Association of mammographic density with blood DNA methylation

Abstract

Background: Altered DNA methylation may be an intermediate phenotype between breast cancer risk factors and disease. Mammographic density is a strong risk factor for breast cancer. However, no studies to date have identified an epigenetic signature of mammographic density. We performed an epigenome-wide association study of mammographic density.

Methods: White blood cell DNA methylation was measured for 385 postmenopausal women using the Illumina Infinium MethylationEPIC BeadChip array. Differential methylation was assessed using genome-wide, probe-level, and regional analyses. We implemented a resampling-based approach to improve the stability of our findings.

Results: On average, women with elevated mammographic density exhibited DNA hypermethylation within CpG islands and gene promoters compared to women with lower mammographic density. We identified 250 CpG sites for which DNA methylation was significantly associated with mammographic density. The top sites were located within genes associated with cancer, including HDLBP, TGFB2, CCT4, and PAX8, and were more likely to be located in regulatory regions of the genome. We also identified differential DNA methylation in 37 regions, including within the promoters of PAX8 and PF4, a gene involved in the regulation of angiogenesis. Overall, our results paint a picture of epigenetic dysregulation associated with mammographic density.

Conclusion: Mammographic density is associated with differential DNA methylation throughout the genome, including within genes associated with cancer. Our results suggest the potential involvement of several genes in the biological mechanisms behind differences in breast density between women. Further studies are warranted to explore these potential mechanisms and potential links to breast cancer risk.

Keywords: DNA methylation; breast cancer; epigenetics; epigenome-wide association study; illumina epic array; mammographic density; postmenopausal.

Figure 1.

Summarizes the data pre-processing and analysis pipeline. Methylation array data were pre-processed according to recommended steps for Illumina methylation BeadChip data [34]. All data processing and analysis was performed in R, version 3.5.1 [35]

Figure 2.

Relationship between mammographic density and genome-wide average methylation, for all probes on HumanMethylationEPIC BeadChip (Figure 2a), for probes in CpG Islands (Figure 2b) and CpG shores (Figure 2c), and for probes by chromatin state predicted by ChromHMM from ENCODE data for the GM12878 cell line: promoters (Figure 2d), enhancers (Figure 2e), transcribed (figure 2f), repressed (Figure 2g) and insulators (Figure 2h). Dashed lines show 95% confidence intervals.

Figure 3.

Volcano plot of results from probe-level differential methylation analysis. Top 10 probes by methylation difference (delta-β) and p-value are labelled with the gene or Illumina identifier (if intergenic).

Figure 4.

Boxplot of methylation (β) value by mammographic density category for top 4 intragenic probes by p-value. A: cg01837485 in HDLBP (p = 1.7 x 10⁻[8]), B: cg06899755 in TGFB2 (p = 2.0 x 10⁻[8]), C: cg27631039 in CCT4 (p = 2.9 x 10⁻[8]), D: cg21610815 in PAX8/PAX8-AS1 (p = 5.4 x 10⁻[8]).

Figure 5.

Enrichment analysis for genomic context of differentially methylated probes (DMPs). P-values are from Χ² test. *** indicates p < 0.001, ** indicates p < 0.01, * indicates p < 0.05.