New algorithm matches any tumor cells to best possible anti-cancer treatments

CHARLOTTESVILLE, Va., July 23, 2007 -- Cancer patients dont have time to waste. Many go through several different treatments, however, to find one that is more effective against their particular type of tumor.

Thus, an algorithm that could help rapidly sort molecular information about a patients particular tumor and could help match this information to the right drug treatment would be a breakthrough of enormous value. Dan Theodorescu, M.D., Ph.D., a University of Virginia oncologist and cancer biologist, and Jae Lee, Ph.D., a computational biologist and bioinformatics statistician, have pioneered just such a system. This work involved collaboration with colleagues at the National Cancer Institute, GeneLogic Inc. and the University of Virginia Computer Sciences Department. They published their results the week of July 23 in the Early Edition of the Proceeedings of the National Academy of Sciences, found online.

Using a panel of 60 diverse, human cancer cell lines from the National Cancer Institute (NCI-60), the researchers devised and tested an algorithm designed to match the best potential treatment(s) for a particular tumor in a particular patient.

Previously, the NCI-60 cell lines were used to screen more than 100,000 chemical compounds for their anticancer activity. These drug responses, however, were not definitely linked to clinical effectiveness in patients. Another issue is that the 60 cell lines did not include all important cancer types (for example, certain bladder cancers, lymphomas, and small cell lung cancers were not among the 60 lines studied).

The researchers investigated whether the drug sensitivity data of the 60 cancer cell lines could be extrapolated into useful information on other tumors or cancer cell lines. In fact, they found that their coexpression extrapolation (COXEN) system could be used to accurately predict drug sensitivity for bladder cancer cell lines to two common chemotherapies, cis

Contact: Mary Jane Gore
University of Virginia Health System

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