Clinical Chemistry Link to Randox Laboratories Web Site
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

Clinical Chemistry 53: 17-23, 2007. First published November 9, 2006; 10.1373/clinchem.2007.072629
This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Data Supplement
Right arrow All Versions of this Article:
clinchem.2007.072629v1
53/1/17    most recent
Right arrow Submit an electronic Letter to
the Editor about this paper
Right arrow Alert me when this article is cited
Right arrow Alert me when eLetters are posted
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via ISI Web of Science (6)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Brakensiek, K.
Right arrow Articles by Lehmann, U.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Brakensiek, K.
Right arrow Articles by Lehmann, U.
Related Collections
Right arrow Molecular Diagnostics and Genetics
(Clinical Chemistry. 2007;53:17-23.)
© 2007 American Association for Clinical Chemistry, Inc.

Molecular Diagnostics and Genetics

Quantitative High-Resolution CpG Island Mapping with PyrosequencingTM Reveals Disease-Specific Methylation Patterns of the CDKN2B Gene in Myelodysplastic Syndrome and Myeloid Leukemia

Kai Brakensiek1, Luzie U. Wingen2, Florian Länger1, Hans Kreipe1 and Ulrich Lehmann1,a

1 Institute of Pathology, Medizinische Hochschule Hannover, Hannover, Germany.
2 Department of Disease and Stress Biology, John Innes Centre, Norwich, England.

aAddress correspondence to this author at: Institute of Pathology, Medizinische Hochschule Hannover, Carl-Neuberg-Str. 1, D-30625 Hannover, Germany. Fax 49-511-532-5799; e-mail Lehmann.Ulrich{at}MH-Hannover.de.


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Background: Gene silencing through aberrant CpG island methylation is the most extensively analyzed epigenetic event in human tumorigenesis and has huge diagnostic and prognostic potential. Methylation patterns are often very heterogeneous, however, presenting a serious challenge for the development of methylation assays for diagnostic purposes.

Methods: We used PyrosequencingTM technology to determine the methylation status of 68 CpG sites in the CpG island of the CDKN2B gene [cyclin-dependent kinase inhibitor 2B (p15, inhibits CDK4)], frequently hypermethylated in myeloid malignancies, in a series of bone marrow samples from patients with myelodysplasia and myeloid leukemia (n = 82) and from 32 controls. A total of 7762 individual methylation sites were quantitatively evaluated. Precision and reproducibility of the quantification was evaluated with several overlapping primers.

Results: The use of optimized sequencing primers and the new Pyro Q-CpGTM software enabled precise and reproducible quantification with a single sequencing primer of up to 15 CpG sites distributed over ~100 bp. Extensive statistical analyses of the whole CpG island revealed for the first time disease-specific methylation patterns of the CDKN2B gene in myeloid malignancies and small regions of differential methylation with high discriminatory power that enabled differentiation of even low-grade myelodysplastic syndrome samples from the controls, a result that was confirmed in an independent group of 9 control and 36 patient samples.

Conclusion: The precise quantitative methylation mapping of whole CpG islands is now possible with Pyrosequencing software in combination with optimized sequencing primers. This method reveals disease-specific methylation patterns and enables the development of specific diagnostic assays.


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
Aberrant DNA methylation of cytosine residues in the promoter region, the most extensively analyzed epigenetic alteration in the development and progression of malignant tumors, constitutes a mechanism that is functionally equivalent to genetic alterations such as deletions or allelic losses and can be found in almost all cancer types (1). In hematopoietic neoplasms, DNA methylation abnormalities are among the most frequent molecular changes and play critical roles (2). Detection of altered methylation has huge diagnostic and prognostic potential (3) but requires a comprehensive characterization of the methylation patterns in patient samples and normal tissues to determine which hypermethylation events are disease specific.

