data. (+, –, or x before each gene in legend denotes methylated, unmethylated, or any methylation status, respectively.)

 
Recursive partitioning analysis of overall survival and methylation status. To examine the proposition that specific methylation signatures may predict patient outcome, we used recursive partitioning methods to define a panel of genes of which methylation status may collectively be associated with overall survival. All eight genes were considered in the recursive partitioning analysis.

We found that methylation status of three (APC, RASSF1A, and ATM) of the eight genes was influential for defining the prognosis. The first split of the data was based on the methylation status of APC. Patients with APC hypermethylation formed terminal group 1. The 2-year survival rate of these patients was 52% (95% CI, 35-78%; n = 25). The methylation status of RASSF1A determined the next split for patients with no APC methylation. Patients with hypermethylation of RASSF1A formed terminal group 4; this group had a 2-year survival rate of 16.7% (95% CI, 5-59%; n = 12). The remaining patients were divided based on their methylation status of ATM: patients with hypermethylated ATM formed terminal group 2, and patients with unmethylated ATM formed terminal group 3. The 2-year survival rates for groups 2 and 3 were 51% (95% CI, 31-83%; n = 23) and 29% (95% CI, 17-49%; n = 45), respectively. None of the five remaining genes (p16, MGMT, hMLH1, DAPK, and ECAD) entered into the final model.

The survival distributions of the prognosis groups are presented in Fig. 3B. It is interesting to note that groups 1 and 2 have similar survival experiences in spite of the fact that, on average, group 1 patients are slightly older (group 1: 71 years; group 2: 67 years) and have a higher percentage of stage III and IV disease (group 1: 56%; group 2: 17.3%).

To determine whether the association between the methylation groups and overall survival remained after adjusting for covariates of disease stage and age, we used a Cox proportional hazard model. Because the survival experiences between methylation groups 1 and 2 were so similar, we combined these two groups and used the resulting group as the reference in this analysis; groups 3 and 4 were each modeled individually. For modeling disease stage, we combined stages III and IV, and we used stages I and II for the referent group. Similarly, age was dichotomized so that the reference was an age of <60 years. The resulting model showed that association between methylation groups 3 and 4 and overall survival remained strong after adjusting for age and disease stage covariates (group 3: HR, 1.81; 95% CI, 1.01-3.25; group 4: HR, 5.25; 95% CI, 2.32-11.88).

Hypermethylation in histologically normal lung. In a small subset of our cohort, histologically uninvolved adjacent lung tissue (n = 32) was available. For this paired set, we did methylation-specific PCR for the same eight genes selected in this study. The frequency of promoter hypermethylation in histologically normal adjacent lung tissue parenchyma is similar to that seen in tumor samples (Fig. 4), a finding in keeping with the field cancerization hypothesis for lung cancer (17, 18).



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  		Fig. 4. The methylation status of paired tumors and surrounding histologically uninvolved lung tissue. Shaded cells, promoter methylation. T and N, tumor and normal lung, respectively. Eachr ow represents one patient. Bottom row, percentage methylation for each representative column.

 

   Discussion
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 Abstract
 Materials and Methods
 Results
 Discussion
References
 
In recent years, researchers have attempted to prognosticate tumors based on biological factors, such as gene expression profiling and specific molecular abnormalities (19, 20). This biological approach represents the potential beginning of new avenues to explore tumor biology and improve disease classification. Such improvement is becoming more clinically relevant with the emergence of targeted cancer drugs and the call to improve patient selection and identify the appropriate candidates for specific therapeutics.

Using recursive partitioning analysis, we defined prognostically divergent subgroups on the basis of combinatorial gene hypermethylation status. When other recognized prognostic factors (stage and patient age) were included in a multivariate analysis, our methylation-based groups retained their prognostic significance. These results lead us to suggest that certain methylation signatures may be predictive of patient survival, however, confirmatory studies using independent data sets are needed to support our findings. Our approach is readily testable because pathology departments have the needed patient material archived with linked outcome data.

