© The Author 2006. Published by Oxford University Press.
ARTICLE |
Quantitation of Promoter Methylation of Multiple Genes in Urine DNA and Bladder Cancer Detection
Affiliations of authors: Department of Otolaryngology–Head and Neck Surgery (MOH, OT, AC, ER, WHW, DS), Department of Biostatistics/Oncology (MZ, SNG), The Johns Hopkins School of Medicine, Baltimore, MD; The James Buchanan Brady Urological Institute (MS), Department of Pathology (SB, WHW), Johns Hopkins Medical Institutions, Baltimore, MD; OncoMethylome Sciences, Sart-Tilman (Liege), Belgium (WVC)
Correspondence to: David Sidransky MD, Director, Division of Head and Neck Cancer Research, The Johns Hopkins School of Medicine, 818 Ross Research Building, 720 Rutland Ave., Baltimore, MD 21205-2196 (e-mail: dsidrans{at}jhmi.edu).
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ABSTRACT |
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 TopNotes Abstract IntroductionMaterials and methodsResultsDiscussionReferences |
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Background: The noninvasive identification of bladder tumors may improve disease control and prevent disease progression. Aberrant promoter methylation (i.e., hypermethylation) is a major mechanism for silencing tumor suppressor genes and other cancer-associated genes in many human cancers, including bladder cancer. Methods: A quantitative fluorogenic real-time polymerase chain reaction (PCR) assay was used to examine primary tumor DNA and urine sediment DNA from 15 patients with bladder cancer and 25 control subjects for promoter hypermethylation of nine genes (APC, ARF, CDH1, GSTP1, MGMT, CDKN2A, RAR
2, RASSF1A, and TIMP3) to identify potential biomarkers for bladder cancer. We then used these markers to examine urine sediment DNA samples from an additional 160 patients with bladder cancers of various stages and grades and from an additional 69 age-matched control subjects. Data were analyzed on the basis of a prediction model and were internally validated using a jacknife procedure. All statistical tests were two-sided. Results: For all 15 patients with paired DNA samples, the promoter methylation pattern in urine matched that in the primary tumors. Four genes displayed 100% specificity. Of the 175 bladder cancer patients, 121 (69%, 95% confidence interval [CI] = 62% to 76%) displayed promoter methylation in at least one of these genes (CDKN2A, ARF, MGMT, and GSTP1), whereas all control subjects were negative for such methylation (100% specificity, 95% CI = 96% to 100%). A logistic prediction model using the methylation levels of all remaining five genes was developed and internally validated for subjects who were negative on the four-gene panel. This combined, two-stage predictor produced an internally validated ROC curve with an overall sensitivity of 82% (95% CI = 75 % to 87%) and specificity of 96% (95% CI = 90% to 99%). Conclusion: Testing a small panel of genes with the quantitative methylation–specific PCR assay in urine sediment DNA is a powerful noninvasive approach for the detection of bladder cancer. Larger independent confirmatory cohorts with longitudinal follow-up will be required in future studies to define the impact of this technology on early detection, prognosis, and disease monitoring before clinical application.
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INTRODUCTION |
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Bladder cancer is the fourth most common cancer in men and the eighth most common cancer in women in the United States, both in terms of incidence and mortality (1–2). Worldwide, an estimated 243 000 cases of bladder cancer occur each year; incidence rates of bladder cancer are highest in industrialized countries, in which more than 90% of bladder cancers are of transitional cell origin (3). Among patients with newly diagnosed transitional cell carcinoma, 70% have superficial tumors (TNM [tumor–node–metastasis] stages Ta, Tis, or T1) (4) and can be treated by transurethral resection of the tumor, with or without adjunctive intravesical therapies. However, even after complete transurethral resection of all visible lesions, 50%–70% of superficial bladder tumors recur and 10%–20% progress in stage and grade (5). Endoscopic evaluation of the bladder (i.e., cystoscopy) along with biopsy of suspicious lesions remains the standard method of bladder cancer diagnosis. Results of recent studies of the efficacy of cystoscopy with biopsy suggest that tumors are missed in 10%–40% of patients who have undergone transurethral resection (6–8). The development of highly reliable, noninvasive tools for bladder cancer diagnosis would facilitate early detection and help to define the role of molecular markers in prognostic evaluation of patients with bladder cancer at the time of initial diagnosis.
