Nucleic Acids Research, 2002, Vol. 30, No. 5 e21
© 2002 Oxford University Press
Tumour class prediction and discovery by microarray-based DNA methylation analysis
Information Sciences, 1Biomedical Research and Development and 2Technology Development, Epigenomics AG, Berlin, Germany, 3Department of Urology, Heinrich Heine University, Düsseldorf, Germany, 4Max Planck Institute for Infection Biology, Berlin, Germany and 5Department of Haematology, Oncology and Tumour Immunology, Charité, Campus Berlin-Buch, Robert-Rössle Klinik, Humboldt University, Berlin, Germany
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.
* To whom correspondence should be addressed. Tel: +49 30 243450; Fax: +49 30 24345 555; Email: olek{at}epigenomics.com The authors wish it to be known that, in their opinion, the first six authors should be regarded as joint First Authors
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