DNA methylation signature in white blood cells (WBC) associated with BRCA1 mutation status

We analysed DNAme of 27,578 CpGs in WBC samples from a total of 72 women with a known BRCA1 mutation and 72 women with no mutation in the BRCA1 or BRCA2 gene (Figure 1 and Additional file 1). The presence of a cancer has been shown to modulate the composition of WBCs and DNAme profiles in peripheral blood [42] and hence we used a mixture of women who did and who did not develop breast cancer in order to be able to adjust for this. Using a multivariate regression model that included age, cohort and cancer status as covariates we were able to rank CpGs according to the significance of the association between their DNAme profile and mutation status. On applying a relaxed threshold of FDR <0.3 we observed a total of 2,514 BRCA1-mutation associated CpGs, of which 1,422 (57%) were hypermethylated (hyperM) and 1,092 (43%) were hypomethylated (hypoM) in women who had a BRCA1 mutation (Figure 1, Additional file 5), representing a highly significant skew towards hypermethylated CpGs (Binomial test P < 1e-10). To arrive at a specific DNAme signature, which would allow classification of independent samples, we used the elastic net (ELNET) framework (see Additional file 4), which resulted in a signature consisting of 1,829 CpGs (Figure 2, Additional file 6).

Figure 2 CpGs (n = 1829), which are differentially methylated in WBCs between BRCA1 mutation carriers and wild type controls and which comprise the ‘ BRCA1 -mutation DNA methylation signature’. Heatmap of normalised methylation values (blue = relative high methylation, yellow = relative low methylation) of CpGs comprising the BRCA1-mutation DNAme signature. The first colour bar at the top denotes the two main clusters where ‘red’ reflects the samples with a BRCA1 mutation whereas ‘green’ reflects samples without a mutation in BRCA1 or BRCA2 gene. The distribution of cancer cases is given in the second colour bar indicating women who had developed a breast cancer in purple. Right panel shows the enrichment of the top components of the gene set enrichment analysis in the hyper- and hypomethylated subset of CpGs; PCGT; Polycomb repressor complex 2 Group Target. Dashed line separates hypermethylated from hypomethylated CpGs. Full size image

Given that PCGT methylation is a hallmark of almost all cancers and that a BRCA1 defect in normal non-neoplastic cells is likely to silence PCGTs and compromise cell differentiation [20], we posited that our BRCA1 DNAme signature may be able to predict sporadic breast cancer. Interestingly, Gene Set Enrichment Analysis (GSEA) [43, 44] on the 1,074 hypermethylated (Additional file 7) and 755 hypomethylated (Additional file 8) CpGs of the BRCA1- mutation signature demonstrated the association of BRCA1 mutation with promoter hypermethylation of PCGTs. Indeed, the top categories of genes, associated with the hypermethylated CpGs in BRCA1 mutation carriers, were significantly (P <10-10) enriched for stem cell PCGTs irrespective of the definition used (Figure 2, Additional file 7). In contrast, none of the gene categories associated with those CpGs which are hypomethylated in BRCA1 mutation carriers reached significance based on adjusted P values (Additional file 8). Even the GSEA on the 105 CpGs with a more stringent FDR (<=0.05) associated with BRCA1 mutation in white blood cells demonstrated the enrichment of PCGTs (P < =0.02) (Additional file 9).

BRCA1-mutation DNAme signature and breast cancer risk in peripheral blood cells in the NSHD

In order to test whether the BRCA1-mutation DNAme signature is able to identify women who will develop breast cancer we analysed one of the best available characterised longitudinal cohorts (Additional file 2). Applying the BRCA1-mutation DNAme signature (out of the 1,829 BRCA1 CpGs, 1,722 were present on the 450 k Illumina methylation array), yielded a breast cancer risk AUC = 0.65 (0.51 to 0.78, P = 0.02) (Figure 3A). Interestingly, the BRCA1 signature also significantly predicted the future development of invasive non-breast cancers (AUC = 0.62; 0.50 to 0.74; P = 0.04) (Additional file 10A).