The most commonly used methods are qualitative and quantitative methylation-specific PCR (4), which are very sensitive and easy to use but can analyze only a very limited number of dinucleotide cytosine-guanosine (CpG)1 dinucleotides (~3–10, depending on the primers/probes used). Furthermore, these methods do not provide precise information about the methylation status of single CpG sites. Bisulfite genomic sequencing, which provides single-CpG resolution over several hundred bps, is the method of choice for comprehensive methylation mapping. This method is not practical in a diagnostic setting, however, because detection and quantification of low methylation levels require analysis of very large numbers of clones, a very tedious and labor-intensive procedure.

Recently, a new real-time DNA sequencing technology called PyrosequencingTM was developed for the analysis of single-base variations. This method employs a sequencing-by-synthesis principle and enables the precise quantification of incorporated nucleotides at polymorphic positions (5)(6). An indirect bioluminometric assay measures quantitatively the amount of pyrophosphate (PPi) that is released from each incorporated deoxynucleotide (dNTP). Through an enzyme cascade, the released PPi is converted into a light signal that is directly proportional to the amount of incorporated dNTP. The measured light signals are sequentially displayed as peaks in a "PyrogramTM". Treatment of the DNA with sodium bisulfite converts the epigenetic difference between methylated and unmethylated cytosine into a single-base variation of the C/T type (5-methylcytosine remains unaltered, whereas cytosine is selectively deaminated to uracil and amplified as thymidine). Therefore, Pyrosequencing is a very suitable tool for methylation analysis, and it is not hampered by the limitations of the techniques mentioned above. Pyrosequencing was shown to be a reliable technique for the precise quantification of methylation at single CpG sites (7)(8)(9)(10) with a very good correlation with other methods (11)(12)(13). If more than 5 variable positions have to be taken into account, however, the current single-base variation software provided by the manufacturer cannot calculate all possible sequence combinations reproducibly and within a reasonable time frame (i.e., less than a few minutes).

The tumor suppressor gene CDKN2B (cyclin-dependent kinase inhibitor 2B (p15, inhibits CDK4)2 is well known to be inactivated by hypermethylation in a wide variety of hematological malignancies (2). The epigenetic inactivation of the CDKN2B gene is one of the most frequent molecular alterations in myelodysplastic syndrome (MDS), a group of clonal stem cell disorders characterized by inefficient hematopoiesis and a considerable risk (up to 30%) of transformation into secondary acute myeloid leukemia (AML) (14). Methylation is heterogeneous in most cases, thus identification of disease-specific methylation pattern is challenging, requiring quantitative analysis with single base pair resolution. We used Pyrosequencing with a set of only 7 sequencing primers to investigate 68 CpG sites (see Fig. 1 ) in patients with myelodysplasia and myeloid leukemia and in controls.


Figure 1
View larger version (9K):
[in this window]
[in a new window]

  		Figure 1. Schematic view of the region of the CDKN2B gene analyzed in this study.

The 7 sequencing primers are represented by arrows, and CpG sites are indicated by vertical bars. The start site of the coding region is indicated by an arrow ("ATG").


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
patient samples
We obtained archived formalin-fixed, paraffin-embedded bone marrow trephine samples from 82 patients with MDS or AML. Samples were classified according to the French-American-British system: refractory anemia (RA; n = 13), RA with ringed sideroblasts (RARS; n = 22), RA with excess blasts (RAEB; n = 24), chronic myelomonocytic leukemia (CMML; n = 24), and AML (n = 11), and 32 control samples (biopsies displaying only mild reactive alterations) from the archive of the Institute of Pathology. MDS and AML samples were obtained from patients with a mean age of 76 years (median, 78; range, 21–91 years), and control samples were obtained from persons with a mean age of 55 years (median, 58; range, 22–86 years). All samples were collected and processed anonymously in accordance with the guidelines of the local Ethics Committee.

dna isolation and sodium bisulfite conversion
Genomic DNA was isolated essentially as previously described (15). DNA samples were treated with sodium bisulfite with the EZ DNA Methylation KitTM (Zymo Research, HiSS Diagnostics) according to the manufacturer’s instructions and then eluted in 40 µL elution buffer.

