Effects of BRCA2 cis-regulation in normal breast and cancer risk amongst BRCA2 mutation carriers
- Ana-Teresa Maia1, 2, 30Email author,
- Antonis C Antoniou3,
- Martin O'Reilly1,
- Shamith Samarajiwa1, 2,
- Mark Dunning1,
- Christiana Kartsonaki3,
- Suet-Feung Chin1, 2,
- Christina N Curtis1, 2, 29,
- Lesley McGuffog3,
- Susan M Domchek4,
- Douglas F Easton3,
- Susan Peock3,
- Debra Frost3,
- D Gareth Evans5,
- Ros Eeles6,
- Louise Izatt7,
- Julian Adlard8,
- Diana Eccles9,
- GEMO Study Collaborators10,
- Olga M Sinilnikova11, 12,
- Sylvie Mazoyer11,
- Dominique Stoppa-Lyonnet13, 14, 15,
- Marion Gauthier-Villars13,
- Laurence Faivre16, 17,
- Laurence Venat-Bouvet18,
- Capucine Delnatte19,
- Heli Nevanlinna20,
- Fergus J Couch21,
- Andrew K Godwin22,
- Maria Adelaide Caligo23,
- Rosa B Barkardottir24, 25,
- kConFab Investigators26,
- Xiaoqing Chen27,
- Jonathan Beesley27,
- Sue Healey27,
- Carlos Caldas1, 2, 28,
- Georgia Chenevix-Trench27 and
- Bruce AJ Ponder1, 2
© Maia et al.; licensee BioMed Central Ltd. 2012
Received: 12 August 2011
Accepted: 18 April 2012
Published: 18 April 2012
Cis-acting regulatory single nucleotide polymorphisms (SNPs) at specific loci may modulate penetrance of germline mutations at the same loci by introducing different levels of expression of the wild-type allele. We have previously reported that BRCA2 shows differential allelic expression and we hypothesize that the known variable penetrance of BRCA2 mutations might be associated with this mechanism.
We combined haplotype analysis and differential allelic expression of BRCA2 in breast tissue to identify expression haplotypes and candidate cis-regulatory variants. These candidate variants underwent selection based on in silico predictions for regulatory potential and disruption of transcription factor binding, and were functionally analyzed in vitro and in vivo in normal and breast cancer cell lines. SNPs tagging the expression haplotypes were correlated with the total expression of several genes in breast tissue measured by Taqman and microarray technologies. The effect of the expression haplotypes on breast cancer risk in BRCA2 mutation carriers was investigated in 2,754 carriers.
We identified common haplotypes associated with differences in the levels of BRCA2 expression in human breast cells. We characterized three cis-regulatory SNPs located at the promoter and two intronic regulatory elements which affect the binding of the transcription factors C/EBPα, HMGA1, D-binding protein (DBP) and ZF5. We showed that the expression haplotypes also correlated with changes in the expression of other genes in normal breast. Furthermore, there was suggestive evidence that the minor allele of SNP rs4942440, which is associated with higher BRCA2 expression, is also associated with a reduced risk of breast cancer (per-allele hazard ratio (HR) = 0.85, 95% confidence interval (CI) = 0.72 to 1.00, P-trend = 0.048).
Our work provides further insights into the role of cis-regulatory variation in the penetrance of disease-causing mutations. We identified small-effect genetic variants associated with allelic expression differences in BRCA2 which could possibly affect the risk in mutation carriers through altering expression levels of the wild-type allele.
Unequal expression of the alleles of autosomal genes is common in the human genome [1, 2]. This differential allelic expression (DAE) is thought to play a major role in intra-species phenotypic variation as well as individual susceptibility to complex diseases. Previous reports have suggested a role for DAE in cancer, including that of the colon [3, 4], the pancreas , the ovary  and the breast [7, 8]. Additionally, several cancer risk-associated SNPs identified through genome-wide association studies (GWAS) have been shown to have a role in gene expression regulation [9–13]. We previously reported that BRCA2 displays DAE in breast and blood samples from healthy individuals . Using a coding single nucleotide polymorphism (SNP) in exon 9 of BRCA2, rs144848, as a marker for quantifying allelic transcripts in heterozygous individuals, we observed differences between alleles as large as four-fold. Additionally, our data suggested that the cis-regulatory variation responsible for DAE is in the same haplotype block as rs144848.
Deleterious germline mutations in BRCA2 are rare but confer a high risk of breast cancer. However, penetrance estimates vary and several lines of evidence indicate that other genetic and environmental factors modify the cancer risks conferred by these mutations [15–17]. Studies by the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA) have demonstrated that common alleles in RAD51, FGFR2, MAP3K1, TOX3/TNRC9, LSP1, SLC4A7/NEK10, a 2q35 locus and a 5p12 locus affect breast cancer risk in BRCA2 mutation carriers [18–20]. These loci account for approximately 4% of the genetic variation observed in the penetrance of BRCA2 mutations in breast cancer.
Most of the deleterious germline mutations in BRCA2 introduce premature termination codons and trigger nonsense-mediated mRNA decay . Haploinsufficiency in BRCA2 +/- cells has been shown to affect DNA damage repair of breaks induced by γ-irradiation and mitomycin C  which can contribute to cancer predisposition in mutation carrier families. Recently, it was also reported that primary breast epithelial cells cultured from BRCA2 mutation carriers and controls have differences in gene expression signatures .
In this study, we set out to identify and characterize cis-regulatory variation responsible for DAE in BRCA2 and to determine whether the penetrance of BRCA2 mutations could be modified by the variable level of expression of the remaining wild-type allele in heterozygous mutation carriers. We identified common haplotypes that associate with different levels of BRCA2 expression and found three SNPs that alter the binding affinity of transcription factors and overlap cis-regulatory elements. The expression haplotypes were also found to correlate with altered expression in breast tissue of genes whose expression has previously been reported to be altered in the presence of inherited BRCA2 mutations. We found one haplotype that was associated with higher expression of BRCA2, for which there is suggestive evidence that it is also associated with a protective effect for the development of breast cancer in BRCA2 mutation carriers.
SNP and haplotype analysis
Human tissue samples
B cells and normal breast tissue from healthy individuals and normal matched breast tissue from cancer patients were obtained from the Blood Centre and the Tissue Bank at Addenbrooke's Hospital as previously described , with approval from the Addenbrooke's Hospital Local Research Ethics Committee (REC reference 04/Q0108/21, 06/Q0108/221 and 07/H0308/161, respectively). The B cell and control normal breast tissue material was used for the functional analysis and the DAE study. The normal matched breast tissue was used for total gene expression studies.
Nucleic acid extraction and processing
DNA was extracted using a conventional SDS/proteinase K/phenol method. Total RNA was extracted from all samples using Qiazol (QIAGEN, Crawley, UK) following the manufacturer's instructions. The RNA was subsequently treated with DNaseI and repurified using acidic phenol-chloroform, and ethanol precipitation. DNA and RNA integrity were checked using the Agilent Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA).
cDNA was prepared from total RNA with the SuperScript® III First Strand SuperMix (Invitrogen, Carlsbad, CA, USA) using random hexamers and oligo-dT primers, according to the manufacturer's instructions.
