Combining genetic data with information on other risk factors could significantly improve the ability to identify women at highest risk of developing breast cancer, according to a new study published in the Journal of the National Cancer Institute (2014; doi:10.1093/jnci/dju305).

Testing for genes that predispose to cancer such as BRCA1 and BRCA2 identifies a small percentage of women at very high risk. Prevention strategies could be improved by including information on multiple gene variants that have a small effect on risk on an individual basis but are more common in the population.

“Genetic testing has the potential to improve strategies for preventing breast cancer, and multiple, small-effect genetic risk factors could be included alongside the major breast cancer genes to assess risk. We hope it helps to start a wider discussion about how to add more accurate risk prediction tools to future prevention programs,” said Monserrat Garcia-Closas, MD, DrPH, professor of epidemiology at The Institute of Cancer Research in London, England.

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Identifying women at highest risk with a combination of genetic and other factors could allow preventive treatments and individual advice to be offered, thereby reducing the number of women who develop breast cancer. The genetic testing can be done using currently available technology.

Researchers used a combination of a questionnaire, genetic profiling, and mammography scans to calculate risk. The risk factors were genetic profile, family history of breast cancer, age at menarche, number of births and age at first live birth, oral contraceptive use, combined menopausal therapy use, body mass index, alcohol consumption, smoking status, personal history of benign breast disease, and breast tissue density.

The most effective model combined analysis of all the risk factors. When this model was applied to 50-year-old women, the model identified the 10% of women at highest risk; these women account for 32.2% of all breast cancer cases.

Researchers stressed that this was a computer modelling analysis and would need to be confirmed by studies to validate the models they used and assess actual prevention approaches.