Gene chips accurately diagnose four complex childhood cancers

Artificial intelligence used with gene expression microarrays for the first time

Bethesda, MD - Scientists at the National Human Genome Research Institute and Lund University in Sweden have developed a method of genetic fingerprinting that can tell the difference between several closely related types of childhood cancer. The method combines, for the first time, the cutting edge technology of gene chips with a form of artificial intelligence called an artificial neural network (ANN). The neural network automatically analyzes the large amounts of data produced by the gene chip to make a highly accurate diagnosis.

Using typical diagnostic technologies, the four types of childhood tumors used in this study can be difficult to tell apart because they look alike under the microscope; their similar appearance can lead to misdiagnosis and improper treatment. Gene chip technology, on the other hand, analyzes the pattern of activity of thousands of genes inside any cell type, including cancer cells. This approach, the researchers reported in the June issue of Nature Medicine, allows their computerized neural network to classify the different cancers with much greater accuracy.

"This research is a very exciting example of how genome technology is advancing the diagnosis of some of the most serious and challenging diseases," says Francis S. Collins, M.D., Ph.D., director of the National Human Genome Research Institute in Bethesda, Maryland. "Studies like this one should help lead to the discovery of genes that are altered in these tumors, and this information may lead to the development of effective new treatments."

The study began by simultaneously analyzing more than 6,000 known genes present in all cells. Among those, the researchers identified 41 genes expressed in the tumors that had not been previously associated with these diseases. "I am convinced that we will find good targets for new drug treatments with this kind of approach," says

Contact: Geoff Spencer
NIH/National Human Genome Research Institute

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