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Molecular Cancer Research 5, 461-471, May 1, 2007. doi: 10.1158/1541-7786.MCR-06-0358
© 2007 American Association for Cancer Research
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Signaling and Regulation

Assessment of Methylation Events during Colorectal Tumor Progression by Absolute Quantitative Analysis of Methylated Alleles

Michiel F.G. de Maat1, Naoyuki Umetani1, Eiji Sunami1, Roderick R. Turner2 and Dave S.B. Hoon1

1 Department of Molecular Oncology, John Wayne Cancer Institute and 2 Department of Surgical Pathology, Saint John's Health Center, Santa Monica, California

Requests for reprints: Dave S.B. Hoon, Department of Molecular Oncology, John Wayne Cancer Institute, 2200 Santa Monica Boulevard, Santa Monica, CA 90404. Phone: 310-449-5267; Fax: 310-449-5282. E-mail: hoon{at}jwci.org


   Abstract
Top
 Notes
 Abstract
Introduction
 Results
 Discussion
 Materials and Methods
 References
 
To date, the epigenetic events involved in the progression of colorectal cancer are not well described. To study, in detail, methylation during colorectal cancer development in high-risk adenomas, we developed an assay combining in situ (on-slide) sodium bisulfite modification (SBM) of paraffin-embedded archival tissue sections with absolute quantitative assessment of methylated alleles (AQAMA). We tested the performance of the assay to detect methylation level differences between paired pre-malignant and malignant colorectal cancer stages. AQAMA assays were used to measure methylation levels at MINT (methylated in tumor) loci MINT1, MINT2, MINT12, and MINT31. Assay performance was verified on cell line DNA and standard cDNA. On-slide SBM, allowing DNA methylation assessment of 1 to 2 mm2 of paraffin-embedded archival tissue, was employed. Methylation levels of adenomatous and cancerous components within a single tissue section in 72 colorectal cancer patients were analyzed. AQAMA was verified as accurately assessing CpG island methylation status in cell lines. The correlation between expected and measured cDNA methylation levels was high for all four MINT AQAMA assays (R ≥ 0.966, P < 0.001). Methylation levels at the four loci increased in 11% and decreased in 36% of specimens comparing paired adenoma and cancer tissues (P < 0.0001 by Kolmogorov-Smirnov test). Single-PCR AQAMA provided accurate methylation level measurement. Variable MINT locus methylation level changes occur during malignant progression of colorectal adenoma. Combining AQAMA with on-slide SBM provides a sensitive assay that allows detailed histology-oriented analysis of DNA methylation levels and may give new, accurate insights into understanding development of epigenetic aberrancies in colorectal cancer progression. (Mol Cancer Res 2007;5(5):461–71)


   Introduction
Top
 Notes
 Abstract
 Introduction
Results
 Discussion
 Materials and Methods
 References
 
Cytosine-5 of CpG dinucleotides is the unique target of methyl-group placement in mammals (1). CpG methylation is a heritable mechanism to assist in silencing of noncoding DNA in mammals (2). In cancer, dense methylation of a gene promoter region, or the region in the vicinity of the 5' region of a gene open reading frame, can silence expression of genes involved in cancer-related processes (3-5). Tumor-specific DNA methylation events have been shown in a variety of human cancers and can encompass both loss and gains in methylation (6). Currently, the clinical utility of detecting CpG methylation status in primary tumors for the management of cancer patient treatment is being evaluated as a useful surrogate marker for disease variables. Some studies have successfully shown clinical correlates and/or prognostic value (7-9). Epigenetic changes may be important as signatures of tumor progression or prognosis, and they may become potential therapeutic targets. Studying epigenetic changes during malignant tumor development would provide valuable additive information on tumor specificity and genesis of key methylation aberrances.