Breast cell lines culture
Breast cancer (PMC42, MCF-7 and SUM-159) and normal breast (MCF-10A) cell lines were cultured as previously described [29–32] for extracting nuclear protein for electrophoretic mobility shift assays and for extracting chromatin for immunoprecipitation assays.
Genotyping of normal blood and breast tissue from healthy individuals
Genotyping for rs144848 was performed using a fluorescent 5' exonuclease TaqMan assay and the ABI PRISM 7900 Sequence Detection Sequence (Applied Biosystems, Foster City, CA, USA). Genotyping for another six SNPs that tag common haplotypes for the DAE region in BRCA2 was carried out by PCR amplification of 150 bp to 250 bp around the SNP of interest. Primer sequences are provided in the Supplementary Material (Additional file 1 Table S1). Products were verified by agarose gel electrophoresis and sequenced using ABI Big Dye chemistry and capillary electrophoresis on an ABI 3730 sequencer (Applied Biosystems).
Thirty-seven normal breast tissue samples were genotyped using Affymetrix Genome-Wide SNP 6.0 arrays (Affymetrix, Santa Clara, CA, USA), following the standard Affymetrix protocol using 0.5 μg DNA, at AROS Applied Biotechnology (Aarhus, Denmark). Affymetrix SNP 6.0 arrays data were pre-processed and analyzed using the crlmm Bioconductor package  implemented in the R statistical programming language  (GEO: GSE32259).
Quantification of differential allelic gene expression
Differential allelic expression assays were performed as previously described . Validation was performed by Sanger sequencing. In brief, replicate PCRs were performed amplifying 100 bp to 200 bp around the coding SNP of interest; products were verified by agarose gel electrophoresis and sequenced using ABI Big Dye chemistry and capillary electrophoresis on an ABI 3730 sequencer (Applied Biosystems). Allelic expression ratios in cDNA samples were normalized against the genomic DNA ratios. Primer sequences are provided as Supplementary Material (Additional file 1 Table S1).
In silicoprediction of transcription factor binding
TRANSFAC professional database  (Biobase, Germany) ver.2009.4 with vertebrate non redundant matrices, and MATCH algorithm with core match and matrix match set to minimize the sum of both false positives and negative motif matches were used to identify putative transcription factor binding sites.
Electrophoretic mobility shift assay (EMSA)
EMSA were performed as previously described . Each EMSA was repeated two or more times for all combinations of cell extract and oligonucleotide, which were also tested in serial diluting amounts. Oligonucleotide sequences are provided as Supplementary Material (Additional file 1 Table S1). Antibodies used for super-shift competition assays are listed in the Chromatin Immunoprecipitation section.
Chromatin Immunoprecipitation (ChIP)
ChIP experiments were performed using chromatin extracted from SUM-159 (cancer), PMC42 (cancer), MCF-7 (cancer) and MCF10-A (normal) breast cell lines with antibodies against RNA polymerase II (Abcam, Cambridge, UK, ab5408-100), H3K79me2 (Abcam, ab3594), HMGA1a/HMGA1b (Abcam, ab4078), DABP (Insight Biotechnology Ltd, Wembley, UK, sc-98411-X), ZFP161 (Abcam, ab68116), C/EBPα (Insight Biotechnology Ltd, sc-9314-X), IgG-Rabbit (Insight Biotechnology Ltd, sc-2027) and IgG Mouse (Insight Biotechnology Ltd, sc-2025) as recently described . The immunoprecipitated material was purified using QIAquick PCR Cleaning kit (Qiagen) and analyzed by PCR for the regions of interest. Primer sequences are supplied in the Supplementary Material (Additional file 1 Table S1).
Gene Expression Analysis
The relative gene expression of BRCA2, SPP1, MUC16, PAX8, BIRC5 and RRM2 was analyzed using Fluidigm 96.96 Dynamic Arrays (Fluidigm, San Francisco, CA, USA), according to the manufacturer's instructions, in a set of normal breast tissue from healthy donors (n = 58). Four housekeeping genes (Actinβ, HPRT1, 18S and GAPDH) were included in the experiments for normalization purposes.
In short, 1.25 μL of cDNA was pre-amplified in a 5 μL reaction for the specific targets (0.2x of each assay), for eight cycles. Pre-amplified samples were diluted 1:5 before being utilized. Chips were run on the BioMark™ Real-Time PCR System (Fluidigm). The cycling program used consisted of 10 minutes at 95°C followed by 40 cycles of 95°C for 15 seconds and one minute at 60°C. Data were analyzed using the BioMark Gene Expression Data Analysis software to obtain Ct values. Each chip (experiment) was run with all assays plated in triplicate and 94 test samples plus two no-template control samples. Analysis was performed using the ΔΔCt method and normalization was done using the average Ct value for the two housekeeping genes (Actinβ and HPRT1) that had the most similar distribution of Ct values across arrays.
Additionally, we analyzed separately 33 normal breast tissue samples from healthy individuals and 200 normal breast tissue samples from cancer patients (normal-matched) without BRCA2 germline mutations, using Illumina HT12 v3 expression arrays (Illumina, San Diego, CA, USA) (GEO: GSE32259 and EGA: EGAS00000000082). Briefly, for each sample set the bead array package  was employed to pre-process and summarize each Illumina H12 v3 BeadChip following quality assessment and the adjustment of spatial artefacts with the BASH tool . Potential outlier arrays were removed by considering the bead-level quality assurance (QA) scores derived using the control probes on each array and the remaining samples were quantile normalized.
Gene expression statistical analysis
All statistical analysis was performed using R version 2.10.1 , unless otherwise stated. Differential allelic expression data were analyzed as previously described . Comparison of proportions between groups for haplotype correlation with DAE was tested using the Chi-square test. The non-parametric Wilcoxon rank sum test was used for the analysis of BRCA2 expression versus haplotype. Specific SNP genotype-gene expression correlations were determined using analysis of variance (ANOVA) testing for the first type of correlation and the Student's t test for the second.
Associations with breast cancer risk for BRCA2 mutation carriers
Associations with breast cancer risk were evaluated using women with BRCA2 mutations from 11 studies from the CIMBA consortium for the seven SNPs defining the gene expression associated haplotypes. Subjects participated in clinical or research studies at the host institutions under ethically approved protocols and written informed consent was obtained (Additional file 2 Table S2).
Studies included per SNP in our analysis are shown in Additional file 3 (Table S3). Sample eligibility and available information were reported in detail previously and are provided in Additional file 4. The DNA samples of ten studies were genotyped by iPLEX Mass Array platform (Sequenom Inc., Newton, MA, USA) at Queensland Institute of Medical Research, with a further study, GEMO, genotyped elsewhere by Taqman assay, using Applied Biosystems reagents (Applied Biosystems), for the same seven SNPs. Further details on the genotyping and quality control are described in Additional file 4.
