Mammography is sufficient to predict breast cancer risk in women aged ≥ 60, but in younger women, combining mammography and genetic testing improves the assessment, according to a retrospective case-control study published in Proceedings of AMIA Joint Summits on Translational Science. “Understanding the predictive power of imaging features and genetic variants in different age groups has the potential to aid clinicians in determining what tests can be used to improve information about the likelihood of malignancy,” said the authors.
The investigators developed logistic regression-based predictive models that compared diagnostic mammography results, selected genetic variants (low-penetrance single-nucleotide polymorphisms [SNPs]), and a combination of the two tests in women aged ≥ 60 and women < 60.
The researchers used data from women enrolled in the Marshfield Clinic Personalized Medicine Research Project (Marshfield PRMP) in Marshfield, Wisconsin. These women had provided a blood sample for genetic testing, answered a brief survey, and given consent for their results to be linked to their medical record. Women who had also undergone diagnostic mammography for breast cancer, had a breast cancer biopsy within a year of the mammogram, and had provided a blood sample that could be analyzed for breast cancer risk were included in the study. Women with a confirmed diagnosis of breast cancer served as cases. Controls had a benign breast biopsy and did not have a breast cancer diagnosis. The cases and controls were age-matched within 5 years of one another. Subjects who had known BRCA1 or BRAC2 mutations, non-white patients, and cases where Breast Cancer Imaging Reporting and Data System (BI-RADS) features were unavailable were excluded.
A total of 738 women aged 29 to 90 (mean age 62) were included in the analysis. The researchers chose one diagnostic mammogram to analyze for each case and each control and found that among women ≥ 60, mammography (area under the curve [AUC] = 0.744, 95% CI = 0.740-0.748) predicted breast cancer risk better than genetic testing alone (AUC = 0.540, 95% CI = 0.532-0.549) or genetic testing combined with mammography (AUC = 0.713, 95% CI =0.705-0.720). The results were both statistically and clinically significant. In women < 60, however, the combined model yielded the most statistically significant predictive results (AUC = 0.724, 95% CI = 0.718-0.731) compared to either mammography alone (AUC = 0.690, 95% CI = 0.686-0.695) or genetic testing alone (AUC = 0.696, 95% CI = 0.692-0.700). This makes sense, according to the authors, since younger women who develop breast cancer are more likely to have a genetic propensity to the disease than are older women. They also noted that genome-wide association studies have allowed for identification of less-well-known, low-penetrance SNPs that are associated with breast cancer risk, in addition to being linked to early-onset disease and poorer prognosis.
The study expands on previous research about the utility of using patient demographics and different tests to analyze breast cancer risk, as well as utilizing SNPs to stratify risk.
The study was limited by a relatively small sample size, which required the researchers to include data from 1989 to 2010 to enroll a sufficient number of cases and controls. There was also a lack of non-Caucasian women among the cases and controls; this was because there were not enough women of other races included in the registry who were eligible for the study, but it limits the ability to generalize the results to other racial and ethnic groups. The researchers also noted that the BI-RADS lexicon has evolved over and since the study period, leading to a possible underestimation of the benefit of mammography alone.