Cytologic analysis of voided urine is the most commonly used noninvasive method for detecting transitional cell carcinoma. Papanicolaou and Marshall (9) were the first to apply cytologic examination to urine sediment in the 1940s, and their innovation created the field of in vitro diagnostics as applied to vesical malignancies. However, the sensitivity of urine cytology for the diagnosis of low-grade bladder tumors is not satisfactory (10). Several other cell-based methods that have been developed to improve detection of bladder cancer include ImmunoCyt and UroVysion. The latter test, which is based on fluorescence in situ hybridization to detect chromosomal abnormalities, was approved by the U.S. Food and Drug Administration (FDA) as an aid for the initial diagnosis of bladder cancer and has a sensitivity of 68.6% and a specificity of 77.7%.
In addition to ImmunoCyt and UroVysion, several other potential diagnostic markers for bladder cancer have been identified, including nuclear matrix protein 22, bladder tumor antigen, and telomerase (11,12). Although these markers are more sensitive than urine cytology for detecting bladder cancer, their use is limited by their low specificity (12). Methods are being developed to detect other urinary markers that can be used for noninvasive diagnosis of bladder cancer, including hyaluronic acid (13), the Lewis X antigen (14), fibrinogen degradation product (15), and minichromosome maintenance 5 protein (16). All of these methods are based on the detection of elevated or altered levels of these proteins in cancer cells. Genetic alterations, including TP53 gene mutations, loss of heterozygosity (LOH), and microsatellite instability are also found in the urine sediment DNA of cancer patients (17,18). We have also demonstrated that allelic imbalance in urine DNA identified by the Affymetrix GeneChip HuSNP Mapping Assay (which contains 1494 single nucleotide polymorphisms) may hold promise for the early detection of bladder cancer (19).
Another kind of alteration that may be used as a marker is aberrant methylation of promoter DNA. DNA methylation refers to the covalent bonding of a methyl group to the dinucleotide CpG that is catalyzed by DNA methyltransferase. Aberrant promoter methylation (i.e., hypermethylation) is a major mechanism for silencing tumor suppressor genes or other cancer-associated genes in many kinds of human cancer (20–24). For example, the promoters of the APC, CDH1, RAR2, and RASSF1A genes have been found to be hypermethylated in more than 35% of bladder tumors tested (25,26). The development of real-time methylation-specific polymerase chain reaction (PCR) methods has simplified the study of genes that are inactivated by promoter hypermethylation in human cancer. One advantage of these methods is increased specificity due to the use of an internally binding fluorogenic hybridization probe for each gene (27,28). Several studies have demonstrated that hypermethylation of various gene promoters is detectable in DNA isolated from bodily fluids, including urine sediment DNA from bladder cancer patients (26,28–30).
In an effort to develop a noninvasive diagnostic test for bladder cancer, we first analyzed the promoter hypermethylation pattern of nine key genes that are methylated in various cancers, including bladder cancer (20–26), in paired samples of primary tumor DNA and urine sediment DNA from 15 bladder cancer patients. We then evaluated each gene as a diagnostic marker for bladder cancer by comparing the pattern of methylation for the nine genes obtained using the urine sediment DNA samples from the bladder cancer patients with that obtained using urine sediment DNA samples from 25 control subjects. Finally, we extended our analysis to an additional 160 urine sediment DNA samples from bladder cancer patients and an additional 69 urine sediment DNA samples from control subjects to evaluate the potential of this assay as a diagnostic test for bladder cancer.
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MATERIALS AND METHODS |
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Sample Collection and Preparation
We obtained samples of bladder tumor tissue and voided urine (50 mL) from 15 patients (nine males and six females) with bladder cancer who underwent curative surgery at The Johns Hopkins University School of Medicine. These patients were chosen consecutively on the basis of tissue availability. Tissue specimens were immediately snap-frozen in liquid nitrogen and stored at –80 °C. Frozen tissue was sectioned (12 µm thick), and every tenth section was stained with hematoxylin–eosin and histologically examined for the presence or absence of tumor cells as well as for tumor density. Only sections that showed more than 70% of neoplastic cells were used for DNA extraction.
