MCF-7 as a model for functional analysis of breast cancer risk variants

Abstract

Background

Breast Cancer (BCa) genetic predisposition is governed by more than 142 loci as revealed by genome-wide association studies (GWAS). The functional contribution of these risk loci to BCa remains unclear and additional post-GWAS analyses are required.

Methods

We identified active regulatory elements (enhancers, promoters, and chromatin organizing elements) by histone H3K27 acetylation and CTCF occupancy and determined the enrichment of risk variants at these sites. We compared these results to previously published data and for other cell lines, including HMEC cells, and related these data to gene expression.

Results

In terms of mapping accuracy and resolution, our data augments previous annotations of the MCF-7 epigenome. After intersection with GWAS risk variants we found 39 enhancers and 15 CTCF occupancy sites that, between them, overlapped 96 BCa credible risk variants at 42 loci. These risk enhancers likely regulate the expression of dozens of genes, which are enriched for GO categories including estrogen and prolactin signaling.

Conclusions

Ten (of 142) BCa risk loci likely function via enhancers which are active in MCF-7 and are well suited to targeted manipulation in this system. In contrast, risk loci cannot be mapped to specific CTCF binding sites and the genes linked to risk CTCF sites did not show functional enrichment. The identity of risk enhancers and their associated genes suggest that some risk may function during later processes in cancer progression.

Impact

Here we report how the ER+ cell line MCF-7 can be used to dissect risk mechanisms for BCa.

Introduction

According to the traditional model of carcinogenesis, normal tissue undergoes rounds of oncogenic mutations during proliferation that eventually leads to metastatic tumor growth. An individual’s inherited genetic predisposition for cancer, which can be measured by linkage analysis or genome-wide association studies (GWAS), influences the rate of those oncogenic somatic mutations and the environment in which the tumors develop (1). In breast cancer (BCa) rare but high-penetrance inherited mutations to genes, such as BRCA1 and BRCA2, contribute about 30% to the familial risk of developing breast cancer and, in general, have well understood biological consequences (2). However, only about 5%−10% of BCa cases are actually associated with this type of germline mutation. In contrast, GWAS show that low-penetrance but common genetic variants explain up to 50% of disease heritability and contribute significant risk to the development of both familial and sporadic BCa (3). Unlike high-penetrance rare mutations, in most cases, these other risk genotypes are poorly understood and do not alter protein coding. Elucidating the functional basis of these common risk variants is therefore of great importance.

A recent GWAS uncovered 65 new breast cancer risk loci contributing to a total of roughly 142 reproducible breast cancer risk loci containing over 38,000 statistically significant (combined p.value < 5×10⁻⁸) or near significant (combined p.value < 1×10⁻⁵) risk variants (4). These variants primarily consist of single nucleotide polymorphisms (SNPs) (we use ‘variant’ and ‘SNP’ interchangeably in this text). Due to the large number of risk-associated SNPs as well as to their presence in noncoding DNA, it can be difficult to pinpoint which SNP or combination of SNPs are causal for a disease, let alone explain the biological mechanisms/genes involved. This will only become more challenging as the number of identified risk SNPs is expected to increase as the sizes of case-control studies grow larger in the near future. Our goal is therefore to prioritize SNPs based on their potential effects on cell type specific genomic activity.

Risk SNPs are non-randomly distributed throughout the genome and have been shown to be enriched in tissue-specific noncoding regulatory elements (REs), mainly in enhancers (5–7). Regulatory elements are defined as regions of non-coding DNA that regulate the transcription of genes. Enhancers are a class of regulatory element that influence cell fate and development through coordinated interaction between transcription factors (TFs) and their target promoters to alter the transcription of genes (8). Enhancers are marked by surrounding histone modifications and nucleosome depletion. The histone modification most often used is H3K27 acetylation, since it has been shown that it marks active (engaged) enhancers (9). Enhancers are also relatively transient, and their activity is dictated by extracellular signals that activate complex enhancer networks to promote cell type and condition specific gene expression. Common breast cancer risk variants present in gene REs are likely to subtly alter gene expression patterns, compared to protective alleles, in ways that predispose some individuals to cancer. For example, studies of the noncoding region 8q24 upstream of MYC have shown enrichment of variants associated with different tumor types, including breast, in multiple enhancers at this location (10–12).

Another important feature shown to coincide with risk SNPs is CTCF (CCCTC binding-factor). CTCF is a highly conserved 11-zinc finger protein and is the only known insulator protein in vertebrates (13). It is most often enriched at loop and topologically associated domain (TAD) boundaries that separate transcriptionally active and repressed genes (14). This implicates its importance in the organization of chromatin compartments to prevent aberrant gene-enhancer interactions. Lupianez et al. (15) showed that removing the CTCF associated boundary elements at the Epha4 TAD causes abnormal interactions between adjacent TADs and expression of genes within those TADs. Others have shown that CTCF is important for spatial organization of the genome for proper induction and silencing of transcription (14,16). Although the relationship between SNPs and CTCF is less clear than that of SNPs within enhancers, SNP mediated disruptions of CTCF consensus sequences has been shown to change the binding affinity of the CTCF protein (17). This could result in gene spatial network rewiring that has a strong influence on gene expression changes that indirectly lead to BCa. Such rewiring was demonstrated by a CRISPR-mediated deletion of prostate cancer risk-associated CTCF sites, which identified repressive chromatin loops [17].

In the present study, we used the MCF-7 cell line as a model of breast cancer. It is one of the most utilized breast cancer cell lines in cancer research due to its long history and expression of the estrogen receptor (ER) (18). The majority of patients (72.7%) with a known HR/HER status (estrogen receptor + progesterone receptor/human epidermal growth factor receptor) are HR+/HER2- (19). MCF-7 cells are one of the few breast cancer cell lines that is also HR+/HER2-, making it an extremely relevant model for the study of invasive breast cancer. Even, so there is currently a lack in the literature of post-GWAS analyses based on experimental manipulation in ER+ systems, including MCF-7 (20). Although a detailed meta-analysis of BCa risk loci was recently conducted and credible risk variants (CRVs; see methods for a detailed definition) genome-wide were defined (4), potential functionality was only alluded to in broad terms. Here, we analyze epidemiologically defined genetic risk loci within the MCF-7 cancer cell line. In so doing, we identified functional targets that can further be tested by genetic manipulation. We report a priority list of BCa risk enhancers and risk CTCF-binding-sites tailored to the MCF-7 cell line.

