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Journal of Clinical Endocrinology & Metabolism , doi:10.1210/jc.2005-0313
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The Journal of Clinical Endocrinology & Metabolism Vol. 90, No. 7 4011-4018
Copyright © 2005 by The Endocrine Society

Quantitative Assessment of Promoter Methylation Profiles in Thyroid Neoplasms

M. O. Hoque, E. Rosenbaum, W. H. Westra, M. Xing, P. Ladenson, M. A. Zeiger, D. Sidransky and C. B. Umbricht

Departments of Otolaryngology, Head and Neck Surgery (M.O.H., W.H.W., D.S.), Oncology (E.R., W.H.W., C.B.U.), Surgery (D.S., M.A.Z., C.B.U.), Pathology (W.H.W., C.B.U.), and Medicine (M.X., P.L.), The Johns Hopkins Medical Institutions, Baltimore, Maryland 21205

Address all correspondence and requests for reprints to: Christopher B. Umbricht, M.D., Ph.D., Division of Endocrine and Oncologic Surgery, The Johns Hopkins Medical Institutions, 720 Rutland Avenue, Ross 743, Baltimore, Maryland 21205. E-mail: cumbrich{at}jhmi.edu.


   Abstract
Top
 Abstract
Introduction
 Patients and Methods
 Results
 Discussion
 References
 
Context: Cancer-specific molecular markers are needed to supplement the cytopathological assessment of thyroid tumors, because a majority of patients with cytologically indeterminate nodules currently undergo thyroidectomy without a definitive diagnosis.

Objective: The aim of this study was the quantitative assessment of promoter hypermethylation and its relation to the BRAF mutation in thyroid tumors.

Design: Quantitative hypermethylation of Rassf1A, TSHR, RAR-ß2, DAPK, S100, p16, CDH1, CALCA, TIMP3, TGF-ß, and GSTpi was tested on a cohort of 82 benign and malignant thyroid tumors and five thyroid cancer cell lines.

Setting: The study was conducted at a tertiary research hospital.

Patients: Patients underwent surgical resection for a thyroid tumor from 2000 to 2003 at our institution.

Interventions: There were no interventions.

Main Outcome Measure: Final surgical pathology diagnosis was the main outcome measure.

Results: Thyroid tumors showed hypermethylation for the following markers: Rassf1A, TSHR, RAR-ß2, DAPK, CDH1, TIMP3, and TGF-ß. A trend toward multiple hypermethylation was evident in cancer tissues, with hypermethylation of two or more markers detectable in 25% of hyperplasias, 38% of adenomas, 48% of thyroid cancers, and 100% of cell lines. A rank correlation analysis of marker hypermethylation suggests that a subset of these markers is epigenetically modified in concert, which may reflect an organ-specific regulation process. Furthermore, a positive correlation was found between the BRAF mutation and RAR-ß2, and a negative correlation was found between the BRAF mutation and Rassf1A.

Conclusions: Methylation-induced gene silencing appears to affect multiple genes in thyroid tissue and increases with cancer progression. Additional markers with better discriminatory power between benign and malignant samples are needed for the diagnostic assessment of cytologically indeterminate thyroid nodules.


   Introduction
Top
 Abstract
 Introduction
Patients and Methods
 Results
 Discussion
 References
 
FINE NEEDLE ASPIRATION (FNA) cytology is currently the best diagnostic tool in the differential diagnosis of a thyroid nodule, identifying thyroid cancer in 5% of aspirates. However, in approximately 20% of FNAs, the results are indeterminate and do not distinguish benign from malignant nodules (1). This has unfortunate clinical implications, because complete thyroidectomy is required in the surgical treatment of a malignant nodule, whereas partial thyroidectomy or observation is preferred for most benign nodules. Consequently, a majority of patients with cytologically indeterminate thyroid nodules who undergo total thyroidectomy do so without a definitive diagnosis of malignancy, which is present in only 12–20% of cases of suspicious FNAs (2). Because thyroid nodules are present in over half the adult population, and are increasingly being detected incidentally by a variety of imaging procedures, this diagnostic dilemma has important implications for today’s health care.