Recently, studies in colorectal cancer have shed new light on the macroscopic and microscopic pathways involved in the transition from normal epithelium to adenomatous polyps to invasive cancer (10, 11). Novel subgroups of colorectal adenomas were identified, indicating differential pathways of colorectal cancer development. On the molecular level, Vogelstein et al. (12) reported specific genomic mutations associated with colorectal cancer carcinogenesis. On the epigenetic level, it is known that aberrant DNA methylation is present at the earliest dysplastic stages as well as in malignant tumors (13). How levels of methylation develop during colorectal cancer formation remains uncertain. In general, the molecular events involved in colorectal cancer development and progression are still not clearly validated. To investigate this, colorectal cancer specimens harboring adenomatous cell components belonging to the precursor lesion would provide an attractive study model. This direct comparison of the pre-malignant lesion with the associated cancer would enable paired analysis of specific events during malignant progression. The adenomatous cells analyzed would represent relevant, high-risk cancer precursors, whereas most studies use randomly selected colorectal adenomas with an unknown likelihood to develop into cancer. We have previously described an approach that enables this direct comparison in colorectal cancer paraffin-embedded archival tissue (PEAT) sections by employing in situ sodium bisulfite modification (on-slide SBM) of the DNA (14). Adding a quantitative method to evaluate PEAT sections would allow accurate analysis of epigenetic events related to tumor histopathologic changes. To date, such detailed studies have been challenging, as reported studies often fail to microscopically confirm the selection of tumor cells for nucleic acid isolation. On-slide SBM enables DNA methylation assessment of tissue areas 1 to 2 mm2 in size with DNA yields 2.5 to 4 times higher and similar efficiency of SBM, compared with standard SBM protocols. Assessment of small areas of tissue allows for more homogenous tumor sample DNA by reducing the risk of selecting uninvolved tissue areas, such as bowel musculature or serosa, especially compared with DNA isolated from whole tissue sections. Using on-slide SBM with absolute quantitative PCR methods would, therefore, give a more reliable representation of methylation levels in a specifically defined small area of a tissue section.

Advances in sequence detection technology have been made with the addition of minor groove binder (MGB) molecules to Taqman probes. MGB probes have been tested to be more sequence specific than standard DNA probes, especially for single base pair mismatches at elevated PCR extension temperatures (15, 16). Zeschnigk et al. (17) applied these improved probe qualities to design a fully quantitative approach, real-time PCR assay for methylation level measurement: quantitative assessment of methylated alleles (QAMA; Fig. 1 ). This method was designed as a relative quantification containing a mathematical derivation using the methylated and unmethylated fluorescent signal threshold value as input. In this study, we used an absolute quantitative version of QAMA (AQAMA) with cDNA standard curves to provide better internal assay control. As methylation biomarkers, we selected four MINT (methylated in tumor) loci, CpG-rich regions (1, 2, 12, and 31), as they have been consistently shown to become methylated in a tumor-related (18-21) and, recently, in a adenoma-related manner in colorectal cancer (22). We showed the accuracy of methylation level assessment of AQAMA alone in evaluating the combination of AQAMA and on-slide SBM to detect changes in methylation levels between paired pre-malignant and malignant colorectal cancer cells.


Figure 1
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  		FIGURE 1. Schematic representation of the AQAMA assay. A universal primer set amplifies a target sequence. A. A methylation-specific probe with FAM-labeled reporter, BHQ, and MGB molecule recognizes sample DNA showing hypermethylation. B. An unmethylated-specific probe with VIC-labeled reporter, BHQ, and MGB molecule recognizes unmethylated sample DNA.

 

   Results
Top
 Notes
 Abstract
 Introduction
 Results
Discussion
 Materials and Methods
 References
 
