Polygene Test Can Predict Risk for Breast Cancer

Ricki Lewis, PhD

April 14, 2015

A new polygenic risk score (PRS) can be used to predict a woman's risk of developing breast cancer, especially when it is used in conjunction with information about breast tissue density and family history.

The PRS is described in the May issue of the Journal of the National Cancer Institute.

"This genetic risk factor adds valuable information to what we already know can affect a woman's chances of developing breast cancer," said study coauthor Celine Vachon, PhD, an epidemiologist at Mayo Clinic in Rochester, Minnesota. "We are currently developing a test based on these results, and although it isn't ready for clinical use yet, I think that within the next few years we will be using this approach for better personalized screening and prevention strategies for our patients," she said in a statement.

PRS Adds Info, Improves Prediction

Many factors are associated with an increased risk for breast cancer. High breast density reduces the sensitivity of mammography and directly contributes to risk, the researchers note. In addition to more than a dozen single genes that, when mutant, cause cancer susceptibility, such as BRCA1 and BRCA2, nearly 80 genes have been identified with common variants that contribute to overall breast cancer risk. Variants in these genes account for approximately 14% of familial breast cancer risk, they note.

The PRS, developed by Dr Vachon and an international team of researchers, is based on 76 single-nucleotide polymorphisms (SNPs). Individual SNPs confer small but not necessarily equal components of overall risk for a condition or trait. The SNPs used in the PRS were identified in studies for a variety of cancers and other disease phenotypes, and because they are associated with breast cancer, were made available on a customized microarray device.

The researchers combined the PRS with breast-density measurements (using the Breast Imaging Reporting and Data System) from three clinical studies that involved 2397 healthy women and 1643 breast cancer patients. They found that the PRS and breast density function are independent variables, which is important in not overestimating risk, they explain.

Next, the researchers examined how the PRS affects the risk-prediction model from the Breast Cancer Surveillance Consortium (BCSC), which calculates risk on the basis of breast density, family history of breast cancer, breast biopsy findings, age, and ethnicity. The 5-year risk prediction, calculated using the BCSC model, was compared with and without the PRS in 334 patients enrolled in the Mayo Mammography Health Study.

For women with extremely dense breasts, PRS further stratified risk. In this group, women with the lowest PRS had a low risk of developing breast cancer (odds ratio [OR], 0.91; 95% confidence interval [CI], 0.53 - 1.56). The risk for breast cancer was 2.7-fold higher in women with extremely dense breasts and a PRS in the highest quartile (95% CI, 1.74 - 4.12) than in women with scattered fibroglandular breast density and an average PRS.

Adding genetic information from the PRS to the 5-year risk prediction would have placed 11% of the women who eventually developed cancer into a higher risk category and would have qualified them for more intensive care, such as MRI, chemoprevention, and prophylactic surgery, the researchers report.

"Our results suggest that genetic risk factors and mammographic density together add significant information to accurately identify women at risk of breast cancer," Dr Vachon told Medscape Medical News. Her group and other researchers are currently developing risk calculators that include density measures and genetic risk factors.

More accurate breast cancer predictions will have clinical repercussions as the risk/benefit ratios of preventive strategies shift, Dr Vachon predicted. "Women at high risk may benefit from more intensive screening, such as magnetic resonance or molecular breast imaging, and those at the lowest risk with low density may be able to reduce their frequency of screening," she said.

"Better risk prediction could also improve targeting of chemoprevention. Tamoxifen, raloxifene, and aromatase inhibitors have been shown to be effective in substantially reducing the risk of breast cancer, especially in those with risks above 3%. The highest-risk women may be more motivated to take preventive therapies and accept their potential complications if they are confident they are at high risk of breast cancer, and thus will receive the greatest benefit from them," Dr Vachon added.

A limitation of this study is that most of the participants were white. The researchers caution that larger studies need to be conducted to evaluate the ability of these models to estimate risk in the general population.

J Natl Cancer Inst. 2015;107:dju397. Abstract


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