Any molecular diagnostic attempt to improve the cytopathological assessment of thyroid FNAs is often confounded by the very low signal-to-noise ratio of the specimens due to contaminating red blood cells, and to a lesser degree, white blood cells. DNA-based assays fare considerably better than RNA-based assays given the absence of nucleated red blood cells in human peripheral blood. A specific epigenetic modification of DNA, promoter methylation-induced gene silencing, has been described in a large number of cancers and involves an increasing number of tumor suppressors. There is evidence that aberrant methylation of genomic DNA is associated, and may be in part responsible for transcriptional silencing during carcinogenesis (3). Furthermore, this epigenetic change can be detected by sensitive DNA-based assays, such as the methylation-specific PCR (MSP) (4). Gene silencing due to aberrant hypermethylation is a common epigenetic event in cancer progression (5), and tumor-specific patterns of hypermethylation can be detected by quantitative MSP (QMSP) on very limited clinical material (6, 7). Because this gene-specific epigenetic modification can be detected with the sensitivity required for FNA samples, it presents a uniquely attractive target for the development of a thyroid cancer-specific diagnostic assay (8, 9). Management of thyroid nodules would be significantly improved by a diagnostic QMSP profile, because persisting diagnostic uncertainty can lead to both overtreatment of benign thyroid tumors, and undertreatment of thyroid cancer.

For this study, we have selected MSP markers that were developed and characterized in thyroid as well as in nonthyroid cancers (6, 7, 8, 9, 10, 11, 12, 13, 14). The markers were tested against our thyroid tumor collection to identify those that may be useful in differentiating benign from malignant thyroid disease.

Using the original MSP assay as well as a more sensitive variant called nested MSP (15), we initially found considerable overlap between benign and malignant tumors. Therefore, we decided to measure the degree of methylation using QMSP (6, 7, 8, 10) to determine whether the degree of methylation could be used to distinguish benign from malignant tumors. The QMSP markers were selected based on their documented relevance in thyroid cancer [Rassf1A (8, 16), TSHR (9), RAR-ß (17, 18), S100 (19, 20), p16 (16, 21, 22), CDH1 (23, 24, 25, 26), GSTpi (27)], other neoplasms [DAPK (28, 29, 30, 31), CALCA (32), TIMP3 (33, 34, 35), TGF-ß (36, 37)], and preliminary results from our pilot studies using standard MSP assays.

In addition, the use of a panel of 11 independent MSP markers allowed us to study the relationship between these genes, as well as with the recently described BRAF mutation, which is detectable in up to two thirds of papillary thyroid cancers (8, 38, 39, 40, 41, 42). Our analysis can be used to begin mapping epigenetic alterations of specific signal transduction pathways in thyroid tumor progression.


   Patients and Methods
Top
 Abstract
 Introduction
 Patients and Methods
Results
 Discussion
 References
 
Tissue samples

We selected 100 patients who underwent surgical resection for a thyroid tumor from 2000–2003 at The Johns Hopkins Hospital (Baltimore, MD) for whom a frozen tumor sample was available for DNA extraction. Collection of tissue and demographic data, including patient age, sex, and race, was performed in accordance to the guidelines of The Johns Hopkins University Institutional Review Board. Tissue was routinely obtained from the center of the lesions and from uninvolved adjacent thyroid tissue and snap frozen in liquid nitrogen. To avoid field-cancerization effects (43, 44, 45), only normal thyroid tissue sampled adjacent to benign nodules was used in this study. All cases were reviewed by an experienced thyroid pathologist (W.H.W.) and scored for the presence of inflammatory cell infiltrates suggestive of thyroiditis.

Thyroid cancer cell lines

The human thyroid cancer cell lines were kindly provided by the following individuals: the thyroid adenoma cell line KAK-1 and the papillary cancer cell lines KAT-5 and KAT-10 were from Dr. K. B. Ain (University of Kentucky Medical Center, Lexington, KY); the follicular cancer cell line WRO-82 and the anaplastic cancer cell line ARO-81 were from Dr. G. J. F. Juillard (University of California Los Angeles School of Medicine, Los Angeles, CA). Cells were cultured in RPMI 1640 (Life Technologies, Inc., Carlsbad, CA) supplemented with 10% calf serum, 1 mM sodium pyruvate, 0.1 mM nonessential amino acids, and penicillin-streptomycin in 5% CO2. Cells were harvested at 75% confluence.

DNA extraction

DNA was extracted from frozen thyroid tissue and tissue culture cells by standard sodium dodecyl sulfate/proteinase K digestion followed by organic extraction and ethanol precipitation (46).

