FORCE's XRAYS program, funded by the CDC, is a reliable resource for young breast cancer survivors and high-risk women to navigate through breast cancer research related news and information.
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Special populations: Women without mutations in BRCA or other genes that are known to increase cancer risk. White women.
A number of factors are known to increase breast cancer risk, but some newly discovered factors have not been incorporated into breast cancer risk prediction models. This study incorporates some of these risk factors, such as genetics, smoking, and drinking, to give white women in the U.S. a more individualized breast cancer risk assessment. (6/29/16)
New research on breast cancer risk factors that can be incorporated into a model to predict a Caucasian woman’s breast cancer risk.
Research has identified lifestyle and environmental factors such as alcohol consumption, smoking, menstrual and/or reproductive history, hormone use, height, and weight that can increase breast cancer risk. We now also know about common changes in DNA called “single nucleotide polymorphisms” (SNPs) that can also increase breast cancer risk. SNPs are different from mutations in BRCA or other genes found on hereditary cancer panels, as they have been shown to only slightly modify cancer risk. Researchers want to incorporate this information into a breast cancer risk prediction model to identify those women who are at higher risk of developing breast cancer.
The researchers of this study developed a model that takes into account information updates about factors that increase breast cancer risk. This information can be used to predict which women have a slightly higher or slightly lower risk of breast cancer. Some risk factors are known as “nonmodifiable,” meaning that their influence on breast cancer risk cannot be changed or is unlikely to be changed. Genetics, family history, and some components of menstrual and or reproductive history are examples of nonmodifiable risk factors. However, the researchers observed that if women with the greatest increase in risk from nonmodifiable factors had low body mass index (BMI), did not drink or smoke, or use hormone therapy, they could still, at age 80, have comparable breast cancer risk as women who did not have an increased risk due to nonmodifiable risk factors.
Although lifestyle changes can help to lower your breast cancer risk, this particular risk prediction model has not been validated for clinical use, meaning health care providers are not currently using it to counsel women on their breast cancer risk. The study authors state that “randomized trials will be needed to understand the true effect of an intervention for the underlying population…” in other words, they do not know with certainty that altering modifiable lifestyle factors will prevent breast cancer. Women should talk to their health care provider to determine what individual measures they should take to reduce their breast cancer risk.
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Some breast cancer risk-increasing factors can be changed—for example, a woman can choose not to smoke. Others are known as “nonmodifiable,” meaning that their ability to alter breast cancer risk cannot be changed or is unlikely to be changed. Genetic factors, such as an individual’s DNA, are a good example of nonmodifiable risk. Researchers do not know how information about nonmodifiable risk factors, such as single nucleotide polymorphisms (SNPs), common DNA changes found in the general population; family history; and some components of menstrual and or reproductive history can guide cancer prevention efforts with modifiable risk factors (such as drinking, smoking, and weight).
Paige Maas and her colleagues from the National Cancer Institute and other institutions published research in the journal JAMA Oncology describing their breast cancer risk model that incorporates both modifiable and nonmodifiable risk factors to predict a woman’s 80-year breast cancer risk.
Can risk factors such as single nucleotide polymorphism and other lifestyle/environmental factors be incorporated into a breast cancer risk prediction model?
This study used information from women participating in the Breast and Prostate Cancer Cohort Consortium. Researchers used information from 17,171 white women with invasive breast cancer, and 19,862 white women without invasive breast cancer (controls) to develop a breast cancer risk model for all white women in the U.S. The information included data on 24 single nucleotide polymorphisms (SNPs) that are known to increase breast cancer risk, and that the researchers included in their model. While SNPs are a DNA alteration, they are commonly found in the general population, and do not increase cancer risk as much as a known cancer-causing gene mutation—92 common SNPs are believed to increase breast cancer risk. For SNPs not included in the Breast and Prostate Cancer Cohort Consortium, the researchers used previously published data in their model. This population did not include women with mutations in BRCA1; it did include women with specific SNPs in BRCA2 and CHEK2, but not all known mutations.
The researchers defined nonmodifiable risks as those that could not be changed or were unlikely to be changed in regard to their ability to alter breast cancer risk. However, some of the factors they considered nonmodifiable do have components that can be changed. For example, height, the age at onset of menstruation, and the age at onset of menopause are partially determined by body size, childhood diet, and exercise habits. Additionally, because the researchers did not evaluate all breast cancer risk factors—they did not collect data directly from these women—they were unable to ask about education level, breastfeeding, physical activity, breast conditions (mammographic density and benign breast disease), and hormone biomarkers (estradiol, testosterone, and prolactin). Nor did they look at genetic mutations, such as BRCA mutations, that cause disease; they looked only at single nucleotide polymorphisms. And because not all the data was available from one single study, the researchers had to combine data from multiple studies, increasing the inconsistencies in the overall data used in this study. Finally, the researchers did not include data on women with many known mutations in cancer risk-increasing genes which, depending on the gene mutation, can increase cancer risk substantially.
This study suggests that maintaining healthy lifestyle choices regarding smoking, drinking, menopausal hormone therapy use, and BMI can help to prevent breast cancer, even in women whose high risk for breast cancer is due to genetics and family history. More work, including a large clinical trial, needs to be done to confirm these findings and to see if altering these lifestyle factors results in fewer breast cancer cases. Models need to be developed for races other than Caucasian. Future studies should also include other factors that increase breast cancer risk, including genetic mutations (while the highest lifetime risk in this study was 24%, the lifetime risk for a woman with a BRCA mutation is as high as 65%). Because of the multiple sources of data used by the researchers, William Dupont and colleagues wrote in an accompanying editorial, “Since [the model] cannot be calibrated, we would not recommend that it be used to counsel individual patients.” While the authors of the editorial praise the progress achieved of the research study, they state that it would be premature to use the model to support clinical decision making. Women should talk to their health care providers to identify lifestyle changes that they can incorporate into their lives to help lower their breast cancer risk. Adopting a healthy lifestyle is important for many aspects of health in addition to lowering cancer risk.
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Dupont WD, Blume JD, Smith JR. “Building and Validating Complex Models of Breast Cancer Risk.” JAMA Oncology. Published online first on May 26, 2016.
Maas P, Barrdahl M, Joshi AD et al. “Breast Cancer Risk From Modifiable and Nonmodifiable Risk Factors Among White Women in the United States.” JAMA Oncology. Published online first on May 26, 2016.
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