We also collected voided urine (50 mL) from an additional 160 patients (119 males and 41 females) with bladder cancer who underwent curative surgery at The Johns Hopkins University School of Medicine (total = 175 urine sediment samples from bladder cancer patients) and from 94 age-matched (median age = 58.5 years, range = 28–84 years) control patients. The control patients (68 males and 26 females) were randomly chosen from among patients with no history of genitourinary malignancy whose urine samples were evaluated by the Cytopathology Department of The Johns Hopkins University. Among the control subjects, nine patients had been diagnosed with benign prostate hyperplasia, 10 patients harbored atypical cells by urine cytology examination, five patients had primary cancers at sites other than the bladder (one non–small-cell carcinoma [lung], one basal cell carcinoma [skin], one malignant melanoma [leg], one Kaposi's sarcoma [leg], and one infiltrating ductal carcinoma [breast]), one patient had a fibroepithelial polyp of the bladder, three patients had a tubular adenoma of the colon, one patient had an organizing thrombus of the vagina, one patient was diagnosed with neurogenic bladder, two patients were diagnosed with bladder papilloma, 20 patients had either macroscopic or microscopic hematuria, and 42 patients had been seen for vague urologic symptoms without malignancy. The absence of genitourinary neoplasm in the control subjects was confirmed by a complete clinical evaluation that included cystoscopy.
All voided urine samples were collected before definitive surgery and centrifuged at 3000g for 10 minutes, and the pelleted urine sediment was washed twice with phosphate-buffered saline and stored at –80 °C. Approval for research on human subjects was obtained from The Johns Hopkins University Institutional Review Boards. This study qualified for exemption under the U.S. Department of Health and Human Services policy for protection of human subjects [45 CFR 46.101(b)].
Frozen urine cell pellets and microdissected tissues were incubated in 1% sodium dodecyl sulfate and 50 µg/mL proteinase K (Boehringer Mannheim, Mannheim, Germany) at 48 °C overnight, followed by phenol/chloroform extraction and ethanol precipitation of DNA as previously described (31). Tissue staging followed American Joint Committee on Cancer guidelines (32).
Bisulfite Treatment
DNA extracted from primary tumors and urine sediment was subjected to bisulfite treatment, which converts unmethylated cytosine residues to uracil residues, as described previously (33) with minor modification. Briefly, 2 µg of genomic DNA from each sample was denatured with NaOH (final concentration, 0.2 M) in a total volume of 20 µL for 20 minutes at 50 °C. The denatured DNA was diluted in 500 µL of a freshly prepared solution of 10 mM hydroquinone and 3 M sodium bisulfite and incubated for 3 hours at 70 °C. Bisulfite-modified DNA was purified using a Wizard DNA Clean-Up System (Promega), treated with 0.3 M NaOH for 10 minutes at room temperature, precipitated with ethanol, resuspended in 120 µL of LoTE (2.5 mM EDTA, 10 mM Tris–HCl [pH 8]), and stored at –80 °C.