Sodium bisulfite treatment

One microgram of genomic DNA was denatured in 0.2 M NaOH for 10 min at 37 C. The denatured DNA was diluted in 550 µl of freshly prepared solution of 10 mM hydroquinone and 3 M sodium bisulfite, and incubated for 3 h at 50 C. After incubation, the DNA sample was desalted through a column (Wizard DNA Clean-Up System; Promega, Madison, WI), treated with 0.3 M NaOH for 10 min at room temperature, and precipitated with ethanol. The bisulfite-modified genomic DNA was resuspended in 120 µl of H2O and stored at –80 C.

Real-time QMSP analysis

Sequences for genes to be analyzed were then used to design gene-specific MSP primers using PrimerExpress (PerkinElmer Cetus, Boston, MA). The details of the design of methylation-specific primers for QMSP have been published elsewhere (7, 8, 47). Quantitative analysis of gene expression was performed in a 7700 Sequence detector (PerkinElmer Cetus), which uses the 5' nuclease activity of Taq DNA polymerase in a real-time quantitative PCR analysis assay. Briefly, oligonucleotide primers are designed to specifically amplify bisulfite-converted DNA of the gene of interest. Fluorogenic PCRs were carried out in triplicate in a reaction volume of 25 µl consisting of 600 nM of each primer, 200 nM of probe, 5 U of Taq polymerase, 200 µM each of dATP, dCTP, and dGTP, 400 µM of dTTP, and 5.5 mM MgCl2. Three microliters of treated DNA solution were used in each real-time MSP reaction. Amplifications were carried out in 384-well plates. Each plate consisted of patient samples and multiple water blanks, as well as positive and negative controls. Leukocytes from a healthy individual were methylated in vitro with excess SssI methyltransferase (New England Biolabs Inc., Beverly, MA) to generate completely methylated DNA, and serial dilutions of this DNA were used for constructing the calibration curves on each plate. For the internal reference gene, ß-actin, the primers and probe were designed to amplify a region that is devoid of CpG nucleotides (Table 1Go). This allowed amplification independent of its methylation status, whereas the amplification of the gene of interest was proportional to the degree of CpG methylation. The methylation ratio was defined as the ratio of the fluorescence emission intensity values for the gene-specific PCR products to those of the ß-actin. QMSP assays were performed for the following genes: Rassf1A, TSHR, RAR-ß2, DAPK, S100A2, p16, CDH1, CALCA, TIMP3, TGF-ß, and GSTpi, using ß-actin (ACTB) as internal load control.


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  		TABLE 1. Primers and fluorescent probes used

 
BRAF mutation analysis

Tumor samples and controls were analyzed for the thymine (T)-adenine (A) missense mutation at nucleotide 1796 in the BRAF gene as described (48). Briefly, PCR primer sequences were designed to amplify a 102-bp fragment of exon 15. PCR amplification was performed using 100 ng of genomic DNA as template. Analysis of the PCR products was performed using the colorimetric Mutector assay (TrimGen, Sparks, MD). A detection primer was designed that does not permit primer extension when the target base is wild-type. When the target base is mutated, primer extension continues and a color reaction is observed. Ten microliters of PCR products were used as a template for the detection assay, which was performed according to the manufacturer’s instructions. The melanoma cell line HTB 72 was used as positive control for the BRAF T1796A mutation, and the cervical cancer cell line ME180, known to be wild-type for BRAF at T1796, served as a negative control (48).

Statistical analysis

Nonparametric tests were used for univariate and multivariate analyses because of the significantly skewed distribution of the QMSP ratios. The Wilcoxon rank sum test was used to compare groups; P values under 0.05 for the {chi}2 test statistic were considered significant. Correlations between markers were analyzed using Spearman’s {rho} correlation coefficient. Diagnostic threshold analyses were performed using receiver operating characteristic curves. All statistical tests were performed using the JMP statistical package (SAS Institute, Cary, NC) on a Macintosh microcomputer.