AQAMA Specificity and Performance
First, we evaluated the CpG island methylation status of the MINT loci selected for this study in gastrointestinal cancer cell lines. To this end, we did capillary array electrophoresis methylation-specific PCR (CAE-MSP) to assess the CpG island methylation status for all four selected MINT loci in five colorectal and five gastric cancer cell lines. These results (Table 1 ) were used to identify cell lines showing complete methylation, heterogeneous methylation, or no methylation. For each MINT locus, we selected completely methylated and unmethylated cell lines that could be used as templates for cloning into vectors and expanded and used as standards in the AQAMA assay. We corroborated the methylation status reported by CAE-MSP of the CpG islands of two of the MINT loci (MINT2 and MINT12) in two of the cell lines used for cloning (AGS and FN-0028) by direct bisulfite sequencing. Sequencing confirmed the methylation status reported by CAE-MSP (see Fig. 2A-D for the sequencing and CAE-MSP results for the MINT12 locus). To gauge the accuracy of AQAMA in assessing various levels of methylation, mixtures of methylated and unmethylated standard cDNA, synthesized from templates with confirmed methylation status, were prepared and measured as unknown samples. The mixtures were prepared from methylated and unmethylated diluted 1 x 103 copy number DNA standards. The AQAMA assay performance for all four MINT loci was assessed. In Fig. 3 , the results of two representative independent experiments for each MINT locus assay are shown. Pearson's correlation coefficient for linearity of the methylation percentage of the known mixture with the AQAMA assay outcome MI-value was not lower than 0.966 (P < 0.001). SD of all measured methylation index (MI) levels between the two independent experiments did not exceed 0.08 for the four MINT locus assays. We subsequently assessed the 10 gastrointestinal cell lines analyzed with CAE-MSP by AQAMA. The results showed that there was 100% agreement between the methylation categories of CAE-MSP and the quantitative result of AQAMA (Table 1). The AQAMA result could be compared with the results from the direct bisulfite sequencing, and there was 100% agreement as well.


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  		TABLE 1. Comparison of MI Assessment by AQAMA and CAE-MSP in Cell Lines

 

Figure 2
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  		FIGURE 2. Methylation assessment results for MINT12. Representative direct bisulfite sequencing (forward direction) for FN-0028 (A) and AGS (B). Arrows, CpG sites. Boxed site, hemi-methylation. C. Representative results of CAE detection of labeled (left, methylated; right, unmethylated) products after MSP for FN-0028 and AGS, respectively.

 

Figure 3
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  		FIGURE 3. A to D. Graphs representing the correlation between result of two independently assessed AQAMA assay MI levels (Y-axis) and input DNA methylation percentage (X-axis). {square} and {blacktriangleup}, results of the two experiments. {diamondsuit}, expected MI.

 
Colorectal Cancer Tumor-Adenoma Methylation Level Differences
To show the value of assessing primary colorectal cancer tissue methylation levels by AQAMA, we investigated application of the technique and its utility when combined with on-slide SBM. We tested whether the AQAMA assay has the ability to detect differences in methylation levels between pre-malignant and malignant colorectal cancer stages. A schematic overview of combining AQAMA with on-slide SBM is given in Fig. 4 . Seventy-two cases were selected based on review of histopathology indicating that, along with invasive cancer cells, the specimen also had an area of tissue containing the precursor adenomatous lesion. The areas were selected to contain a minimum number of contaminating normal cells by a surgical pathologist. Each sample was measured in triplicate, and the SDs were 0.04, 0.04, 0.05, and 0.06 for MINT1, MINT2, MINT12, and MINT31, respectively.


Figure 4
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  		FIGURE 4. Schematic representation of histology-oriented tissue isolation followed by AQAMA. Left, AQAMA PCR plot. The adenomatous tissue component (bottom marked area) shows only unmethylated fluorescent signal (triplicate results), whereas the cancerous component (top marked area) shows both unmethylated and methylated fluorescent signal. Both signals are visualized here; however, in the raw data analysis, the CT is analyzed separately.

 
The boxplots of the measured MI values in adenoma and cancer tissue (Fig. 5A ) show that the MI values are not normally distributed and samples showing methylation are outliers. The experiments testing the linearity of the quantitative qualities of AQAMA showed that AQAMA can reliably discriminate a minimum difference of 5% from MI = 0 or among samples. An MI ≥ 0.05 was detected in 12% versus 11% MINT1, 35% versus 29% MINT2, 22% versus 22% MINT12, and 22% versus 15% MINT31 in adenoma versus cancer cells, respectively. None of the proportions differed significantly. Colorectal cancers with methylation at MINT loci form subgroups, as the majority of colorectal cancers is unmethylated. The total number of MINT loci with MI ≥ 0.05 per sample did not differ significantly (P = 0.27) between adenomas and cancer samples. We also analyzed whether MI levels differed significantly when methylation of all MINT loci was added up. For this analysis, we first considered samples with total MI ≥ 0.05 as "methylated" and, subsequently, samples with total MI ≥ 0.2. The latter cutoff was chosen because AQAMA was tested to discriminate 5% difference from zero at a single locus and, subsequently, 20% from zero at four loci. No significant overall event of gain or loss of methylation could be shown in both analyses (P = 0.11 for samples with MI ≥ 0.05 and P = 0.20 for samples with MI ≥ 0.20) at the four MINT loci between pre-malignant and malignant colorectal cancer lesions. Figure 5B shows the measured absolute change in MI level for each MINT locus in the 72 adenoma-cancer pairs. Positive and negative changes in MI level at each locus were detected in some cases, and this explains why there is no clear event in methylation that occurs during malignant transformation. Therefore, we also analyzed the distributions of the measured MI changes at individual and at the four combined loci to determine whether substantial increases or decreases were measured by AQAMA.


