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Computer tool helps pinpoint risky gene mutations

Certain cancer risks can be passed down through families, the result of tiny changes in a family's genetic code. But not all genetic changes are deadly. To help medical counselors and physicians identify the mutations that pose the greatest health risks, researchers at four institutions, including Johns Hopkins, have developed and validated a new computer tool.

The system, described in the Feb. 16 issue of Public Library of Science Computational Biology, evaluates 16 "predictive features" to help answer a critical question: Is a particular mutation a harmless variation or a genetic glitch that could set the stage for cancer? In blind biochemical tests involving 36 samples containing genetic mutations whose association with breast and ovarian cancer was unknown, the computer tool demonstrated an accuracy rate exceeding 94 percent in identifying protein functions that are believed to be linked to a higher risk of cancer.

The researchers cautioned that the computer tool by itself cannot yet predict future cancer cases. But they believe it can be a fast and useful supplement to traditional biochemical tests, which are far more time-consuming, costly and labor-intensive, and do not always yield conclusive results.

"When people are diagnosed with certain types of cancer, other family members sometimes get genetic testing to find out if they, too, are predisposed to this disease," said Rachel Karchin, an assistant professor of biomedical engineering at Johns Hopkins and lead author of the journal article. "But sometimes, the standard tests find small genetic variations that may be harmful or benign. Our computational test may help pinpoint which one it is. We hope the system will eventually give counselors and doctors an important new tool to help them advise patients about whether they need to take preventive steps to keep cancer from developing."

Karchin, who earned a doctorate in computer science from the University of Californ
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Contact: Phil Sneiderman
prs@jhu.edu
443-287-9960
Johns Hopkins University
15-Feb-2007


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