   Results
Top
 Abstract
 Introduction
 Patients and Methods
 Results
Discussion
 References
 
We tested promoter hypermethylation of Rassf1A, TSHR, RAR-ß2, DAPK, S100, p16, CDH1, CALCA, TIMP3, TGF-ß, and GSTpi by QMSP in a cohort of 20 hyperplastic nodules, including multinodular hyperplasias and adenomatoid nodules; 24 adenomas, including follicular and Hürthle cell types; four follicular carcinomas, including two Hürthle cell carcinomas; 23 papillary carcinomas, including 10 follicular variant papillary carcinomas; and five medullary carcinomas, as well as six cases with lymphocytic thyroiditis as sole diagnosis. Thyroid tumors with lymphocytic thyroiditis as secondary diagnosis were excluded from this analysis. Figure 1Go shows scatterplots of the methylation ratios of the markers tested. The scatterplots are arranged into four principal thyroid tissue classes, i.e. normal thyroid tissue (n = 15), hyperplastic nodules (n = 20), adenomas (n = 24), and epithelial carcinomas (n = 27). Results for lymphocytic thyroiditis, medullary carcinomas, and thyroid cancer cell lines are not shown. The scatterplots show considerable overlap between methylation levels of the markers analyzed in the different diagnostic groups. Significant differences between the benign vs. cancer groups were found only for RAR-ß2 (P < 0.02), and possibly for DAPK, which had a result of borderline significance (P < 0.06).



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  		FIG. 1. Scatterplots of the methylation ratios (marker/actin) of the markers tested. nl, Normal thyroid tissue (n = 15); HN, hyperplastic nodules (n = 20); AD, adenomas (n = 24); and CA, epithelial carcinomas (n = 27). The horizontal bars indicate means and SD values within the four groups.

 
In light of the levels of detectable methylation in benign tissues and adjacent normal tissues, we chose a high diagnostic cutoff for hypermethylation using the 90th percentile of methylation levels detectable in the benign hyperplastic nodules. As shown in Fig. 2Go, the cancer cell lines showed a very high incidence of hypermethylation (80–100%) for RAR-ß2, S100, CALCA, and TIMP3, whereas samples with lymphocytic thyroiditis showed a high incidence of hypermethylation (60–80%) for RAR-ß2, CDH1, and TIMP3.



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  		FIG. 2. Bar graph illustrating the fraction of hypermethylated samples, defined as showing methylation ratios above the 90th percentile of the hyperplastic nodules, for each histological tumor group. LT, Lymphocytic thyroiditis; HN, hyperplastic nodule; AD, thyroid adenoma; TC, thyroid carcinoma; MC, medullary carcinoma; CL, cancer cell line. Each methylation marker is plotted as a separate category on the x-axis.

 
Hypermethylation was found in the following percentages of thyroid cell cancers: Rassf1A (15%), TSHR (33%), RAR-ß2 (22%), CDH1 (22%), and TIMP3 (22%). However, hypermethylation of TSHR (30%) and DAPK (16%) was also detected in adenomas, limiting their diagnostic usefulness. Relative hypermethylation levels of cancer vs. noncancer tissues were significantly different only for RAR-ß2 (P < 0.05).

Scoring individual tumor samples across the panel of markers, a clear trend toward hypermethylation of multiple markers was evident with cancer progression among the tissues, with hypermethylation of two or more markers detectable in 25% of hyperplasias, 38% of adenomas, 48% of thyroid cell cancers, 60% of medullary carcinomas, and 100% of thyroid cancer cell lines tested (Fig. 3Go). Receiver operator curve analysis demonstrated optimal discrimination between benign and malignant tumors with three hypermethylated markers, resulting in a correct classification of 63% of samples (area under curve = 58%), a false-positive rate of 9%, and a false-negative rate of 28% (data not shown).



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  		FIG. 3. Bar graph illustrating the fraction of samples showing hypermethylation of multiple (≥2) markers. Abbreviations are identical to Fig. 2Go.

 
To understand how the changes in methylation of markers related to each other in individual tumor samples, we grouped the samples into two sets of similar size, consisting of either neoplastic tissue (i.e. adenomas and carcinomas) or nonneoplastic tissue (i.e. normal and hyperplastic tissue), and performed a Spearman’s {rho} rank correlation analysis. As shown in Fig. 4Go, six of the markers, CALCA, Rassf1A, CDH1, TGF-ß, TIMP3, and TSHR, showed remarkable congruence in their methylation patterns. Moreover, these patterns were nearly identical in neoplastic and nonneoplastic tissue. Differences between neoplastic and nonneoplastic tissues were found for the correlation between S100 and TGF-ß and TSHR, as well as for CDH1 and DAPK. With the single exception of S100, which showed a strong discordance in methylation levels compared with other markers, the correlations among marker methylations were positive. None of the markers examined showed significant correlations with age, sex, or race variables.