generation of the pcr product
PCR products were generated in a 50-µL reaction volume with 400 nmol/L of forward and reverse PCR primers (see Table S1 in the Data Supplement that accompanies the online version of this article at http:www.clinchem.org/content/vol53/issue1), 200 µmol/L of each dNTP, 1.5 mmol/L or 2.5 mmol/L MgCl2 (see Table S1 in the online Data Supplement), 1 xPlatinum-Taq reaction buffer, and 1.25 units PlatinumTaqTM (Invitrogen). PCR conditions were 95 °C for 5 min, followed by 50 cycles with denaturation at 95 °C for 30 s, annealing at 55 °C or 60 °C for 45 s (see Table S1 in the online Data Supplement), and elongation at 72 °C for 30 s, finished with 1 cycle final elongation at 72 °C for 5 min.

pyrosequencing
PCR products (45–50 µL) were added to a mix consisting of 3 µL Streptavidin Sepharose HPTM (Amersham Biosciences) and 37 µL binding buffer (Biotage) and mixed at 1200 rpm for 5 min at room temperature.

We used the Vacuum Prep ToolTM (Biotage) to prepare single-stranded PCR products according to the manufacturer’s instructions. The Sepharose beads with the single-stranded templates attached were released into a PSQ 96 Plate LowTM (Biotage) containing a mix of 45 µL annealing buffer (Biotage) and 500 nmol/L of the corresponding sequencing primer (see Table S1 in the online Data Supplement). Pyrosequencing reactions were performed in a PSQ 96MATM System (Biotage) according to the manufacturer’s instructions, with the PyroGold SQATM Reagent Kit (Biotage). CpG site quantification was performed with the new methylation Software PyroQ-CpGTM.

Criteria for Pyrogram selection were as follows: sufficient peak height of >15 units (arbitrary units for light emission calculated by the software), symmetric peaks without any irregularities or side-peaks, wide reading length with a high reliability until the end of the sequence, and absence of any significant signals at the positions where a bisulfite treatment control was included or where control nucleotides were dispensed to check for unspecific background signals (Fig. 2 ).


Figure 2
View larger version (30K):
[in this window]
[in a new window]

  		Figure 2. Representative Pyrogram.

The incorporation of each dNTP during the sequencing reaction releases pyrophosphate, which is converted into a light flash by an enzymatic cascade. These sequentially generated light signals are measured by a charge-coupled device camera and are displayed as peaks in a "Pyrogram" [see text and reference (6) for details]. Shown is the analysis of the AML-derived cell line KG1a. The sequence in the upper part of each Pyrogram represents the sequence under investigation. The sequence below the Pyrogram indicates the sequentially added nucleotides. The gray regions highlight the analyzed C/T sites, with percentage values for the respective cytosine above them. Yellow parts highlight the positions where a cytosine was added to verify the complete conversion from unmethylated cytosine to thymine.

statistical analysis
We performed statistical analyses with the R software package (version 2.1.0). We used the Wilcoxon rank-sum test for pairwise comparison of medium methylation levels per site within each sample group.

For the identification of differentially methylated regions with highest discriminatory power, we used different window sizes (3 to 8 CpG sites) to compare methylation levels over the whole sequence. The mean methylation levels of the 3 to 8 CpG sites within each window were compared among the different sample groups, and the resulting P values were plotted against the start point of the window. P values <0.05 were considered statistically significant.


   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
assay development and validation
Using the PSQ assay design softwareTM from Biotage as well as conventional primer design software (Primer ExpressTM, Applied Biosystems), we designed primers to amplify a total of 4 PCR products with a maximum size of ~300 bp (see Table S1 in the online Data Supplement). To analyze every CpG site within these fragments, 7 sequencing primers resulting in high-quality PyrogramsTM were selected (Fig. 2Up ; see Materials and Methods for selection criteria).

