Briefs

Researchers at the University of Maryland Greenebaum Cancer Center in Baltimore have devised a new method to differentiate and diagnose several types of colon tumors.

The method, which uses "artificial neural networks," or ANNs, to analyze thousands of genes at one time, could ultimately help doctors to identify the cancers earlier and spare some patients from unnecessary, debilitating surgery, says Stephen J. Meltzer, M.D., professor of medicine at the University of Maryland School of Medicine. Dr. Meltzer is the senior author of a study to be featured on the cover of the March issue of Gastroenterology, the journal of the American Gastroenterological Association.

Patients with Crohn�s disease and ulcerative colitis, the two forms of inflammatory bowel disease (IBD), have an increased risk of developing cancer, but the cancer can be one of two forms. "Sporadic," or common, colon cancers can often be removed without radical surgery, while IBD-related growths and cancers are much more aggressive and are generally treated by taking out the entire colon.

Until now, we had no reliable way to discriminate between these two types of lesions, especially in their early stages," says Dr. Meltzer, who is also associate director for core sciences at the University of Maryland Greenebaum Cancer Center and director of the cancer center�s Genomics Core Facility.

The researchers extracted the DNA from the samples and then used high-tech gene microarray equipment to analyze 8,064 genes to determine the level at which they were present in each colon sample, according to Florin M. Selaru, M.D., research associate in the Department of Medicine at the University of Maryland School of Medicine, director of bioinformatics and data analysis at the Greenebaum Cancer Center, and the lead author of the study.

These "gene expression" levels were translated into numbers, which were processed by "artificial neural networks,� multi-layer mathematical programs that operate much like the human brain and are capable of recognizing complex patterns in large amounts of data.

Using gene information from 27 of the 39 samples, researchers "trained" the neural network to recognize the two types of colon cancer, and then gave it information from 12 samples it had never seen. It made the correct diagnosis in all 12 cases.

The researchers were also able to reduce the number of genes necessary to make the correct diagnosis from 8,064 to 97, which would make the method easier and less expensive if this technology became more widely available.

See http://www.sciencedaily.com/releases/2002/02/020226074507.htm