In this data set, the methylation status of no individual gene had a statistically significant effect on survival. Furthermore, our results do not suggest a correlation between survival and a methylation phenotype, which we defined as the presence of at least two hypermethylated genes in a given tumor (Fig. 3A). In contrast to findings in esophageal cancer (21), NSCLC tumors affected by hypermethylation of four or more genes seem to be associated with better outcome (HR, 0.526; 95% CI, 0.31-0.91; P = 0.019). This cumulative hypermethylation model, however, is biologically indiscriminate because it gives no weight to the types of genes included in a "methylator" tumor.

The prognostic capacity for methylation abnormalities has been proposed and investigated in lung cancer (6–10). The approach in most studies assessed the influence of individual genes on outcome to discern a prognostic value for promoter hypermethylation. The results have been generally inconclusive, and occasionally results were not reproducible by the same group (7, 9). Promoter hypermethylation is a heterogeneous and gradual process, a feature that likely contributes to the aforementioned discrepancies. Variations in demographic factors and carcinogen exposures on methylation patterns cannot be ruled out. Biologically, assigning a prognostic value to an individual methylated gene is complicated because of the possibility of a similar functional effect in the comparator group, mediated by "genetic" inactivation (e.g., mutation) in the very same gene. For example, among NSCLCs lacking p16, hypermethylation accounted for no more than 36% of p16 silencing, the remaining tumors having lost p16 via a different mechanism (usually mutation; ref. 22). From a different point of view, compared with the homogenous and immediate disruptions produced by genetic insults (e.g., mutation or DNA deletion/translocation), promoter hypermethylation instigates a more gradual and often incomplete suppression of the protein encoded by the involved gene (1). Accordingly, a tumor using epigenetic mechanism to disrupt an essential cancer pathway may fare better (due to the incomplete loss of the gene product) than one using genetic means.

In this study, we used paraffin-embedded tissue samples as the source of DNA for PCR-based evaluation of methylation. It is well recognized that formaldehyde, which is ubiquitously used in clinical processing of tumors, reacts with proteins and subsequently inhibits proteolysis (23), causes single-strand breaks in DNA, and cross-links DNA to protein (24–26). It is unclear whether formaldehyde damages DNA directly or simply makes it difficult to extract, possibly because it remains strongly associated with proteins (23). The size of DNA molecules extracted from formalin-fixed tissue is generally small (less than 1 kb; ref. 27), and bisulfite modification of DNA also reduces molecular weight of DNA to a similar size range. When using these sources of template DNA, it is recommended that target PCR products are no larger than 300 bp (28). Thus, formalin-fixed tissue should yield a lower amount of target PCR product than does fresh tissue, but it should generate very similar PCR results provided the sequence to be amplified is short (i.e., <300 bp). The methylation-specific PCR assay we used targets short sequences (<200 nt) and is well suited to assay archival tissue.

Seven of the eight genes selected for our panel have been reported (individually and in combinations) as targets for promoter hypermethylation in NSCLC (2, 4, 12). The exception is ATM, a tumor suppressor gene operating upstream of p53. Hypermethylation of ATM has been reported in head and neck cancers (12) but, to our knowledge, our report is the first to document this abnormality in lung cancer.

Notably, we found promoter hypermethylation in adjacent uninvolved lung parenchyma of cancer patients (Table 3). In keeping with the known heterogeneity of the underlying process, the methylation concordance between tumor tissue and adjacent lung parenchyma is incomplete. This finding supports the efforts to investigate the chemopreventive potential of demethylating agents and positions the methylation signal as a prime candidate for biological surrogate in these studies.

Methylation testing of archived tumor specimens seems to be a promising resource with multitudes of clinical applications. To address a specific question, archived material is often the most realistically useful resource and, in our opinion, utilization of this material is essential if we are to prevent major delays in evaluating the clinical relevance and contribution of gene methylation to clinical oncology. In contrast to mRNA-based approaches, this system can be easily validated and is not restricted by availability of suitable tumor material or outcome data.


   Acknowledgments
 
We thank Drs. Anne-Marie Maddox, Robert McGehee, and Thomas Kieber-Emmons for helpful discussions. We also thank the Office of Grants and Scientific Publications at the University of Arkansas for Medical Sciences for editorial assistance during the preparation of this manuscript.


   Footnotes
 
Grant support: Arkansas Cancer Research Center (A.M. Safar).

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 11/20/04; revised 3/11/05; accepted 3/24/05.


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 Abstract
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 Results
 Discussion
 References
 

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