Details of the statistical analysis methods for evaluating the associations between SNP genotypes and breast cancer risk have been published previously and are provided in Additional file 4. In short, we tested the frequency of each SNP in affected (cases) and unaffected (control) BRCA2 germline mutation carriers. The effect of each SNP was modelled either as a per-allele HR (multiplicative model) or as separate HRs for heterozygotes and homozygotes, and these were estimated on the log scale. Analyses were carried out with the pedigree-analysis software MENDEL . Heterogeneity between studies was tested by comparing the models that allowed for study-specific log-HRs against models in which the same log-HR was assumed to apply to all studies.
Disease associations for the five common expression haplotypes were also explored using the software HAPSTAT , under a standard cohort analysis model and using the low expression haplotype 2 as the reference. It should be noted that this analysis does not account for the sampling design and familial relationships of the participating studies.
Haplotype correlation with levels of expression
Recently, we reported differential allelic expression of BRCA2 in both normal breast and blood tissue samples from control individuals . Using the coding SNP rs144848 for allelic transcript quantification and an allelic expression ratio threshold of 1.2 (log2 = 0.263), we found that in 45 heterozygous samples tested 29 (64%) significantly expressed more of the G allele. All heterozygotes showing DAE preferentially expressed the same allele, indicating that the coding SNP rs144848 is in linkage disequilibrium (LD) with the regulatory variant(s). Linkage disequilibrium is however not complete between the coding and the regulatory SNP(s) as suggested by the fraction of heterozygotes that did not show DAE, who are likely homozygous for the regulatory variant(s).
Haplotype frequencies in BRCA2, tag SNPs and DAE distribution
Freq (%) HapMap CEU
Gene expression profile of breast tissue is dependent on haplotype
In normal breast tissue from healthy individuals, higher expression of SPP1 was found in cells homozygous for the rare allele of rs206081, an SNP in strong LD with the tag for the high-expression haplotype 3 (P-value = 0.039, Figure 2A). This correlation was in concordance with the expression haplotype analysis in blood of healthy individuals using real-time PCR data, in which expression of SPP1 was found to be significantly higher in individuals carrying two high expression haplotypes (P-value = 0.006, difference expression = 1.74, Figure 2B), when compared with individuals carrying one high and one low expression haplotype (no individuals were identified with two low expression haplotypes in these samples). This correlation was found to be in the opposite direction in the normal tissue of breast cancer patients, with lower SPP1 expression associated with a low BRCA2 expression haplotype (P-value = 0.038, Figure 2A). The gene PAX8 showed a concordant correlation in normal breast from healthy controls and cancer patients, showing upregulation in rare homozygous samples for SNPs that correspond to haplotypes 3 (rs206081) and 4 (rs4942448), both associated with higher expression of BRCA2 (P-value = 0.009 in healthy individuals, P-value = 0.018 in cancer patients; Figure 2A). The expression of MUC16 in the normal tissue of patients was negatively correlated with the A allele of rs4942448 (haplotype 4) (P-value = 0.05 in Figure 2A). The upregulation of MUC16 in individuals carrying two low-expression haplotypes was confirmed with the second analysis method (P-values = 0.004 in Figure 2B). The difference in expression between the high-expression and low-expression haplotype groups was -0.125 for the two probes. In the normal tissue of patients, the expression of IGFBP5 was significantly lower in samples homozygous for rs4942448 (haplotype 4), which is associated with lower BRCA2 expression (P-values = 0.014, Figure 2A).
These results suggest that even small differences in the expression level of BRCA2, as those associated with the different haplotypes that we identified, can have an impact on the gene expression profile of normal breast tissue in individuals carrying two copies of BRCA2.
Associations with breast cancer risk for BRCA2mutation carriers
rs4942440 genotype frequencies in BRCA2 mutation carriers by disease status and hazard-ratio estimate
To address this fact we performed a haplotype analysis using standard cohort analysis in which the low-expression haplotypes 2 and 5 were considered as reference. This revealed that each copy of the higher-expression haplotype 4 was associated with a reduced risk of breast cancer for BRCA2 mutation carriers (HR = 0.84, 95%CI: 0.73 to 0.97, P = 0.014) (Additional file 7 Table S5). However, this analysis does not take into account the non-random sampling of mutation carriers with respect to disease phenotype, or possible family relations within the individuals studied, so inference based on this analysis is not directly comparable with the single SNP analysis which was based on modelling the retrospective likelihood. Nevertheless, this is also suggestive of an association between a higher expression haplotype of BRCA2 in mutation carriers with lower risk of developing breast cancer.
Identification of regulatory variation
Three other SNPs passed our selection criteria for analysis by EMSA to evaluate binding of the predicted TFs in vitro, using nuclear protein extracts from two breast cancer and one normal cell line. Two of these three SNPs showed the ability to bind in vitro in an allelic-preferential or allele-specific manner to the predicted TFs (Figure 3B and 3C) and were further characterized by ChIP analysis.
SNP rs9567576 (Functional SNP 2 in Figure 5), tagging haplotypes 2 and 5, located in a regulatory feature in intron 11, resides within a putative binding site for the high-mobility group protein HMGA1, with the T allele better matching the consensus sequence. The band observed in the EMSA experiment (Figure 3B) was consistently stronger for the T allele in replicate experiments, corroborating the in silico predictions, although the G allele could still bind. In a competition EMSA experiment, an oligonucleotide centered on SNP rs9567576 competed more efficiently for binding when the SNP position was a T as compared to a G. Additionally, a consensus probe and a specific antibody against HMGA1 competed strongly for binding. The physical presence of HMGA1 at the rs9567576 site was demonstrated by site-specific ChIP analysis in both MCF-7 and MCF-10A cells (homozygous for the T allele and heterozygous at rs9567576, respectively) (Figure 4B). Although a stronger ChIP result was obtained for MCF-10A cells (genotype TG), comparative sequencing of the immunoprecipitate and the input chromatin, showed that HMGA1 binds this site in the presence of both alleles but there is a preferential binding for the T allele (Figure 4B).
SNP rs4942485 (Functional SNP 3 in Figure 5), which is tagged by rs4942440 and haplotype 4, maps inside a regulatory feature in intron 20. It was predicted that the A allele would bind the albumin D-binding protein (DBP), while the G allele could bind the zinc finger protein 161 homolog (ZF5). EMSA experiments (Figure 3C) showed strong binding for the labelled oligonucleotide containing the A allele in the form of two bands. This binding was more efficiently competed by the same unlabelled oligonucleotide than the unlabelled probe containing the G allele. Using consensus competition probes and specific antibodies for DBP and antibodies for two negative controls (OCT-1 and IgG), we confirmed that the top band corresponded to the binding of DBP. For the G allele, EMSA studies suggested potential binding of ZF5 in the form of a third lower band when in the presence of Zn2+ in the binding buffer. Competition was detected when using a specific antibody against ZF5. However, stronger competition was observed in the same experiment by an antibody against DBP. These data suggest that DBP binding is stronger to the A allele of the rs4942485 sequence, but that possible binding can exist in the presence of the G allele. Our chromatin immunoprecipitation experiments in this region using specific antibodies support the in vivo occupancy of this site by DBP in MCF-7 cells and ZF5 in both MCF7 and MCF-10A cells (Figure 4C). Due to the low frequency of this SNP, all cell lines available for these studies were homozygous for the common A allele, preventing us from establishing allele preferences in vivo.