Figure 5
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  		FIGURE 5. A. Boxplots showing the distribution of the measured MI values for each individual MINT locus in adenoma and cancer tissue. B. Scatter plot of the measured MI changes detected by the AQAMA assay between adenoma and cancer tissue areas in the same colorectal cancer tissue section for all individual MINT loci. Y-axis, change in MI level calculated as MIcancer – MIadenoma.

 
Kurtosis is based on the size of a distribution tail. Distributions with relatively large tails are referred to as "leptokurtic," and a distribution with the same kurtosis as the normal distribution is referred to as "mesokurtic" (Fig. 6 ). Kurtosis of the data distributions of the methylation level differences between adenoma and tumor cells was high except for MINT2 (Table 2 ). However, this was still greater than zero and therefore leptokurtic. This suggests that methylation levels in some colorectal cancers change considerably at individual MINT loci, and that a global event at multiple loci may occur. To identify how many tumors increase or decrease MINT methylation, we did one-sample Kolmogorov-Smirnov analysis on the data distribution of individual MINT loci and overall MINT methylation (Table 2). The strong significance in Kolmogorov-Smirnov analysis for individual and total MINT loci shows a clear deviation from the null hypothesis (normal distribution), implying that the found positive and negative outlier values do not result from variance by chance. The Kolmogorov-Smirnov test can calculate the most extreme differences as the largest positive and negative points of divergence between the tested data set and normal distribution (Fig. 6). For total methylation, 26 (36%) cases were identified with a decrease in methylation (MI difference <0.12), and 8 (11%) cases were identified with increases (MI difference >0.27). Fifty-three percent of cases had change in methylation levels that did not exceed the tested normal distribution (Fig. 7 ).


Figure 6
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  		FIGURE 6. Explanatory representation of leptokurtic and mesokurtic distribution. Vertical bars, cutoff value calculated by Kolmogorov-Smirnov analysis to identify extreme differences between assumed mesokurtic and measured leptokurtic distributions.

 

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  		TABLE 2. Distribution Characteristics of Individual MINT Locus MI Differences between Paired Colorectal Carcinoma Adenoma and Cancer Cells

 

Figure 7
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  		FIGURE 7. Distribution of values of summed up MINT methylation level differences (Y-axis) between adenoma and cancer tissue from the same patient at MINT1, MINT2, MINT12, and MINT31 assessed by AQAMA. X-axis, different cases (dimensionless). Horizontal reference lines, cutoff values as calculated by Kolmogorov-Smirnov analysis from Table 3. Vertical bars, dividing lines grouping into cases with extreme positive methylation differences, no differences, and extreme negative differences.

 

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  		TABLE 3. Association between MI Change Category and Tumor Location

 
Currently, there is no established technique to adequately confirm the measured differences in MI value between the small areas of paraffin tissue by AQAMA. It has been consistently reported that colorectal cancers with increased methylation are found in the right colon (23-25). As an external validation, we therefore analyzed whether the positive MI change category identified by the Kolmogorov-Smirnov analysis correlated with the location of the tumors in the large bowel (Table 3 ). Tumor location did significantly correlate to MI change category (P = 0.03). Seven of the eight identified cases with an increase in MI were in the right colon. The single positive MI change case that was identified in the rectum was from a 48-year-old female with an undifferentiated tumor. Cases with extreme negative MI change were equally distributed over the right and left colon. Sixty-four percent of cases with no change were in the left colon. Additionally, we analyzed whether the MI change categories were correlated with age, sex, or tumor differentiation. No associations were seen between the assigned MI change categories and these variables.