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  		FIG. 4. Summary of Spearman rank correlations. The diagonal row of boxes lists the markers analyzed; correlations are indicated by arrows at the horizontal and vertical intersections of the markers. A single arrow represents P < 0.05; a double arrow represents P < 0.01. Upward arrows represent positive correlations; downward arrows represent negative correlations. The samples were split into two groups of similar size, with correlations between neoplastic tumors (adenomas and carcinomas, n = 51) indicated in the upper half of the plot and correlations between nonneoplastic tumors (hyperplasias and normal thyroid tissue, n = 41) indicated in the lower half of the plot. Gray shading indicates similar correlations found in both groups. BRAF was not analyzed in nonneoplastic tissues, as indicated by –.

 
We then investigated the relationship of the promoter methylation levels with BRAF mutations found in the thyroid tumor samples. In our cohort of 55 thyroid neoplasms, seven of 12 papillary carcinomas, two of 10 follicular variant papillary carcinomas, and none of the follicular carcinomas, medullary carcinomas, or adenomas harbored the BRAF mutation. Of the 11 methylation markers investigated, BRAF mutations were significantly associated with RAR-ß2 methylation (P < 0.04), as well as with an absence of Rassf1A methylation (P < 0.03) by Wilcoxon test analysis (Fig. 5Go). The Spearman’s {rho} rank correlation analysis confirmed that both of these associations were significant (P < 0.05; Fig. 4Go).



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  		FIG. 5. Scatterplots of methylation ratios in BRAF mutation positive (1) and negative (0) neoplastic tumors (adenomas and carcinomas, n = 55). The P value refers to the Wilcoxon rank sum test. The results for Rassf1A and RAR-ß2 are shown; all other markers showed no difference in their distributions relative to their BRAF status. The boxed bars show the medians and quartiles; the additional horizontal whisker bars indicate quartile ± 1.5 * (interquartile range).

 

   Discussion
Top
 Abstract
 Introduction
 Patients and Methods
 Results
 Discussion
References
 
This quantitative analysis of promoter methylation indicates that epigenetic alteration of genomic DNA is a common finding in thyroid tissue, regardless of histological tumor subtype. We also found a considerable overlap in methylation levels between benign and malignant tumors, which is in contrast to the cancer-specific aberrant methylation found in nonendocrine epithelial tumors to date. This raises the possibility that endocrine tissue may use methylation- induced silencing in ways different than other epithelial organs. Most available hypermethylation data reported have focused on nonendocrine tumors, in which hypermethylation was generally found to be relatively cancer-specific. There have been a small number of reports showing hypermethylation in endocrine and neuroendocrine tumors, but they used nonquantitative approaches and did not have corresponding control tissue, e.g. using exocrine pancreas tissue as control for pancreatic endocrine tumors (35, 49, 50, 51, 52, 53). It remains to be seen whether the trend seen in this study extends to other MSP markers in thyroid tissue, and whether it is also observed in other endocrine tissue such as the adrenal or pituitary gland.

The only marker that we found to be significantly hypermethylated in thyroid cancers compared with benign thyroid tumors was RAR-ß2. The cutoff we chose identified 22% of papillary and follicular carcinomas, vs. 4% of adenomas in our cohort, and confirms the importance of RAR-ß2 alterations in epithelial cancer, as shown previously for cancers of the breast, prostate, colon, and lung, among others (10, 13, 54, 55). We were unable to show a significant difference in methylation status between cancer and noncancer tissue for the other markers tested, including several markers that had shown promise in previous reports. Our previous results for the TSHR, for which very little overlap between cancer and benign samples was found, were recently reported (9). This study illustrates how methodological differences can influence results. Specifically, we did not microdissect our tissue samples, because this is not a practical approach for large-scale cohorts, and it is likely that QMSP is more sensitive to contaminants than standard MSP assays. Moreover, no FNA material was used for the analysis presented here. In the earlier study (9), tissue samples underwent careful tissue microdissection before routine MSP analysis, whereas QMSP was performed on FNAs only, thereby presumably avoiding contamination by stromal elements in the undissected tissue used in the current study.

However, our results are consistent with a recent report on the methylation status of Rassf1A in thyroid cancer, in which significant overlap between adenomas and carcinomas was also observed (8). An earlier report on Rassf1A methylation in the thyroid was primarily focused on cancer tissue (16), lacking benign thyroid tumor samples for comparison. Similarly, hypermethylation of E-cadherin (CDH1) in thyroid cancer has been reported in a small study using nonquantitative methods (23), but a 20% incidence of methylation in adenomas was described there as well.