In a first step, to validate the newly developed Pyrosequencing assays using the new Pyro Q-CpGTM software with long reading lengths for multiple methylation sites, we analyzed placental DNA, which is known to be unmethylated for most genes, and DNA from the cell line KG1a, which is known to be nearly fully methylated at the CDKN2B CpG island. As expected, results indicated that placental DNA was completely unmethylated whereas KG1a DNA was nearly completely methylated at all 68 CpG sites; mean 94%, range 74%–100% [mean (SD) for all CpG sites: 1.1%, data not shown].

Sequencing primers with slightly staggered binding sites gave nearly identical results for every CpG site analyzed, demonstrating that the quantification is independent of the sequencing primer (compare primers no. 1 and 2 in Fig. 3 .). Several sequencing primer combinations were positioned in such a way that the last CpG sites reached by 1 primer (CpG no. 13–15 of primer no. 2 in Fig. 3 , 60–90 bp away from the primer binding site) were the first CpG sites covered by the 2nd primer (CpG no. 1–3 of primer no. 4 in Fig. 3 ). All of these measurements gave highly concordant results, demonstrating the reliability of Pyrosequencing for the quantitative evaluation of sequences up to 100 bp in length.


Figure 3
View larger version (10K):
[in this window]
[in a new window]

  		Figure 3. Demonstration of assay reliability with overlapping primers.

Examples of analysis results with overlapping primers. Sequencing primers are represented by rectangles. The numbering of the CpG sites at the top of the figure is the same as in Fig. 4Up . CpG sites are indicated by vertical bars with corresponding methylation values below them. The last 3 CpG sites analyzed with sequencing primer no. 2 have a distance of ~90 bp to the primer. Nevertheless, these values are in very good agreement with the values obtained with sequencing primer no. 4.

analysis of patient samples
To demonstrate the feasibility and power of this approach, we analyzed the very heterogeneous methylation pattern of the CpG islands of the tumor suppressor gene CDKN2B in bone marrow samples from patients with different MDS subtypes (RA, RARS, and RAEB) and from patients with CMML and AML, and in 32 control samples displaying only mild reactive changes. A total of 7762 individual CpG sites were analyzed successfully (for only 2.5% of potential methylation sites, for technical reasons reproducible results were not obtained). The high frequency of aberrant methylation and the marked intra- and interindividual heterogeneity reported in the literature for the CDKN2B gene (16)(17) were clearly confirmed by the quantitative high resolution mapping obtained in this study. For a subset of samples (21 CMML cases), a comparison between direct bisulfite sequencing and Pyrosequencing was possible. Overall, the concordance was quite high (623 of 893 CpG sites showed very similar methylation levels). However, in our hands Pyrosequencing produced much more reliable results in replicates, and direct sequencing showed greater sample-to-sample variation in the overall quality of the data. The discrepancies clustered at certain sites (difficult sequence context) or in certain samples (DNA quality).

A detailed overview displaying all methylation data for each sample is available as an online supplement (Table S2 in the online Data Supplement). Comparison of different measurements of the same patient sample showed a deviation of <5% in methylation levels at each single CpG site. The mean methylation level within each group for every CpG site under investigation is shown in Fig. 4 . The control group showed considerable methylation around CpG no. 25 and beyond CpG no. 50, with a mean (SD) methylation level at all 68 CpG sites of 5% (5%). In the patient samples, mean (SD) methylation levels were 6% (5%), 6% (6%), 11% (8%), 11% (9%), and 21% (13%), for RA, RARS, RAEB, CMML, and AML, respectively. Although the average methylation levels were similar for most subgroups, a clear correlation existed between different risk groups (control, RA+RARS, RAEB, and AML) and average methylation level (Spearman’s correlation r2 = 1.00, P <0.01)). More important, the distribution of methylated cytosine residues displayed pronounced regional differences (see Fig. 4 ). Classification of all cases according to WHO guidelines (18) gave a very similar picture (see Fig. S1 in the online Data Supplement).