The list of the candidate SNPs for functional analysis as well as information on the gene expression levels of the transcription factors analyzed in this study, are provided as Additional files 10 and 11, respectively.
Overall, we identified three SNPs linked to haplotypes 2, 4 and 5, which are located in previously described regulatory elements, have the potential to alter the binding of TFs and are occupied in vivo in breast cancer and normal cell lines (Figure 5). Although rs1799943 is the tag for haplotype 2 and rs9567576 is a tag for haplotypes 2 and 5, rs4942485 tags a subset of haplotype 4 (pairwise rs4942485 versus rs4942440 r2 = 0.55), which suggests that further cis-variants might be acting on this haplotype.
The aim of our study was to investigate the effect of regulatory genetic variation of the BRCA2 tumor suppressor gene on disease risk. To this end, we identified haplotypes associated with different expression levels of BRCA2 in normal breast tissue (expression haplotypes) and characterized three cis-acting polymorphisms that alter the binding of transcription factors at regulatory sites. We show that these expression haplotypes have an impact on the expression profile of breast cells. Also, we found some evidence of association between a high expression haplotype and lower risk of developing breast cancer among carriers of germline mutations in BRCA2.
The cells of individuals carrying inactivating germline mutations in the BRCA2 gene rely on the expression of BRCA2 from the remaining wild-type allele. There is evidence that these cells have a different gene expression profile and ability to repair double-strand DNA breaks compared to cells carrying two wild-type alleles [21, 22]. We have shown previously that BRCA2 shows differential allelic expression in normal breast tissue. These observations raised the hypothesis that cis-regulatory variation could modify the penetrance of germline mutations in BRCA2, by varying the level of expression of the remaining wild-type allele, if different levels of haploinsufficiency are important. The functional effects of haploinsufficiency at a given locus may vary from tissue to tissue . Many critical biological processes are context specific and the overlap of cis-regulatory variants across different tissues is predicted to be between 10% to 80% [43–45], suggesting that the expression haplotypes can vary from tissue to tissue. It is therefore critical to study the relevant tissue when evaluating mechanisms of effect on disease risk. The functional data that we present were obtained from the study of normal breast tissue from control individuals and breast cell lines (normal and cancer).
Haplotypes associated with different levels of gene expression were mapped in unrelated individuals, an approach that has proven powerful to identify cis-acting loci . The difference of expression that we observed between the expression haplotypes is small, which suggests that any effect in disease will also be subtle, as expected for common variants. Further functional analysis in breast cells revealed three cis-acting variants affecting the binding of transcription factors which can explain at least in part the differences in expression level associated with the expression haplotypes. These cis-acting variants were mapped to the promoter and two intronic regulatory elements of BRCA2. The regulatory scenario suggested by our findings is complex, with diverse regulatory variants playing a role in different expression haplotypes. The catalogue of genetic variation is improving constantly through studies like the 1000 Genomes Project. The most recent data show more than ten predicted haplotype blocks for the region we studied here. We have analyzed a fraction of the known variation and believe that there may be more cis-variants regulating the expression of BRCA2 in breast tissue, as in others.
In a recent report, Bellacosa and colleagues have shown that BRCA2 +/- heterozygous cells have an altered gene expression profile compared with normal cells from individuals without mutations and that this effect is tissue-specific . Even though the expression haplotypes we identified are associated with small differences in the expression of BRCA2 itself, our analyses revealed that the gene expression level of some of the same genes reported by Bellacosa et al. could be correlated with the expression haplotypes. These genes were, unsurprisingly, the ones with the most significant differences in the previous report. These findings suggest that even subtle changes in the expression of BRCA2 can have an impact on the phenotype of normal breast tissue, which can be particularly important when only one expressing allele is present.
In our association study we found some evidence that the minor allele of SNP rs4942440 (corresponding to a high expression haplotype) is associated with a protective effect for the development of breast cancer in mutation carriers. We hypothesize that the higher the expression of BRCA2 from the remaining wild-type allele the better the cells of a mutation carrier individual will carry out DNA repair. The evidence of association in the present study is weak. Mutation phase information for the samples used in the association study was not available. As a consequence, the group of BRCA2 mutation carriers who are heterozygous for this SNP consists of a mixture of individuals who have either the minor or the common allele on the wild type haplotype. We are currently collecting detailed pedigree information and will be able to use phase information in the future, which will strengthen our analysis. Genotyping of additional mutation carriers in the future will further clarify the involvement of this SNP in the penetrance of BRCA2 mutations in breast cancer. The selection of SNPs for the disease association analysis, as well as the gene expression studies, was performed based on our previous data associating these SNPs/haplotypes with different levels of BRCA2 expression. It is difficult therefore to apply an exact multiple testing, but taken together, the gene expression and association results are suggestive for the involvement of this SNP in breast cancer risk for BRCA2 mutation carriers.
Our findings support that quite subtle levels of haploinsufficiency of a tumor suppressor gene can have biologically relevant effects. Others have reported similar effects for mutations in the PTEN and TP53 genes in mouse models and APC in Familial Adenomatous Polyposis in humans [47–49]. Common regulatory variation affecting the expression of APC and TGFBR1 has also been shown to contribute to susceptibility to cancer in humans [3, 4]. Furthermore, common variants of the BRCA1 wild-type allele have also been recently suggested to modify the risk of breast cancer in BRCA1 mutations carriers . In the future, a confirmation of these findings could be performed by direct measurement of BRCA2 expression levels in mutation carriers and even BRCA2 protein levels. The functional interactions between genetic variability, transcriptional regulation and cancer susceptibility are largely unexplored. Our findings highlight the importance of investigating the regulatory genetic variation of tumour suppressor genes in the search for genetic modifiers of mutation penetrance.
analysis of variance
Utah residents with Northern and Western European ancestry from the CEPH collection
differential allelic expression
electrophoretic mobility shift assay
fetal calf serum
polymerase chain reaction
single nucleotide polymorphism
The authors would like to thank the women who contributed samples for this study and Dr R Russell, Dr A Hurtado, Ms R Prathalingam, Dr M Wilson, Dr K Meyer and Dr A Lynch for useful discussion and assistance with methodology, Dr E Dicks for assisting with access to data. We would like to acknowledge the support of the Core Genomics team at Cambridge Research Institute, in particular Mr C Bourne. This work was supported by a Breast Cancer Research Foundation grant to ATM and BAJP; the University of Cambridge, Cancer Research UK, Hutchison Whampoa Limited and NIHR Cambridge Biomedical Research Centre. BAJP is the Li Ka Shing Professor of Oncology at the University of Cambridge. ACA is a Cancer Research - UK Senior Cancer Research Fellow. The CIMBA data management and analysis is supported by Cancer Research - UK.
CIMBA Collaborating Centres
University of Pennsylvania (UPENN): This study was supported by the Breast Cancer Research Foundation; Cancer Genetics Network, Marjorie Cohen Foundation.
