Survivors of breast cancer face a substantially increased risk of developing contralateral breast cancer (CBC). We assessed whether risk prediction models for CBC are improved by integrating mammographic density (MD) and polygenic risk scores (PRS). We analyzed data from 399 European-ancestry breast cancer survivors in the WECARE Study, an international, population-based case-control study. Cases were women who developed CBC, and controls were women with unilateral breast cancer (UBC). All participants had genome-wide genotyping and MD measurements at three intensity levels (Cumulus, Altocumulus, and Cirrocumulus) using the CUMULUS software. A weighted PRS was constructed comprised of 64 previously identified genome-wide significant single nucleotide polymorphisms (SNPs) associated with MD (PRSMD). Linear and logistic regression models were used to assess the associations between PRSMD, MD measurements, and CBC risk, adjusting for potential confounders. PRSMD was significantly associated with Cumulus and Altocumulus densities, but not Cirrocumulus. In multivariable-adjusted predictive models, the inclusion of PRSMD improved adjusted R-squared values for Cumulus (from 20. 6% to 22. 8%) and Altocumulus (22. 7% to 24. 7%). However, for Cirrocumulus the PRSMD was not a significant predictor of CBC risk, with an effect estimate of 0. 27 (95% CI: -0. 9, 1. 4; P = 0. 69). PRSMD was not independently associated with CBC risk and adding it to MD models resulted in only small, non‑significant gains in AUC. Exploratory interaction analyses did not indicate that PRSMD modified the association between MD and CBC risk. MD remains a robust independent predictor of CBC risk. Although PRSMD captures inherited predisposition to MD, the current PRS explains only a small fraction of MD variance and does not enhance CBC risk prediction beyond measured MD. Further research is needed to elucidate the genetic underpinnings of MD and their relevance to CBC susceptibility.
Naderi et al. (Wed,) studied this question.