Blood cells of BRCA1 mutation and epimutation carries appear to acquire specific epigenetic signatures

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However, the effect of BRCA1 epimutation as well as germline mutations on the methylomes of carriers has not been investigated. Methods We performed a genome-wide methylation screening of blood cells from three cohorts of women: BRCA1 germline mutation carriers, BRCA1 epimutation carriers and women who were negative for both BRCA1 mutation and epimutation but had blood samples collected an average of 4.7 years prior to a breast cancer diagnosis. We then compared the methylomes of these cohorts to control individuals who were tested negative for both BRCA1 mutation and epimutation and remained cancer-free for more than eight years prior to the study. We also assessed whether methylation changes associated with BRCA1 germline mutation and epimutation were present in the tumor methylomes of TNBC cases. Results We identified specific methylation signatures in blood cells of BRCA1 mutation and epimutation carriers. These signatures were absent in the blood of cancer-free women as well as in blood samples collected years before cancer diagnosis. We subsequently linked the identified methylation changes to physiological processes and genomic regions previously implicated in breast cancer pathogenesis. Moreover, unsupervised clustering analyses confirmed the presence of identified methylation changes in the tumor methylomes of TNBC cases. Conclusions BRCA1 mutation and epimutation carriers display genome-wide methylation signatures that affect specific genomic regions and biological processes known to contribute to breast cancer pathogenesis when disrupted. Notably, these signatures are absent in the blood cells of individuals sampled years before a breast cancer diagnosis but are detectable in the tumor methylomes of TNBC, further suggesting their relevance to breast cancer development. DNA methylation BRCA1 BRCA1 epimutation BRCA1 mutation Figures Figure 1 Figure 2 Figure 3 Figure 4 Background The Breast Cancer Susceptibility Gene 1 ( BRCA1 ) is a well-established tumor suppressor involved in numerous physiological processes, including regulation of homologous recombination (HR) or non-homologous end joining (NHEJ) repair of double-strand breaks (DSBs), cell cycle checkpoint control, transcriptional regulation, response to oxidative stress, maintenance of heterochromatin integrity, interstrand cross-link repair, stabilization of stalled DNA replication forks, modulation of RNA-DNA hybrid (R-loop) formation, and silencing of non-coding pericentromeric satellite RNA expression [ 1 – 6 ]. Each of these functions contributes to its tumor suppressor role; however, no single mechanism has been universally recognized as the predominant source of BRCA1’s tumor-suppressive activity. Germline mutations impair tumor suppression function of BRCA1 , increasing the lifetime risk of breast cancer from 12% in the general population to 55%-72% by age 70, and the risk of ovarian cancer from 1–2–39%-44% [ 7 ]. A recent meta-analysis showed that germline BRCA1 mutations are identified in approximately 7.8% (6.8%-8.9%) of breast cancer cases worldwide[ 8 ], whereas in the Polish population, germline mutations in this gene are found in about 4% of cases [ 9 ]. The methylation of BRCA1 promoter (epimutation), which impacts BRCA1 activity in similar fashion as mutations, has been shown to occur in 13–40% of sporadic breast cancers [ 10 – 12 ]. Interestingly, recent data from large scale studies including ours indicate that presence of detectable in blood cells methylation in BRCA1 promoter increases the risk of TNBC and ovarian cancers [ 13 , 14 ]. The origin of detectable in blood cells BRCA1 epimutation is still not clearly understood but regardless the origin, this epimutation has already been proposed to be an underlying cause of around 20% of TNBC and low-ER expression breast cancers [ 15 ]. Moreover, studies from different populations repeatedly showed that about 10% of women is positive for this epimutation [ 10 , 16 – 22 ]. This is far more frequent than germline BRCA1 mutation. The question however remains if BRCA1 epimutation affects only BRCA1 gene or is a proxy of methylome-wide changes in methylomes of BRCA1 epimutation carriers. Moreover, a question whether, methylomes of BRCA1 epimutation carriers differ from the methylomes of BRCA1 germline mutation carriers is also opened. We have shown here that methylomes of BRCA1 mutation and epimutation carriers display specific genome-wide methylation signatures that may contribute to their elevated breast cancer risk. Methods Study participants and clinical material Blood samples were collected from women recruited at the International Hereditary Cancer Center (IHCC), Pomeranian Medical University in Szczecin, Poland. All participants were cancerfree at the time of blood sampling and were tested for three BRCA1 founder mutations common in the Polish population (c.5266dupC-5382insC; c.181T > G‐300T > G; c.4035delA‐4153delA) as well as for BRCA1 promoter methylation. The study included three groups of women: BRCA1 germline mutation carriers (confirmed negative for promoter methylation), with a mean of 12.9 years of cancer-free follow-up prior to inclusion in the study (n = 43); BRCA1 epimutation carriers (confirmed negative for germline mutations), with a mean of 7.22 years of cancer-free follow-up (n = 29); and women negative for both germline mutation and epimutation who developed breast cancer on average 4.62 years after sample collection (n = 19). The control group comprised healthy women who tested negative for both BRCA1 germline mutation and promoter methylation and remained cancer-free for an average of 8.17 years before methylome analysis (n = 21). Clinical characteristics of all study participants are presented in Table 1 . Genomic DNA was extracted from whole blood samples using the method described in [23]. Table 1 Characteristics of study participants Healthy BRCA1 mutation carriers Healthy BRCA1 epimutation carriers Women with cancer diagnosis, neither BRCA1 mutation nor epimutation sampled prior breast cancer diagnosis Healthy women with neither BRCA1 mutation nor epimutation (controls) Health status healthy healthy healthy at the time of sampling healthy n 43 29 19 21 Mean age in years (range) 52.91 (34–74) 62.69 (43–90) 55.16 (39–73) 57.62 (50–69) Mean time to diagnosis in years (range) N/A N/A 4.62 (2.05-7) N/A Mean observation time in years (range) 12.9 (3.29-20.) 7.22 (3-9.08) N/A 8.17 (6.49–9.44) BRCA1 mutation testing Mutation analysis for the three BRCA1 founder mutations prevalent in Polish population was performed using multiplex polymerase chain reaction (PCR) assay, as previously described [ 24 ]. Briefly, the 5382insC mutation in exon 20 and the 4153delA mutation in exon 11 were detected using allele-specific amplification PCR (ASA-PCR). The third mutation (C61G), a substitution c.181T > G in exon 5 was identified by restriction fragment length polymorphism PCR (RFLP-PCR) employing the Ava II restriction enzyme. Additional methodological details are provided in [ 25 ]. BRCA1 epimutation testing We have previously shown that detecting constitutional BRCA1 promoter methylation in blood samples, which consist of a heterogenous mixture of cell types, is challenging when using array-based technologies such as BeadChip. In contrast, this epimutation can be readily detected using PCR-based methods [ 26 ]. These findings are consistent with recent studies utilizing deep next-generation sequencing to detect BRCA1 epimutation [ 13 , 15 ]. Therefore, in this study, blood samples were analyzed for BRCA1 epimutation using a PCR-based EpiMelt BRCA1 MS-HRM methylation screening kit (MethylDetect ApS, Denmark) in combination with LightCycler® 480 High-Resolution Melting Master (Roche, Germany). Testing was performed on Q – qPCR Instrument (QuantaBio, UK), as previously described in [ 26 ]. Genome-wide methylation analysis The genome-wide methylation screening was performed on DNA extracted from diagnostic blood samples using Illumina MethylationEPIC BeadChip (EPIC, Illumina Inc.). Raw data were processed with ChAMP pipeline and normalized with BMIQ method. The data processing resulted in 732 043 informative CpG sites uniformly available across all study participants. The proportion of different cell types in the blood samples were estimated using the outperforming robust partial correlation method implemented in EpiDISH R package [ 27 – 29 ], with the “cent12CT.m” dataset [ 27 ] as reference. We then used Wilcoxon signed-rank test with FDR correction (q-value ≤ 0.05) to test for cell fraction differences between analyzed samples and we did not find any statistically significant differences in cell type proportions between compared groups. To identify methylome differences between cohorts in our study, we focused on assessment of size effect (differences in methylation levels) [ 30 ]. We first selected CpG sites with an average DNA methylation difference (delta β-value) of at least 0.05 between cases and controls. The average methylation difference however, does not account for the variance of methylation level measurement. Thus, to account for variance in our analyses we calculated Hedges' g size effect for each of the analyzed CpG sites and included in further analyses only CpG with a Hedges' g effect size larger than 0.5. Additionally, we used linear regression to confirm that methylation changes at CpG sites we selected in our analyses are statistically significant with FDR corrected p-value ≤ 0.05. The examples of CpG sites from our analysis with indicated methylation levels between comparison groups are shown in ( Figures S1 - S4 , Additional File 1 ). We also performed Spearman correlation of methylation levels at all CpG sites that met quality control with age, and none of the CpG sites included in our analysis showed significant correlation with age after correction for multiple testing. Genomic regions enrichment analysis To assess the genomic context of the identified methylation changes, we examined the distribution of identified CpG sites relative to chromatin states defined by histone modifications across 11 blood-derived cell types including, monocytes, neutrophils, B cells, natural killers, mononuclear cells, and seven types of T cells (samples IDs: E029, E030, E032, E034, E037, E039, E043, E044, E046, E047, E048), based on the Roadmap Epigenomics 15-state Core Model [ 31 ]. Enrichment analysis was performed using Locus Overlap Analysis (LOLA) tool [ 32 ]. All CpG sites that passed quality control during data processing were used as the background set. Genomic regions marked by specific histone modifications were considered significantly enriched if they met both an odds ratio (OR) threshold > 2 and FDR-corrected p-value ≤ 0.05, as determined by Fisher’s exact test. Gene ontology term enrichment analyses To identify physiological processes potentially affected by the observed methylation changes, we performed a Gene Set Enrichment Analysis (GSEA) using GENE2FUNC module available on the Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA) platform [ 33 ]. Validation data set To validate our results, we used data from five publicly available datasets deposited in the Gene Expression Omnibus (GEO) database, including 