Methylation Analysis
Bisulfite-modified DNA was used as a template for fluorescence-based real-time PCR, as previously described (34). Amplification reactions were carried out in triplicate in a volume of 20 µL that contained 3 µL of bisulfite-modified DNA; 600 nM concentrations of forward and reverse primers; 200 nM probe; 5 U of platinum Taq polymerase (Invitrogen); 200 µM concentrations each of dATP, dCTP, and dGTP; 400 µM dTTP; and 5.5 mM MgCl2. Primers and probes were designed to specifically amplify the promoters of the nine genes of interest and the promoter of a reference gene, ACTB; primer and probe sequences and annealing temperatures are provided in Supplemental Table1 (available at http://jncicancerspectrum.oxfordjournals.org/jnci/content/vol98/issue14). Amplifications were carried out using the following profile: 95 °C for 3 minutes, followed by 50 cycles at 95 °C for 15 seconds and 60–62 °C for 1 minute. Supplemental Table 2 (available at http://jncicancerspectrum.oxfordjournals.org/jnci/content/vol98/issue14) lists the nine genes whose promoters were examined, their proposed functions, and the tumors in which these promoters have been shown to be hypermethylated. Amplification reactions were carried out in 384-well plates in a 7900 sequence detector (Perkin-Elmer Applied Biosystems) and were analyzed by a sequence detector system (SDS 2.2.1; Applied Biosystems). Each plate included patient DNA samples, positive (in vitro methylated leukocyte DNA) and negative (normal leukocyte DNA or DNA from a known unmethylated cell line) controls, and multiple water blanks. Leukocyte DNA from a healthy individual was methylated in vitro with excess SssI methyltransferase (New England Biolabs Inc., Beverly, MA) to generate completely methylated DNA, and serial dilutions (90–0.009 ng) of this DNA were used to construct a calibration curve for each plate. All samples were within the assay's range of sensitivity and reproducibility based on amplification of internal reference standard (threshold cycle [CT] value for ACTB of 
40). The relative level of methylated DNA for each gene in each sample was determined as a ratio of methylation specific PCR-amplified gene to ACTB (reference gene) and then multiplied by 1000 for easier tabulation (average value of triplicates of gene of interest divided by the average value of triplicates of ACTB x 1000). The samples were categorized as unmethylated or methylated based on the sensitivity of the assay.
Statistical Analysis
The primary statistical endpoint in this study was the mean methylation level for each gene in bladder cancer patients and in control subjects. We used the methylation levels for each of the nine genes to construct receiver operating characteristic (ROC) curves for the detection of bladder cancer. We also used multivariable logistic models to explore the value of using a binary cutoff for promoter methylation (no methylation versus any methylation) versus the quantitative level of promoter methylation for the detection of bladder cancer. We constructed a two-step decision rule that was based on our finding that four of the nine genes showed 100% specificity (i.e., they were methylated in bladder cancer patients but not in control subjects). In the first step, we identified an initial group of patients who had tumors in which any of these four gene promoters was methylated. In the second step, we performed logistic regression analysis (35) of the remaining five genes among those patients in whom none of the four genes initially found to have 100% specificity was methylated. ROC curves were produced by combining the sensitivity and 100% specificity achieved from the first step with the logistic regression results from the second step. Internal validation of the logistic regression models was done by using an approximation to the leave-one-out jackknife procedure provided by the classification table option in SAS statistical software (36). All multivariable procedures were preceded by univariate analyses.
We used box plots (37) to compare methylation values between bladder cancer patients and control subjects. Cross-tabulation and logistic regression were used to examine whether methylation of the nine genes was associated with clinical parameters. Correlations of the methylation levels of genes were calculated with Spearman correlation coefficients. Statistical computations were performed using SAS statistical software (36), all reported P values are two-sided, and P.05 was considered statistically significant.
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RESULTS |
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TopNotesAbstractIntroductionMaterials and methodsResultsDiscussionReferences |
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We initially measured the levels of promoter methylation for the APC, ARF, CDH1, GSTP1, MGMT, CDKN2A, RAR
2, RASSF1A, and TIMP3 genes in paired primary bladder tumor and urine sediment DNA samples from 15 bladder cancer patients. Figure 1 summarizes the methylation profiles of each of the nine genes for the 15 paired samples. In paired samples, methylation in urine sediment DNA was always accompanied by methylation of tumor DNA, whereas methylation of tumor DNA was not always accompanied by methylation of urine sediment DNA. We detected no aberrant methylation (i.e., hypermethylation) in the urine of bladder cancer patients who did not also have aberrant methylation of the same promoter in the corresponding tumor sample. In general, relative methylation levels, which reflect the number of methylated alleles, were higher in tumor DNA than in urine sediment DNA (data not shown). We then measured the promoter methylation levels of the nine genes in urine sediment DNA from 25 randomly chosen control subjects who had no evidence of a genitourinary malignancy and found that the promoters of CDKN2A, ARF, MGMT, and GSTP1 were not methylated in any control subject and that the promoters of the remaining five genes had low levels of methylation. Thus, these four genes showed 100% specificity.