Materials and Methods

Cell Culture:

MCF-7 cells were obtained from ATCC and cultured in DMEM (ATCC, cat # 30–2003) supplemented with 10% FBS and 0.01 mg/ml human recombinant insulin (Gibco, ref # 12585–014). They were incubated in a humidified 37°C, 5% CO₂ incubator. For routine passaging, cells were grown in T25 and T75 culture flasks and passaged using 0.25% Trypsin/EDTA.

ChIP-Seq

For ChIP we followed previously published protocols from Rhie et al (21) with slight modifications. Roughly 30–40 × 10⁶ cells were used per ChIP. Upon reaching 70–80% confluency, cells were directly fixed in T75 flasks by adding 16% formaldehyde to the culture medium to a final concentration of 1%. The reaction was quenched for 5 minutes at room temperature with 10X (1.15 M) glycine. Using a Bioruptor Pico (Diagenode, Cat # B01060001) the isolated chromatin was sonicated for 30 second on and 30 second off cycles to yield DNA fragments between 200 and 500 base pairs. 100 ug of sonicated chromatin was used for immunoprecipitation and 1 ug (1%) was used for the input control. To probe for CTCF or H3K27ac, samples were incubated at 4 °C overnight with a primary antibody [CTCF: Cell Signaling monoclonal (D31H2), Cat# 3418; H3K27ac: Active Motif, Cat #39133] or an IgG control (Sigma, Cat # R9133). A/G magnetic beads (Pierce, Cat # 88802) were then incubated with the samples for 2 hours at 4 °C. Following this incubation, the beads were washed with a series of buffers of varying salt concentrations before overnight elution at 67 °C. The ChIP, IgG, and Input samples were all purified using a QIAprep Spin Miniprep Kit (Qiagen, Cat # 27104).

Construction and Sequencing of ChIP-Seq Libraries

Libraries for input and IP samples were prepared by the Van Andel Genomics Core from 10 ng of input material and all available IP material using the KAPA Hyper Prep Kit (v5.16) (Kapa Biosystems, Wilmington, MA USA). Prior to PCR amplification, end-repaired and Atailed DNA fragments were ligated to Bio Scientific NEXTflex Adapters (Bio Scientific, Austin, TX, USA). The quality and quantity of the finished libraries were assessed using a combination of Agilent DNA High Sensitivity chip (Agilent Technologies, Inc.), QuantiFluor dsDNA System (Promega Corp., Madison, WI, USA), and Kapa Illumina Library Quantification qPCR assays (Kapa Biosystems). Sequencing (75 bp, single end) was performed on an Illumina NextSeq 500 sequencer using a 75-bp sequencing kit (v2) (Illumina Inc., San Diego, CA, USA). Base calling used Illumina NextSeq Control Software (NCS) v2.0, and the output of NCS was demultiplexed and converted to FastQ format with Illumina Bcl2fastq v1.9.0.

Identification of ChIP-Seq Peaks

Two biological replicates of MCF-7 were used for input and ChIP for H3K27Ac and separately for CTCF. Following sequencing, fastq files were aligned to the HG19 genome assembly using default setting for BWA v0.7.15 (22). Mapped sequencing depth was 40 – 50 million reads ChIP, ~110 million reads input for CTCF and 60–70 million reads ChIP, ~150 million reads input for H3K27ac. Aligned reads were called using MACS2 v2.1 at a liberal FDR cutoff of 0.1 (23). For CTCF comparison to ENCODE data (ENCODE1 = ENCSR000DMV, ENCODE2 = ENCSR560BUE, primary antibody: Millipore polyclonal (07–729); ENCODE3 = ENCSR000DWH primary antibody: Cell Signaling (2899)), analysis started with fastq files and were aligned with BWA as above, except: reads less than 50 bp in our data and reads less than 20 bp in the ENCODE data were removed using Trim Galore prior to peak calling. Peaks were filtered by IDR (V2.0.2) according to default settings. For H3K27Ac, Trim Galore was not used and ENCODE comparison was done starting with peaks files (also generated using BWA and MACS2). Existing data for MCF-7 H3K27ac ChIP-Seq ENCODE1 (ENCSR000EWR, read length: 32 bp, ChIP depth = ~20 million reads, input = ~10 million reads), ENCODE2 (ENCSR752UOD, read length: 36 bp, ChIP depth: rep1, 60 million and rep2, 20 million reads, input: 70 – 80 million reads), and for HMEC (ENCSR000ALW) were used for comparison. NarrowPeak calls (i.e. ENCFF187RUK, ENCFF37ORFF, ENCFF537JMI, and ENCFF208IPB) were filtered by IDR to generate plots in Figure 1 and for peak coordinates. All H3K27Ac peak data was filtered by IDR (V2.0.2) with the following parameters for peak-merge: --rank p.value --soft-idr-threshold .01 --peak-merge-method max (24). FRiP was calculated using deepTools (V.3.1.3) (25). Intersected datasets are defined by coverage, not peaks, i.e. every bp annotated in all datasets is included; overlapping peaks are not merged. Prediction for allele dependent CTCF binding was made using MotifBreakR (V.1.8) (26).

