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Alexander Olek

Publications and source records attributed to Alexander Olek.

4 recordsLinked to original sources
PubMed2003

Oligonucleotide-based microarray for DNA methylation analysis: principles and applications.

Gene silencing via promoter CpG island hypermethylation offers tumor cells growth advantages. This epigenetic event is pharmacologically reversible, and uncovering a unique set of methylation-silenced genes in tumor cells can bring a new avenue to cancer treatment. However, high-throughput tools capable of surveying the methylation status of multiple gene promoters are needed for this discovery process. Herein we describe an oligonucleotide-based microarray technique that is both versatile and sensitive in revealing hypermethylation in defined regions of the genome. DNA samples are bisulfite-treated and PCR-amplified to distinguish CpG dinucleotides that are methylated from those that are not. Fluorescently labeled PCR products are hybridized to arrayed oligonucleotides that can discriminate between methylated and unmethylated alleles in regions of interest. Using this technique, two clinical subtypes of non-Hodgkin's lymphomas, mantle cell lymphoma, and grades I/II follicular lymphoma, were further separated based on the differential methylation profiles of several gene promoters. Work is underway in our laboratory to extend the interrogation power of this microarray system in multiple candidate genes. This novel tool, therefore, holds promise to monitor the outcome of various epigenetic therapies on cancer patients.

CpG Islands↗
PubMed2002

Tumour class prediction and discovery by microarray-based DNA methylation analysis.

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.

Algorithms↗
PubMed2002

Diabetes: a candidate disease for efficient DNA methylation profiling.

Methylation has been implied in a number of biological processes and has been shown to vary under environmental influences as well as in age. Most results on the correlation of methylation patterns with phenotypic characteristics of cells have been obtained by analysis of very few or even single genomic fragments for methylation. However, variation of methylation may more often than not be a phenomenon that affects multiple genomic loci. The role of methylation has been most conclusively demonstrated in complex disease, with cancer being the most prominent example. The influence of aging and environmental influences such as diet seems to be on global methylation patterns, in turn exerting local effects on groups of genes. Hence, methylation seems literally to be orchestrating complex genetic systems. It could, therefore, be considered an archetypal "genomics" parameter. In consequence, technologies used to analyze methylation patterns should be as industrialized as possible to capture the local events across the entire genome. Epigenomics' research team is the first to have achieved the industrialized production of genome sequence-specific wide methylation data. Our microarray and mass-spectrometry-based detection platform currently allow the analysis of up to 50,000 methylation positions per day, for the first time making methylation data amenable to sophisticated information mining. The information content of methylation position has never been analyzed using the high-dimensional statistical methods that are recognized to be required for the analysis of, for example, mRNA expression profiles or proteomic data. As methylation patterns are nothing but a quasi-digital form of expression data, their information content must be evaluated using similar but adapted algorithms. This article presents a broad set of studies that demonstrate that methylation yields information that is comparable or even superior to the current state of the art, namely, mRNA profiling. We argue that the resulting robust, digital and-because of the highly stable nature of DNA as the analyte-more reproducible information could become the "gold standard" for clinical diagnostics and disease gene identification in age-related, environmentally influenced and epigenetic disease in general, substituting for mRNA expression.

DNA Methylation↗