Epidemiological study of BRCA1 & BRCA2 mutation carriers (EMBRACE): Douglas F. Easton is the Principal Investigator of the study. EMBRACE Collaborating Centers are: Coordinating Centre, Cambridge: Susan Peock, Debra Frost, Steve Ellis, Elena Fineberg, Radka Platte. North of Scotland Regional Genetics Service, Aberdeen: Zosia Miedzybrodzka, Helen Gregory. Northern Ireland Regional Genetics Service, Belfast: Patrick Morrison, Lisa Jeffers. West Midlands Regional Clinical Genetics Service, Birmingham: Trevor Cole, Kai-ren Ong, Jonathan Hoffman. South West Regional Genetics Service, Bristol: Alan Donaldson, Margaret James. East Anglian Regional Genetics Service, Cambridge: Joan Paterson, Sarah Downing, Amy Taylor. Medical Genetics Services for Wales, Cardiff: Alexandra Murray, Mark T. Rogers, Emma McCann. St James's Hospital, Dublin & National Centre for Medical Genetics, Dublin: M. John Kennedy, David Barton. South East of Scotland Regional Genetics Service, Edinburgh: Mary Porteous, Sarah Drummond. Peninsula Clinical Genetics Service, Exeter: Carole Brewer, Emma Kivuva, Anne Searle, Selina Goodman, Kathryn Hill. West of Scotland Regional Genetics Service, Glasgow: Rosemarie Davidson, Victoria Murday, Nicola Bradshaw, Lesley Snadden, Mark Longmuir, Catherine Watt, Sarah Gibson, Eshika Haque, Ed Tobias, Alexis Duncan. South East Thames Regional Genetics Service, Guy's Hospital London: Louise Izatt, Chris Jacobs, Caroline Langman, Anna Whaite. North West Thames Regional Genetics Service, Harrow: Huw Dorkins. Leicestershire Clinical Genetics Service, Leicester: Julian Barwell. Yorkshire Regional Genetics Service, Leeds: Julian Adlard, Carol Chu, Julie Miller. Cheshire & Merseyside Clinical Genetics Service, Liverpool: Ian Ellis, Catherine Houghton. Manchester Regional Genetics Service, Manchester: D Gareth Evans, Fiona Lalloo, Jane Taylor. North East Thames Regional Genetics Service, NE Thames, London: Lucy Side, Alison Male, Cheryl Berlin. Nottingham Centre for Medical Genetics, Nottingham: Jacqueline Eason, Rebecca Collier. Northern Clinical Genetics Service, Newcastle: Fiona Douglas, Oonagh Claber, Irene Jobson. Oxford Regional Genetics Service, Oxford: Lisa Walker, Diane McLeod, Dorothy Halliday, Sarah Durell, Barbara Stayner. The Institute of Cancer Research and Royal Marsden NHS Foundation Trust: Ros Eeles, Susan Shanley, Nazneen Rahman, Richard Houlston, Elizabeth Bancroft, Lucia D'Mello, Elizabeth Page, Audrey Ardern-Jones, Kelly Kohut, Jennifer Wiggins, Elena Castro, Anita Mitra, Lisa Robertson. North Trent Clinical Genetics Service, Sheffield: Jackie Cook, Oliver Quarrell, Cathryn Bardsley. South West Thames Regional Genetics Service, London: Shirley Hodgson, Sheila Goff, Glen Brice, Lizzie Winchester, Charlotte Eddy, Vishakha Tripathi, Virginia Attard. Wessex Clinical Genetics Service, Princess Anne Hospital, Southampton: Diana Eccles, Anneke Lucassen, Gillian Crawford, Donna McBride, Sarah Smalley. EMBRACE is supported by Cancer Research UK Grants C1287/A10118 and C1287/A11990. D. Gareth Evans and Fiona Lalloo are supported by an NIHR grant to the Biomedical Research Centre, Manchester. The Investigators at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust are supported by an NIHR grant to the Biomedical Research Centre at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust. Ros Eeles, Elizabeth Bancroft and Lucia D'Mello are also supported by Cancer Research UK Grant C5047/A8385.
Genetic Modifiers of cancer risk in BRCA1/2 mutation carriers (GEMO): We wish to thank all the GEMO collaborating Cancer Genetics Network 'Groupe Génétique et Cancer' and Fédération Nationale des Centres de Lutte Contre le Cancer, France. The study was supported by the Ligue National Contre le Cancer; Association for International Cancer Research Grant (AICR-07-0454); and the Association "Le cancer du sein, parlons-en!" Award. We wish to thank all the GEMO collaborating groups for their contribution to this study. GEMO Collaborating Centers are: Coordinating Centres, Unité Mixte de Génétique Constitutionnelle des Cancers Fréquents, Centre Hospitalier Universitaire de Lyon/Centre Léon Bérard, & Equipe «Génétique du cancer du sein», Centre de Recherche en Cancérologie de Lyon: Olga Sinilnikova, Sylvie Mazoyer, Laure Barjhoux, Carole Verny-Pierre, Sophie Giraud, Mélanie Léone; and Service de Génétique Oncologique, Institut Curie, Paris: Dominique Stoppa-Lyonnet, Marion Gauthier-Villars, Bruno Buecher, Claude Houdayer, Virginie Moncoutier, Muriel Belotti, Carole Tirapo, Antoine de Pauw. Institut Gustave Roussy, Villejuif: Brigitte Bressac-de-Paillerets, Audrey Remenieras, Véronique Byrde, Olivier Caron, Gilbert Lenoir. Centre Jean Perrin, Clermont-Ferrand: Yves-Jean Bignon, Nancy Uhrhammer. Centre Léon Bérard, Lyon: Christine Lasset, Valérie Bonadona. Centre François Baclesse, Caen: Agnès Hardouin, Pascaline Berthet. Institut Paoli Calmettes, Marseille: Hagay Sobol, Violaine Bourdon, Tetsuro Noguchi, François Eisinger. Groupe Hospitalier Pitié-Salpétrière, Paris: Florence Coulet, Chrystelle Colas, Florent Soubrier. CHU de Arnaud-de-Villeneuve, Montpellier: Isabelle Coupier, Pascal Pujol. Centre Oscar Lambret, Lille: Jean-Philippe Peyrat, Joëlle Fournier, Françoise Révillion, Philippe Vennin, Claude Adenis. Hôpital René Huguenin/Institut Curie, St Cloud: Etienne Rouleau, Rosette Lidereau, Liliane Demange, Catherine Nogues. Centre Paul Strauss, Strasbourg: Danièle Muller, Jean-Pierre Fricker. Institut Bergonié, Bordeaux: Emmanuelle Barouk-Simonet, Françoise Bonnet, Virginie Bubien, Nicolas Sevenet, Michel Longy. Institut Claudius Regaud, Toulouse: Christine Toulas, Rosine Guimbaud, Laurence Gladieff, Viviane Feillel. CHU de Grenoble: Dominique Leroux, Hélène Dreyfus, Christine Rebischung, Magalie Peysselon. CHU de Dijon: Fanny Coron, Laurence Faivre. CHU de St-Etienne: Fabienne Prieur, Marine Lebrun, Caroline Kientz. Hôtel Dieu Centre Hospitalier, Chambéry: Sandra Fert Ferrer. Centre Antoine Lacassagne, Nice: Marc Frénay. CHU de Limoges: Laurence Vénat-Bouvet. CHU de Nantes: Capucine Delnatte. CHU Bretonneau, Tours: Isabelle Mortemousque. Creighton University, Omaha, USA: Henry T.Lynch, Carrie L.Snyder.
