Selective estrogen receptor modulators (SERMs) decrease the risk of breast cancer in high-risk women. We sought to determine the association between germline DNA methylation and single nucleotide polymorphism (SNP) biomarkers and benefit from SERM prevention.

DNA from blood at time of entry on NSABP P-1 or P-2 was utilized from a matched case–control study of women receiving a SERM. Methylation was determined with Illumina Infinium Methylation EPIC BeadChipv2 that targets 937,055 sites. Differential methylation analysis was performed utilizing the Wilcoxon signed-rank test. Analysis was performed on 22 potentially relevant genes. Five modeling strategies for predicting case–control status were performed. Polygenic risk scores (PRS) were integrated with methylation data. Risk SNPs and methylation data were jointly analyzed.

DNA methylation data were available for 1706 participants (587 cases, 1119 controls). Wilcoxon signed-rank analysis was performed on 887,318 CpG sites and 25,746 methylated regions. Six CpGs were significantly associated with breast cancer risk with smallest p-value of 3.70E−08 for the top CpG that was related to AFF3, which has been associated with resistance to tamoxifen. Dfferences in beta (methylation) values between cases and controls were small. No CpG regions were significantly associated. CYP1A1 and CYP3A7 were significantly associated with case–control status. Five prediction modeling strategies revealed median AUCs of 0.524–0.590. Integration of PRS with methylation did not improve predictive performance. Joint analysis of two previously published SNPs (related to ZNF423 and CTSO) and cg11423397 revealed an odds ratio of 16.5 for differences in breast cancer risk for those with all protective factors versus all risk factors.

We identified differentially methylated CpG sites that achieved statistical significance but minimal differences in beta values. No CpG regions were significant. Differential methylation in two CYP genes was identified but of unclear importance. No improvement in performance from prediction models or integrating PRS and methylation was identified. Joint analysis of cg11423397 and SNPs from ZNF423 and CTSO suggest further discrimination of breast cancer risk. While current methylation approaches utilizing germline DNA show limited predictive utility for breast cancer risk in women receiving a SERM, our results point to specific methylation and genetic biomarkers that warrant further study.