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The frequency of methylation of all nine genes in primary tumors and the analytical sensitivity of the quantitative methylation–specific PCR assay are summarized in Table 1. Among the 15 primary tumor samples, the frequency of promoter methylation at each of the nine loci ranged from 33% (for ARF) to 93% (for MGMT and TIMP3), and the analytical sensitivity of the individual genes ranged from 100% (for GSTP1) to 50% (for MGMT and TIMP3) (Table 1).
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We then extended our quantitative analysis of promoter methylation to the urine sediment DNA samples from an additional 160 bladder cancer patients and an additional 69 control subjects. The demographic and clinical characteristics of the 175 bladder cancer patients included in this study are summarized in Table 2. The study population was predominantly male (73%) and had a median age of 67 years (interquartile range = 29–90 years). Bladder cancer cases were identified by cystoscopy and/or cytology, and all cases were eventually confirmed by standard pathology. Most of the tumors were transitional cell carcinomas of all stages and grades (Table 2).
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Among the urine samples from the 175 bladder cancer patients, we detected aberrant methylation of the CDKN2A promoter in 79 samples (45%, 95 confidence interval [CI] = 38% to 53%), of the ARF promoter in 49 samples (28%, 95 CI = 21% to 35%), of the MGMT promoter in 61 samples (35%, 95% = CI 28% to 42%), and of the GSTP1 promoter in 75 samples (43%, 95% CI = 35% to 51%). The box plots in Fig. 2 show the distribution of relative methylation values for each gene of interest versus ACTB obtained by quantitative methylation–specific PCR using urine sediment DNA from cancer patients and control subjects.
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Figure 3 depicts the combined two-stage algorithm that we used for disease classification. The ROC curves obtained by using the two-stage approach, which was based on four markers with 100% specificity followed by logistic regression analysis on the remaining five markers, are shown in Fig. 4. An algorithm that incorporated the four genes that had 100% specificity (CDKN2A, ARF, MGMT, and GSTP1) correctly identified 69% (95% CI = 62% to 76%) of the 175 bladder cancer patients (Fig. 4). For patients who did not have methylated versions of any of these genes, addition of a logistic regression score that was based on the remaining five genes improved sensitivity from 69% to 82% (95% CI = 75% to 87%) but decreased the specificity from 100% to 96% (95% CI = 90% to 99%) (Fig. 4). The detailed regression coefficients for the additional genes are listed in Supplemental Table 3 (available at http://jncicancerspectrum.oxfordjournals.org/jnci/content/vol98/issue14). We then compared this overall ROC curve to those obtained by adding each of the five genes individually to the model (data not shown). As expected, we found that the individual genes performed less well as predictors than did the multivariable logistic score using the entire group. We also examined how the multivariable logistic score model performed for detecting tumors of different stages. The sensitivity of the model increased with more advanced tumor stage, ranging from 75% for non-–muscle-invasive tumors to 85% of muscle-invasive tumors detectable by quantitative methylation–specific PCR with high specificity (96%) (Fig. 5).
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pT2) were detected at 75% and 85% sensitivity, respectively, by quantitative methylation-specific polymerase chain reaction with high specificity (i.e., near 96%). Both curves were corrected for overfitting via internal validation.We examined associations between several clinicopathologic and demographic parameters (age at diagnosis, sex, tumor stage, tumor grade, cytology result, cystoscopy result, presence of metastasis, invasiveness, and histories of smoking and alcohol consumption) and the methylation patterns identified by using urine sediment DNA. Contingency table and logistic regression analysis was performed to determine whether the associations between the promoter methylation of individual genes or combination of genes and clinicopathologic and demographic characteristics were associated with bladder cancer prognosis. In a univariate analysis, tumor grade, TNM stage, presence of metastasis, positive cytology, positive cystoscopy, and invasiveness were associated with methylation of at least one of the nine gene promoters (Table 3). Specifically, promoter methylation of both ARF and MGMT was statistically significantly associated with increasing tumor stage (odds ratio [OR] = 2.4, 95% CI = 1.1 to 4.8 and OR = 2.0, 95% CI = 1.1 to 3.9, respectively). Promoter methylation of ARF (OR = 3.5, 95% CI = 1.5 to 8.5), MGMT (OR = 2.8, 95% CI = 1.3 to 6.0), GSTP1 (OR = 2.5, 95% CI = 1.2 to 4.8), and TIMP3 (OR = 2.0, 95% CI = 1.0 to 4.0) was statistically significantly associated with invasive tumors (Table 3). Promoter methylation of GSTP1 (OR = 2.9, 95% CI = 1.4 to 5.8) and RASSF1A (OR = 1.9, 95% CI = 0.9 to 4.0) was statistically significantly associated with positive cytology. Aberrant methylation of any of the nine genes investigated in urine sediment DNA of bladder cancer patients was not associated with other clinical or demographic characteristics, including age at the time of diagnosis, sex, histologic subtype of tumor, and recurrence of tumor (data not shown).