450K BeadChip Microarray data (GSE51032, GSE104942), and EPIC BeadChip Microarray data (GSE148748, GSE163521, GSE184159). Specifically, the GSE51032 dataset contains data from EPIC Italy cohort, from which we used blood cells methylation profiles for healthy women (n = 116) and women who developed breast cancer after sample collection (n = 168). From GSE104942, we used microarray data from the blood of 87 Australian women sampled both prior to and at breast cancer diagnosis (n = 87), as well as from cancer-free women at the time of sampling (n = 123). From the GSE148748 dataset, we analyzed microarray data from freshly frozen tumor tissues of TNBC patients (n = 82), including 25 cases with BRCA1 mutation and 57 cases with BRCA1 promoter methylation. The GSE163521 and GSE184159 datasets provided data from FFPE TNBC tumor samples (n = 13 and n = 32, respectively), in which neither BRCA1 mutation nor epimutation had been determined. All methylomics data in this study were processed and normalized using a consistent pipeline, with the exception of data from GSE104942, which were provided as text files containing pre-calculated beta-values and therefore could not be reprocessed using our pipeline. Results Blood cells methylomes of neither BRCA1 germline mutation nor epimutation carriers are highly stable We first, compared methylomes of blood cells from women with neither BRCA1 germline mutation nor epimutation (n = 19) collected on average 4.62 years prior to breast cancer diagnosis with the controls (n = 21) that also did not have BRCA1 mutation or epimutation and did not develop cancer during 8.17 years. This analysis identified only 12 differentially methylated positions (DMPs), including nine hypomethylated and three hypermethylated sites ( Supplementary Table 1, Additional File 2 ). These results suggest that the blood cell methylomes of individuals without BRCA1 germline mutation or epimutation are highly similar, regardless of whether they eventually developed breast cancer. Although we were not able to identify an independent dataset containing confirmed BRCA1 mutation and epimutation status to directly validate these findings, we performed a comparable analysis using data from the EPIC Italy cohort [ 34 ]. This dataset included women whose blood samples were collected on average 4.75 years prior to cancer diagnosis (n = 116), as well as healthy women without any signs of cancer (n = 168). While BRCA1 germline mutation and epimutation status were not available for this cohort, the prevalence of such alterations is generally low. Therefore, even if a small number of cases carried BRCA1 -related alterations, the overall impact on the results is expected to be negligible due to the relatively large sample size. This analysis identified only 39 DMPs ( Supplementary Table 2, Additional File 2 ), including 21 hypomethylated and 18 hypermethylated positions, further supporting the general stability of blood cell methylomes in women without BRCA1 germline mutation or epimutation. Additionally, we analyzed data from a separate study [ 35 ] comparing methylomes of blood samples collected both prior to and at the time of breast cancer diagnosis (n = 87) with samples from cancer-free individuals (n = 123). No significant methylation differences were detected in this cohort, again supporting the notion of peripheral blood cells methylome stability of BRCA1 mutation and epimutation negative individuals. Methylomes of blood cells from healthy women with BRCA1 mutation or epimutation display specific methylation signatures Next, we compared the blood cells methylomes of BRCA1 germline mutation carriers (n = 43) with those of women confirmed to carry neither BRCA1 mutation nor epimutation (n = 21). This analysis identified 2473 DMPs in mutation carriers ( Supplementary Table 3, Additional File 2 ), including 1839 hypomethylated (74.4%) and 634 hypermethylated (25.6%) CpG sites. We then performed the same analysis for epimutation carriers (n = 29) and identified more than twice as many DMPs (5163), including 4637 hypomethylated (89.8%) and 526 hypermethylated (10.2%) CpG sites ( Supplementary Table 4, Additional File 2 ). Among the DMPs identified, 1155 CpG sites were common to both mutation and epimutation carriers (Fig. 1 A; and Supplementary Table 5, Additional File 2 ), including 982 hypomethylated (Fig. 1 B) and 173 hypermethylated sites (Fig. 1 C). These results show that methylomes of blood cells of women tested positive for germline mutation and epimutation carry specific, but to a large extent non overlapping methylation signatures. Figure 1 . Venn diagrams illustrating overlap analysis between DMPs identified in BRCA1 germline mutation and epimutation related methylation signatures. A) all identified DMPs in both signatures, B) hypomethylated DMPs and C) hypermethylated DMPs. Methylation changes identified in BRCA1 mutation and epimutation carriers are enriched in repressed chromatin regions Then, to approximate biological context of the identified methylation signatures we used Locus Overlap Analysis (LOLA) [ 32 ] and assessed whether methylation changes present in BRCA1 mutation and epimutation carriers are enriched in regions marked by histones with specific modifications. This analysis was performed across 11 cell types from Roadmap Epigenomics 15-state Core Model (see Methods for details). Despite the distinct methylation signatures associated with BRCA1 mutation and epimutation, our results indicate that the DMPs in both groups map to similar regulatory regions. Specifically, the methylation changes identified in both mutation carriers (Fig. 2 Panel A ) and epimutation carriers (Fig. 2 Panel B ) were consistently enriched in regions characterized by constitutive heterochromatin (Het) histone marks. Interestingly, maintaining global heterochromatin integrity has been linked to BRCA1 tumor suppression function [ 3 ]. Also, methylation changes identified in BRCA1 germline mutation related signature were enriched for several cell types within regions of the genome associated with histone marks characteristic for repressed PolyComb (ReprPC) and ZNF genes and repeats (ZNF/Rpts). And aggressive breast cancer phenotype was linked to the function of the Polycomb-repressive complex 2 (PRC2) [ 36 ]. Biological processes associated with BRCA1 mutation- or epimutation-specific methylation signatures link these alterations to breast cancer development To explore the functional relevance of the BRCA1 mutation- and epimutation-associated methylation signatures, we performed GSEA of genes associated with identified methylation signatures using two ontology terms databases “Hallmark gene sets” and “Curated gene sets” within the FUMA platform. The analysis based on “Hallmark gene sets” database, identified one and four ontology terms associated with the BRCA1 germline mutation and epimutation related signatures, respectively ( Supplementary Tables 1 and 2, Additional File 3 ). The majority of gene ontology terms identified in these analyses were related to carcinogenesis. Specifically, the single gene ontology term linked to BRCA1 mutation associated signature was HALLMARK_KRAS_SIGNALING_DN. KRAS is the most frequently mutated RAS gene in breast cancer, and has been linked to poor prognosis and an elevated metastatic rate [ 37 ]. Three of the four ontology terms associated with methylation changes identified in BRCA1 epimutation carriers were also cancer-related, including HALLMARK_APICAL_JUNCTION, HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION, and HALLMARK_MYOGENESIS. The apical junction complex, is considered a hub of signal transduction, regulates cell-cell adhesion, gene transcription, and cell proliferation and differentiation [ 38 ]. E-Cadherin, a component of the apical junction complex is encoded by CDH1 . The loss of CDH1 expression is a hallmark of epithelial-mesenchymal transition, a key process related to metastasis [ 38 ]. Also, a meta-analysis reported that individuals diagnosed with dermatomyosis, which involves impaired myogenesis, have increased risk of developing breast cancer [ 39 ]. The analysis based on “Curated gene sets” database, identified 29 and 204 ontology terms associated with the BRCA1 germline mutation and epimutation related signatures, respectively ( Supplementary Tables 3 and 4, Additional File 3 ). Interestingly, 24 of those terms were common between the two analyses ( Supplementary Table 5, Additional File 3 ), including those previously associated with breast cancer: REACTOME_NEUREXINS_AND_NEUROLIGINS [ 40 – 43 ], REACTOME_REGULATION_OF_COMMISSURAL_AXON_PATHFINDING_BY_SLIT_AND_ROBO [ 44 , 45 ] and MANALO_HYPOXIA_UP [ 46 – 48 ]; as well as terms associated with poor clinical outcome of breast cancer patients, including BENPORATH_SUZ12_TARGETS, BENPORATH_EED_TARGETS, and BENPORATH_PRC2_TARGETS [ 49 ]. Moreover, nine of these ontology terms were related to histone modifications, including H3K4me2, H3K27me3 and H3K4me3 [ 48 , 50 – 54 ] and the changes of histone-related chromatin structure is one of the hallmarks of cancer. Interestingly, the term LIM_MAMMARY_STEM_CELL_UP, which characterizes a subset of genes consistently upregulated in mammary stem cells in both mouse and human species [ 55 ] was also one of terms common in this analysis. Moreover, among the terms uniquely associated with methylation signature identified in BRCA1 mutation carriers ( Supplementary Table 6, Additional File 3 ), two were previously associated with breast cancer development: MIKKELSEN_ES_HCP_WITH_H3K27ME3 [ 52 , 53 ] and GRESHOCK_CANCER_COPY_NUMBER_UP [ 56 , 57 ]. In turn, amongst terms associated with methylation signature identified in BRCA1 epimutation carriers ( Supplementary Table 7, Additional File 3 ) nine was directly linked to breast cancer, five described processes related to the carcinogenesis (e.g. methylated in cancer, pathways in cancer, neoplastic transformation), and two were associated with metastasis. Three terms in this analysis were related to inflammatory response, which has been shown to promote or inhibit the cancer development [ 58 ]. Five terms were related to collagen metabolism, a fundamental component of the tumor microenvironment contributing to cancer fibrosis [ 59 ], and six to metabolisms of glycosylation, a process influencing malignant transformation or accelerating tumorigenesis [ 60 ]. Moreover, this analysis identified term KYNG_DNA_DAMAGE_BY_4NQO, and DNA damage is recognized as a critical factor in cancer development and progression [ 61 ], terms such as KEGG_FOCAL_ADHESION, WP_FOCAL_ADHESION, WP_HIPPO_SIGNALING_REGULATION_PATHWAYS, or REACTOME_CELL_CELL_COMMUNICATION which were previously described in the context of processes involved in cell proliferation and migration [ 38 ] and GOZGIT_ESR1_TARGETS_DN describing a set of down-regulated genes in breast cancer cell line that do not express ESR1 [ 62 ]. Also, three terms in this analysis were related to class 1 histone deacetylases (HDAC), including HDAC1 , HDAC2 and HDAC3 . Previously, HDAC1 has been described to be significantly correlated with the molecular subtypes of breast tumors and overexpressed in luminal A tumors [ 63 ], whereas HDAC2 and HDAC3 have been shown to displayed higher expression in aggressive breast cancer subtypes [ 64 ]. Lastly, a number of remaining terms contained genes