According to a recent study published by JAMA, single-nucleotide polymorphisms (SNPs) risk panels can improve predicting breast cancer and to ultimately identify women who may benefit from preventive therapy of additional in-depth mammogram screenings.
"Single-nucleotide polymorphisms (SNPs) have demonstrated an association with breast cancer susceptibility, but there is limited evidence on how to incorporate them into current breast cancer risk prediction models," according to the study's lead author Elke M. van Veen, MSc, from the Manchester Academic Health Science Center at the University of Manchester in the United Kingdom.
According to study methods, researchers aimed to determine whether a panel of 18 SNPs can predict breast cancer from traditional risk factors and mammographic density. A total of 9,363 women between 46 and 73 years old were enrolled for the study from October 2009 to June 2015, all without a previous breast cancer diagnosis. Follow-up appointments continued through January 5, 2017, according to the study.
"The predictive ability of SNP18 for breast cancer diagnosis (invasive and ductal carcinoma in situ) was assessed using logistic regression odds ratios per interquartile range of the predicted risk," researchers wrote.
Of the 9,363 women, 466 were diagnosed with breast cancer (271 prevalent and 195 incident cases). SNP18 was predictive when unadjusted or adjusted for mammographic density and classic factors, according to study results. Additionally, a combined risk assessment noted that 18 percent of the sub cohort was at a five percent or greater 10-year risk ("compared with 30 percent of all cancers, 35 percent of interval-detected cancers, and 42 percent of stage 2+ cancers"). However, researchers explained that 33 percent of the sub-cohort were at less than a two percent risk ("but accounted for only 18 percent, 17 percent and 15 percent of the total, interval and stage 2+ breast cancers").
"SNP18 added substantial information to risk assessment based on the Tyrer-Cuzick model and mammographic density," the researchers concluded. "A combined risk is likely to aid risk-stratified screening and prevention strategies."