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Finally, we performed a correlation analysis for all pairs of markers (Table 4). Promoter methylation of every pair of genes was statistically significantly correlated (all P
.001). The strongest correlations (r>.50) were between CDH1 and APC, ARF, RASSF1A, and TIMP3. In addition, promoter methylation of TIMP3 and ARF was highly correlated (r = .53), as was promoter methylation of GSTP1 and RASSF1A (r = .52).
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DISCUSSION |
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TopNotesAbstractIntroductionMaterials and methodsResultsDiscussionReferences |
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We evaluated promoter methylation for several genes that are important in bladder carcinogenesis in tumor epithelium and urine sediment DNA from patients with bladder cancer and in urine sediment DNA from control subjects without any genitourinary malignancy. We found high levels of promoter methylation in the urine sediments of the majority of the cancer patients and generally low levels of promoter methylation in the urine DNA from control subjects.
Disease-free survival and prognosis of patients with bladder cancer may improve with early detection. Moreover, definitive noninvasive diagnosis may improve the management of some bladder tumors and may help to predict the individualized therapeutic scheme (the administration of Herceptin to women with HER2-amplified breast cancers is just one example). Our finding of the high sensitivity of quantitative promoter methylation analysis for the detection of bladder cancer confirms the diagnostic potential of emerging epigenetic markers for bladder and other cancers. Expansion of the current panel of markers to other relevant tumor suppressor genes may increase the sensitivity of the assay. This approach might be particularly useful for the diagnosis of superficial (i.e., noninvasive) bladder tumors, of which we could detect a maximum of only 75% (Fig. 5). However, it is possible that some bladder cancers may not carry any epigenetic alterations and, thus, may need to be detected by another approach, such as TP53 mutation analysis, LOH analysis, or detection of homozygous deletions (17–18,38). However, it is interesting that all of the 15 primary tumors tested in this study harbored at least one methylated marker. Moreover, the methylation status of the urine sediment sample always matched that of the tumor sample from the same patient, indicating that hypermethylation detected in urine sediment was specific for bladder cancer cells and did not reflect methylation of DNA from other cell population in urine.
Diagnostic tests for cancers at internal sites are severely constrained by their low specificity and sensitivity and their high cost and associated morbidity. Cystoscopy is considered the gold standard for bladder cancer diagnosis and offers the potential to both find and remove small lesions, but it is associated with high cost, substantial patient discomfort, and variable sensitivity. Other studies have used conventional methylation–specific PCR for the amplification of promoter regions of some cancer-related genes to detect occult cancer cells of different cancer types in plasma, serum, lymph nodes, and bronchoalveolar lavage fluid and/or sputum (39–41). However, conventional MSP is limited as a technique to detect cancer because scoring for methylation based on visualization of bands can be subjective and is not automated. By contrast, a quantitative analysis of PCR products is critical for reproducible interpretation of results, and the quantitative methylation–specific PCR assay we used here provides a highly sensitive automated approach for the detection of methylated alleles. Moreover, this assay enables the identification of one methylated allele in the presence of more than 1000 unmethylated alleles (33), and the specificity of the assay is enhanced by hybridization of the methylation-specific PCR product with a labeled internal probe. In addition, quantitative methylation–specific PCR that incorporates a panel of methylation markers may enhance detection of cancer over single-marker methods by accounting for tumor cell heterogeneity that may exist among patients, as well as among the primary tumors, adjacent margins, and metastases.