previously implicated in breast cancer development and progression, including TP53 [ 65 , 66 ], TGFB [ 67 ], VEGFR1 [ 68 , 69 ], CDH1 [ 70 ], EGFR [ 71 , 72 ], EGF [ 73 ], TNC [ 74 , 75 ], GR [ 76 , 77 ], STAT5A [ 78 , 79 ], MYC [ 80 – 83 ], TP63 [ 84 – 86 ], PTP1B [ 87 – 89 ], SATB1 [ 90 – 93 ], NF1 [ 94 – 97 ], PDEF [ 83 , 98 , 99 ], SMARCE1 [ 100 ] or SUZ12 [ 101 ]. Methylation changes observed in BRCA1 mutation and epimutation carriers are also present in breast cancer cells There is already evidence available indicating that at least to some extent methylation changes observed in blood cells can be considered a proxy of changes observed in the tissue that cancer originates from [ 102 – 104 ]. Due to the lack of available datasets from healthy breast tissues of individuals with confirmed BRCA1 germline mutation or epimutation, we were unable to evaluate the presence of these signatures in non-tumor mammary epithelial cells. However, we were able to access methylomics data from TNBC samples with BRCA1 mutation (n = 25), epimutation (n = 57) [ 105 ] and cases with neither BRCA1 mutation nor epimutation (n = 45) [ 106 , 107 ]. To assess the presence of the methylation changes constituting BRCA1 germline mutation and epimutation related signatures in methylomes of TNBC, we performed unsupervised clustering of TNBC cases, based on identified in blood of BRCA1 epimutation and germline mutation methylation signatures. Clustering based on the BRCA1 germline mutation-associated signature effectively distinguished TNBC samples without BRCA1 alterations from those harboring BRCA1 mutations or epimutations (Fig. 3 A). Similarly, although slightly less precise, clustering based on the epimutation-associated signature also separated samples according to BRCA1 status (Fig. 3 B). Most interestingly, the unsupervised clustering analyses based on methylation changes common for both signatures, remarkably accurately separated TNBC without BRCA1 mutation nor epimutation from TNBC positive for BRCA1 mutation or epimutation (Fig. 3 C). BRCA1 germline mutation and epimutation related signatures in the context of previously reported blood-based methylation markers of breast cancer risk A number of previous studies have reported methylation changes in blood cells associated with breast cancer risk [ 35 , 108 – 114 ]. We analyzed the results of these studies, including types of recruited patients and methods the authors used to identify methylation changes. We also compared reported methylation signatures between these studies with our results (Fig. 4 ). In general, there was no or only a minor overlap between DMPs reported in analyzed studies and the signatures we identified. This is however not surprising because overall, there was no consistency between analyzed studies including patient selection and data processing methodologies used. A detailed comparison of these datasets and methodological differences is provided in Additional File 4 . Discussion We have shown that blood cells methylomes of healthy at the time of sampling BRCA1 mutation and epimutation carriers display aberrations, that are not present in blood cells of healthy women without detectable genetic or epigenetic BRCA1 lesions and women that eventually developed sporadic breast cancer during our study. These results indicate that malfunctioning of BRCA1 , may result in genome-wide methylation aberrations which also may contribute to increased cancer risk, especially that we were able to link identified epigenetic changes to the pathways that have previously been associate with breast cancer pathology. The methylation signatures that we identified in methylomes of epimutation and mutation carriers overlapped only to certain extent. However, GSEA linked methylation changes from each of the signatures to very similar cancer related pathways and physiological processes, which when affected, are key contributors to cancer phenotype. This suggests that the effect of genetic and epigenetic inactivation of BRCA1 may be to a large extant synergic. Moreover, the methylation changes constituting both signatures were predominantly losses of methylation and most importantly uniformly and specifically annotated to heterochromatin regions of genome. It has been shown that DNA methylation marks heterochromatin at pericentromeres and is a key factor for the maintenance of the heterochromatin integrity [ 115 ]. Moreover, losses of methylation at these regions of genome during neoplastic transformation have been shown to lead to genomic instability and aberrant gene expression [ 116 – 119 ]. That in the context of finings indicating that maintaining heterochromatin integrity is one of the functions of BRCA1 tumor suppressor activity [ 3 ], allows to speculate that observed in our analysis methylation aberrations at heterochromatin may be attributed to epigenetic or genetic inactivation of BRCA1 . Moreover, observed in mice models increased levels of heterochromatic repetitive satellite RNAs which have been shown to induce tumor formation [ 120 ], may also be consequence of loss of stability of methylation patterns at heterochromatin, attributed to malfunctioning BRCA1 gene. Adding to mechanism by which BRCA1 mutation and epimutation related methylation aberrations in heterochromatin, contribute to breast cancer development. Our analysis also linked identified methylation changes to genomic regions of Polycomb-repressive complex 2 (PRC2) and Zinc-finger genes (ZNFs). Interestingly, the loss of BRCA1 expression have been shown to inhibit the differentiation of embryonic stem cells and promote an aggressive breast cancer phenotype by impacting the function of Polycomb-repressive complex 2 (PRC2) [ 106 ]. We did not find studies describing direct association between BRCA1 function and Zinc-finger genes (ZNFs) family, however these proteins have been reported to be involved in tumorigenesis via their prooncogenic/tumor suppressed properties, as well as transcription factor function [ 107 ]. Lastly, we showed that methylation changes constituting identified BRCA1 epimutation- and germline mutation-associated signatures are present in methylomes of TNBC. This suggests that BRCA1 deficiency is associated with methylation changes in blood cells but only in progenitors of breast cancer cells leads to phenotypic transformation. Obvious limitation of our study is the number of patients and our results need to be confirmed in larger cohorts of patients which we are lacking at the moment. Moreover, our attempt to validate our findings in the context of methylation signatures previously identified in blood of breast cancer patients was only partly successful and illustrated the need for the methodologically unified study of methylation changes associated with BRCA1 deficiency. Nevertheless, the evidence we present appears to be sufficient to accelerate research aiming to elaborate the link between BRCA1 malfunctioning and genome-wide epigenetic changes that may be involved in breast cancer development. Conclusions Our study adds to the increasing body of evidence that warrant further research to determine contribution of constitutional methylation of tumor suppressor genes in somatic cells as cancer risk factors. For the first time, we demonstrate that that entire methylome may be involved in development of increased risk of breast cancer observed for the carriers of genetic and epigenetic defects of BRCA1 gene. Declarations Ethics approval and consent to participate The study protocol was approved by the ethics committee of the Pomeranian Medical University and conformed to the ethical guidelines of the 1975 Declaration of Helsinki. All participants gave written consent to provide a blood sample for research purposes and to participate in the study. Consent for Publication Not applicable. Availability of Data and Materials The datasets generated and/or analyzed during the current study are available in the Gene Expression Omnibus repository, GSE272644 – our data, GSE51032 [34], GSE104942 [35], GSE148748 [105], GSE163521 [106], GSE184159 [107]. Competing interests The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding The study was financed by PRELUDIUM BIS 2 grant (2020/39/O/NZ2/02943 Grant Number) and OPUS22 grant (2021/43/B/NZ2/02979 Grant Number) from Polish National Science Centre. Authors’ Contributions Tomasz Kazimierz Wojdacz, Jan Lubiński, Tomasz Huzarski conceived the concept and design of the study. Tomasz Kazimierz Wojdacz obtained funding for this project. Jan Lubiński and Tomasz Huzarski recruited participants. Jacek Antoniewski prepared biological samples for DNA methylation analysis and performed MS-HRM, which was supported by Katarzyna Ewa Sokolowska. Dominik Strapagiel and Marta Sobalska-Kwapis performed wet-lab microarray experiments. Katarzyna Ewa Sokolowska analyzed the quality of data, performed methylation and bioinformatics analyses, prepared data visualization, with the support of Tomasz Kazimierz Wojdacz. Katarzyna Ewa Sokolowska and Tomasz Kazimierz Wojdacz wrote, reviewed and edited the manuscript. All authors read and approved the final manuscript. Acknowledgements Not applicable. References Savage, K.I. and D.P. Harkin, BRCA1, a 'complex' protein involved in the maintenance of genomic stability. FEBS J, 2015. 282 (4): p. 630-46. 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Supplementary Files AdditionalFile1.docx File name and format: Additional_File_1.docx Title: Additional Figures S1-S4 Description: Histograms represent the frequency of DMP identified in specific analyses and boxplots showing four example DMPs from each analysis AdditionalFile2.xlsx File name and format: Additional_File_2.xlsx Title: DMPs identified in data analyses Description: Supplementary Table 1. DMPs identified for women prior breast cancer diagnosis with neither BRCA1 germline mutation nor epimutation (Polish cohort) Supplementary Table 2. DMPs identified for women prior breast cancer diagnosis with neither BRCA1 germline mutation nor epimutation (EPIC Italy cohort) Supplementary Table 3. DMPs identified for BRCA1 germline mutation carriers Supplementary Table 4. DMPs identified for BRCA1 epimutation carriers Supplementary Table 5. Common DMPs for BRCA1 germline mutation and epimutation carriers AdditionalFile3.xlsx File name and format: Additional_File_3.xlsx Title: GSEA Results Description: Supplementary Table 1. All significant GSEA results for BRCA1 mutation carriers in "Hallmark gene sets" database Supplementary Table 2. All significant GSEA results for BRCA1 epimutation carriers in "Hallmark gene sets" database Supplementary Table 3. All significant GSEA results for BRCA1 mutation carriers in "Curated gene sets" database Supplementary Table 4. All significant GSEA results for BRCA1 epimutation carriers in "Curated gene sets" database Supplementary Table 5. Common significant GSEA results for BRCA1 mutation and epimutation carriers in "Curated gene sets" database Supplementary Table 6. Unique significant GSEA results for BRCA1 mutation carriers in "Curated gene sets" database Supplementary Table 7. Unique significant GSEA results for BRCA1 epimutation carriers in "Curated gene sets" database AdditionalFile4.xlsx File name and format: Additional_File_4.xlsx Title: Studies reported results of analysis blood samples/PBMC of women prior and at breast cancer diagnosis in a case-control