Figure 4
View larger version (69K):
[in this window]
[in a new window]

  		Figure 4. Quantitative methylation mapping of the CDKN2B CpG island.

(A), for every single CpG site (no. 1–68), the mean methylation level in the 6 different sample groups (classified according to FAB) is displayed. (B), to highlight the differences between control group and patient samples, the relative mean methylation level of every single CpG site is displayed in the panel. This relative methylation level is calculated by subtracting the mean value in the control group from the mean value in the patient group for every single CpG site. The corresponding figure with all samples classified according to WHO criteria can be found in the online Data Supplement (Fig. S1). The raw data used for generating Fig. 4, A and B, can be found in Table S2 in the online Data Supplement.

identification of small regions with high discriminatory significance
To identify the regions showing the largest differences in methylation, we used the Wilcoxon rank-sum test to systematically compare mean methylation levels of all CpG sites within a small window (3–8 CpG sites in size) across the whole CpG island between all groups. The obtained P values were plotted against the start point of the window. In a comparison of the control group with low-risk MDS (RA and RARS) (Fig. 5 ) regions displaying statistically highly significant differences are clearly discernible. All statistical data are available as an online supplement (Table S3 in the online Data Supplement). Even for comparison of RA and RARS cases, which are sometimes difficult to differentiate on clinical and morphological grounds alone, statistical differences were found (P values <0.02, see Table S3 in the online Data Supplement). Only the comparison of RAEB and CMML did not show significant differences in any region. Very similar regions with high discriminatory significance were also found if all cases were classified according to the new WHO guidelines (see Table S4 in the online Data Supplement).


Figure 5
View larger version (23K):
[in this window]
[in a new window]

  		Figure 5. Detection of differentially methylated regions with discriminatory power.

With a window size of 8 nucleotides, the whole sequence of all 68 CpG sites was screened for discriminatory relevant regions. The analysis was started at each nucleotide from CpG no. 1 to 61 (x axis). The corresponding P values for each individual window are plotted against the start point of each window. In this figure, the comparison of controls vs low risk MDS cases (classified according to FAB) is shown as an example. Statistically significant differences were found for the CpG sites 1–15 and 22–41. The P values for all other comparisons and window sizes can be found in the online data supplement (Table S3 in the online Data Supplement).

We confirmed these results in an independent patient cohort with 8 RA, 9 RAEB, 8 RARS, and 11 AML cases as well as 9 controls. In this independent series, we analyzed a subregion of CpG no. 17 to no. 41 and found statistically significant differences for all groups for a window from CpG no. 31 to 38 used for pairwise comparison. Only the comparison of RA and RARS did not reach statistical significance (P = 0.07), most probably because of the smaller sample size (see Table S5 in the online Data Supplement for all statistical data). For a hierarchical cluster analysis of all methylation data see Fig. S2 in the online Data Supplement.


   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
Hypermethylation-associated silencing of tumor suppressor genes is the most important and most extensively analyzed epigenetic mechanism in human tumorigenesis. Techniques for the analysis of this aberration have been hampered by limitations such as no quantification of methylation events, limited numbers of analyzable CpG sites, very labor-intensive or no single-CpG resolution. Pyrosequencing, a new real-time sequencing technology, possesses the potential to overcome these limitations, which makes it especially useful for the analysis of genes with heterogeneous methylation patterns. Initial reports demonstrated the reliability of this technique for the precise quantification of methylation levels at single CpG sites (7)(8)(9)(10) and showed very good correlation with other methods such as COBRA (12), SNaPmeth(11), mass spectrometry (13), and PCR/LDR/Universal array assay (19). The new Pyro Q-CpGTM software (Biotage) substantially increases the number of CpG sites that can be analyzed reliably with a single sequencing primer, thereby facilitating the analysis of large CpG rich regions (up to whole CpG islands), with only a few sequencing primers. With this advancement we analyzed for the first time up to 15 CpG sites spread over ~100 nucleotides with one single-sequencing primer. The reliability until the end of the sequencing reaction was confirmed by 1 or 2 additional overlapping sequencing primers.