It is surprising that some patients in our study lacked detectable methylated DNA in their urine despite having detectable promoter methylation in the primary tumor. This situation may occur because in some cases detectable amounts of cancer cells or neoplastic DNA may not shed into urine when it was collected, and some cancers may not shed enough amounts of neoplastic DNA into urine at the time it was collected. Alternatively, methylated markers may have been present in some of the urine sediment samples, but at levels below the limit of detection of our quantitative assay. We believe that this possibility is unlikely because, in our hands, methylation-specific PCR is a sensitive and specific assay that can detect as few as 15 copies of an amplified sequence in a single PCR reaction that contains approximately eight cells' worth of genomic DNA, similar to the detection limit that has been reported by others (42). Increasing the amount of input DNA might overcome the problem of sensitivity. Methylation-specific PCR involves an additional chemical modification of DNA, in which bisulfite modification is used to convert unmethylated cytosine residues to thymine residues (42). The bisulfite modification also results in DNA breakage, which could lower the sensitivity of methylation-specific PCR relative to that of conventional PCR.
Our combination marker approach provides evidence that increasing the number of markers in the assay panel increases the sensitivity but can decrease the specificity of the assay and increases its cost. Here we amplified each gene individually. In the future, development of multiplex PCR assays with genes that are highly specific for bladder cancer detection may continue to improve the sensitivity of the assay. Moreover, other technological developments, such as hardware and software for automated signal enumeration and robotic pipeting, are being developed and will greatly facilitate the use of such marker sets as diagnostic tools in pathology laboratories.
Hypermethylation of the TIMP3 and ARF promoters in urine sediment DNA from bladder cancer patients often occurred together, and Rassf1A promoter methylation was strongly correlated with methylation of the APC, CDH1, and GSTP1 promoters. Given that hypermethylation of these genes is common in bladder cancer, these associations could have happened by chance alone and should be interpreted with caution. However, a possible interaction between these genes in bladder cancer deserves further evaluation. Our finding that GSTP1 and RASSF1A promoter methylation was associated with positive urine cytology suggests that the methylation of these gene promoters may influence cell morphology. On the other hand, markers associated with invasion, such as ARF and TIMP3 promotor methylation, may identify tumors that have a poor prognosis or that require early aggressive treatment. Some of the methylation markers used in our assay have been tested individually or in limited panels of markers for detection of and association with tumor progression in some studies in primary bladder cancer and in urine (25,43–46) by conventional methylation–specific PCR.
High specificity and sensitivity are important for any diagnostic test, particularly when treatment options for the diagnosed disease include major surgery. Cell-based and protein assays for the diagnosis of bladder cancer that have been approved by the FDA report inferior sensitivity and specificity compared with that of the quantitative methylation–specific PCR assay reported here. However, no direct comparison of these assays in prospective studies has been done. Urine testing may also provide complementary information to enhance current methods for staging disease. The clinical implications of our findings await the results of larger cohort studies with appropriate follow-up. Testing for relevant epigenetic markers in voided urine holds promise for the early detection of bladder cancer and may eventually contribute to more individualized therapeutic strategies.
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NOTES |
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This work was supported by National Cancer Institute Grant U01-CA84986 and Oncomethylome Sciences, SA. The funding agency had no role in the design of the study, data collection, or analysis; in the interpretation of the results; in the preparation of the manuscript; or in the decision to submit the manuscript for publication.
Under a licensing agreement between Oncomethylome Sciences, SA, and The Johns Hopkins University, D. Sidransky is entitled to a share of royalty received by the University upon sales of diagnostic products described in this article. D. Sidransky owns Oncomethylome Sciences, SA, stock, which is subject to certain restrictions under University policy. Dr. Sidransky is a paid consultant to Oncomethylome Sciences, SA, and is a paid member of the company's Scientific Advisory Board. The Johns Hopkins University in accordance with its conflict of interest policies is managing the terms of this agreement.
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Manuscript received May 6, 2005; revised May 3, 2006; accepted May 15, 2006.
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