studies Description: Table illustrating comparison of methodology used in our and previous studies aiming to identified methylation signatures associated with breast cancer Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7301339","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":498141369,"identity":"b2f6b9a7-ab70-49ad-9e27-6ef60dfe4543","order_by":0,"name":"Katarzyna Ewa Sokolowska","email":"","orcid":"","institution":"Pomeranian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Katarzyna","middleName":"Ewa","lastName":"Sokolowska","suffix":""},{"id":498141372,"identity":"58d400da-a0c4-432b-80f8-bd6ca80df55e","order_by":1,"name":"Jacek Antoniewski","email":"","orcid":"","institution":"Pomeranian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jacek","middleName":"","lastName":"Antoniewski","suffix":""},{"id":498141373,"identity":"5b005c5f-2456-4bb8-bec3-a4a27bf7640f","order_by":2,"name":"Marta Sobalska-Kwapis","email":"","orcid":"","institution":"University of Łódź","correspondingAuthor":false,"prefix":"","firstName":"Marta","middleName":"","lastName":"Sobalska-Kwapis","suffix":""},{"id":498141375,"identity":"48b7ad85-af5f-4476-ba7b-138abd108b02","order_by":3,"name":"Dominik Strapagiel","email":"","orcid":"","institution":"University of Łódź","correspondingAuthor":false,"prefix":"","firstName":"Dominik","middleName":"","lastName":"Strapagiel","suffix":""},{"id":498141377,"identity":"d9c488ba-60e9-4496-bd3a-2ccb13da0632","order_by":4,"name":"Jan Lubiński","email":"","orcid":"","institution":"Pomeranian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jan","middleName":"","lastName":"Lubiński","suffix":""},{"id":498141379,"identity":"1286ac79-40bc-472b-8476-4d8ea6b6aa47","order_by":5,"name":"Tomasz Huzarski","email":"","orcid":"","institution":"Pomeranian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tomasz","middleName":"","lastName":"Huzarski","suffix":""},{"id":498141383,"identity":"92feb6fe-8e22-405a-8524-7ef54eea04df","order_by":6,"name":"Tomasz Kazimierz Wojdacz","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYHCDAwZAwgbC5gFiPhzqeNC0pCGE2QhrYQBpOUxYiz17d+LHH38YEvsZD2/88OPP+TyDawcYH7xtY8jDaQvP2c3SPDwMiTMbjhVL9vDcLja4ncBsOLeNoRinFoncDdIMEgy5Gw6cMQAybiduuJ3AJs3bxpDYhlvL5p8/DMBajH8zGJwDaWH/TUDLNgmeBLAWM2mGhANgW5jxajlzdps1zwGJeqBfyix7DiQXS95ObJacc04Cp1/Y23s33/zxx8aYX+Lw5hs//tjl8d1OPvjhTZlNHj8OLVAgAUQHwKwEBgbGBpBIAn4dIMDfANPCgMoYBaNgFIyCEQ8Aa1pcC6K/cyUAAAAASUVORK5CYII=","orcid":"","institution":"Pomeranian Medical University","correspondingAuthor":true,"prefix":"","firstName":"Tomasz","middleName":"Kazimierz","lastName":"Wojdacz","suffix":""}],"badges":[],"createdAt":"2025-08-05 13:53:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7301339/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7301339/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88897545,"identity":"777a1cde-ae98-491b-9c4a-cb007f98e55d","added_by":"auto","created_at":"2025-08-12 13:12:43","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":401618,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVenn diagrams illustrating overlap analysis between DMPs identified in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eBRCA1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e germline mutation and epimutation related methylation signatures.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA) all identified DMPs in both signatures, B) hypomethylated DMPs and C) hypermethylated DMPs.\u003c/p\u003e","description":"","filename":"Figure1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7301339/v1/71914171f9834ea266b3a6e3.jpg"},{"id":88895817,"identity":"a43ed4c1-7cff-4ab9-8525-2b23584e551d","added_by":"auto","created_at":"2025-08-12 13:04:43","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1344629,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnrichment analysis of regions marked by histones with specific modifications in 11 cell types according to 15-state Model from Roadmap Epigenomics Core.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePanel A - Results for \u003cem\u003eBRCA1\u003c/em\u003e mutation carriers. Panel B - Results for \u003cem\u003eBRCA1\u003c/em\u003e epimutation carriers. Figures on the left - Scatter plot with cell names on y axis, modification names on x axis. The size of dots determines oddsRatio for the observed enrichment, color gradient from blue to red indicate decreasing FDR corrected for multiple testing p-values (Fisher exact test). Figures on the right - Histograms with oddsRatio on y axis and modification names on x axis. Every color of bar indicates specific cell type as described in the legend.\u003c/p\u003e","description":"","filename":"Figure2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7301339/v1/1c0329109e4d6c9be04b3110.jpg"},{"id":88898375,"identity":"c3baef73-a470-4fb1-ae0c-38e3b2cc8df5","added_by":"auto","created_at":"2025-08-12 13:20:43","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1571640,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUnsupervised clustering analyses of TNBC based on\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e BRCA1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e germline mutation and epimutation related methylation signatures.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHeatmap illustrating the results of unsupervised clustering of the TNBC tissue samples with \u003cem\u003eBRCA1\u003c/em\u003e mutation (grey), \u003cem\u003eBRCA1\u003c/em\u003eepimutation (pink) and TNBC tissues with neither \u003cem\u003eBRCA1\u003c/em\u003e mutation nor epimutation (purple) based on signatures identified in blood of A) \u003cem\u003eBRCA1\u003c/em\u003egermline mutation carriers, B) \u003cem\u003eBRCA1\u003c/em\u003e epimutation carriers, C) common DMPs identified for \u003cem\u003eBRCA1\u003c/em\u003e germline mutation and epimutation carriers.\u003c/p\u003e","description":"","filename":"Figure3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7301339/v1/56e9dc7106563488a04fda49.jpg"},{"id":88898377,"identity":"2652ac97-4aff-4401-aa6d-9701baab3b75","added_by":"auto","created_at":"2025-08-12 13:20:43","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":699162,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUpset plot with „distinct” mode type illustrating comparison of the \u0026nbsp;DMPs identified in our and previous studies.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe rows of the matrix correspond to the sets of DMPs from specific studies, the columns correspond to the intersections between studies. Horizontally, the number of identified DMPs in specific studies is plotted as bar charts on the left side, studies are listed on the right side. Vertically, the number of overlapping DMPs (size of the intersections) are shown aligned with the columns, also as bar charts.\u003c/p\u003e","description":"","filename":"Figure4.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7301339/v1/cc337c05b8afa6bdd930cbcb.jpg"},{"id":97250643,"identity":"a12bb7d4-8276-497d-bf3e-29161bc2a202","added_by":"auto","created_at":"2025-12-02 13:14:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5914808,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7301339/v1/e28df658-3583-405b-9c21-f8004c2ee0a5.pdf"},{"id":88895818,"identity":"ab61196f-3244-4faa-b49e-bf5255a18162","added_by":"auto","created_at":"2025-08-12 13:04:43","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2489701,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFile name and format: \u003c/strong\u003eAdditional_File_1.docx\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTitle: \u003c/strong\u003eAdditional Figures S1-S4\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDescription: \u003c/strong\u003eHistograms represent the frequency of DMP identified in specific analyses and boxplots showing four example DMPs from each analysis\u003c/p\u003e","description":"","filename":"AdditionalFile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7301339/v1/46cbefbb875f77ec3562bf53.docx"},{"id":88898374,"identity":"6f48146f-9694-4563-a11b-4fd988334630","added_by":"auto","created_at":"2025-08-12 13:20:43","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":908998,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFile name and format: \u003c/strong\u003eAdditional_File_2.xlsx\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTitle: \u003c/strong\u003eDMPs identified in data analyses\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDescription:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 1.\u003c/strong\u003e DMPs identified for women prior breast cancer diagnosis with neither \u003cem\u003eBRCA1\u003c/em\u003e germline mutation nor epimutation (Polish cohort)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 2.\u003c/strong\u003e DMPs identified for women prior breast cancer diagnosis with neither \u003cem\u003eBRCA1\u003c/em\u003e germline mutation nor epimutation (EPIC Italy cohort)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 3.\u003c/strong\u003e DMPs identified for \u003cem\u003eBRCA1\u003c/em\u003egermline mutation carriers\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 4.\u003c/strong\u003e DMPs identified for \u003cem\u003eBRCA1\u003c/em\u003eepimutation carriers\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 5.\u003c/strong\u003e Common DMPs for \u003cem\u003eBRCA1\u003c/em\u003egermline mutation and epimutation carriers\u003c/p\u003e","description":"","filename":"AdditionalFile2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7301339/v1/779e5affe9ad2e05b1d45c0c.xlsx"},{"id":88895824,"identity":"88fa7e43-4674-4b5e-969f-7884d5a0116c","added_by":"auto","created_at":"2025-08-12 13:04:43","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":95920,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFile name and format: \u003c/strong\u003eAdditional_File_3.xlsx\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTitle: \u003c/strong\u003eGSEA Results\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDescription:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 1.\u003c/strong\u003e All significant GSEA results for \u003cem\u003eBRCA1\u003c/em\u003e mutation carriers in \"Hallmark gene sets\" database\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 2.\u003c/strong\u003e All significant GSEA results for \u003cem\u003eBRCA1\u003c/em\u003e epimutation carriers in \"Hallmark gene sets\" database\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 3.\u003c/strong\u003e All significant GSEA results for \u003cem\u003eBRCA1\u003c/em\u003e mutation carriers in \"Curated gene sets\" database\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 4.\u003c/strong\u003e All significant GSEA results for \u003cem\u003eBRCA1\u003c/em\u003e epimutation carriers in \"Curated gene sets\" database\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 5.\u003c/strong\u003e Common significant GSEA results for \u003cem\u003eBRCA1\u003c/em\u003e mutation and epimutation carriers in \"Curated gene sets\" database\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 6.\u003c/strong\u003e Unique significant GSEA results for \u003cem\u003eBRCA1\u003c/em\u003e mutation carriers in \"Curated gene sets\" database\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 7.