Rather than testing for individual marker genes or proteins, researchers at the University of California, San Diego (UC San Diego) and the Moores UCSD Cancer Center have evidence that groups, or networks, of interactive genes may be more reliable in determining the likelihood that a form of leukemia is fast-moving or slow-growing.
One of the problems in deciding on the right therapy for chronic lymphocytic leukemia (CLL) is that it is difficult to know which type a patient has. One form progresses slowly, with few symptoms for years. The other form is more aggressive and dangerous. While tests exist and are commonly used to help predict which form a patient may have, their usefulness is limited.
Han-Yu Chuang, a Ph.D. candidate in bioinformatics and systems biology program in the department of bioengineering in the UC San Diego Jacobs School of Engineering, senior author Thomas Kipps, M.D., Ph.D., professor of medicine and deputy director for research at the Moores UCSD Cancer Center, and their colleagues analyzed the activity and patterns of gene expression in cancer cells from 126 patients with aggressive or slow-growing CLL. The researchers, using complex algorithms, matched these gene activity profiles with a huge database of 50,000 known protein complexes and signaling pathways among nearly 10,000 genes/proteins, searching for "subnetworks" of aggregate gene expression patterns that separated groups of patients. They found 30 such gene subnetworks that, they say, were better in predicting whether a disease is aggressive or slow-growing than current techniques based on gene expression alone.
They presented their results Monday, December 8, 2008 at the annual meeting of the American Society of Hematology in San Francisco.