The results indicate that AQAMA can identify colorectal cancers with gains and losses of DNA methylation levels at individual and combined multiple MINT loci between adenomatous dysplastic epithelial cells and invasively growing adenocarcinoma cells. MINT loci MINT1, MINT2, MINT12, and MINT31 were originally identified to be methylated in colorectal cancer and not in normal colorectal epithelial cells. Our study analyzed adenomatous components of existing colorectal cancers with common histopathology and therefore focuses on sporadic pre-malignant lesions that will develop into cancer. This novel approach quantitatively shows that divergent MINT methylation changes accompany the malignant turning point of colorectal cancer subsets.


   Discussion
Top
 Notes
 Abstract
 Introduction
 Results
 Discussion
Materials and Methods
 References
 
Hypermethylation of CpG islands is an early event in the development of colorectal cancer (22, 26, 27). Better identification of methylation changes when adenomatous epithelial cells manifest invasive growth could greatly enhance our knowledge of the malignant turning point. To date, the assessment of confined areas with specific histopathology in PEAT specimens by PCR techniques for methylation status has not been efficient. Relatively large amounts of DNA are required to compensate for the inevitable loss of DNA during the standard protocol for SBM. We have previously employed a model of comparing, by regular MSP, the methylation status of colorectal cancer cells within the same tissue section showing invasion with cells from the adenomatous precursor lesion (14). In this study, we applied an informative, quantitative technique, providing more detailed information about the methylation status than the dichotomous results of standard MSP. The absolute quantitative quality of AQAMA in combination with on-slide SBM improves the approach for assessing small areas of tissue sections. The original report of QAMA describes that input DNA before sodium bisulfite treatment was standardized to 1 µg (17). The amount of input DNA in the AQAMA assay isolated from 1 to 2 mm2 of tissue of a 7-µm section is difficult to standardize. Therefore, the control on the linearity of the PCR reaction that the standard curve provides over a wide range of input concentrations of sample DNA complies well with on-slide SBM. Importantly, the standard curve approach in AQAMA allows comparison of results between different PCR runs. The homogenous quality of cloned DNA standards contributes to assuring consistency of results. This is highly important for quality control in comparison of multiple assays of many samples. We showed that levels of MINT locus methylation in colorectal cancers can not only accumulate at multiple genomic CpG island loci (predominantly in the right colon) but can also decrease during malignant change in colorectal cancer. It is interesting to note that there seems to be no uniform event that most colorectal cancers undergo during malignant transformation for methylation at the MINT loci. In addition, the data indicate that subgroups of colorectal cancers may exist that can lose, stabilize, or gain methylation in the gene promoter region(s). Considering the silencing effect of methylation, the divergent development of methylation patterns could lead to differential gene expression signatures proven to be clinically relevant in colorectal cancer (28, 29).

The capacity of AQAMA to discern differences in methylation levels was excellent as it was measured in a range of MI = 0.05 through MI = 0.6 with increments of 0.1. However, it was noted that the accuracy decreases for methylation levels containing a MI < 0.05. Single-reaction AQAMA, therefore, is likely to have less value in picking up the so-called "needle in a hay stack" from a large population of normal cells, as in micrometastatic tumor cells of colorectal cancer in lymph nodes. To use AQAMA for such purposes of detection, PCR reactions with methylated and unmethylated probes may be run separately.

On-slide SBM reduces the risk of non–cancer cell contamination compared with DNA isolated from whole PEAT sections, where normal colon tissue areas, such as muscle layers and serosal layers, are usually present. Because the studied tissue area can be confined to a specific 1 to 2 mm2 tumor sample, the DNA source is usually more homogeneous, resulting in a more reliable representation of the amount of methylated alleles in the tumor. Another important aspect in measurement of DNA methylation levels is that human error and inter-assay variability is kept to a minimum. The control that the single reaction AQAMA assay provides is that results can be analyzed directly without the need to compensate for the variability of two or three separate PCR reactions with different settings and reaction kinetics (30, 31).

