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Rewriting Life

Who Really Needs Chemotherapy?

New diagnostic tests can predict which patients are most likely to benefit from chemotherapy.

The vast majority of breast cancer patients who get chemotherapy don’t actually need it. But since it’s difficult to pick out the few who do, almost all patients receive chemotherapy–and with it the fatigue, nausea, and pain that often accompany the treatment.

In the Oncotype DX test, tissue samples from tumor biopsies are analyzed for their gene expression profile. (Credit: Genomic Health)

But that could be about to change. A number of new diagnostic tests that predict who is most likely to benefit from chemotherapy are now under development or being tested in clinical trials. “If these tests catch on, physicians will start to prescribe chemotherapy more sparingly and save women the toxicity of it,” says A. Raymond Frackelton, a cancer research at Brown University in Providence, RI, who is developing one such test.

Thanks to new genomics techniques, scientists can rapidly test the DNA of tumor tissues collected during biopsies. By searching for genetic and molecular markers that correlate with a particular patient’s outcome, they have identified hundreds of potential markers that signal when one patient’s tumor will be more aggressive than another’s, or more responsive to a certain type of therapy.

Collections of such markers are now being turned into diagnostic tests. One example is the Oncotype DX test, marketed by Genomic Health in Redwood City, CA, which measures the expression of 21 genes in breast cancer tumors. Originally developed to determine a patient’s prognosis–how likely she is to have a recurrence of the cancer–the test was recently shown to predict which patients will benefit from chemotherapy. According to Sheila Taube, associate director of the cancer diagnosis program at the National Cancer Institute, women classified by the test as having a high risk of recurrence showed a clear benefit from chemotherapy–their recurrence rate dropped by 27 percent. Women with a good prognosis showed little benefit.

Such tests could solve a huge problem in breast cancer treatment. Using the current guidelines for treatment of women with a certain type of early-stage breast cancer, about 90 percent of women would be prescribed chemo and hormone therapy. “But 70 percent of these women would still be alive 15 years later with surgery alone. Add hormone therapy, and it’s about 85 percent,” says Taube. “Clearly, we are over-treating patients. But because there hasn’t been a test that would predict who would recur and die from their disease, the community felt it was better to give chemo to everyone to benefit the few.”

The Oncotype test is already commercially available. “These products seem to be quite powerful,” says Harold Burstein, a breast oncologist at the Dana Farber Cancer Institute in Boston, who uses it in his clinical practice. He adds that he thinks tests that look at many genes, such as the Oncotype test, are likely to be the most successful. “Looking at one or two genes is insufficient for developing assays,” he suggests.

However, some questions remain about how best to use the results of such tests. For example, treatment choice is clear for patients who fall on the high or low end of the testing spectrum. “But for the vast majority of people–those who aren’t clearly at the highest and lowest risk–it’s still an open question,” says Sridhar Ramaswamy, a cancer specialist at Massachusetts General Hospital in Boston. The National Cancer Institute is now sponsoring a trial to address that question.

Scientists eventually hope to develop tests indicating the appropriateness of chemotherapy for other cancers, as well as tests that predict exactly what type of therapy is best. While many potential markers have been identified–a meeting of the American Association of Cancer Research in Chicago last week listed presentation after presentation of potential diagnostic markers–the real challenge is turning these markers into a truly predictive test. Says Taube, “the process of evaluating a test is much more difficult than finding some potentially interesting markers.”

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