\u003c/strong\u003e Unique significant GSEA results for \u003cem\u003eBRCA1\u003c/em\u003e epimutation carriers in \"Curated gene sets\" database\u003c/p\u003e","description":"","filename":"AdditionalFile3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7301339/v1/b272d9c04b581cdb3394a6ff.xlsx"},{"id":88895822,"identity":"87f56d4e-2f65-4773-b93a-54495d75e8ec","added_by":"auto","created_at":"2025-08-12 13:04:43","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":12935,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFile name and format: \u003c/strong\u003eAdditional_File_4.xlsx\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTitle: \u003c/strong\u003eStudies reported results of analysis blood samples/PBMC of women prior and at breast cancer diagnosis in a case-control studies\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDescription: \u003c/strong\u003eTable illustrating comparison of methodology used in our and previous studies aiming to identified methylation signatures associated with breast cancer\u003c/p\u003e","description":"","filename":"AdditionalFile4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7301339/v1/251f403368109389e6769152.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Blood cells of BRCA1 mutation and epimutation carries appear to acquire specific epigenetic signatures","fulltext":[{"header":"Background","content":"\u003cp\u003eThe Breast Cancer Susceptibility Gene 1 (\u003cem\u003eBRCA1\u003c/em\u003e) is a well-established tumor suppressor involved in numerous physiological processes, including regulation of homologous recombination (HR) or non-homologous end joining (NHEJ) repair of double-strand breaks (DSBs), cell cycle checkpoint control, transcriptional regulation, response to oxidative stress, maintenance of heterochromatin integrity, interstrand cross-link repair, stabilization of stalled DNA replication forks, modulation of RNA-DNA hybrid (R-loop) formation, and silencing of non-coding pericentromeric satellite RNA expression [\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e–\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Each of these functions contributes to its tumor suppressor role; however, no single mechanism has been universally recognized as the predominant source of \u003cem\u003eBRCA1’s\u003c/em\u003e tumor-suppressive activity.\u003c/p\u003e\u003cp\u003eGermline mutations impair tumor suppression function of \u003cem\u003eBRCA1\u003c/em\u003e, increasing the lifetime risk of breast cancer from 12% in the general population to 55%-72% by age 70, and the risk of ovarian cancer from 1–2–39%-44% [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. A recent meta-analysis showed that germline \u003cem\u003eBRCA1\u003c/em\u003e mutations are identified in approximately 7.8% (6.8%-8.9%) of breast cancer cases worldwide[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], whereas in the Polish population, germline mutations in this gene are found in about 4% of cases [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe methylation of \u003cem\u003eBRCA1\u003c/em\u003e promoter (epimutation), which impacts \u003cem\u003eBRCA1\u003c/em\u003e activity in similar fashion as mutations, has been shown to occur in 13–40% of sporadic breast cancers [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e–\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Interestingly, recent data from large scale studies including ours indicate that presence of detectable in blood cells methylation in \u003cem\u003eBRCA1\u003c/em\u003e promoter increases the risk of TNBC and ovarian cancers [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The origin of detectable in blood cells \u003cem\u003eBRCA1\u003c/em\u003e epimutation is still not clearly understood but regardless the origin, this epimutation has already been proposed to be an underlying cause of around 20% of TNBC and low-ER expression breast cancers [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Moreover, studies from different populations repeatedly showed that about 10% of women is positive for this epimutation [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR17 CR18 CR19 CR20 CR21\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e–\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This is far more frequent than germline \u003cem\u003eBRCA1\u003c/em\u003e mutation. The question however remains if \u003cem\u003eBRCA1\u003c/em\u003e epimutation affects only \u003cem\u003eBRCA1\u003c/em\u003e gene or is a proxy of methylome-wide changes in methylomes of \u003cem\u003eBRCA1\u003c/em\u003e epimutation carriers. Moreover, a question whether, methylomes of \u003cem\u003eBRCA1\u003c/em\u003e epimutation carriers differ from the methylomes of \u003cem\u003eBRCA1\u003c/em\u003e germline mutation carriers is also opened.\u003c/p\u003e\u003cp\u003eWe have shown here that methylomes of \u003cem\u003eBRCA1\u003c/em\u003e mutation and epimutation carriers display specific genome-wide methylation signatures that may contribute to their elevated breast cancer risk.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy participants and clinical material\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBlood samples were collected from women recruited at the International Hereditary Cancer Center (IHCC), Pomeranian Medical University in Szczecin, Poland. All participants were cancerfree at the time of blood sampling and were tested for three \u003cem\u003eBRCA1\u003c/em\u003e founder mutations common in the Polish population (c.5266dupC-5382insC; c.181T \u0026gt; G‐300T \u0026gt; G; c.4035delA‐4153delA) as well as for \u003cem\u003eBRCA1\u003c/em\u003e promoter methylation. The study included three groups of women: \u003cem\u003eBRCA1\u003c/em\u003e germline mutation carriers (confirmed negative for promoter methylation), with a mean of 12.9 years of cancer-free follow-up prior to inclusion in the study (n = 43); \u003cem\u003eBRCA1\u003c/em\u003e epimutation carriers (confirmed negative for germline mutations), with a mean of 7.22 years of cancer-free follow-up (n = 29); and women negative for both germline mutation and epimutation who developed breast cancer on average 4.62 years after sample collection (n = 19). The control group comprised healthy women who tested negative for both \u003cem\u003eBRCA1\u003c/em\u003e germline mutation and promoter methylation and remained cancer-free for an average of 8.17 years before methylome analysis (n = 21). Clinical characteristics of all study participants are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Genomic DNA was extracted from whole blood samples using the method described in [23].\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of study participants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHealthy \u003cem\u003eBRCA1\u003c/em\u003e mutation carriers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHealthy \u003cem\u003eBRCA1\u003c/em\u003e epimutation carriers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWomen with cancer diagnosis, neither \u003cem\u003eBRCA1\u003c/em\u003e mutation nor epimutation sampled prior breast cancer diagnosis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHealthy women with neither \u003cem\u003eBRCA1\u003c/em\u003e mutation nor epimutation (controls)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHealth status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehealthy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ehealthy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ehealthy at the time of sampling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ehealthy\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMean age in years (range)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52.91 (34–74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62.69 (43–90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55.16 (39–73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e57.62 (50–69)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMean time to diagnosis in years (range)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.62 (2.05-7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMean observation time in years (range)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.9 (3.29-20.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.22 (3-9.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.17 (6.49–9.44)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003cb\u003eBRCA1 mutation testing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMutation analysis for the three \u003cem\u003eBRCA1\u003c/em\u003e founder mutations prevalent in Polish population was performed using multiplex polymerase chain reaction (PCR) assay, as previously described [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Briefly, the 5382insC mutation in exon 20 and the 4153delA mutation in exon 11 were detected using allele-specific amplification PCR (ASA-PCR). The third mutation (C61G), a substitution c.181T \u0026gt; G in exon 5 was identified by restriction fragment length polymorphism PCR (RFLP-PCR) employing the Ava II restriction enzyme. Additional methodological details are provided in [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eBRCA1 epimutation testing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe have previously shown that detecting constitutional \u003cem\u003eBRCA1\u003c/em\u003e promoter methylation in blood samples, which consist of a heterogenous mixture of cell types, is challenging when using array-based technologies such as BeadChip. In contrast, this epimutation can be readily detected using PCR-based methods [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. These findings are consistent with recent studies utilizing deep next-generation sequencing to detect \u003cem\u003eBRCA1\u003c/em\u003e epimutation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Therefore, in this study, blood samples were analyzed for \u003cem\u003eBRCA1\u003c/em\u003e epimutation using a PCR-based EpiMelt BRCA1 MS-HRM methylation screening kit (MethylDetect ApS, Denmark) in combination with LightCycler® 480 High-Resolution Melting Master (Roche, Germany). Testing was performed on Q – qPCR Instrument (QuantaBio, UK), as previously described in [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eGenome-wide methylation analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe genome-wide methylation screening was performed on DNA extracted from diagnostic blood samples using Illumina MethylationEPIC BeadChip (EPIC, Illumina Inc.). Raw data were processed with ChAMP pipeline and normalized with BMIQ method. The data processing resulted in 732 043 informative CpG sites uniformly available across all study participants. The proportion of different cell types in the blood samples were estimated using the outperforming robust partial correlation method implemented in EpiDISH R package [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e–\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], with the “cent12CT.m” dataset [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] as reference. We then used Wilcoxon signed-rank test with FDR correction (q-value ≤ 0.05) to test for cell fraction differences between analyzed samples and we did not find any statistically significant differences in cell type proportions between compared groups.