BCa Risk SNPs

All SNPs used here derived from the recent BCa GWAS meta-analysis by Michailidou, et. al. (4). Their report provided a set of 11.8 million 1000 Genomes SNPs which were associated with BCa and 20,989 SNPs with significant or near significant (combined p < 1 × 10⁻⁵) association values. Across 142 regions they selected the most significant SNP or SNPs and identified those nearby SNPs within 500 Kb and 2 orders of magnitude significance. These they called credible risk variants (CRVs). The risk loci correspond roughly to sets of significant risk SNPs (combined p. value < 5×10⁻⁸) with at least 400 to 500 Kb intervening distance. We combined the sets of CRVs and associated annotations from their Supplemental Tables: 2, 6, 8, 11, 13, and 14 to produce a list of 4,453 BCa CRVs corresponding to 65 new risk loci and 77 re-confirmed previously published risk loci. Across these regions, we also selected the most significant SNPs per locus and used RaggR (27) to obtain the set of 8,687 phase 3 1000 genomes in LD R² > 0.8 based on European linkage maps.

Overlap and Enrichment of BCa Risk SNPs in Regulatory Elements

SNP enrichment was obtained using Bedtools v2.26.0 to overlap risk SNP genomic locations (hg19) with RE locations (28). The ratios of overlapping risk SNP/total risk SNPs were compared to the background ratio: either based on of the overlap of all 11.8 million background SNPs with association values by Michailidou, et. al. (4) or based on the ratio of only those 11.8 million background SNPs within 1Mb of each risk locus (1.25 million SNPs). The hypergeometric distribution was used to obtain significance values of overall overlap frequencies (Sup. Table 2).

These enrichment ratios are depicted in Figure 2B–D and are based on the overlap of all BCa risk variants with all enhancers, compared to a single background set. This is useful for determining in general whether the entire set of risk SNPs is related to a specific type of genomic element or to tissue-specific activity. The metric quantifies how relevant any particular model is likely to be for examining risk. Unfortunately, when examining each locus individually, this sort of enrichment calculation can be misleading. This is because, while the background set of SNPs are independent at both the level of genome and single locus, due to genetic linkage, risk SNPs are not independent of each other at the level of a locus. Although multiple significant risk SNPs can be present at a single locus, they should not be treated as independent, but instead represent a single risk signal. As such, an enrichment score for a single locus can be a function of the local LD structure, which we do not expect to be a good indicator of disease relevance. For this reason, the background rate of overlap should be adjusted separately for each locus.

To compare the significance of risk span/RE overlap at each locus separately, the most distant CRVs for each locus were used to define a CRV span and the proportion of that span, in bp, which overlapped RE peak coverage was calculated using Bedtools. That value was then ranked against a background distribution of overlap proportions for each of the 142 loci. The background distributions were calculated by permutation testing using R, wherein background SNPs within 1 MB were randomly drawn 10,000 times and used to form the center of a span equal in length to the risk span, and then overlapped with REs. This comparison generates a probability value corresponding to the proportion of the background distribution with an equal or greater amount of overlap as the risk span. The same set of random spans for each locus were used to compare overlap with different tissue or types of REs.

These comparisons were used to generate the heatmap shown in Figure 3. This was done, in addition to using more common enrichment scores, primarily because we think that the location of individual risk variants within a locus is affected by how risk association is propagated during imputation according to LD related to some single risk element. Thus, these multiple variants represent a single signal. Enrichment calculations, and in particular, statistical tests which assume multiple independent tests are not valid. Randomly drawing individual SNPs from a large set of background SNPs for comparison is likewise not appropriate. Instead we asked: given a span of DNA associated with breast cancer risk, what percentage overlaps an MCF-7 RE and how does that compare to a background of equal sized spans nearby? We believe that the identification of likely risk region is probably more accurate for small, well defined REs in inactive regions, than for large REs in regions of high activity. For instance, Supplemental Figure 2 shows 2 loci with high risk SNP enrichment but with widely spaced CRVs so that they are characterized by a low span overlap significance and correspond to ambiguity in risk RE identification.

Construction and Sequencing of Directional mRNA-Seq Libraries

Libraries were prepared by the Van Andel Research Institute Genomics Core from 1 μg of material using the KAPA Stranded mRNAseq Kit (v4.16) (Kapa Biosystems, Wilmington, MA USA). RNA was sheared to 250–300 bp. Prior to PCR amplification, cDNA fragments were ligated to Bio Scientific NEXTflex Adapters (Bioo Scientific, Austin, TX, USA). The quality and quantity of the finished libraries were assessed using a combination of Agilent DNA High Sensitivity chip (Agilent Technologies, Inc.), QuantiFluor dsDNA System (Promega Corp., Madison, WI, USA), and Kapa Illumina Library Quantification qPCR assays (Kapa Biosystems).

Differential Gene Expression Analysis

Existing RNA-seq data from ENCODE for MCF-7 (ENCSR000CPT) and HMEC (ENCFF000GDZ) and from NCBI for HMEC (GSE47933) were used for comparison, without alteration. To these we compared newly generated expression data from 8 WT biological replicates of MCF-7, which were sequenced in two separate experiments. For our experiments 8 different MCF-7 WT clones were expanded until reaching 80% confluency in a T25 flask. RNA was isolated using a Qiagen RNeasy mini kit (Cat # 74104). Paired-end mRNA libraries were then prepared by the Van Andel Research Institute Genomics Core as described in the previous section. Following sequencing, fastq files were aligned to HG19 using STAR v2.5 (29). Alignments (bam files) were converted to feature counts using HTSeq v0.6.0 referenced against the ENSEMBLE annotation of HG19: Homo_- sapiens.GRCh37.87.gtf counting against the feature “exon”, grouped by “gene_id”, and using the strand parameter “reverse”. This set included exon locations for 57,905 genomic entities including pseudogenes, lncRNAs, and 20,356 protein coding genes. The resulting gene_id map counts were normalized using edgeR (TMM) and tested for significant differential expression with Limma and Voom, in R (v3.3.1) (30–32). The normalized count data for all 8 datasets revealed, through principle component analysis (PCA), that there was very high similarity between our WT MCF-7 which was distinct from the encode data (Fig. S3). In total 57,905 genes were mapped, however in order to combine our data with existing MCF-7 and HMEC RNA-seq data gene_ids, which were not reported in the published studies, were filtered out.

Gene Ontology Analysis

Gene ontology enrichment analysis was done using String v.10.5 (33).

