Common Genetic Variants Modestly Improve Breast Cancer Risk Models
More than a dozen common genetic variants have been linked to breast cancer in recent years, largely through genome wide association studies. While each variant—a single nucleotide polymorphism or SNP—contributes modestly to a woman’s overall inherited risk of the disease, some researchers have suggested (in the Journal of the National Cancer Institute and New England Journal of Medicine) that current breast cancer risk models could be improved by profiling women for panels of these variants.
This idea has now been tested for the first time in a large population of women. The researchers found that a model based on the profile of 10 common SNPs (“snips”) associated with breast cancer risk did a better job of predicting which women would develop the disease than a standard risk-prediction model based on a questionnaire that asks about traditional risk factors, though the improvement was minor.
“We found that adding SNPs to the questionnaire model made a noticeable difference in the model’s performance—that is, it improved the ability to stratify women according to different levels of risk,” said lead investigator Dr. Sholom Wacholder of NCI’s Division of Cancer Epidemiology and Genetics (DCEG). “We also found, however, that the stratification provided by the model was still not adequate to inform clinical practice.”
The addition of the SNPs to the standard model led to a tool that was slightly better than the other two models (SNPs alone or the standard model alone). But even with this improvement, it is not yet possible to identify women at reduced or increased risks of the disease in a clinically useful way, the researchers concluded in the March 18 New England Journal of Medicine.
New Genetic Information
“One way to look at the results,” said Dr. David Hunter, the study’s senior author and director of the Program in Molecular and Genetic Epidemiology at the Harvard School of Public Health, “is that the new genetic information, acquired in just the last 3 years, gives as much predictive value as the classic risk factors that were the fruits of the previous many decades of breast cancer epidemiological research.”
The study compared the SNP-based model with NCI’s Breast Cancer Risk Assessment Tool, which takes into account a woman’s medical and reproductive history as well as her family history of the disease. Commonly known as the Gail model, this tool is used by health care providers to discuss breast cancer risk with their female patients.
The addition of the genetic variants did shift a small proportion of women from one risk group to another. But only 34 percent of the women who actually had breast cancer in the study were assigned to the top 20 percent risk group by the combination model, the researchers found. In a highly effective risk-prediction model, 80 percent of the women with breast cancer would have been assigned to this group, according to the researchers.
“The message of this study is that current genomic markers of risk for breast cancer do not offer significant information beyond traditional methods of estimating risk,” said Dr. Kenneth Offit, chief of Clinical Genetics Service at Memorial Sloan-Kettering Cancer Center, who was not involved in the research. “The study also reminds us that traditional methods of breast cancer risk assessment need to be further refined.”
Effective models for assessing breast cancer risk would allow doctors to determine where an individual woman falls along a continuum of risk levels. Women at the highest levels of risk could consider aggressive prevention measures, such as more frequent mammograms and taking the drug tamoxifen.
For women seeking advice on their individual risk of breast cancer, it is too early to incorporate SNP testing into a counseling procedure, although such tests for this purpose are already advertised on the Internet, noted the authors of an accompanying editorial, Drs. Peter Devilee of Leiden University Medical Center and Matti Rookus of the Netherlands Cancer Institute.
To compare risk estimates, the study authors assembled genetic and clinical information on 5,590 women with breast cancer and nearly 6,000 without the disease. The women in the study were predominantly white, between the ages of 50 and 79, and had enrolled in four prospective studies in the United States and one case-control study in Poland.
“We have concluded that more knowledge about what causes breast cancer is needed—both from genetic and environmental epidemiology,” said co-author Dr. Patricia Hartge of DCEG. “Until we have that information, the predictive accuracy of these models is going to be limited.”
Tip of the Iceberg
Clearly, additional SNPs and other types of inherited genetic variants that influence breast cancer risk, including structural changes to chromosomes and gene mutations, remain to be found. The 10 SNPs in the study account for less than 5 percent of the familial risk for breast cancer in the population, Drs. Devilee and Rookus said. By comparison, mutations in the breast cancer susceptibility genes BRCA1 and BRCA2 are rare but account for nearly 25 percent of the familial risk for the disease.
“This study provides an important and sobering look at the complex nature of the inherited genetic contributions to breast cancer,” said coauthor Dr. Stephen Chanock, who directs the Laboratory of Translational Genomics in DCEG. “With this preliminary look, we are starting to see the usefulness of SNPs, but there are additional common, uncommon, and rare genetic variants still to be discovered.”
In terms of genetic variants associated with breast cancer, the 10 SNPs represent “no more than the tip of the iceberg,” the editorial authors noted. As a more complete picture emerges over time, researchers can use the new information to improve the models, Dr. Chanock said.
Meanwhile, researchers are trying to uncover the precise sources of the genetic risk captured by each SNP. The SNPs are essentially markers of chromosome regions that harbor genetic variation associated with breast cancer risk. Information about the functional effects of the SNPs could ultimately lead to more accurate risk predictions, the researchers said.
The best way to think about the study, said Dr. Offit, is as part of a process of developing evidence for starting to translate genomic discoveries into the practice of preventive medicine. Once a more complete catalog of genetic variations involved in breast cancer risk has been developed, it will be critical to consider the effects of these markers together and not individually, he continued.
“When this range of studies is completed,” Dr. Offit added, “it may then be possible to provide genomic profiles to personalize the prevention of breast cancer.”
—Edward R. Winstead