Figure 3 Validation of the BRCA1 -mutation DNAme signature in two independent prospective cohorts. ROC curves and AUC statistics to predict future breast cancer (BC) incidence applying the BRCA1-mutation DNAme signature in white blood cells (WBCs) (A) and in buccal (BUCC) cells (B) of the NSHD cohort and in serum DNA of the UKCTOCS cohort (C). Overlap of the top CpGs differently methylated in WBC between BRCA1 mutant and wild type (BRCA1 study) and the top CpGs differently methylated in serum DNA between women who have developed oestrogen receptor positive BCs and women who remained cancer-free (D). ROC curve and AUC statistics to predict deadly BCs applying the BRCA1-mutation DNAme signature in serum DNA in the UKCTOCS cohort (E) and Kaplan Meier curve (and hazard ratio (HR)) of future breast cancer patients with a high and low BRCA1-mutation DNAme score in serum DNA (F). Full size image

Consistent with the view that DNAme is tissue-specific, our DNAme signature - derived from peripheral blood cells from women with known BRCA1 status - was not able to predict invasive breast cancer (Figure 3B) or invasive non-breast cancer (Additional file 10B) in the buccal cell DNAme profiles obtained at the same time from the same women who provided blood DNA.

BRCA1- mutation DNA methylation signature and breast cancer risk in serum DNA in the UKCTOCS cohort

Less than 10% of invasive breast cancers are due to a BRCA1 mutation [45] and therefore it is unlikely that the predictive capacity of the BRCA1-mutation DNAme signature in the NSHD cohort was due to the correct identification of BRCA1 mutation carriers. Nevertheless in order to further substantiate that the BRCA1-mutation DNAme signature identifies sporadic cancers, we performed a nested case–control study within the UKCTOCS cohort (a 202,638 postmenopausal women cohort, who based on their family history were not at an increased risk of ovarian or breast cancer - see Additional files 3 and 4). As BRCA1-associated cancers are far more likely (75%) to be oestrogen receptor (ER) negative [46], we solely focused our analysis on women who provided a blood sample between 0.42 and 4.18 years (average 2 years) before they developed an ER positive invasive breast cancer (n = 119) and matched (on age at blood donation and recruitment centre) them to 122 women who did not develop a breast cancer during the follow-up period (5.61 to 12 years, average follow-up 11.92 years). As there was no whole blood DNA samples available from the women in UKCTOCS, we used serum-free DNA as a source of material for this analysis. Since >95% of blood samples were only spun down 24 to 48 h after the blood draw, it was important for us to identify the likely source of DNA in the serum samples. Although we were not able to definitely identify the source, the evidence clearly pointed towards an enriched for WBC DNA (see Additional file 11). The BRCA1-mutation DNAme signature predicted the development of an ER positive breast cancer with an AUC = 0.57 (0.50 to 0.64; P = 0.03) (Figure 3C) independent of whether the sample was taken less or more than 2 years prior to diagnosis (see Additional file 12). Importantly, the BRCA1- mutation DNAme signature also substantially overlapped with an ER + breast cancer specific risk signature (Additional file 13), which we derived de novo in the UKCTOCS cohort (P <2 x 10-33, Figure 3D). Of note, in the breast cancer specific risk signature, we also observed enrichment of biological terms, all crucially involved in stem cell differentiation and biology (Additional file 14). Again, these stem cell gene categories were only enriched among CpGs hypermethylated in cases, but not among CpGs hypomethylated in cases (Additional file 15). This observation is particularly pertinent given that NIPP1, PRC2, MSX1 and NANOG all suppress differentiation through occupation and suppression of specific gene sets.

BRCA1-mutation DNAme signature identifies women years in advance of fatal breast cancer diagnosis

In order to test whether the BRCA1-mutation DNAme signature is able to predict not only incidence but also breast cancer mortality we performed ROC statistics in the UKCTOCS set comparing women who died from breast cancer (n = 10) during the follow-up period with women who did not develop breast cancer (Figure 3E) and found an AUC = 0.67 (0.51 to 0.83; P = 0.02). In line with these findings women with a higher than average BRCA1-mutation DNAme signature score were 8.46 (95% CI 1.06 to 67.69) -fold more likely to die from breast cancer (P = 0.04) than those with lower than average scores (Figure 3F). Interestingly, apart from the number of nodes, none of the other clinico-pathological features or treatment modalities was associated with the BRCA1-mutation DNAme signature in these ER positive breast cancers (Additional file 16).

BRCA1-mutation DNAme signature and association with epidemiological and hormonal risk markers

Next, we were interested whether our DNAme signature could be explained by any of the breast cancer risk factors we had available for the UKCTOCS cohort. Interestingly, neither any of the epidemiological breast cancer risk factors nor any of the hormones (Tables 1, 2 and 3) we have analysed in the same serum samples was associated with our BRCA1-mutation DNAme signature. Interestingly, when we analysed women with and without a family history [47] separately, both BC incidence and death was predicted by our BRCA1-DNAme signature only in the group without family history (Additional file 17), but not in the (obviously very small) group of women with any family history (Additional file 18).

Table 1 Characteristics of the samples used from the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) Full size table

Table 2 Additional characteristics of the samples used from the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) Full size table