Availability of Data and Material

Publicly available MCF-7 H3K27ac and CTCF ChIP-Seq, and HMEC RNA-Seq datasets used during the current study are available from the encyclopedia of DNA elements (ENCODE), [https://www.encodeproject.org/]

All data generated during this study are included in this published article [and its supplementary information files] or have been deposited in NCBI’s Gene Expression Omnibus (34) and are accessible through GEO Series accession number GSE130852.

Results

MCF-7 ChIP-Seq and RNA-seq Analyses

With the goal of identifying which genetic BCa risk variants are functional in the MCF-7 cell line, we first attempted to locate REs that are active in MCF-7 cells using Chromatin Immunoprecipitation followed by high-throughput sequencing (ChIP-Seq). ChIP-Seq has become widely-used in many cell types, including MCF-7 to map genome wide histone modifications and transcription factor binding sites. Whereas this method has been integral in locating gene REs, it can be highly variable (35). Antibody quality, sample preparation, sequencing depth, and MCF-7 cell line heterogeneity are the main sources of technical variability. Furthermore, following successful immunoprecipitation and sequencing, various algorithms then must be employed to define peaks: regions showing a high number of mapped reads relative to background. In particular, sufficiently deep sequencing is important for this part of the process wherein active REs are defined. Deeper sequencing allows for a lower relative detection threshold and so can capture low-expression transcripts, but also generates greater discrimination among peaks and more signal relative to control and so can map features with higher resolution and more reproducibly. This is important for the detection of variable regions that may not have robust enrichment but are still highly active and functionally important (35,36). An example at one locus showing 3 different H3K27ac CHIP-Seq experiments, including our own reported here, is shown in Figure 1a.

In addition to technical variation, MCF-7 is prone to instability and major genetic and expression changes can be present between subclones (18). Therefore, prior to any genetic manipulation of MCF-7 for disease modeling, recent and accurate characterization is recommended. We reviewed existing ENCODE MCF-7 CHIP-Seq H3K27ac and CTCF datasets and compared these studies to our own (Figure 1B–D). We mapped a total of 14,722 H3K27ac peaks, corresponding to a coverage of roughly 35.2 Mb of DNA (Sup. Table 1). In comparison, the Encode 1 and Encode 2 datasets generate 11,304 and 20,102 reproducible peaks covering about 13.3 and 49.4 Mb, respectively (Figure 1). However, peak size can vary, even if the same peaks has been called in multiple datasets. By comparing our peak locations (labeled ‘Coetzee’) to the ENCODE datasets, we found that only 37% of our peaks overlapped H3K27ac regions from both ENCODE datasets (by at least 1bp). In addition, 21% are completely unique to our data, 1% overlap peaks in Encode dataset 1 (but not 2), and 41% overlap peaks present in Encode dataset 2 (but not 1). Examining unique DNA coverage across the three datasets yields a total of 61.4 Mb, which are annotated as H3K27ac in at least 1 experiment and 9.7 Mb (15%), which are annotated in all three datasets (Figure 1E). Based upon an analysis of the proportion of reads in peaks (FRiP), which indicates the specificity of library following immunoprecipitation, our data is similar to that of ENCODE 2, both studies of which used the same primary antibody for H3K27Ac [Sup. Figure 1]. The Encode 1 dataset in contrast (the oldest study) used a different antibody and shows a lower proportion of reads in peaks. Analyzing the reproducibility of peak calling between the 3 studies showed larger differences, as seen by irreproducible discovery rate (IDR) analysis. This analysis filters out peaks which are not similar across two biological replicates. In this case our data retained 62% of defined peaks, ENCODE 2 retained 44%, and the oldest data, ENCODE 1 retained only 23% of peaks. ENCODE 1 used both a different primary antibody and was sequenced to a much lower depth for both input and control.

Figure 1. Comparison of MCF-7 ChIP-Seq Data.

A) Genome browser view of representative locus near ESR1. B) IDR plot showing the correspondence between replicates 1 and 2 and the threshold for filtering (in black < 0.01) for our H3K27Ac ChIP-Seq (Coetzee) C) for Encode dataset 2, and D) for ENCODE dataset 1. E) Comparison of H3K27Ac ChIP-Seq annotation coverage. F) Proportion of IDR defined H3K27Ac peaks from the our (Coetzee) data that overlap peaks in ENCODE1, ENCODE2, both, or neither; by at least 1 bp. G) Comparison of CTCF ChIP-Seq annotation coverage. H) Proportion of IDR defined CTCF peaks from the our (Coetzee) data that overlap peaks in ENCODE CTCF 2, ENCODE CTCF 3, both, or neither; by at least 1 bp.

We used the same approach in comparing our MCF-7 CTCF data with three separate experiments from ENCODE, (labeled 1–3 and unrelated to the H3K27Ac ENCODE data). We detected almost double the number of peaks in our data set (65,997 peaks) compared to the other three ENCODE datasets with 38,553, 33,312, and 36,057 peaks respectively, that pass an IDR cutoff of q < 0.01 (IDR score > 830) (Sup. Table 1). When combining all three datasets, they only share 20,601 common peaks. After removing CTCF ENCODE1, the least reproducible data, we further evaluated CTCF ENCODE datasets 2 and 3 in comparison to our own. With our CTCF ChIP-Seq data we detected a substantially greater amount of unique CTCF bound DNA (20.5 Mb) across the genome in comparison to CTCF ENCODE 2 and 3 (0.5 and 0.03 MB) (Figure 1G). The majority of CTCF sites detected by the CTCF ENCODE 2 and 3 datasets were also detected in our data. Out of the 65,997 CTCF peaks found in our experiment, 39% were unique to our dataset, 10% were shared with CTCF ENCODE 3 but not CTCF ENCODE 2, 7% were shared with CTCF ENCODE 2 but not CTCF ENCODE 3, and 44% were shared between all three datasets (Figure 1H). These data illustrate that CTCF peaks are largely conserved across datasets, but deeper sequencing may improve the detection of weaker CTCF binding signals.