Two alternative tools for the analysis of complex methylation patterns are base-specific cleavage and subsequent matrix-assisted laser desorption ionization time-of-flight mass spectrometry of cleavage fragments (MALDI-TOF-MS; (20)(21) and array-based analysis of DNA methylation patterns (22)(23)(24). MALDI-TOF-MS is a very promising approach that enables high-throughput analysis of a large number of CpG sites in many samples, but its usefulness for analyzing individual CpG sites in regions with very high CpG density is often limited by the absence of suitable cleavage fragments or the generation of identical fragments from different regions. The success of an array-based methylation analysis is heavily dependent on the composition of the sequence of interest. Even with a comprehensive assay design and the application of very stringent filter criteria for the selection of reliable oligonucleotides, Mund et al. (24) and Kimura et al. (23) were unable to accurately differentiate between individual CpG sites in difficult regions.

A systematic comparison of the methylation patterns revealed region-specific differences in the CDKN2B CpG island concerning distribution and level of CpG methylation. The identification of small regions with high discriminatory relevance implies that large areas of the CpG island do not have discriminatory relevance, a finding that demonstrates the necessity for a comprehensive quantitative methylation analysis before high-throughput assays targeting only a few potential methylation sites (e.g., methylation-specific PCR) can be developed and implemented into routine diagnostics. The functional consequences of this heterogeneity in molecular terms (e.g., accessibility for transcription factors) have to be addressed in future studies.

Occasionally very weak methylation signals were found at positions where control nucleotides were dispensed or where a conversion control was implemented. Consequently, we consider methylation values ≤5% as potential background signals with questionable significance, in concordance with other publications. For example, Shaw et al. (10) regard values of 0%–5% methylation as background noise and Jones et al. (25) defined a threshold of detection of 5% for their JAK2 genotyping assay because they found residual wild-type signals of ≤5% in nearly all of their homozygous cases.

In conclusion, we demonstrate that quantitative long-read Pyrosequencing is a reliable quantitative approach that enables the high-resolution mapping of a whole CpG island in a very efficient semi-highthroughput fashion. Employing this technology, we showed that only a few quite small regions of the CDKN2B CpG island provide relevant information for differential methylation analysis. Our results show, again, the advantage of quantitative compared to a merely qualitative methylation analysis (10)(26)(27) as well as demonstrating that analyzing the methylation of every individual CpG site is the only procedure that allows a detailed characterization of methylation patterns and identification of relevant regions with high discriminatory power, thus forming the basis for the development of clinically useful assays.


   Acknowledgments
 
The authors thank Britta Hasemeier and Kathleen Metzig for their excellent technical assistance and Dr. Michael Utting (Biotage) for initial support in designing primers as well as for providing a beta version of the Pyro Q-CpG-SoftwareTM. We thank Dr. Masyar Monazahian, Niedersächsisches Landesgesundheitsamt, Hannover, Germany for the opportunity to perform the Pyrosequencing assay on the PSQ 96MA instrument and Dr. Holly Sundberg for critical reading of the manuscript.

This study was supported by Deutsche Krebshilfe, grant number 10-1842-Le I and Deutsche Forschungsgemeinschaft, KFO 119/TP2.


   Footnotes
 
1 Nonstandard abbreviations: CpG, dinucleotide cytosine-guanosine; dNTP, deoxynucloeside triphosphate; MDS, myelodysplastic syndrome; RA, refractory anemia; RARS, refractory anemia with ringed sideroblasts; RAEB, refractory anemia with excess blasts; CMML, chronic myelomonocytic leukemia; AML, acute myeloid leukemia.