In summary, AQAMA is a very sensitive assay that can reliably detect 10% differences in methylation between samples. It uses a real-time PCR technique with reported robustness and reproducibility (32). The single-reaction assay makes AQAMA suitable for the assessment of large clinical sample sizes, as required in biomarker studies. The technique can be applied to widely accessible PEAT specimens and uses a minimal amount of tissue (a single 7-µm section), making it suitable for retrospective analysis. We showed that, when combined with on-slide SBM, AQAMA forms a useful assay that can give new insights in the development of epigenetic patterns during colorectal carcinogenesis using archival paraffin-embedded specimens.


   Materials and Methods
Top
 Notes
 Abstract
 Introduction
 Results
 Discussion
 Materials and Methods
References
 
Cancer Cell Lines and Patient Specimens
For assay validation, DNA was isolated from eight cancer cell lines obtained from the American Type Culture Collection: gastric cancer (AGS, SNU-1, and KATO-III) and colorectal cancer (SW480, SW620, DLD-1, HT-29, Colo320DM, and LoVo). All cell lines were cultured and maintained according to the American Type Culture Collection recommendations. Additionally, we obtained DNA from two gastric cancer cell lines (RL-0380 and FN-0028) from the John Wayne Cancer Institute cell line bank. Seventy-two colorectal cancer PEAT blocks were obtained from the surgical pathology department of Saint John's Health Center. All human specimens were collected under research protocols reviewed and approved by the combined institutional review board of Saint John's Health Center/John Wayne Cancer Institute.

DNA Preparation, Quantitation, and SBM
Genomic DNA from cell lines was isolated as previously described (33) with DNAzol (Molecular Research Center) and quantified and assessed for purity with UV spectrophotometry. DNA from PEAT was modified according to our previously published protocol (14). Briefly, from each tissue block, a single 4-µm section was cut and stained by H&E. Seven-micrometer PEAT sections were cut consecutively and mounted on adhesive silane-coated slides for DNA studies. Adenomatous and cancer tissue components were identified and marked on the H&E-stained section by an expert surgical pathologist (R.R.T.). Sections for DNA studies were deparaffinized, soaked in 0.2 mol/L NaOH for 15 min at room temperature, incubated for 8 h in sodium bisulfite solution at 60°C, rinsed twice with H2O, soaked in 0.3 mol/L NaOH for 10 min, and desalted in double-distilled water for 2 h at 60°C. Subsequently, sections were lightly stained with hematoxylin, and specific tissue areas were carefully isolated by manual dissection under an inverted light microscope. The isolated tissue was digested in 30 µL lysis buffer containing proteinase K and Tween 20 at 50°C for 16 h. The proteinase K enzyme was than denatured at 95°C for 15 min, and the lysate was stored at –30°C. For cell line DNA, SBM was done on 1 µg DNA as described previously (34).

AQAMA Assay Design
Four sets of PCR primers and probes were designed for SBM-converted sequences. For a single marker, the assay contains four oligonucleotides. One forward (5') and one reverse (3') primer will amplify the target sequence independent from the markers methylation status, as they do not anneal to any CpGs. The methylation status is assessed by two MGB molecule–containing probes (Applied Biosystems): one methylation specific and one unmethylated specific. Forward and reverse primer sets were designed using Primer 3 software.3 The MGB probes were designed with Primer Express software (version 2.0, Applied Biosystems) with the MGB probe test document according to the recommendations. Probe length was as short as possible (≥13 bp) while keeping the annealing temperature and GC percentage of both the methylated and unmethylated probe as similar as possible. Methylated probes were FAM(6-carboxyfluorescein)–labeled, and unmethylated probes were VICtm-labeled for optimal discrimination of the two fluorescent signals by the detection system. Black hole quenchers (BHQ) were used to silence the probe fluorescent signal when not hybridized. Selected markers were "methylated in tumor" loci MINT1, MINT2, MINT12, and MINT31. The 5' primer, 3' primer, methylation-specific probe, and unmethylated-specific probe are listed as follows, respectively: MINT1 (GGTTGGGTATTTGGATTTATATTTTT, TTCTTTCAAACTCTCTCAACACTTACT, FAM-5'-AAATCCCCGCCGAAA-3'-MGB-BHQ, VIC-5'-AAAATCCCCACCAAAA-3'-MGB-BHQ), MINT2 (GTGGAAAGTGTTAGAAAAATGTGTTGTA, TCAACACTTTAACAAAATCCAAAATC, FAM-5'-TTTCGTCGAATTTT-MGB-BHQ, VIC-5'-TTTTTTTGTTGAATTTTAG-MGB-BHQ), MINT12 (GGGTTTTAGTTTTGAGG ATTAGG, CAAAACCATATCTAAATCACTAACCTT, FAM-5'-AACGACCGCAA ACA-MGB-BHQ, VIC-5'-CCAACAACCACAAAC-3'-MGB-BHQ), MINT31 (TAAAGTGAGGGGTGGTGATG, AAAAACACTTCCCCAACATCT, FAM-5'-AGGTTTCGTCGTGTTT-3'-MGB-BHQ, VIC-5'-AGGTTTTGTTGTGTTTAT-3'-MGB-BHQ).