Coincidence of BCa Risk Variants with Regulatory Regions

Although most risk variants do not alter protein coding, they must still show allelic differences in some functional aspect of genome biology if they are causally related to the disease. Identifying active regulatory regions that coincide with risk variant locations can point to such mechanisms. In order to identify BCa risk processes which are active in MCF-7, we intersected the locations of risk variants with both our own and the ENCODE MCF-7 ChIP-Seq H3K27ac peaks and CTCF binding peaks. To maximize both sensitivity and specificity multiple sets of SNPs were used in this analysis based on different definitions of which variants are most likely to confer BCa risk, all deriving from Michailidou, et. al. (4) and based on different thresholds for significance (see Methods for details, Sup. Table 2). Overall, we examined GWAS BCa risk variants corresponding to 142 separate loci. These loci each had one or small number of equally most significant variants per locus, with 210 in total. Additionally, Michailidou, et. al. (4) reported a set of credible risk variants (CRVs) for each locus, with an average of 31 (stdev. 50) CRVs per locus that spanned an average of 114 Kb (stdev. 179 Kb). CRVs are most likely to be functional and are based on a locus specific significance threshold (see methods).

By examining the location of genomic regulatory activity with respect to risk variants, we can most easily infer which risk loci are not functional within a particular cell line. In MCF-7, 76 (of 142) breast cancer risk loci, and 93% of total reported CRVs, do not overlap any active REs. It is therefore very unlikely that these 76 loci confer risk via processes which are active in MCF-7. In contrast, the remaining 66 risk loci may function via processes that are intrinsic to, and active in, some breast cancer cells, making them logical targets for experimental manipulated in MCF-7. However, the possibility of spurious coincidence remains, as some rSNPs will overlap active REs simply by chance. Out of the 4,453 reported BCa CRVs only 70 (1%) CRVs overlap (31) H3K27ac peak locations present in all three datasets, while 191 CRVs overlap (80) H3k27ac peaks based on our data alone (Sup. Table 2). Similarly, only 13 CRVs overlap a CTCF binding peak present in all of the MCF-7 CTCF ChIP-seq datasets, and 96 CRVs overlap (70) peaks based on our data alone. For CTCF, of the 13, just 5 (rs3008455, rs8103622, rs1800437, rs10231350, rs10116233), (and of the 96: 16 (Sup. Table 4)) are predicted to also cause allele dependent disruption of a known CTCF binding motif.

Coincidence of BCa Risk Variants with Regulatory Elements in MCF-7

If GWAS-measured BCa risk is functional in MCF-7 cells than we predict that the location of BCa risk SNPs is correlated with the locations of active MCF-7 enhancers and/or CTCF binding sites. That is, we expect that the character of MCF-7 as breast derived cancer cells can be captured uniquely by the location of regulatory elements and this unique activity pattern will predict the location of BCa CRVs. To test this hypothesis, we compared the location of BCa risk SNPs to entire set of imputed SNPs generated by Michailidou, et. al., the vast majority of which are not statistically associated with BCa. Polymorphisms are distributed non-randomly throughout the genome and tend to occur near active regions of transcription and so some BCa-unrelated SNP set is required to define a background rate of coincidence. The simplest calculation for SNP enrichment is to compare two ratios: the proportion of risk variants overlapping enhancers or CTCF sites and the proportion background SNPs overlapping the same. We made these enrichment calculations using slightly different definitions of MCF-7 enhancer or CTCF peaks, based on merging our data with the ENCODE data, and for different definitions of risk SNPs, as described above.

To calculate enrichment of BCa risk in MCF-7 then, we first measured the ratios of CTCF peaks overlap by both risk variants and with the entire set of imputed SNPs. By using this comparison, we found that overall enrichment of SNPs coinciding with CTCF binding sites is very modest and is close the overlap expected by chance (Figure 2D). Though the difference in enrichment scores between datasets was not large, CRVs showed the strongest enrichment for all ChIP datasets, indicating that this definition of risk SNPs may be the most relevant to BCa (see Sup. Table 2 for CTCF sites that overlap CRVs). Out of the 210 most significant risk variants dataset, our ChIP data were the only ones to coincide with any of them (rs4971059, rs56069439, rs12449271, and rs35383942). The effects of a CTCF motif disruption may apply across a broader range of cell types as CTCF occupancy is highly consistent (37,38). To demonstrate this in the scope of our work with BCa, we compared the location of HMEC CTCF peaks (from ENCODE) with our CTCF peaks and found that 99% (15,095/15,138) of HMEC peaks were shared (at least 1 base pair) with our MCF-7 CTCF peaks.

Figure 2. Enrichment of BCa Risk Variants in MCF-7 Regulatory Elements.

A) Genome browser view of representative locus near ESR1, showing in red: the location of risk SNPs and background SNPs, and in orange: the location of H3K27ac peaks, based on our data (Coetzee), our data intersected with ENCODE 2 (Intersect 2), our data intersected with both ENCODE data sets (Intersect 3), or the union of the 3 datasets. B) Heat map showing risk the SNP enrichment ratio in each of the 4 MCF-7 datasets of MCF-7 H3K27ac peaks, or in HMEC (which has no H3K27ac at the ESR1 locus) for each of the sets of BCa risk SNPs. C) Heat map showing the SNP enrichment ratio for enhancers that are unique to MCF-7, for MCF-7 enhancers that overlap HMEC enhancers, HMEC enhancers that overlap MCF-7 enhancers, or enhancers unique to HMEC. D) Heat map showing risk the SNP enrichment ratio for CTCF peaks.

In contrast to CTCF, we found highly significant enrichment of BCa risk SNPs in H3k27ac peaks (enhancers and promoters) for all sets of MCF-7 enhancers and all definitions of BCa risk variants (Figure 2B). For example, we found that BCa CRVs overlapped 81 MCF-7 H3K27ac peaks (based on our data alone) and were 2.3-fold more likely to coincide with peaks than were the background SNPs overall. This enrichment means that the location of MCF-7 enhancer activity is predictive of risk SNP location. Therefore, as expected, at least some gene regulation that is active in MCF-7 also increases the risk of developing BCa.

