Assessing Breast Cancer Risk with Genetics


One of the primary goals in oncology research is finding ways to predict each woman’s individual risk of breast cancer.

One of the primary goals in oncology research is finding ways to predict each woman’s individual risk of breast cancer. Many oncologists use the Gail model, which calculates a woman’s probability of developing breast cancer over the next 5 years and at age 90 years. The Gail model calculates probability based on answers to a series of questions about the patient’s familial history of breast cancer (mother and sisters), current age, age at first menses and first live birth, how many breast biopsies the patient has undergone and their results, and the patient’s race. Since the introduction of the Gail model in 1990, it has gone through several updates; a newer version appears on the National Cancer Institute’s Website.

In an article published in the October 2008 issue of Journal of Clinical Oncology, researchers from the Mayo Clinic presented data showing the Gail model was no more accurate “than a coin flip” at predicting the risk of invasive breast cancer for patients with atypia. Kathie Dalessandri, MD, a physician researcher from the University of California, San Francisco, and the Buck Institute for Age Research, and colleagues also questioned the reliability of the Gail model in assessing cancer risk. They applied the Gail model to 169 women in Marin County, California, given a diagnosis of breast cancer in 1997-1999, and 177 women without breast cancer (the control group). Dr. Dalessandri said patients in the two cohorts were well matched in regard to age, height, and other factors. To the researchers’ surprise, the Gail model showed “few differences” in predicting risk between the women who developed breast cancer and the controls group.

Dr. Dalessandri’s team decided to apply the OncoVue clinical breast cancer model to stored samples from the women in their study. OncoVue combines elements of the Gail model and genotypes DNA for 22 variations in single nucleotide polymorphisms in 19 genes. OncoVue performed 2.5 times better than the Gail model at accurately assessing which women were high risk and which were low risk. The OncoVue model identified 19 more of the breast cancer patients as “high risk,” which the investigators said was a 51% improvement over the Gail model.

Researchers are increasingly turning to genetic profiling to assess cancer risks and design more targeted approaches to prevention and treatment. Dr. Dalessandri predicted “within the next few years there is going to be a definite paradigm shift toward prevention by analysis of genetic material rather than traditional risk factors.”

Dr. Dalessandri noted that the FDA is currently formulating guidelines for genetic breast cancer screening tests like OncoVue and has not yet approved them. OncoVue’s manufacturer, the Oklahoma City-based Intergenetics, Inc currently offers the tests only through clinicians. Dr. Dalessandri said additional studies are underway, including one at Cedar Sinai in Los Angeles, to evaluate the OncoVue system. The OncoVue test averages $397 per patient, whereas the Gail model costs nothing. The results of ongoing and future studies will help determine whether these tests are a cost-effective method for assessing a patient’s risk of developing breast cancer. Even before it gains widespread acceptance as a tool for patients, Dr. Dalessandri said the OncoVue test could be “important for evaluating women for clinical prevention trials.”

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