The researchers reported their findings using breast cancer as a test case in the April 26, 2004, online early edition of the Proceedings of the National Academy of Sciences.
"Currently, it is primarily traditional clinical information alone that aids in understanding a patient's risk profile," said Mike West, Ph.D., Arts & Sciences professor of statistics and decision sciences at Duke and lead author on the study. "However, the resulting predictions typically lump patients into broad categories. Access to detailed genomic information now provides the opportunity to move far beyond this -- toward customized risk predictions and prognoses more widely, for the individual patient."
Nevertheless, most previous studies that have focused on developing genomic-based predictors of cancer recurrence risk have only broadly defined patients as high versus low risk, leaving considerable room for error about an individual's true chance of recurrence, the researchers said. The power of the Duke team's approach to improve such predictions lies in the combined use of multiple sources of clinical and genomic data, they said.
In their case study of breast cancer, the Duke team developed methods that utilize diverse information including traditional clinical variables, such as lymph node and estrogen receptor status, and multiple, complex patterns of gene activity, or "genetic fingerprints," of a patient's tumor. They integrated these data to formulate unique predictions about individual patients' recurrence risk. While the Duke study focused on patients with breast cancer, the approach is applicable more broadly to any form of cancer and can in
Contact: Kendall Morgan
Duke University Medical Center