Apr. 4, 2008 Combining a breast cancer patient's clinical characteristics with a genomic profile of her tumor may provide important information for predicting an individual patient's prognosis and accurately guiding treatment options, according to a new study led by researchers in the Duke Comprehensive Cancer Center (DCCC) and Duke's Institute for Genome Sciences & Policy (IGSP).
"Our goal is to treat patients on a more individualized basis, matching the right drugs with the right patients," said Anil Potti, M.D., an oncologist and researcher in the DCCC and the IGSP. "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."
The findings appear in the April 2, 2008 issue of the Journal of the American Medical Association. The study was funded by the Jimmy V Foundation, the American Cancer Society and the Emilene Brown Research Fund.
Researchers looked at almost 1000 breast tumor samples, and corresponding patient data, and applied existing technology -- a computerized system called Adjuvant! -- to assess clinical characteristics and make predictions of recurrence based on them. By then comparing gene expression in these tumor samples, the researchers were able to see specific genomic patterns among patients with aggressive cancers, and those whose cancers were less likely to recur.
"We knew from previous studies that Adjuvant! tends to overestimate disease recurrence in younger patients," Potti said. "We hypothesized that genomic profiling could be a complementary tool that would more precisely define clinical outcomes, and could also help to aid in selecting the right drug for a given patient."
By using the clinical and genomic tools together and cross-comparing data, the researchers were able to not only say that a particular patient has a "high" risk of recurrence, but they could be more specific; for instance, they could predict that a particular patient was 90 percent likely to see her cancer recur, Potti said.
"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 percent likelihood of recurrence," he said. "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."
The findings have already been put into practice as part of several clinical trials at Duke for cancer patients. A tumor's genomic make-up is being used to dictate the choice between a traditional chemotherapy regimen and an alternate drug that is more likely to benefit an individual patient. One such trial involving almost 300 patients with breast cancer is expected to start at Duke this spring.
Other researchers involved with this study include Chaitanya Acharya, David Hsu, Kelly Marcom, Carey Anders, Ariel Anguiano, Kelly Salter, Kelli Walters, Bradford Perez, Richard Redman, Sascha Tuchman, Cynthia Moylan, Sayan Mukherjee, William Barry, Holly Dressman, Geoffrey Ginsburg, Katherine Garman, Gary Lyman and Joseph Nevins.
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