\u003c/p\u003e\u003cp\u003eTo identify methylome differences between cohorts in our study, we focused on assessment of size effect (differences in methylation levels) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. We first selected CpG sites with an average DNA methylation difference (delta β-value) of at least 0.05 between cases and controls. The average methylation difference however, does not account for the variance of methylation level measurement. Thus, to account for variance in our analyses we calculated Hedges' g size effect for each of the analyzed CpG sites and included in further analyses only CpG with a Hedges' g effect size larger than 0.5. Additionally, we used linear regression to confirm that methylation changes at CpG sites we selected in our analyses are statistically significant with FDR corrected p-value ≤ 0.05. The examples of CpG sites from our analysis with indicated methylation levels between comparison groups are shown in (\u003cb\u003eFigures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-\u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e, Additional File 1\u003c/b\u003e). We also performed Spearman correlation of methylation levels at all CpG sites that met quality control with age, and none of the CpG sites included in our analysis showed significant correlation with age after correction for multiple testing.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGenomic regions enrichment analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo assess the genomic context of the identified methylation changes, we examined the distribution of identified CpG sites relative to chromatin states defined by histone modifications across 11 blood-derived cell types including, monocytes, neutrophils, B cells, natural killers, mononuclear cells, and seven types of T cells (samples IDs: E029, E030, E032, E034, E037, E039, E043, E044, E046, E047, E048), based on the Roadmap Epigenomics 15-state Core Model [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Enrichment analysis was performed using Locus Overlap Analysis (LOLA) tool [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. All CpG sites that passed quality control during data processing were used as the background set. Genomic regions marked by specific histone modifications were considered significantly enriched if they met both an odds ratio (OR) threshold \u0026gt; 2 and FDR-corrected p-value ≤ 0.05, as determined by Fisher’s exact test.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGene ontology term enrichment analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo identify physiological processes potentially affected by the observed methylation changes, we performed a Gene Set Enrichment Analysis (GSEA) using GENE2FUNC module available on the Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA) platform [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eValidation data set\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo validate our results, we used data from five publicly available datasets deposited in the Gene Expression Omnibus (GEO) database, including 450K BeadChip Microarray data (GSE51032, GSE104942), and EPIC BeadChip Microarray data (GSE148748, GSE163521, GSE184159). Specifically, the GSE51032 dataset contains data from EPIC Italy cohort, from which we used blood cells methylation profiles for healthy women (n = 116) and women who developed breast cancer after sample collection (n = 168). From GSE104942, we used microarray data from the blood of 87 Australian women sampled both prior to and at breast cancer diagnosis (n = 87), as well as from cancer-free women at the time of sampling (n = 123). From the GSE148748 dataset, we analyzed microarray data from freshly frozen tumor tissues of TNBC patients (n = 82), including 25 cases with \u003cem\u003eBRCA1\u003c/em\u003e mutation and 57 cases with \u003cem\u003eBRCA1\u003c/em\u003e promoter methylation. The GSE163521 and GSE184159 datasets provided data from FFPE TNBC tumor samples (n = 13 and n = 32, respectively), in which neither \u003cem\u003eBRCA1\u003c/em\u003e mutation nor epimutation had been determined. All methylomics data in this study were processed and normalized using a consistent pipeline, with the exception of data from GSE104942, which were provided as text files containing pre-calculated beta-values and therefore could not be reprocessed using our pipeline.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eBlood cells methylomes of neither\u003c/b\u003e \u003cb\u003eBRCA1\u003c/b\u003e \u003cb\u003egermline mutation nor epimutation carriers are highly stable\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe first, compared methylomes of blood cells from women with neither \u003cem\u003eBRCA1\u003c/em\u003e germline mutation nor epimutation (n\u0026thinsp;=\u0026thinsp;19) collected on average 4.62 years prior to breast cancer diagnosis with the controls (n\u0026thinsp;=\u0026thinsp;21) that also did not have \u003cem\u003eBRCA1\u003c/em\u003e mutation or epimutation and did not develop cancer during 8.17 years. This analysis identified only 12 differentially methylated positions (DMPs), including nine hypomethylated and three hypermethylated sites (\u003cb\u003eSupplementary Table\u0026nbsp;1, Additional File 2\u003c/b\u003e). These results suggest that the blood cell methylomes of individuals without \u003cem\u003eBRCA1\u003c/em\u003e germline mutation or epimutation are highly similar, regardless of whether they eventually developed breast cancer.\u003c/p\u003e\u003cp\u003eAlthough we were not able to identify an independent dataset containing confirmed \u003cem\u003eBRCA1\u003c/em\u003e mutation and epimutation status to directly validate these findings, we performed a comparable analysis using data from the EPIC Italy cohort [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This dataset included women whose blood samples were collected on average 4.75 years prior to cancer diagnosis (n\u0026thinsp;=\u0026thinsp;116), as well as healthy women without any signs of cancer (n\u0026thinsp;=\u0026thinsp;168). While \u003cem\u003eBRCA1\u003c/em\u003e germline mutation and epimutation status were not available for this cohort, the prevalence of such alterations is generally low. Therefore, even if a small number of cases carried \u003cem\u003eBRCA1\u003c/em\u003e-related alterations, the overall impact on the results is expected to be negligible due to the relatively large sample size. This analysis identified only 39 DMPs (\u003cb\u003eSupplementary Table\u0026nbsp;2, Additional File 2\u003c/b\u003e), including 21 hypomethylated and 18 hypermethylated positions, further supporting the general stability of blood cell methylomes in women without \u003cem\u003eBRCA1\u003c/em\u003e germline mutation or epimutation.\u003c/p\u003e\u003cp\u003eAdditionally, we analyzed data from a separate study [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] comparing methylomes of blood samples collected both prior to and at the time of breast cancer diagnosis (n\u0026thinsp;=\u0026thinsp;87) with samples from cancer-free individuals (n\u0026thinsp;=\u0026thinsp;123). No significant methylation differences were detected in this cohort, again supporting the notion of peripheral blood cells methylome stability of \u003cem\u003eBRCA1\u003c/em\u003e mutation and epimutation negative individuals.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethylomes of blood cells from healthy women with\u003c/b\u003e \u003cb\u003eBRCA1\u003c/b\u003e \u003cb\u003emutation or epimutation display specific methylation signatures\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNext, we compared the blood cells methylomes of \u003cem\u003eBRCA1\u003c/em\u003e germline mutation carriers (n\u0026thinsp;=\u0026thinsp;43) with those of women confirmed to carry neither \u003cem\u003eBRCA1\u003c/em\u003e mutation nor epimutation (n\u0026thinsp;=\u0026thinsp;21). This analysis identified 2473 DMPs in mutation carriers (\u003cb\u003eSupplementary Table\u0026nbsp;3, Additional File 2\u003c/b\u003e), including 1839 hypomethylated (74.4%) and 634 hypermethylated (25.6%) CpG sites. We then performed the same analysis for epimutation carriers (n\u0026thinsp;=\u0026thinsp;29) and identified more than twice as many DMPs (5163), including 4637 hypomethylated (89.8%) and 526 hypermethylated (10.2%) CpG sites (\u003cb\u003eSupplementary Table\u0026nbsp;4, Additional File 2\u003c/b\u003e). Among the DMPs identified, 1155 CpG sites were common to both mutation and epimutation carriers (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA; and \u003cb\u003eSupplementary Table\u0026nbsp;5, Additional File 2\u003c/b\u003e), including 982 hypomethylated (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) and 173 hypermethylated sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). These results show that methylomes of blood cells of women tested positive for germline mutation and epimutation carry specific, but to a large extent non overlapping methylation signatures. \u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. \u003cb\u003eVenn diagrams illustrating overlap analysis between DMPs identified in\u003c/b\u003e \u003cb\u003eBRCA1\u003c/b\u003e \u003cb\u003egermline mutation and epimutation related methylation signatures.\u003c/b\u003e A) all identified DMPs in both signatures, B) hypomethylated DMPs and C) hypermethylated DMPs.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethylation changes identified in\u003c/b\u003e \u003cb\u003eBRCA1\u003c/b\u003e \u003cb\u003emutation and epimutation carriers are enriched in repressed chromatin regions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThen, to approximate biological context of the identified methylation signatures we used Locus Overlap Analysis (LOLA) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and assessed whether methylation changes present in \u003cem\u003eBRCA1\u003c/em\u003e mutation and epimutation carriers are enriched in regions marked by histones with specific modifications. This analysis was performed across 11 cell types from Roadmap Epigenomics 15-state Core Model (see Methods for details). Despite the distinct methylation signatures associated with \u003cem\u003eBRCA1\u003c/em\u003e mutation and epimutation, our results indicate that the DMPs in both groups map to similar regulatory regions. Specifically, the methylation changes identified in both mutation carriers (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003ePanel A\u003c/b\u003e) and epimutation carriers (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003ePanel B\u003c/b\u003e) were consistently enriched in regions characterized by constitutive heterochromatin (Het) histone marks. Interestingly, maintaining global heterochromatin integrity has been linked to \u003cem\u003eBRCA1\u003c/em\u003e tumor suppression function [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Also, methylation changes identified in \u003cem\u003eBRCA1\u003c/em\u003e germline mutation related signature were enriched for several cell types within regions of the genome associated with histone marks characteristic for repressed PolyComb (ReprPC) and ZNF genes and repeats (ZNF/Rpts). And aggressive breast cancer phenotype was linked to the function of the Polycomb-repressive complex 2 (PRC2) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eBiological processes associated with\u003c/b\u003e \u003cb\u003eBRCA1\u003c/b\u003e \u003cb\u003emutation- or epimutation-specific methylation signatures link these alterations to breast cancer development\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo explore the functional relevance of the \u003cem\u003eBRCA1\u003c/em\u003e mutation- and epimutation-associated methylation signatures, we performed GSEA of genes associated with identified methylation signatures using two ontology terms databases \u0026ldquo;Hallmark gene sets\u0026rdquo; and \u0026ldquo;Curated gene sets\u0026rdquo; within the FUMA platform.