Helsinki Breast Cancer Study (HBECS): HEBCS would like to thank Dr. Kristiina Aittomäki and Tuomas Heikkinen and research nurse Irja Erkkilä for their help with the patient data and samples. The HEBCS study has been financially supported by the Helsinki University Central Hospital Research Fund, Academy of Finland (132473), the Finnish Cancer Society, and the Sigrid Juselius Foundation.
Mayo Clinic (MAYO): The Mayo Study was supported by NIH grants CA128978 and a Breast Cancer Specialized Program of Research Excellence (SPORE), CA116167, the Komen Foundation for the Cure and the Breast Cancer Research Foundation.
Fox Chase Cancer Center (FCCC): A.K.G. was funded by U01CA69631, 5U01CA113916, and the Eileen Stein Jacoby Fund. The author acknowledges support from The University of Kansas Cancer Center and the Kansas Bioscience Authority Eminent Scholar Program. A.K.G. is the Chancellors Distinguished Chair in Biomedical Sciences endowed Professor.
The Pisa Breast Cancer Study (PBCS): We would like to acknowledge the 'Tumour Tuscany Institute' for a grant (n. AOOGRT/0011780/Q.30.11) to Maria Adelaide Caligo.
The Swedish BRCA1 and BRCA2 Study (SWE-BRCA): Per Karlsson, Margareta Nordling, Annika Bergman and Zakaria Einbeigi, Gothenburg, Sahlgrenska University Hospital; Marie Stenmark-Askmalm and Sigrun Liedgren, Linköping University Hospital; Åke Borg, Niklas Loman, Håkan Olsson, Ulf Kristoffersson, Helena Jernström, Katja Harbst and Karin Henriksson, Lund University Hospital; Annika Lindblom, Brita Arver, Anna von Wachenfeldt, Annelie Liljegren, Gisela Barbany-Bustinza and Johanna Rantala, Stockholm, Karolinska University Hospital; Beatrice Melin, Henrik Grönberg, Eva-Lena Stattin and Monica Emanuelsson, Umeå University Hospital; Hans Ehrencrona, Richard Rosenquist Brandell and Niklas Dahl, Uppsala University Hospital.
Iceland Landspitali--University Hospital (ILUH): The ILUH group was supported by the Icelandic Association 'Walking for Breast Cancer Research' and by the Landspitali University Hospital Research Fund.
Kathleen Cunningham Foundation Consortium for Research into Familial Breast Cancer (kConFab): We wish to thank Heather Thorne, Eveline Niedermayr, all the kConFab research nurses and staff, the heads and staff of the Family Cancer Clinics, and the Clinical Follow Up Study (funded 2001-2009 by NHMRC and currently by the National Breast Cancer Foundation and Cancer Australia #628333) for their contributions to this resource, and the many families who contribute to kConFab. kConFab is supported by grants from the National Breast Cancer Foundation, the National Health and Medical Research Council (NHMRC) and by the Queensland Cancer Fund, the Cancer Councils of New South Wales, Victoria, Tasmania and South Australia, and the Cancer Foundation of Western Australia.
- Morley M, Molony CM, Weber TM, Devlin JL, Ewens KG, Spielman RS, Cheung VG: Genetic analysis of genome-wide variation in human gene expression. Nature. 2004, 430: 743-747. 10.1038/nature02797.PubMedPubMed CentralView ArticleGoogle Scholar
- Lo H, Wang Z, Hu Y, Yang H, Gere S, Buetow K, Lee M: Allelic variation in gene expression is common in the human genome. Genome Res. 2003, 13: 1855-1862.PubMedPubMed CentralView ArticleGoogle Scholar
- Valle L, Serena-Acedo T, Liyanarachchi S, Hampel H, Comeras I, Li Z, Zeng Q, Zhang H-T, Pennison MJ, Sadim M, Pasche B, Tanner SM, de la Chapelle A: Germline allele-specific expression of TGFBR1 confers an increased risk of colorectal cancer. Science. 2008, 321: 1361-1365. 10.1126/science.1159397.PubMedPubMed CentralView ArticleGoogle Scholar
- Yan H, Dobbie Z, Gruber S, Markowitz S, Romans K, Giardiello F, Kinzler K, Vogelstein B: Small changes in expression affect predisposition to tumorigenesis. Nat Genet. 2002, 30: 25-26. 10.1038/ng799.PubMedView ArticleGoogle Scholar
- Tan AC, Fan J-B, Karikari C, Bibikova M, Garcia EW, Zhou L, Barker D, Serre D, Feldmann G, Hruban RH, Klein AP, Goggins M, Couch FJ, Hudson TJ, Winslow RL, Maitra A, Chakravarti A: Allele-specific expression in the germline of patients with familial pancreatic cancer: an unbiased approach to cancer gene discovery. Cancer Biol Ther. 2008, 7: 135-144. 10.4161/cbt.7.1.5199.PubMedPubMed CentralView ArticleGoogle Scholar
- Shen J, Medico L, Zhao HW: Allelic imbalance in BRCA1 and BRCA2 gene expression and familial ovarian cancer. Cancer Epidemiol Biomarkers Prev. 2010, 20: 50-56.PubMedView ArticleGoogle Scholar
- Azzato EM, Lee AJX, Teschendorff A, Ponder BAJ, Pharoah P, Caldas C, Maia AT: Common germ-line polymorphism of C1QA and breast cancer survival. Br J Cancer. 2010, 102: 1294-1298. 10.1038/sj.bjc.6605625.PubMedPubMed CentralView ArticleGoogle Scholar
- Chen X, Weaver J, Bove B, Vanderveer L, Weil S, Miron A, Daly M, Godwin A: Allelic imbalance in BRCA1 and BRCA2 gene expression is associated with an Increased breast cancer risk. Hum Mol Genet. 2008, 17: 1336-1348. 10.1093/hmg/ddn022.PubMedView ArticleGoogle Scholar
- Meyer K, Maia AT, O'Reilly M, Teschendorff A, Chin S, Caldas C, Ponder B: Allele-specific up-regulation of FGFR2 increases susceptibility to breast cancer. PLoS Biol. 2008, 6: e108-10.1371/journal.pbio.0060108.PubMedPubMed CentralView ArticleGoogle Scholar
- Dunning AM, Healey CS, Baynes C, Maia AT, Scollen S, Vega A, Rodríguez R, Barbosa-Morais NL, Ponder BA, for S, Low YL, Bingham S, for E, Haiman CA, Le Marchand L, for MEC, Broeks A, Schmidt MK, for A, Hopper J, Southey M, for A, Beckmann MW, Fasching PA, for B, Peto J, Johnson N, for B, Bojesen SE, Nordestgaard B, et al: Association of ESR1 gene tagging SNPs with breast cancer risk. Hum Mol Genetics. 2009, 18: 1131-1139. 10.1093/hmg/ddn429.View ArticleGoogle Scholar