To begin to define risk element functionality, we first categorized MCF-7 H3K27ac peaks as enhancer or promoter based on gene transcription start site locations. As expected, promoter peaks were uniformly less likely than enhancer peaks to show enrichment for risk variants compared to background. Focusing on enhancers, we then compared MCF-7 active enhancer locations to normal human mammary epithelial cell (HMEC) active enhancer locations, based on ENCODE H3K27ac CHIP-Seq data (Sup. Table 1). We reasoned that HMEC exhibits a noncancerous phenotype and so represents the active-enhancer profile of normal tissue, whereas MCF-7 cells represent the active enhancer profile of ER+ cancerous breast epithelial tissue. Therefore, they can be used to categorize risk enhancers active in two distinct cell types representing two extremes of breast epithelial cells. Although not yet demonstrated, we speculate that risk enhancers that are present only in HMEC point to risk imposed through pre-cancer cell functions (such as apoptotic checkpoints) that must be altered or bypassed for carcinogenic initiation. Risk enhancers present only in MCF-7 could affect mechanisms involved in tumor progression and malignancy (such as cell adhesion and immune avoidance). Finally, risk enhancers present in both cell types may regulate processes involved in BCa at all stages of oncogenic promotion (such as DNA synthesis and cell proliferation). The data presented in Figure 2C demonstrate that while all enhancer subtypes are enriched for BCa associated SNPs compared to background, MCF-7-only enhancers are most correlated with risk SNPs. This indicates that a greater proportion of MCF-7 specific processes are involved in conferring BCa risk compared to HMEC. It also suggests that some of GWAS measured risk is involved with later stage processes of cancer development.

Next, we measured the significance of overlap between risk SNPs and MCF-7 enhancers at each locus individually. The total number and relative locations of risk SNPs in a locus is influenced by both the underlying disease biology as well as the LD structure. Therefore, we sought to control for LD and simultaneously treat the rSNPs at each locus as fundamentally connected (see methods for more details). As a very simple partial solution to this problem we chose to merge the set of risk SNPs within a locus into a single peak or span. We then used permutation testing to compare that risk span (the distance between the outer most significant CRVs at a locus) against a background set of randomly sampled equal sized spans across the same locus (e.g. Figure 2A). We measured the proportion of the risk span that overlaps active REs and generated a percentile score based on the number of background comparisons with greater overlap (Figure 3, Sup. Figure 2, Sup. Table 3). A low value indicates greater confidence in the identification of a specific risk RE within a locus. This span overlap metric is helpful, in addition to SNP enrichment, and p-value ranking, in order to determine which REs are most suitable for follow up testing.

We calculated the risk-span-overlap at each locus for enhancers active in 11 related roadmap tissues as well as HMEC and MCF-7 (figure 3). Notably, the largest group of loci show low overlap in any tissue. The remaining loci are easier to link to likely REs. For instance, at locus 6:151952332 near ESR1, the actual BCa CRV risk span overlaps a greater proportion of MCF-7 enhancers than more than 98% of other possible cases within 1Mb. In contrast, no other cell types examined showed significant overlap. It is likely that the enhancer(s) at this locus, which completely overlaps BCa credible risk SNPs, is enhancing ESR1 expression. In the other cell types, no enhancer is present and ESR1 expression is probably off, such as it is in HMEC cells. BCa risk may be imposed through altered function of this enhancer, and this can be experimentally verified in MCF-7. Thirteen other loci show a greater overlap of risk SNPs with MCF-7 enhancers than expected by chance. Unlike ESR1, the risk locus, 19:13954571, near NANOS3, shows greater than expected overlap with MCF-7 enhancers as well as those of multiple other tissues. NANOS3 is expressed in many tissues and is likely involved in general proliferation. The function of this locus, then, may be related to and can be queried in MCF-7 in addition to the other cell types.

Figure 3. Analysis of Individual Risk Loci and Tissue-specific Regulatory Elements.

ChIP-Seq data and Roadmap segmentations (subset for enhancers) were used to examine the significance of overlap with CRV risk spans for each of the 142 BCa risk loci (columns). Significance levels are depicted in red with darker red indicating a greater risk-span/RE overlap proportion compared to background levels for that locus. Dendrograms are based on complete linkage using city-block distance measurements of −log(p.value). The individual loci showing the most significant overlap are identified by chromosome and position underneath. These are the loci with the most specific correspondence between tissue-specific regulatory elements and BCa CRVs.

The risk span overlap metric may also be misleading in some cases. For example, widely spaced CRVs can give spans that overlap large amounts of RE even when no individual risk SNPs do. However, using this measure in conjunction with a simple enrichment calculation and also with GWAS significance-based SNP ranking is an improvement over any one method alone. Table 1 shows 10 BCa loci that are highly appropriate for study in MCF-7 based on all three metrics.

Table 1. MCF-7 BCa Risk Enhancers.

Listed are the 10 best BCa risk loci in MCF-7 and the corresponding 10 MCF-7 risk enhancers which are most suitable for follow up testing. Each enhancer overlaps the most significant GWAS SNP at the locus, and the locus shows both an enrichment greater than 2 (frequency of BCa credible risk variants (CRVs) overlapping enhancers compared to background overlap frequency), as well as a proportion of the CRV risk span that overlaps enhancers greater than more than 95% background overlap proportions. The enrichment and overlap values may be modified by overlap of risk SNPs with other enhancers at the same locus. The overall GWAS significance, ER+ GWAS significance, and reported genes from Michailidou et. al. are listed. Additionally, all but the enhancer at locus 19 are unique to MCF-7 and do not overlap HMEC enhancers locations, so the differentially expressed genes (relative to HMEC) within 500 Kb or 1 MB of each lead SNP are identified as putative risk genes in MCF-7.