\u003c/p\u003e\u003cp\u003eThe analysis based on \u0026ldquo;Hallmark gene sets\u0026rdquo; database, identified one and four ontology terms associated with the \u003cem\u003eBRCA1\u003c/em\u003e germline mutation and epimutation related signatures, respectively (\u003cb\u003eSupplementary Tables\u0026nbsp;1 and 2, Additional File 3\u003c/b\u003e). The majority of gene ontology terms identified in these analyses were related to carcinogenesis. Specifically, the single gene ontology term linked to \u003cem\u003eBRCA1\u003c/em\u003e mutation associated signature was HALLMARK_KRAS_SIGNALING_DN. \u003cem\u003eKRAS\u003c/em\u003e is the most frequently mutated \u003cem\u003eRAS\u003c/em\u003e gene in breast cancer, and has been linked to poor prognosis and an elevated metastatic rate [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Three of the four ontology terms associated with methylation changes identified in \u003cem\u003eBRCA1\u003c/em\u003e epimutation carriers were also cancer-related, including HALLMARK_APICAL_JUNCTION, HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION, and HALLMARK_MYOGENESIS. The apical junction complex, is considered a hub of signal transduction, regulates cell-cell adhesion, gene transcription, and cell proliferation and differentiation [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. E-Cadherin, a component of the apical junction complex is encoded by \u003cem\u003eCDH1\u003c/em\u003e. The loss of \u003cem\u003eCDH1\u003c/em\u003e expression is a hallmark of epithelial-mesenchymal transition, a key process related to metastasis [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Also, a meta-analysis reported that individuals diagnosed with dermatomyosis, which involves impaired myogenesis, have increased risk of developing breast cancer [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe analysis based on \u0026ldquo;Curated gene sets\u0026rdquo; database, identified 29 and 204 ontology terms associated with the \u003cem\u003eBRCA1\u003c/em\u003e germline mutation and epimutation related signatures, respectively (\u003cb\u003eSupplementary Tables\u0026nbsp;3 and 4, Additional File 3\u003c/b\u003e). Interestingly, 24 of those terms were common between the two analyses (\u003cb\u003eSupplementary Table\u0026nbsp;5, Additional File 3\u003c/b\u003e), including those previously associated with breast cancer: REACTOME_NEUREXINS_AND_NEUROLIGINS [\u003cspan additionalcitationids=\"CR41 CR42\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], REACTOME_REGULATION_OF_COMMISSURAL_AXON_PATHFINDING_BY_SLIT_AND_ROBO [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] and MANALO_HYPOXIA_UP [\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]; as well as terms associated with poor clinical outcome of breast cancer patients, including BENPORATH_SUZ12_TARGETS, BENPORATH_EED_TARGETS, and BENPORATH_PRC2_TARGETS [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Moreover, nine of these ontology terms were related to histone modifications, including H3K4me2, H3K27me3 and H3K4me3 [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan additionalcitationids=\"CR51 CR52 CR53\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] and the changes of histone-related chromatin structure is one of the hallmarks of cancer. Interestingly, the term LIM_MAMMARY_STEM_CELL_UP, which characterizes a subset of genes consistently upregulated in mammary stem cells in both mouse and human species [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] was also one of terms common in this analysis.\u003c/p\u003e\u003cp\u003eMoreover, among the terms uniquely associated with methylation signature identified in \u003cem\u003eBRCA1\u003c/em\u003e mutation carriers (\u003cb\u003eSupplementary Table\u0026nbsp;6, Additional File 3\u003c/b\u003e), two were previously associated with breast cancer development: MIKKELSEN_ES_HCP_WITH_H3K27ME3 [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] and GRESHOCK_CANCER_COPY_NUMBER_UP [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. In turn, amongst terms associated with methylation signature identified in \u003cem\u003eBRCA1\u003c/em\u003e epimutation carriers (\u003cb\u003eSupplementary Table\u0026nbsp;7, Additional File 3\u003c/b\u003e) nine was directly linked to breast cancer, five described processes related to the carcinogenesis (e.g. methylated in cancer, pathways in cancer, neoplastic transformation), and two were associated with metastasis. Three terms in this analysis were related to inflammatory response, which has been shown to promote or inhibit the cancer development [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Five terms were related to collagen metabolism, a fundamental component of the tumor microenvironment contributing to cancer fibrosis [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], and six to metabolisms of glycosylation, a process influencing malignant transformation or accelerating tumorigenesis [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Moreover, this analysis identified term KYNG_DNA_DAMAGE_BY_4NQO, and DNA damage is recognized as a critical factor in cancer development and progression [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], terms such as KEGG_FOCAL_ADHESION, WP_FOCAL_ADHESION, WP_HIPPO_SIGNALING_REGULATION_PATHWAYS, or REACTOME_CELL_CELL_COMMUNICATION which were previously described in the context of processes involved in cell proliferation and migration [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] and GOZGIT_ESR1_TARGETS_DN describing a set of down-regulated genes in breast cancer cell line that do not express \u003cem\u003eESR1\u003c/em\u003e [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Also, three terms in this analysis were related to class 1 histone deacetylases (HDAC), including \u003cem\u003eHDAC1\u003c/em\u003e, \u003cem\u003eHDAC2\u003c/em\u003e and \u003cem\u003eHDAC3\u003c/em\u003e. Previously, \u003cem\u003eHDAC1\u003c/em\u003e has been described to be significantly correlated with the molecular subtypes of breast tumors and overexpressed in luminal A tumors [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], whereas \u003cem\u003eHDAC2\u003c/em\u003e and \u003cem\u003eHDAC3\u003c/em\u003e have been shown to displayed higher expression in aggressive breast cancer subtypes [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Lastly, a number of remaining terms contained genes previously implicated in breast cancer development and progression, including \u003cem\u003eTP53\u003c/em\u003e [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], \u003cem\u003eTGFB\u003c/em\u003e [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], \u003cem\u003eVEGFR1\u003c/em\u003e [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], \u003cem\u003eCDH1\u003c/em\u003e [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e], \u003cem\u003eEGFR\u003c/em\u003e [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e], \u003cem\u003eEGF\u003c/em\u003e [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e], \u003cem\u003eTNC\u003c/em\u003e [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e], \u003cem\u003eGR\u003c/em\u003e [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e], \u003cem\u003eSTAT5A\u003c/em\u003e [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e], \u003cem\u003eMYC\u003c/em\u003e [\u003cspan additionalcitationids=\"CR81 CR82\" citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e], \u003cem\u003eTP63\u003c/em\u003e [\u003cspan additionalcitationids=\"CR85\" citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e], \u003cem\u003ePTP1B\u003c/em\u003e [\u003cspan additionalcitationids=\"CR88\" citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e], \u003cem\u003eSATB1\u003c/em\u003e [\u003cspan additionalcitationids=\"CR91 CR92\" citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e], \u003cem\u003eNF1\u003c/em\u003e [\u003cspan additionalcitationids=\"CR95 CR96\" citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e], \u003cem\u003ePDEF\u003c/em\u003e [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e, \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e], \u003cem\u003eSMARCE1\u003c/em\u003e [\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e] or \u003cem\u003eSUZ12\u003c/em\u003e [\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethylation changes observed in\u003c/b\u003e \u003cb\u003eBRCA1\u003c/b\u003e \u003cb\u003emutation and epimutation carriers are also present in breast cancer cells\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThere is already evidence available indicating that at least to some extent methylation changes observed in blood cells can be considered a proxy of changes observed in the tissue that cancer originates from [\u003cspan additionalcitationids=\"CR103\" citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e]. Due to the lack of available datasets from healthy breast tissues of individuals with confirmed \u003cem\u003eBRCA1\u003c/em\u003e germline mutation or epimutation, we were unable to evaluate the presence of these signatures in non-tumor mammary epithelial cells. However, we were able to access methylomics data from TNBC samples with \u003cem\u003eBRCA1\u003c/em\u003e mutation (n\u0026thinsp;=\u0026thinsp;25), epimutation (n\u0026thinsp;=\u0026thinsp;57) [\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e] and cases with neither \u003cem\u003eBRCA1\u003c/em\u003e mutation nor epimutation (n\u0026thinsp;=\u0026thinsp;45) [\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e]. To assess the presence of the methylation changes constituting \u003cem\u003eBRCA1\u003c/em\u003e germline mutation and epimutation related signatures in methylomes of TNBC, we performed unsupervised clustering of TNBC cases, based on identified in blood of \u003cem\u003eBRCA1\u003c/em\u003e epimutation and germline mutation methylation signatures. Clustering based on the \u003cem\u003eBRCA1\u003c/em\u003e germline mutation-associated signature effectively distinguished TNBC samples without \u003cem\u003eBRCA1\u003c/em\u003e alterations from those harboring \u003cem\u003eBRCA1\u003c/em\u003e mutations or epimutations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Similarly, although slightly less precise, clustering based on the epimutation-associated signature also separated samples according to \u003cem\u003eBRCA1\u003c/em\u003e status (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Most interestingly, the unsupervised clustering analyses based on methylation changes common for both signatures, remarkably accurately separated TNBC without \u003cem\u003eBRCA1\u003c/em\u003e mutation nor epimutation from TNBC positive for \u003cem\u003eBRCA1\u003c/em\u003e mutation or