- Ware MD, DeSilva D, Sinilnikova OM, Stoppa-Lyonnet D, Tavtigian SV, Mazoyer S: Does nonsense-mediated mRNA decay explain the ovarian cancer cluster region of the BRCA2 gene?. Oncogene. 2006, 25: 323-328.PubMedGoogle Scholar
- Schmidt D, Wilson MD, Ballester B, Schwalie PC, Brown GD, Marshall A, Kutter C, Watt S, Martinez-Jimenez CP, Mackay S, Talianidis I, Flicek P, Odom DT: Five-vertebrate ChIP-seq reveals the evolutionary dynamics of transcription factor binding. Science. 2010, 328: 1036-1040. 10.1126/science.1186176.PubMedPubMed CentralView ArticleGoogle Scholar
- Pomerantz MM, Ahmadiyeh N, Jia L, Herman P, Verzi MP, Doddapaneni H, Beckwith CA, Chan JA, Hills A, Davis M, Yao K, Kehoe SM, Lenz HJ, Haiman CA, Yan C, Henderson BE, Frenkel B, Barretina J, Bass A, Tabernero J, Baselga J, Regan MM, Manak JR, Shivdasani R, Coetzee GA, Freedman ML: The 8q24 cancer risk variant rs6983267 shows long-range interaction with MYC in colorectal cancer. Nat Genet. 2009, 41: 882-884. 10.1038/ng.403.PubMedPubMed CentralView ArticleGoogle Scholar
- Maia AT, Spiteri I, Lee AJ, O'Reilly M, Jones L, Caldas C, Ponder BA: Extent of differential allelic expression of candidate breast cancer genes is similar in blood and breast. Breast Cancer Res. 2009, 11: R88-10.1186/bcr2458.PubMedPubMed CentralView ArticleGoogle Scholar
- Begg CB, Haile RW, Borg A, Malone KE, Concannon P, Thomas DC, Langholz B, Bernstein L, Olsen JH, Lynch CF, Anton-Culver H, Capanu M, Liang X, Hummer AJ, Sima C, Bernstein JL: Variation of breast cancer risk among BRCA1/2 carriers. JAMA. 2008, 299: 194-201. 10.1001/jama.2007.55-a.PubMedPubMed CentralView ArticleGoogle Scholar
- Antoniou A, Pharoah PDP, Narod S, Risch HA, Eyfjord JE, Hopper JL, Loman N, Olsson H, Johannsson O, Borg A, Pasini B, Radice P, Manoukian S, Eccles DM, Tang N, Olah E, Anton-Culver H, Warner E, Lubinski J, Gronwald J, Gorski B, Tulinius H, Thorlacius S, Eerola H, Nevanlinna H, Syrjäkoski K, Kallioniemi O-P, Thompson D, Evans C, Peto J, et al: Average risks of breast and ovarian cancer associated with BRCA1 or BRCA2 mutations detected in case series unselected for family history: a combined analysis of 22 studies. Am J Hum Genet. 2003, 72: 1117-1130. 10.1086/375033.PubMedPubMed CentralView ArticleGoogle Scholar
- Rebbeck TR: Inherited predisposition and breast cancer: modifiers of BRCA1/2-associated breast cancer risk. Environ Mol Mutagen. 2002, 39: 228-234. 10.1002/em.10050.PubMedView ArticleGoogle Scholar
- Antoniou AC, Sinilnikova OM, Simard J, Léoné M, Dumont M, Neuhausen SL, Struewing JP, Stoppa-Lyonnet D, Barjhoux L, Hughes DJ, Coupier I, Belotti M, Lasset C, Bonadona V, Bignon Y-J, Rebbeck TR, Wagner T, Lynch HT, Domchek SM, Nathanson KL, Garber JE, Weitzel J, Narod SA, Tomlinson G, Olopade OI, Godwin A, Isaacs C, Jakubowska A, Lubinski J, et al: RAD51 135G--> C modifies breast cancer risk among BRCA2 mutation carriers: results from a combined analysis of 19 studies. Am J Hum Genet. 2007, 81: 1186-1200. 10.1086/522611.PubMedPubMed CentralView ArticleGoogle Scholar
- Antoniou AC, Spurdle AB, Sinilnikova OM, Healey S, Pooley KA, Schmutzler RK, Versmold B, Engel C, Meindl A, Arnold N, Hofmann W, Sutter C, Niederacher D, Deissler H, Caldes T, Kämpjärvi K, Nevanlinna H, Simard J, Beesley J, Chen X, Neuhausen SL, Rebbeck TR, Wagner T, Lynch HT, Isaacs C, Weitzel J, Ganz PA, Daly MB, Tomlinson G, et al: Common breast cancer-predisposition alleles are associated with breast cancer risk in BRCA1 and BRCA2 mutation carriers. Am J Hum Genet. 2008, 82: 937-948. 10.1016/j.ajhg.2008.02.008.PubMedPubMed CentralView ArticleGoogle Scholar
- Antoniou AC, Beesley J, McGuffog L, Sinilnikova OM, Healey S, Neuhausen SL, Ding YC, Rebbeck TR, Weitzel JN, Lynch HT, Isaacs C, Ganz PA, Tomlinson G, Olopade OI, Couch FJ, Wang X, Lindor NM, Pankratz VS, Radice P, Manoukian S, Peissel B, Zaffaroni D, Barile M, Viel A, Allavena A, Dall'olio V, Peterlongo P, Szabo CI, Zikan M, Claes K, et al: Common breast cancer susceptibility alleles and the risk of breast cancer for BRCA1 and BRCA2 mutation carriers: implications for risk prediction. Cancer Res. 2010, 70: 9742-9754. 10.1158/0008-5472.CAN-10-1907.PubMedPubMed CentralView ArticleGoogle Scholar
- Arnold K, Kim M-K, Frerk K, Edler L, Savelyeva L, Schmezer P, Wiedemeyer R: Lower level of BRCA2 protein in heterozygous mutation carriers is correlated with an increase in DNA double strand breaks and an impaired DSB repair. Cancer Lett. 2006, 243: 90-100. 10.1016/j.canlet.2005.11.041.PubMedView ArticleGoogle Scholar
- Bellacosa A, Godwin AK, Peri S, Devarajan K, Caretti E, Vanderveer L, Bove B, Slater C, Zhou Y, Daly M, Howard S, Campbell KS, Nicolas E, Yeung AT, Clapper ML, Crowell JA, Lynch HT, Ross E, Kopelovich L, Knudson AG: Altered gene expression in morphologically normal epithelial cells from heterozygous carriers of BRCA1 or BRCA2 mutations. Cancer Prev Res (Phila). 2010, 3: 48-61. 10.1158/1940-6207.CAPR-09-0078.View ArticleGoogle Scholar
- The International HapMap Consortium: A haplotype map of the human genome. Nature. 2005, 437: 1299-1320. 10.1038/nature04226.PubMed CentralView ArticleGoogle Scholar
- Consortium TGP: A map of human genome variation from population-scale sequencing. Nature. 2010, 467: 1061-1073. 10.1038/nature09534.View ArticleGoogle Scholar