2 Human gene: CDKN2B, cyclin-dependent kinase inhibitor 2B (p15, inhibits CDK4).


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

  1. Esteller M. Aberrant DNA methylation as a cancer-inducing mechanism. Annu Rev Pharmacol Toxicol 2005;45:629-656.
  2. Lehmann U, Brakensiek K, Kreipe H. Role of epigenetic changes in hematological malignancies. Ann Hematol 2004;83:137-152.[CrossRef][ISI][Medline] [Order article via Infotrieve]
  3. Laird PW. The power and the promise of DNA methylation markers. Nat Rev Cancer 2003;3:253-266.[CrossRef][ISI][Medline] [Order article via Infotrieve]
  4. Cottrell SE. Molecular diagnostic applications of DNA methylation technology. Clin Biochem 2004;37:595-604.[CrossRef][ISI][Medline] [Order article via Infotrieve]
  5. Ronaghi M, Uhlen M, Nyren P. A sequencing method based on real-time pyrophosphate. Science 1998;281:363, 5.
  6. Marsh S, King CR, Garsa AA, McLeod HL. Pyrosequencing of clinically relevant polymorphisms. Methods Mol Biol 2005;311:97-114.[Medline] [Order article via Infotrieve]
  7. Tost J, Dunker J, Gut IG. Analysis and quantification of multiple methylation variable positions in CpG islands by pyrosequencing. Biotechniques 2003;35:152-156.[ISI][Medline] [Order article via Infotrieve]
  8. Yang AS, Estecio MR, Doshi K, Kondo Y, Tajara EH, Issa JP. A simple method for estimating global DNA methylation using bisulfite PCR of repetitive DNA elements. Nucleic Acids Res 2004;32:e38.[Abstract/Free Full Text]
  9. Issa JP, Gharibyan V, Cortes J, Jelinek J, Morris G, Verstovsek S, et al. Phase II study of low-dose decitabine in patients with chronic myelogenous leukemia resistant to imatinib mesylate. J Clin Oncol 2005;23:3948-3956.[Abstract/Free Full Text]
  10. Shaw RJ, Liloglou T, Rogers SN, Brown JS, Vaughan ED, Lowe D, et al. Promoter methylation of P16, RARbeta, E-cadherin, cyclin A1 and cytoglobin in oral cancer: quantitative evaluation using pyrosequencing. Br J Cancer 2006;94:561-568.[CrossRef][ISI][Medline] [Order article via Infotrieve]
  11. Uhlmann K, Brinckmann A, Toliat MR, Ritter H, Nurnberg P. Evaluation of a potential epigenetic biomarker by quantitative methyl-single nucleotide polymorphism analysis. Electrophoresis 2002;23:4072-4079.[CrossRef][ISI][Medline] [Order article via Infotrieve]
  12. Colella S, Shen L, Baggerly KA, Issa JP, Krahe R. Sensitive and quantitative universal pyrosequencing methylation analysis of CpG sites. Biotechniques 2003;35:146-150.[ISI][Medline] [Order article via Infotrieve]
  13. Dupont JM, Tost J, Jammes H, Gut IG. De novo quantitative bisulfite sequencing using the pyrosequencing technology. Anal Biochem 2004;333:119-127.[CrossRef][ISI][Medline] [Order article via Infotrieve]
  14. Steensma DP, Tefferi A. The myelodysplastic syndrome(s): a perspective and review highlighting current controversies. Leuk Res 2003;27:95-120.[CrossRef][ISI][Medline] [Order article via Infotrieve]
  15. Lehmann U, Hasemeier B, Lilischkis R, Kreipe H. Quantitative analysis of promoter hypermethylation in laser-microdissected archival specimens. Lab Invest 2001;81:635-638.[ISI]
  16. Aggerholm A, Guldberg P, Hokland M, Hokland P. Extensive intra- and interindividual heterogeneity of p15INK4B methylation in acute myeloid leukemia. Cancer Res 1999;59:436-441.[Abstract/Free Full Text]
  17. Tessema M, Langer F, Dingemann J, Ganser A, Kreipe H, Lehmann U. Aberrant methylation and impaired expression of the p15(INK4b) cell cycle regulatory gene in chronic myelomonocytic leukemia (CMML). Leukemia 2003;17:910-918.[CrossRef][ISI][Medline] [Order article via Infotrieve]
