April 1, 2008 -- Each woman's breast cancer has several genetic signatures that help predict her risk -- and help pinpoint the most effective treatments.
Breast cancers with the same clinical features have very different genetic profiles. These "gene expression signatures" can be used to predict an individual woman's risk of a bad outcome -- and to identify treatments most likely to help individual patients.
That's the conclusion of Duke University researchers Anil Potti, MD, and colleagues, who looked at whether various breast cancer gene signatures could help individualize treatment. They report their findings in the April 2 issue of The Journal of the American Medical Association.
"There is an opportunity to apply genomic signatures that can best capture the biological nature of a tumor on the patient," Potti and colleagues write. "Incorporating that knowledge with relevant clinical and pathological data may improve prognostic ability, obtain a better understanding of the underlying biology of breast cancer, and identify effective therapeutic options for an individual patient."
Potti and colleagues identified six clusters of gene profiles linked to risk of cancer recurrence in 573 patients. They then used these clusters to predict outcomes in 391 additional patients.
"Our goal is to treat patients on a more individualized basis, matching the right drugs with the right patients," Potti says in a news release. "The combination of these two methods, one of which uses the clinical description of patient's breast cancer and the other which looks at gene expression at a molecular level in a patient's tumor, may allow us to do that with unprecedented accuracy. This represents a robust approach to personalizing treatment strategies in patients suffering from breast cancer."
Current systems identify breast cancer patients as having low, intermediate, or high risk of seeing their cancer come back. The new system promises more accuracy. For example, Potti says, instead of saying a patient is at "high risk," doctors could say she has a 90% risk of recurrence.
"This is important because with this data, we might decide to treat this person more aggressively even than someone else who is considered 'high risk' but may have only a 60% likelihood of recurrence," he says. "Moreover, we can identify specific options for chemotherapy in such patients as well, by correlating gene expression in a tumor with its response, or non-response, to certain chemotherapies."