Confirmation by MassARRAY (EpiTYPER) data 108

Mass spectrometry yields quantitative methylation data of short stretches of subsequent CpGs in a high-throughput manner and consequently allows for the validation of large genomic regions. Approximately 350 genes that were differentially methylated between AML patients and normal monocytes were selected based on the array results (Figure 5-23). For all genes a total of 670 PCR amplicons were designed. Before analyzing the patient samples, all primers were tested with the cell lines (THP-1 and U937). The results of all amplicons were screened for selection for follow-through methylation analysis in the AML samples. Several criteria were considered when choosing the best amplicons:

1. Spectral quality: All amplicons designed across a CGI were assessed for spectral quality from the MALDI-TOF MS output to determine the success of PCR amplification and the presence / absence of primer dimers or amplification bias.

2. Cleavage pattern: The cleavage pattern of each amplicon following base-specific cleavage was also assessed to determine whether a sufficient amount of cleavage fragments fell within the mass range of detection for MALDI-TOF MS (1,500 Da - 6,500 Da). In some situations, a large proportion of cleaved fragments were either too small or too large for detection.

3. CpG density and length: Larger (>400 bp), CpG-dense amplicons were preferable, in order to maximize the quantity of data available.

4. Location: Amplicons adjacent to or upstream of the transcription start site were considered ideal, in order to cover any putative transcription factor binding sites.

5. Methylation levels: When methylation ratios were considered across several amplicons covering a CGI, amplicons in regions where methylation levels changed dramatically between samples (from unmethylated to methylated or vice versa) or where tumor sampes were methylated were preferable, rather than amplicons where no methylation was observed.

Following manual inspection of all methylation data quality, a final set of about 400 amplicons (˃7000 CpG sites) in about 300 genes were chosen for validation of the microarray data with AML patient samples.

Again, MassARRAY EpiTYPER data correlated well with microarray data. Examples for the excellent consistency of the two different techniques are shown in Figure 5-25.

We finally asked, if specific markers for disease diagnosis or prognosis can be identified. To address this issue, a further 200 AML patients were screened using the 400 amplicons as described above to identify relevant disease markers. (The complete MALDI-TOF MS data will be available online upon publication.)

Figure 5-26 shows exemplary methylation patterns of two different gene amplicons (CEBPA and RHOB) for 165 AML patients, CD34+ cells derived from three different healthy donors and monocytes derived from four healthy 20-year-old donors and eleven healthy 60-year-old donors. CEBPA (CCAAT/enhancer binding protein α) is a basic leucine zipper transcription factor that regulates differentiation-dependent genes during granulocyte differentiation. While hypermethylation of the CEBPA promoter has already been reported in AML as well as in other malignancies (Figueroa et al., 2009), the distal CpG island which is located about 20 kb downstream of the promoter was often hypomethylated in AMLs but methylated in normal monocytes and stem cells (Figure 5-26). In contrast, more than one third of the analyzed AML patients showed significant hypermethylation of an amplicon within the RHOB gene whereas hematopoietic stem cells (CD34+ cells) as well as monocytes of all healthy donors were unmethylated. It is already known that the expression of the RHOB gene, a member of the Rho family of small GTPases, is often downregulated in lung cancer (Sato et al., 2007). Computational analyses of the EpiTYPER data set was still in progress at the time of writing this thesis. Therefore, no correlations between methylation profiles and clinical parameters could be detected or, likewise, no potential marker genes could be identified at this time.

Figure 5-25 Examples of aberrantly methylated CpG islands in AML samples

Microarray and MassARRAY data are shown for CpG islands of four different genes in AML samples and two blood monocytes from two different healthy donors. Each sample is represented by one column. Each line of the microarray results represents one probe and each line of the EpiTYPER results represents one CpG unit. The same region is detected by microarray or EpiTYPER analysis, respectively. DNA methylation values regarding EpiTYPER results are represented on a continuous scale from non-methylated (white) to fully methylated (dark blue) (non-detectable CpGs are marked in gray), whereas signal log ratios (tumor versus normal) are represented on a continuous scale from blue (strongly hypermethylated in tumor) to yellow (strongly hypomethylated in tumor). The top diagrams were extracted from the Genome Browser showing the relative position of transcripts, CpG islands (green) as well as position of amplicons detected by MassARRAY experiments.

To find out if monocytes also show age-related differences in methylation patterns similar to colon samples, and therefore to make sure that the identified potential marker genes are really methylated due to tumorigenesis and not due to aging, DNA samples derived from monocytes of about 60-year-old donors were analyzed using MALDI-TOF MS for the 400 CpG island regions. Unlike colon samples, monocyte samples did not show age-dependent changes in DNA methylation (Figure 5-26). One explanation could be that crypt stem cells possess an exceptionally high rate of proliferation, resulting in further DNA methylation due to the higher mitosis rate.

Figure 5-26 Examples of abnormal methylation patterns in AML patients

Mass spectrometry analysis of CpG island fragments of two different genes (CEBPA (on the left) and RHOB (on the right)) in 165 AML patients, three CD34+ samples and monocytes derived from four healthy 20-year-old donors and eleven healthy 60-year-old donors. Samples (rows) are clustered according to the average methylation degree of all CpG units (columns) within the amplicon. DNA methylation values are depicted by a color scale as indicated (methylation increases from white (non-methylated) to dark blue (fully methylated)). Gray denotes data of poor quality. The top diagrams were extracted from the Genome Browser showing the relative position of transcripts (black), the transcription start sites (arrows), CpG islands (green) and the position of

5.4 General transcription factor binding at CpG islands in

normal cells correlates with resistance to de novo

methylation in cancer

The data from this section have been published in the journal Cancer Research. Microarray data were deposited with GEO (gene expression analyses: GSE16076; comparative MCIp hybridizations: GSE17455, GSE17510, GSE17512; ChIP-on-chip hybridizations: GSE16078).

Cancer is associated with disease-related epigenetic abnormalities, including the aberrant hypermethylation of CpG islands leading to loss of tumor suppressor gene expression. Methylation profiling studies have demonstrated that although there may be hundreds of different CpG islands methylated in any one tumor, some are methylated in multiple tumor types, whereas others are methylated in a tumor-type specific manner. Moreover, each tumor type tends to exhibit a characteristic set of aberrantly methylated genes. However, despite numerous examples of methylation–associated gene silencing events in human cancer cells, the molecular pathways underlying aberrant DNA methylation remain elusive. Different mechanisms for cancer-dependent, aberrant de novo methylation have been proposed so far, largely based on the behavior of individual CpG islands (see section 1.7.2). Besides other proposed mechanisms, one possible mechanism suggests that Alu and other repetitive elements may serve as foci from which de novo methylation can spread (Feltus et al., 2003), whereas other elements could provide a “protective” function. The absence of such ”protective” transcription factors may lead to the spreading of DNA methylation into affected CpG islands (Turker, 2002). To address this issue, methylation-prone and methylation-resistant CpG islands were defined by analyzing the methylation status of 23,000 CpG islands of the human genome in acute leukemia cell lines as well as normal blood monocytes. Understanding the nature of these differences could provide insight into the molecular basis for aberrant methylation. The corresponding MCIp-on-chip experiments as well as the extensive validation by bisulfite conversion and subsequent MALDI-TOF are described in 5.3.2.