  18. Harris NL, Jaffe ES, Diebold J, Flandrin G, Muller-Hermelink HK, Vardiman J, et al. The World Health Organization classification of hematological malignancies report of the Clinical Advisory Committee Meeting, November 1997, Airlie House, Virginia. Mod Pathol 2000;13:193-207.[CrossRef][ISI][Medline] [Order article via Infotrieve]
  19. Cheng YW, Shawber C, Notterman D, Paty P, Barany F. Multiplexed profiling of candidate genes for CpG island methylation status using a flexible PCR/LDR/Universal Array assay. Genome Res 2006;16:282-289.[Abstract/Free Full Text]
  20. Schatz P, Dietrich D, Schuster M. Rapid analysis of CpG methylation patterns using RNase T1 cleavage and MALDI-TOF. Nucleic Acids Res 2004;32:e167.[Abstract/Free Full Text]
  21. Ehrich M, Nelson MR, Stanssens P, Zabeau M, Liloglou T, Xinarianos G, et al. Quantitative high-throughput analysis of DNA methylation patterns by base-specific cleavage and mass spectrometry. Proc Natl Acad Sci U S A 2005;102:15785-15790.[Abstract/Free Full Text]
  22. Yan PS, Shi H, Rahmatpanah F, Hsiau TH, Hsiau AH, Leu YW, et al. Differential distribution of DNA methylation within the RASSF1A CpG island in breast cancer. Cancer Res 2003;63:6178-6186.[Abstract/Free Full Text]
  23. Kimura N, Nagasaka T, Murakami J, Sasamoto H, Murakami M, Tanaka N, et al. Methylation profiles of genes utilizing newly developed CpG island methylation microarray on colorectal cancer patients. Nucleic Acids Res 2005;33:e46.[Abstract/Free Full Text]
  24. Mund C, Beier V, Bewerunge P, Dahms M, Lyko F, Hoheisel JD. Array-based analysis of genomic DNA methylation patterns of the tumour suppressor gene p16INK4A promoter in colon carcinoma cell lines. Nucleic Acids Res 2005;33:e73.[Abstract/Free Full Text]
  25. Jones AV, Kreil S, Zoi K, Waghorn K, Curtis C, Zhang L, et al. Widespread occurrence of the JAK2 V617F mutation in chronic myeloproliferative disorders. Blood 2005;106:2162-2168.[Abstract/Free Full Text]
  26. Brakensiek K, Langer F, Kreipe H, Lehmann U. Low level of DAP-kinase DNA methylation in myelodysplastic syndrome. Blood 2004;104:1586-1587author reply 7–8.[Free Full Text]
  27. Lehmann U, Berg-Ribbe I, Wingen LU, Brakensiek K, Becker T, Klempnauer J, et al. Distinct methylation patterns of benign and malignant liver tumors revealed by quantitative methylation profiling. Clin Cancer Res 2005;11:3654-3660.[Abstract/Free Full Text]



The following articles in journals at HighWire Press have cited this article:


Home page
 haematolHome page
T. Ripperger, N. von Neuhoff, K. Kamphues, M. Emura, U. Lehmann, M. Tauscher, M. Schraders, P. Groenen, B. Skawran, C. Rudolph, et al.
Promoter methylation of PARG1, a novel candidate tumor suppressor gene in mantle cell lymphomas
Haematologica, April 1, 2007; 92(4): 460 - 468.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Data Supplement
Right arrow All Versions of this Article:
clinchem.2007.072629v1
53/1/17    most recent
Right arrow Submit an electronic Letter to
the Editor about this paper
Right arrow Alert me when this article is cited
Right arrow Alert me when eLetters are posted
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via ISI Web of Science (6)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Brakensiek, K.
Right arrow Articles by Lehmann, U.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Brakensiek, K.
Right arrow Articles by Lehmann, U.
Related Collections
Right arrow Molecular Diagnostics and Genetics

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS