Analysis and Visualization of Gene Expression Microarray Data in Human Cancer Using Self-Organizing Maps

cDNA microarrays permit massively parallel gene expression analysis and have spawned a new paradigm in the study of molecular biology. One of the significant challenges in this genomic revolution is to develop sophisticated approaches to facilitate the visualization, analysis, and interpretation of the vast amounts of multi-dimensional gene expression data. We have applied self-organizing map (SOM) in order to meet these challenges. In essence, we utilize U-matrix and component planes in microarray data visualization and introduce general procedure for assessing significance for a cluster detected from U-matrix. Our case studies consist of two data sets. First, we have analyzed a data set containing 13,824 genes in 14 breast cancer cell lines. In the second case we show an example of the SOM in drug treatment of prostate cancer cells. Our results indicate that (1) SOM is capable of helping finding certain biologically meaningful clusters, (2) clustering algorithms could be used for finding a set of potential predictor genes for classification purposes, and (3) comparison and visualization of the effects of different drugs is straightforward with the SOM. In summary, the SOM provides an excellent format for visualization and analysis of gene microarray data, and is likely to facilitate extraction of biologically and medically useful information.     

Integration of genomic technologies for accelerated cancer drug development

New technologies have greatly increased the scientist's ability to investigate complex molecular interactions that occur in cancer development and to identify genetic alterations and drug targets. However, these new capabilities have not accelerated drug development efforts; rather, they may be contributing to increased research and development costs because the large number of new drug targets discovered through genomics need to be investigated in great detail to characterize their putative functional involvement in the disease process. One solution to this bottleneck in functional analysis is the use of high-throughput technologies to produce efficient processes that can rapidly handle the large flood of information at every stage of disease. This review examines the use of new and emerging DNA, tissue, and live-cell transfection microarray technologies that can be used to discover, validate, and translate information resulting from the completion of the Human Genome Project.