epimutation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eBRCA1\u003c/b\u003e \u003cb\u003egermline mutation and epimutation related signatures in the context of previously reported blood-based methylation markers of breast cancer risk\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA number of previous studies have reported methylation changes in blood cells associated with breast cancer risk [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan additionalcitationids=\"CR109 CR110 CR111 CR112 CR113\" citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e]. We analyzed the results of these studies, including types of recruited patients and methods the authors used to identify methylation changes. We also compared reported methylation signatures between these studies with our results (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In general, there was no or only a minor overlap between DMPs reported in analyzed studies and the signatures we identified. This is however not surprising because overall, there was no consistency between analyzed studies including patient selection and data processing methodologies used. A detailed comparison of these datasets and methodological differences is provided in \u003cb\u003eAdditional File 4\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe have shown that blood cells methylomes of healthy at the time of sampling \u003cem\u003eBRCA1\u003c/em\u003e mutation and epimutation carriers display aberrations, that are not present in blood cells of healthy women without detectable genetic or epigenetic \u003cem\u003eBRCA1\u003c/em\u003e lesions and women that eventually developed sporadic breast cancer during our study. These results indicate that malfunctioning of \u003cem\u003eBRCA1\u003c/em\u003e, may result in genome-wide methylation aberrations which also may contribute to increased cancer risk, especially that we were able to link identified epigenetic changes to the pathways that have previously been associate with breast cancer pathology.\u003c/p\u003e\u003cp\u003eThe methylation signatures that we identified in methylomes of epimutation and mutation carriers overlapped only to certain extent. However, GSEA linked methylation changes from each of the signatures to very similar cancer related pathways and physiological processes, which when affected, are key contributors to cancer phenotype. This suggests that the effect of genetic and epigenetic inactivation of \u003cem\u003eBRCA1\u003c/em\u003e may be to a large extant synergic.\u003c/p\u003e\u003cp\u003eMoreover, the methylation changes constituting both signatures were predominantly losses of methylation and most importantly uniformly and specifically annotated to heterochromatin regions of genome. It has been shown that DNA methylation marks heterochromatin at pericentromeres and is a key factor for the maintenance of the heterochromatin integrity [\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e]. Moreover, losses of methylation at these regions of genome during neoplastic transformation have been shown to lead to genomic instability and aberrant gene expression [\u003cspan additionalcitationids=\"CR117 CR118\" citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e]. That in the context of finings indicating that maintaining heterochromatin integrity is one of the functions of \u003cem\u003eBRCA1\u003c/em\u003e tumor suppressor activity [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], allows to speculate that observed in our analysis methylation aberrations at heterochromatin may be attributed to epigenetic or genetic inactivation of \u003cem\u003eBRCA1\u003c/em\u003e. Moreover, observed in mice models increased levels of heterochromatic repetitive satellite RNAs which have been shown to induce tumor formation [\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e], may also be consequence of loss of stability of methylation patterns at heterochromatin, attributed to malfunctioning \u003cem\u003eBRCA1\u003c/em\u003e gene. Adding to mechanism by which \u003cem\u003eBRCA1\u003c/em\u003e mutation and epimutation related methylation aberrations in heterochromatin, contribute to breast cancer development.\u003c/p\u003e\u003cp\u003eOur analysis also linked identified methylation changes to genomic regions of Polycomb-repressive complex 2 (PRC2) and Zinc-finger genes (ZNFs). Interestingly, the loss of \u003cem\u003eBRCA1\u003c/em\u003e expression have been shown to inhibit the differentiation of embryonic stem cells and promote an aggressive breast cancer phenotype by impacting the function of Polycomb-repressive complex 2 (PRC2) [\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e]. We did not find studies describing direct association between \u003cem\u003eBRCA1\u003c/em\u003e function and Zinc-finger genes (ZNFs) family, however these proteins have been reported to be involved in tumorigenesis via their prooncogenic/tumor suppressed properties, as well as transcription factor function [\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLastly, we showed that methylation changes constituting identified \u003cem\u003eBRCA1\u003c/em\u003e epimutation- and germline mutation-associated signatures are present in methylomes of TNBC. This suggests that \u003cem\u003eBRCA1\u003c/em\u003e deficiency is associated with methylation changes in blood cells but only in progenitors of breast cancer cells leads to phenotypic transformation.\u003c/p\u003e\u003cp\u003eObvious limitation of our study is the number of patients and our results need to be confirmed in larger cohorts of patients which we are lacking at the moment. Moreover, our attempt to validate our findings in the context of methylation signatures previously identified in blood of breast cancer patients was only partly successful and illustrated the need for the methodologically unified study of methylation changes associated with \u003cem\u003eBRCA1\u003c/em\u003e deficiency.\u003c/p\u003e\u003cp\u003eNevertheless, the evidence we present appears to be sufficient to accelerate research aiming to elaborate the link between \u003cem\u003eBRCA1\u003c/em\u003e malfunctioning and genome-wide epigenetic changes that may be involved in breast cancer development.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study adds to the increasing body of evidence that warrant further research to determine contribution of constitutional methylation of tumor suppressor genes in somatic cells as cancer risk factors. For the first time, we demonstrate that that entire methylome may be involved in development of increased risk of breast cancer observed for the carriers of genetic and epigenetic defects of \u003cem\u003eBRCA1\u003c/em\u003e gene.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the ethics committee of the Pomeranian Medical University and conformed to the ethical guidelines of the 1975 Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003eAll participants gave written consent to provide a blood sample for research purposes and to participate in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available in the Gene Expression Omnibus repository, GSE272644 \u0026ndash; our data,\u0026nbsp;GSE51032 [34],\u0026nbsp;GSE104942\u0026nbsp;[35],\u0026nbsp;GSE148748\u0026nbsp;[105], GSE163521\u0026nbsp;[106], GSE184159\u0026nbsp;[107].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was financed by PRELUDIUM BIS 2 grant (2020/39/O/NZ2/02943 Grant Number) and\u0026nbsp;OPUS22 grant (2021/43/B/NZ2/02979 Grant Number)\u0026nbsp;from Polish National Science Centre.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTomasz Kazimierz Wojdacz, Jan Lubiński, Tomasz Huzarski conceived the concept and design of the study. Tomasz Kazimierz Wojdacz obtained funding for this project. Jan Lubiński\u0026nbsp;and\u0026nbsp;Tomasz Huzarski\u0026nbsp;recruited participants.\u0026nbsp;Jacek Antoniewski prepared biological samples for DNA methylation analysis and performed MS-HRM, which was supported by Katarzyna Ewa Sokolowska. Dominik Strapagiel and Marta Sobalska-Kwapis performed wet-lab microarray experiments. Katarzyna Ewa Sokolowska analyzed the quality of data, performed methylation and bioinformatics analyses, prepared data visualization, with the support of Tomasz Kazimierz Wojdacz. Katarzyna Ewa Sokolowska and Tomasz Kazimierz Wojdacz wrote, reviewed and edited the manuscript.\u0026nbsp;All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSavage, K.I. and D.P. 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However, the effect of \u003cem\u003eBRCA1\u003c/em\u003e epimutation as well as germline mutations on the methylomes of carriers has not been investigated.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe performed a genome-wide methylation screening of blood cells from three cohorts of women: \u003cem\u003eBRCA1\u003c/em\u003e germline mutation carriers, \u003cem\u003eBRCA1\u003c/em\u003e epimutation carriers and women who were negative for both \u003cem\u003eBRCA1\u003c/em\u003e mutation and epimutation but had blood samples collected an average of 4.7 years prior to a breast cancer diagnosis. We then compared the methylomes of these cohorts to control individuals who were tested negative for both \u003cem\u003eBRCA1\u003c/em\u003e mutation and epimutation and remained cancer-free for more than eight years prior to the study. We also assessed whether methylation changes associated with \u003cem\u003eBRCA1\u003c/em\u003e germline mutation and epimutation were present in the tumor methylomes of TNBC cases.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eWe identified specific methylation signatures in blood cells of \u003cem\u003eBRCA1\u003c/em\u003e mutation and epimutation carriers. These signatures were absent in the blood of cancer-free women as well as in blood samples collected years before cancer diagnosis. We subsequently linked the identified methylation changes to physiological processes and genomic regions previously implicated in breast cancer pathogenesis. Moreover, unsupervised clustering analyses confirmed the presence of identified methylation changes in the tumor methylomes of TNBC cases.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003e\u003cem\u003eBRCA1\u003c/em\u003e mutation and epimutation carriers display genome-wide methylation signatures that affect specific genomic regions and biological processes known to contribute to breast cancer pathogenesis when disrupted. Notably, these signatures are absent in the blood cells of individuals sampled years before a breast cancer diagnosis but are detectable in the tumor methylomes of TNBC, further suggesting their relevance to breast cancer development.\u003c/p\u003e","manuscriptTitle":"Blood cells of BRCA1 mutation and epimutation carries appear to acquire specific epigenetic signatures","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-12 13:04:38","doi":"10.21203/rs.3.rs-7301339/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a7afcc18-6c65-4a0c-8a92-bdf37449571a","owner":[],"postedDate":"August 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-02T11:53:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-12 13:04:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7301339","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7301339","identity":"rs-7301339","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}