Cancer is the result of accumulations of genetic changes (gene mutations, chromosomal imbalances) and epigenetic changes (DNA methylation and histone methylation/acetylation) in mammalian cells. In contrast to genetic changes, the epigenetic changes are reversible, thus presenting a promising target in drug discovery for novel epigenetic cancer therapies. Demethylating drugs cause changes in global DNA methylation that can be visualized as large reorganizations of the genome within a cell's nucleus by light microscopy. These changes can be measured as significant differential distribution patterns in the methylated DNA of individual cells using high-resolution (HR) three-dimensional (3-D) fluorescence microscopy. Thus, quantitative DNA methylation imaging (QDMI) will be a vital tool in the characterization of cancer cells in epigenetic therapy, for both drug discovery and personalized cancer diagnostics and prognostics. To the best of our knowledge there is no high-content (HC) informatics system supporting HR 3-D imaging-based evaluation of cancer cells, with automatic analysis of differential DNA methylation. For advancing this field beyond existing HC solutions, we propose our new statistics-based tools to be integrated into the methylation pattern recognition workflow. The system envisaged will perform the following tasks: (i) automatic extraction of nuclear DNA methylation patterns and features, (ii) evaluation of cell culture dissimilarity by means of Kullback-Leibler's divergence, and (iii) feature based classification to distinguish nuclei with hypermethylated DNA and hypomethylated DNA. The drug response in the cell culture will be assessed as a combined product from the two last steps, visualized as pseudo-colored maps superimposed on the original images for experts'visual and quantitative assessment. System performance will be tested and validated on the prostate cancer cell line DU145, treated with three anticancer drugs: doxorubicin, 5-azacytidine, and valproic acid, with different doses for seven days. A dedicated database will be used to archive, link and integrate image, drug, treatment and quantitative data. The HC Informatics System will support the evolution of image-based DNA methylation analysis from a proven technique into a diagnostic system.
We aim to bring a fundamental contribution to a quickly growing research field that combines multidisciplinary efforts required for producing fast, automated image-based techniques. The goal is high-throughput epigenetic screening of thousands of mammalian cells in their native environment, with long-term potential benefits including: (i) finding targets for epigenetic treatment and predicting responsiveness to cancer therapy, (ii) assessing nutritional and environmental factors that impact the epigenomic makeup of cells, (iii) characterization of complex epigenomics-related diseases on a cellular basis, and (iv) enabling the use of cellular models in epigenetic drug development.
The aim of this research is to develop a high-content analysis method for imaging-based cancer cell screening and evaluation of drug effects in epigenetic cancer treatment. Automatic analysis and quantification of global DNA methylation patterns in individual cells allows the evaluation of drug response and treatment effects.