The high frequency of hypermethylation of markers in lymphocytic thyroiditis was not unexpected and confirmed our earlier findings using standard MSP assays. This highlights the importance of taking into account tissue heterogeneity when assessing molecular markers. Eliminating cases with histologically apparent thyroiditis improved our signal-to-noise ratio, but it is difficult to exclude the possibility that some cases with subclinical infiltrates may have obscured the specificity of some markers, such as TSHR, in this cohort.

In addition, the use of a panel of 11 independent markers in a quantitative assay as well as the size of our cohort provided sufficient statistical power to study the relationships in the methylation levels of these genes. A coordinated methylation-induced gene silencing across multiple markers would provide a new window on signal transduction and regulatory pathways in thyroid tumors. We observed a striking concordance of methylation patterns for six of the markers (CALCA, CDH1, Rassf1A, TGF-ß, TIMP3, TSHR) in a given tumor. If one of these markers showed low methylation, the other five did as well. Conversely, if one was hypermethylated, all were hypermethylated in a coordinated fashion and to a similar extent in that tumor. Moreover, this pattern was entirely consistent between neoplastic and nonneoplastic tissue, suggesting that these methylation changes occurred independent of the thyroid disease process, and may reflect an organ-specific regulation process. Although such a pattern could be expected from the well-known relationship between progressive age and increasing methylation levels (54, 56, 57, 58, 59), we did not detect any significant correlations of methylation levels with age, sex, or race in this cohort. A stromal origin of this coordinated methylation signal appears unlikely, because thyroid tumors are relatively homogenous, and one might expect a more pronounced effect in nonneoplastic tissue than in neoplastic tissue. A definitive answer to this possibility awaits a selective analysis of stromal elements in thyroid tumors.

Correlations specific for neoplasia were seen with a close coordination of CDH1 and DAPK methylation, and for discordant methylation of S100 and TGF-ß and TSHR. It remains to be seen how these relationships can be interpreted in the context of cellular processes, because the patterns of hypermethylation we found in this study do not readily fit in currently known regulatory pathways.

An analysis of the relationship between BRAF mutations and gene methylation confirmed the inverse relationship recently reported between Rassf1A silencing and BRAF activation (8), as well as a positive correlation between silencing of RAR-ß2 and BRAF mutations. This would support the view that activation of the ras-raf-map kinase pathway plays an important role in thyroid cancer, which could occur either by activating BRAF by mutation or by silencing of Rassf1A. The role of RAR-ß2 inactivation in thyroid cancer and its relationship to the map kinase signaling pathway remain to be elucidated.

Finally, the progressive hypermethylation of multiple markers is characteristic of tumor progression (29, 58, 60, 61, 62) and makes it possible that an optimal combination of markers may improve the discriminatory power of MSP assays (63). Our study supports the role of methylation-induced gene silencing as a pathogenetic mechanism in thyroid carcinogenesis, but the level of background methylation found in routine tissue samples shows that the markers we have studied so far lack discriminatory power for diagnostic purposes. This may be improved by the discovery of more thyroid cancer-specific markers, or more selective sampling of tissue, be it by microdissection procedures or by using cytological material rather than tissue samples. The latter is appealing because thyroid FNAs are currently the primary diagnostic procedure for thyroid nodules, but any clinical application of methylation assays must clearly await the development of markers with better discriminatory power between benign and malignant samples.

Finally, the pattern of comethylation we observed across several markers, which was evident in both benign and malignant subsets, also raises the possibility that in certain tissues, methylation-induced silencing may occur in a context other than malignant transformation. These results should encourage a more coordinated approach in the assessment of biological markers, since such relationships may not be apparent in a marker-specific study.


   Acknowledgments
 
We thank Dante Trusty and Dañelle Smith for their excellent technical support.


   Footnotes
 
This work was supported by National Institutes of Health Grants CA 095703 and CA 96784-01.

First Published Online April 19, 2005

Abbreviations: FNA, Fine needle aspiration; MSP, methylation- specific PCR; QMSP, quantitative MSP.

Received February 14, 2005.

Accepted April 12, 2005.


   References
Top
 Abstract
 Introduction
 Patients and Methods
 Results
 Discussion
 References
 

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