Locus	Alt. Locus ID	CRVs Enh. Overlap	CRVs in Locus	Hyper. P.Val. (−log)	Ratio Enrich.	Span Overlap Signif. (−log)	Lead SNP ID	Comb. GWAS P.Val.	Comb. GWAS P. Val., ER	Risk Enhancer Start	Risk Enhancer Stop	Gene	MCF-7 DE Genes 0–500Kb	MCF-7 DE Genes 500Kb −1Mb
1p36.22	1_10566215	8	50	4.29	5.79	1.72	rs2506889	2.38E-20	1.10E-07	10595877	10599012	PEX14	APITD1, TARDBP, UBE4B, PEX14	SRM, SLC25A33, PIK3CD, LZIC, PTCHD2, CLSTN1, EXOSC10, MTOR
3p26.1	3_4742276	1	22	0.65	3.31	1.71	rs6787391	9.07E-19	1.20E-15	4727872	4732087	EGOT, ITPR1	BHLHE40, ARL8B, ITPR1,	EDEM1
6q25	6_151952332	10	10	12.50	17.64	2.19	rs60954078	2.84E-54	1.70E-22	151948050	151959756	ESR1	ZBTB2, RMND1, AKAP12, C6orf211, SYNE1, ESR1	MTHFD1L, PLEKHG1
8q22.3	8_102478959	2	8	1.50	6.62	1.66	rs514192	5.61E-09	9.90E-09	102478133	102481107	YWHAZ, GRHL2, KLF10	ZNF706, GRHL2	ANKRD46, YWHAZ, RRM2B, UBR5, PABPC1
10p14	10_9088113	30	42	23.50	8.93	2.15	rs60954078	1.73E-10	6.00E-08	9074392	9089320	GATA3	NA	GATA3
12q24.31	12_120832146	2	26	0.80	2.32	1.54	rs206966	3.79E-08	1.40E-06	120831824	120833249	DYNLL1, GATC	TRIAP1, SRSF9, POP5, PXN, MSI1, DYNLL1, COQ5, UNC119B, CCDC64, RNF10	PRKAB1, P2RX4, CIT, ANAPC5
16q23.2	16_80650805	8	17	9.69	26.12	2.66	rs7500067	4.06E-27	2.90E-21	80647147	80651103	CDYL2	ATMIN, CENPN, CDYL2	GAN, CMIP
17q25.3	17_77781725	3	13	2.56	9.98	1.90	rs8082452	1.14E-10	2.40E-06	77769769	77772332	NA	CBX2, CBX8, CBX4, CARD14, SGSH, SLC26A11, TBC1D16, EIF4A3	TIMP2, LGALS3BP, ENDOV, USP36, RPTOR
19pl3.12	19_13954571	19	43	13.35	8.74	2.74	rs2594714	1.08E-08	1.70E-05	13948902	13955856	JUNB, NANOS3	MIR24–2, PALM3, SAMD1, CCDC130, ZSWIM4, C19orf57, PODNL1, ASF1B,	CALR, SYCE2, TRMT1, CD97, GIPC1, DNAJB1, HOOK2, MAST1,
21q21.1	21_16520832	2	2	4.32	143.22	2.52	rs2403907	1.87E-32	4.20E-23	16570278	16574820	NRIP1	NRIP1	HSPA13, USP25

Putative MCF-7 BCa-Risk Genes

In order to begin to characterize the processes active in MCF-7 that confer risk for BCa via GWAS-identified risk variants we sought to link risk enhancers to the genes which they may regulate. Therefore, we performed RNA-seq on MCF-7 cells and also compared that expression data to existing RNA-Seq data for both MCF-7 and HMEC cells (Sup. Figure 3). Our data corresponded well to published MCF-7 data (Sup. Figure 3a). Combining the MCF-7 datasets, we found 11,309 protein coding genes expressed at an average of at least 1 count per million (CPM), whereas 12,294 genes were expressed in HMEC (Sup. Table 5). Comparing the two cell lines, 10,657 genes (82.3%) were minimally expressed in both, and 8,087 genes were differentially expressed (adjusted p.value < 0.05 and fold change > 2) with 4,899 more highly expressed in HMEC and 3,188 more in MCF-7 cells. The expression changes between the cell lines were not unexpected. MCF-7 genes were, roughly, over-enriched for differentiated cell anatomical functions, migration, and intercellular interactions, and also enriched for KEGG cancer pathways. The HMEC genes were, roughly, over-enriched for proliferation, metabolism, and nuclear organization. For instance, the top 1000 genes most significantly upregulated in MCF-7 were statistically enriched for 382 GO biological processes including “vasculature development”, “anatomical structure morphogenesis”, “regulation of cellular component movement”, and “extracellular matrix organization” (FDRs = 5.2E-12, 5.2E-12, 2.0E-11, 2.2E-11) (Sup. Table 5. The top 1000 most significant HMEC genes were enriched for 63 processes including “RNA metabolic process”, “nucleic acid-templated transcription”, “nucleobase-containing compound metabolic process”, “RNA biosynthetic process” (FDRs = 7.0E-8, 7.0E-8, 2.1E-7, 2.2E-7) (Sup. Table 5).

Multiple methods exist that attempt to pair enhancers with target genes (39–41). The simplest method, which we use here, is to identify nearby genes which are co-expressed with active enhancers specifically in a particular cell type, such as MCF-7. For this purpose, we used only one definition of MCF-7 enhancers based on our own H3K27ac data (see Figure 1) following the removal of promoter peaks. The majority of previously reported enhancer-gene regulatory pairing occur at distances less than 1Mb, though far cis and even inter-chromosomal interactions do exist (42,43). Based on our previous work we used here a distance threshold of 500 kb distance to identify putative risk genes expressed in MCF-7. By this proximity cutoff, 522 genes are near risk enhancers in MCF-7 cells. These potential risk-associated genes expressed in MCF-7 are statistically enriched for 102 GO biological process terms including “nucleosome assembly”, “DNA methylation”, “chromatin silencing”, and “positive regulation of DNA repair” (FDR = 1.4E-5, 7.9E-4, 9.6E-3, 1.5E-2) (Sup. Table 6). Likewise, there are 386 putative risk genes expressed in HMEC and 260 genes common to both the HMEC and MCF-7 sets. HMEC were statistically enriched for 42 biological processes. However, almost no significantly enriched HMEC processes were unique, 80% were also significant for the set of MCF-7 risk genes. The exception being enrichment for GO pathways related to Notch signaling or apoptosis (Sup. Table 6). This suggests that few BCa risk processes are unique to HMEC, compared to MCF-7.