AQAMA PCR
One microliter of modified DNA from cell lines or 1 µL of digested tumor tissue DNA was amplified in a total volume of 10 µL on a 384-well plate using fluorescence-based, real-time PCR with the ABI prism 7900HT Sequence Detection System (Applied Biosystems) and SDS software version 2.2.2. The reaction mixture for each AQAMA PCR consisted of DNA template, 0.4 µmol/L each of forward primer and reverse primer, 1.4 units of iTaq DNA polymerase (Bio-Rad Laboratories), 350 µmol/L of each deoxynucleotide triphosphate, and 0.025 pmol of each MGB probe with 5 mmol/L Mg2+. The master mix contained ROX(6-carboxy-X-rhodamine) dye for passive reference fluorescence. Samples were amplified with a pre-cycling hold at 95°C for 10 min to heat-activated DNA polymerase followed by 40 cycles of denaturation at 95°C for 15 s, and annealing and extension at 60°C for 1 min for all MINT loci. The final value of data analysis is expressed as a sample MI = methylated copy number / (methylated copy number + unmethylated copy number). Sample DNA was added to each reaction plate as controls for specificity of the methylation-specific (AGS and Raji DNA) and unmethylated-specific probe (RL-0380, FN-0028 DNA, and donor peripheral blood lymphocyte DNA). PCR and bisulfite reagent controls for nonspecific amplification are also included in each plate. Equal PCR efficiency of the methylated and unmethylated reactions was controlled by a duplicated sample that contained equal amounts of methylated and unmethylated cDNA standard.

AQAMA DNA Standard Construction
The standard curve for quantifying methylated and unmethylated copy numbers was established by amplifying five-aliquot duplicates of templates with known copy numbers (105 to 101 copies). To obtain high-quality, homogeneous, and consistent DNA standards, we synthesized DNA constructs as follows. We selected cell lines that were confirmed by MSP or bisulfite sequencing to be methylated or unmethylated at the target MINT locus. Regular PCR with only the AQAMA forward and reverse primer on the selected cell line SBM modified DNA as a template was done in a 50-µL reaction volume for 35 cycles, and the product was run on a 2% agarose gel. Specific amplification was confirmed by visualization of a single band. The band was cut out, and DNA was extracted using the QIAquick gel extraction method (Qiagen) according to the manufacturer's instructions. The completely methylated and unmethylated PCR product was ligated into a pCR 2.1-TOPO cloning vector (Invitrogen); the clones were transformed into Escherichia coli DH5-{alpha} cells; and cultures were expanded as described previously (35). Plasmids containing the target gene were purified and quantified by UV spectrophotometry.

