ABSTRACT
DNA methylation-based biomarkers have been discovered that could potentially be used for the diagnosis of cancer by detection of circulating, tumor-derived DNA in bodily fluids. Any methylation detection assay that would be applied to these samples must be capable of detecting small amounts of tumor DNA in the presence of background normal DNA. We have developed a real-time PCR assay, called HeavyMethyl, that is well suited for this application. HeavyMethyl uses methylation-specific oligonucleotide blockers and a methylation-specific probe to achieve methylation-specific amplification and detection. We tested the assays on unmethylated and artificially methylated DNA in order to determine the limit of detection. After careful optimization, our glutathione-S-transferase pi1 and Calcitonin assays can amplify as little as 30 and 60 pg of methylated DNA, respectively, and neither assay amplifies unmethylated DNA. The Calcitonin assay showed a highly significant methylation difference between normal colon and colon adenocarcinomas, and methylation was also detected in serum DNA from colon cancer patients. These assays show that HeavyMethyl technology can be successfully employed for the analysis of very low concentrations of methylated DNA, e.g. in serum of patients with tumors.
Subject(s)
DNA Methylation , DNA/analysis , DNA/metabolism , Oligonucleotides/metabolism , Polymerase Chain Reaction/methods , Adenocarcinoma/blood , Adenocarcinoma/diagnosis , Adenocarcinoma/genetics , Base Sequence , Calcitonin/genetics , Colonic Neoplasms/blood , Colonic Neoplasms/diagnosis , Colonic Neoplasms/genetics , DNA/genetics , DNA Primers/antagonists & inhibitors , DNA Primers/genetics , DNA Primers/metabolism , Glutathione S-Transferase pi , Glutathione Transferase/genetics , Humans , Isoenzymes/genetics , Molecular Sequence Data , Oligonucleotides/genetics , Sensitivity and Specificity , Sulfites/metabolism , Time FactorsABSTRACT
Aberrant DNA methylation of CpG sites is among the earliest and most frequent alterations in cancer. Several studies suggest that aberrant methylation occurs in a tumour type-specific manner. However, large-scale analysis of candidate genes has so far been hampered by the lack of high throughput assays for methylation detection. We have developed the first microarray-based technique which allows genome-wide assessment of selected CpG dinucleotides as well as quantification of methylation at each site. Several hundred CpG sites were screened in 76 samples from four different human tumour types and corresponding healthy controls. Discriminative CpG dinucleotides were identified for different tissue type distinctions and used to predict the tumour class of as yet unknown samples with high accuracy using machine learning techniques. Some CpG dinucleotides correlate with progression to malignancy, whereas others are methylated in a tissue-specific manner independent of malignancy. Our results demonstrate that genome-wide analysis of methylation patterns combined with supervised and unsupervised machine learning techniques constitute a powerful novel tool to classify human cancers.