A potentially more accurate method to link REs with target genes is by using expression quantitative trait loci (eQTL) data, from sources such as GTEx. These results are biased by tissue sample type and population origin and cannot identify changes is high variable or lowly expressed genes. However, an eQTL describes a direct association between the allelic variation at a SNP and an expression change for a gene within 1Mb. In order to integrate eQTL data we identified all SNPs (not just BCa risk SNPs) located within MCF-7 risk enhancers and queried the GTEx database for all significant eQTL genes, associated with those SNPs in breast tissue. We then removed from this set those genes which are not expressed in MCF-7. Doing so produced 48 genes (Sup. Table 7). These genes were not enriched for any GO annotations and may be too stringently filtered to include likely risk genes. For instance, ESR1, a gene known to be important in BCa, and near a risk enhancer (Figure 1) has no identified significant eQTLs in the GTEx dataset. Moreover, the CRVs located at that risk locus, one of the most significant of all loci, have also not been measured to be eQTLs for any gene. Thus, this clear test case fails reidentification by GTEx. So, while eQTL can provide strong support for linking a risk locus to a gene, we think it is currently too restrictive for further use here.

Because enhancer regions active in MCF-7, and not active in HMEC, were most highly correlated with the locations of risk variants we sought to associate this subset of enhancers with putative genes. Enhancers alter the expression of nearby genes so we can assume that most genes that are regulated by this subset of risk enhancers will appear differentially expressed relative to HMEC expression levels, in which the risk enhancers are not active. Although most enhancers upregulate nearby genes, H3K27ac ChIP-Seq will also identify regulatory regions that can downregulate nearby genes. For this reason, we considered DE genes that were both up and downregulated with respect to HMEC. Based on 500 KB proximity, 138 DE genes are associated with MCF-7-only risk enhancers (Sup. Table 7). These are enriched for multiple broad GO categories, but also including the KEGG pathways for estrogen and prolactin signaling, identified via 6 and 5 genes, respectively (Sup. Table 7).

Finally, in table 1 we list risk MCF-7 enhancers at 10 BCa risk loci, which are highly enriched for SNPs, coincide with the span of DNA containing BCa CRVs above background levels, and show overlap for the most significant risk SNP. We linked MCF-7 genes to these 10 enhancers based on expression in MCF-7, significant difference in expression relative to HMEC, and a distance closer than 1 Mb. These represent the most promising enhancer targets for further functional analysis.

Discussion

MCF-7 is a highly utilized breast cancer cell line. However, the range of its potential use for the dissection BCa GWAS risk is not immediately obvious. Based on the classical theory of carcinogenesis, cancer arises from normal tissue and proceeds through the stages of initiation, promotion, and progression. Genetic factors affecting any of these stages may be picked up in GWAS studies, but only a subset of these risk mechanisms are likely to be active in MCF-7 itself. Only those processes active in MCF-7 can in turn be easily manipulated. By comparing MCF-7 to HMEC we believe that risk arising from gene regulation involved in all three stages is active in MCF-7, but that MCF-7 is most suited for studying the risk mechanisms exacerbating tumor progression. In particular, estrogen and prolactin signaling gene networks are especially enriched in BCa GWAS risk biology in MCF-7. Although MCF-7 cells are classified as luminal A/ER+, it is worth noting that this cell line may still be relevant for tumorigenic processes in other subtypes including ER- breast cancer. The developmental lineage of breast tumor subtypes is complex and not fully understood. Based on the hypothesis that different subtypes may be derived from the same cell type of origin, some of the active enhancer-driven processes leading to cancer in each subtype are most likely not mutually exclusive. Further studies need to be done to evaluate the overlapping risk in multiple breast cancer cell lines of different classifications.

We found that the work reported here is more reproducible in defining MCF-7 specific H3K27ac histone marks and CTCF occupancy than that in previously reported ENCODE datasets. The discrepancy in the total amount of H3K27ac or CTCF DNA and in continuously designated regions (peaks) is likely due both to differences in sample preparation and sequencing depth as well as to intrinsic differences between subclones.

A potential next step in dissecting BCa risk in MCF-7 is perform allelic replacement or to delete the entire risk regulatory elements using CRISPR-CAS9 gene disruption. We found that multiple enhancers are well suited for follow-up experiments and a smaller number of CTCF sites may also be suitable. The CTCF protein function is well characterized and compared to enhancers, for which multiple unknown transcription factors may be acting, is fairly simple. Moreover, allele dependent CTCF binding can potential disrupt large TAD regions leading to large expression changes to nearby genes. For these reasons CTCF can be an ideal target. Unfortunately, we found very low enrichment of rSNPs, implying more BCa risk loci function via enhancers in MCF-7. It is possible that the enrichment scores maybe less informative for CTCF, though. CTCF binding sites/peaks are much more specific and narrower than that of H3K27ac peaks and are less likely to overlap multiple risk SNPs simply due to the close proximity of risk SNPs to each other. This and the greater number of CTCF peaks (which are largely tissue invariant) may contribute to lower overall enrichment. In total we found 5 loci which are good targets for further CTCF examination.

In contrast, at least 10 loci point to specific enhancers as casual in BCa risk. For enhancers we found the greatest degree of enrichment of risk variants in the MCF-7 cell line as compared to a normal precursor model (HMEC), indicating that MCF-7 cells are highly relevant for the study of processes leading to abnormal cell growth and tumor formation.

Overall, our results reported here bring into focus how MCF-7 can be used as a model to reveal BCa risk mechanisms in ER-positive genetic predisposition. For enhancers we found the greatest degree of enrichment of risk variants in the MCF-7 cell line as compared to a normal precursor model (HMEC), indicating that MCF-7 cells are highly relevant for the study of processes leading to abnormal cell growth and tumor formation.