MINT Locus CpG Methylation Status Confirmation
For assessment of the CpG methylation status of the MINT loci in a non-quantitative approach, we employed CAE-MSP as previously described (7, 36). The MINT locus methylation-specific forward (MF), methylation-specific reverse (MR), unmethylated-specific forward (UF), and unmethylated-specific reverse (UR), primers are listed here, respectively: MINT1, 5'-TTGTTAGCGTTTGTATTTTTTACGT-3' (MF), 5'-AATTACCTCGATAACTTATTTACTACGAT-3' (MR), 5'-AGGTTTTTTGTTAGTGTTTGTATTTTTTAT-3' (UF), and 5'-AAAATTACCTCAATAACTTATTTACTACAA-3' (UR); MINT2, 5'-CGTCGAATTTTAGTATTTAAGTTCGT-3' (MF), 5'-AATAATAACGACGATTCCGTACG-3' (MR), 5'-TTTTGTTGAATTTTAGTATTTAAGTTTGT-3' (UF), and 5'-AATAATAACAACAATTCCATACACC-3' (UR); MINT12, 5'-GTTTTTTCGTAGATTGTGTTTGC-3' (MF), 5'-CGTTTTATTTAATTTAAAATCCGAA-3' (MR), 5'-GGTTTTTTTGTAGATTGTGTTTGTG-3' (UF), and 5'-AAAACATTTTATTTAATTTAAAATCCAAA-3'; MINT31, 5'-ATATAATTTTGTGTATGGATTCGGC-3' (MF), 5'-AATTAAAATCGTCTCAATTCCCG-3' (MR), 5'-ATAATTTTGTGTATGGATTTGGTGA-3' (UF), and 5'-TTAAAATCATCTCAATTCCCACC-3' (UR). Primers were dye-labeled with different labels for methylation- and unmethylated-specific sets so that PCR products of the predicted base pair size could be detected by the CEQ 8000XL CAE system (Beckman Coulter, Inc.) with CEQ 8000 software version 6.0 (Beckman Coulter). MI was calculated from the detected PCR product signal intensities at the predicted base pair size as [MI = signal intensity methylated PCR product / (signal intensity methylated PCR product + signal intensity unmethylated PCR product)]. Methylation status of the samples was assigned unmethylated (U) if MI < 0.1, heterogenous (M/U) if 0.1 < MI < 0.9, or methylated (M) if MI > 0.9.

Additionally, bisulfite sequencing was also done to further confirm methylation of the AQAMA target sequences for MINT2 and MINT12, as described previously (7, 37). Briefly, the sequencing primer sets were designed to flank the region amplified by the AQAMA assay. When it was not possible to design flanking primer sets, either the forward or the reverse AQAMA assay primer was used. The primer sets used for sequencing were MINT2, 5'-TTTTAGTTTTAGTAGTTGTTTTTAATGGAA-3' (forward) and 5'-TCAACACTTTAACAAAATCCAAAATC-3' (reverse) and MINT12, 5'-GGGTTTT-AGTTTTGAGGATTAGG-3' (forward) and 5'-CAAAACCATATCTAAATCCTAACCTT-3' (reverse). The amplified PCR product was run on a 2% agarose gel, and the single band was confirmed and cut out. DNA was purified from the gel and sequenced with the dye terminator cycle sequencing quick start kit (Beckman Coulter) according to the manufacturer's instructions. Sequencing fragments were analyzed by CAE (Beckman Coulter) and analyzed by the instrument software.

Statistical Analyses
AQAMA assay performance was tested by comparing the linearity of input and measured MI by Pearson's correlation coefficient. Proportions of non-normally distributed data sets were compared using nonparametric Mann-Whitney U tests. We evaluated whether AQAMA can identify marked differences between methylation levels of MINT loci in paired colorectal cancer adenoma and cancer cells diverging from normal variance. We calculated Kurtosis of the data distribution. A positive (>0) Kurtosis denotes that fewer observations cluster near the average, and more observations populate the extremes either far above or far below the average compared with the bell curve shape of the normal distribution. To identify outlier values of the measured methylation differences, we did the Kolmogorov-Smirnov test. The one-sample Kolmogorov-Smirnov test compares the empirical distribution function with the cumulative distribution function specified by the null hypothesis (a normal distribution). A significant P here indicates that the tested data set does not adhere to the null hypothesis. Positive and negative extreme differences at which the tested data set exceeds the normal distribution were calculated.


   Notes
Top
 Notes
Abstract
 Introduction
 Results
 Discussion
 Materials and Methods
 References
 
Grant support: Gonda Laboratories and Martin H. Weil Foundation at the John Wayne Cancer Institute, Saint John's Health Center, Santa Monica, CA.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

3 Available online at http://frodo.wi.mit.edu/cgi-bin/primer3/primer3_www.cgi. Back

Received 10/23/06; revised 1/22/07; accepted 3/ 1/07.


   References
Top
 Notes
 Abstract
 Introduction
 Results
 Discussion
 Materials and Methods
 References
 

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