- The 1000 Genomes Consortium. [http://www.1000genomes.org/]
- The International HapMap Project. [http://hapmap.ncbi.nlm.nih.gov]
- Barrett JC, Fry B, Maller J, Daly MJ: Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005, 21: 263-265. 10.1093/bioinformatics/bth457.PubMedView ArticleGoogle Scholar
- Haploview. [http://www.broad.mit.edu/mpg/haploview]
- Whitehead RH, Bertoncello I, Webber LM, Pedersen JS: A new human breast carcinoma cell line (PMC42) with stem cell characteristics. I. Morphologic characterization. J Natl Cancer Inst. 1983, 70: 649-661.PubMedGoogle Scholar
- Soule HD, Vazguez J, Long A, Albert S, Brennan M: A human cell line from a pleural effusion derived from a breast carcinoma. J Natl Cancer Inst. 1973, 51: 1409-1416.PubMedGoogle Scholar
- Flanagan L, Van Weelden K, Ammerman C, Ethier SP, Welsh J: SUM-159PT cells: a novel estrogen independent human breast cancer model system. Breast Cancer Res Treat. 1999, 58: 193-204. 10.1023/A:1006331716981.PubMedView ArticleGoogle Scholar
- Soule HD, Maloney TM, Wolman SR, Peterson WD, Brenz R, McGrath CM, Russo J, Pauley RJ, Jones RF, Brooks SC: Isolation and characterization of a spontaneously immortalized human breast epithelial cell line, MCF-10. Cancer Res. 1990, 50: 6075-6086.PubMedGoogle Scholar
- Carvalho B, Bengtsson H, Speed TP, Irizarry RA: Exploration, normalization, and genotype calls of high-density oligonucleotide SNP array data. Biostatistics. 2007, 8: 485-499.PubMedView ArticleGoogle Scholar
- R Development Core Team: R: a language and environment for statistical computing. Edited by: Team RDC. 2010Google Scholar
- Wingender E: The TRANSFAC project as an example of framework technology that supports the analysis of genomic regulation. Brief Bioinform. 2008, 9: 326-332. 10.1093/bib/bbn016.PubMedView ArticleGoogle Scholar
- Schmidt D, Schwalie PC, Ross-Innes CS, Hurtado A, Brown GD, Carroll JS, Flicek P, Odom DT: A CTCF-independent role for cohesin in tissue-specific transcription. Genome Res. 2010, 20: 578-588. 10.1101/gr.100479.109.PubMedPubMed CentralView ArticleGoogle Scholar
- Dunning MJ, Smith ML, Ritchie ME, Tavaré S: Beadarray: R classes and methods for Illumina bead-based data. Bioinformatics. 2007, 23: 2183-2184. 10.1093/bioinformatics/btm311.PubMedView ArticleGoogle Scholar
- Cairns JM, Dunning MJ, Ritchie ME, Russell R, Lynch AG: BASH: a tool for managing BeadArray spatial artefacts. Bioinformatics. 2008, 24: 2921-2922. 10.1093/bioinformatics/btn557.PubMedPubMed CentralView ArticleGoogle Scholar
- Antoniou A, Sinilnikova O, McGuffog L, Healey S, Nevanlinna H, Heikkinen T, Simard J, Spurdle A, Beesley J, Chen X, Neuhausen S, Ding Y, Couch F, Wang X, Fredericksen Z, Peterlongo P, Peissel B, Bonanni B, Viel A, Bernard L, Radice P, Szabo C, Foretova L, Zikan M, Claes K, Greene M, Mai P, Rennert G, Lejbkowicz F, et al: Common variants in LSP1, 2q35 and 8q24 and breast cancer risk for BRCA1 and BRCA2 mutation carriers. Hum Mol Genet. 2009, 18: 4442-4456. 10.1093/hmg/ddp372.PubMedPubMed CentralView ArticleGoogle Scholar
- Lange K, Weeks D, Boehnke M: Programs for Pedigree Analysis: MENDEL, FISHER, and dGENE. Genet Epidemiol. 1988, 5: 471-472. 10.1002/gepi.1370050611.PubMedView ArticleGoogle Scholar
- Lin DY, Zeng D, Millikan R: Maximum likelihood estimation of haplotype effects and haplotype-environment interactions in association studies. Genet Epidemiol. 2005, 29: 299-312. 10.1002/gepi.20098.PubMedView ArticleGoogle Scholar
- Guenther MG, Levine SS, Boyer LA, Jaenisch R, Young RA: A chromatin landmark and transcription initiation at most promoters in human cells. Cell. 2007, 130: 77-88. 10.1016/j.cell.2007.05.042.PubMedPubMed CentralView ArticleGoogle Scholar
- Campbell C, Kirby A, Nemesh J, Daly M, Hirschhorn J: A survey of allelic imbalance in F1 mice. Genome Res. 2008, 18: 555-563. 10.1101/gr.068692.107.PubMedPubMed CentralView ArticleGoogle Scholar
- Dimas AS, Deutsch S, Stranger BE, Montgomery SB, Borel C, Attar-Cohen H, Ingle C, Beazley C, Gutierrez Arcelus M, Sekowska M, Gagnebin M, Nisbett J, Deloukas P, Dermitzakis ET, Antonarakis SE: Common regulatory variation impacts gene expression in a cell type-dependent manner. Science. 2009, 325: 1246-1250. 10.1126/science.1174148.PubMedPubMed CentralView ArticleGoogle Scholar
- Emilsson V, Thorleifsson G, Zhang B, Leonardson AS, Zink F, Zhu J, Carlson S, Helgason A, Walters GB, Gunnarsdottir S, Mouy M, Steinthorsdottir V, Eiriksdottir GH, Bjornsdottir G, Reynisdottir I, Gudbjartsson D, Helgadottir A, Jonasdottir A, Jonasdottir A, Styrkarsdottir U, Gretarsdottir S, Magnusson KP, Stefansson H, Fossdal R, Kristjansson K, Gislason HG, Stefansson T, Leifsson BG, Thorsteinsdottir U, Lamb JR, et al: Genetics of gene expression and its effect on disease. Nature. 2008, 452: 423-428. 10.1038/nature06758.PubMedView ArticleGoogle Scholar
- Pastinen T, Ge B, Gurd S, Gaudin T, Dore C, Lemire M, Lepage P, Harmsen E, Hudson TJ: Mapping common regulatory variants to human haplotypes. Hum Mol Genet. 2005, 14: 3963-3971. 10.1093/hmg/ddi420.PubMedView ArticleGoogle Scholar
- Alimonti A, Carracedo A, Clohessy JG, Trotman LC, Nardella C, Egia A, Salmena L, Sampieri K, Haveman WJ, Brogi E, Richardson AL, Zhang J, Pandolfi PP: Subtle variations in Pten dose determine cancer susceptibility. Nat Genet. 2010, 42: 454-458. 10.1038/ng.556.PubMedPubMed CentralView ArticleGoogle Scholar
- Venkatachalam S, Shi YP, Jones SN, Vogel H, Bradley A, Pinkel D, Donehower LA: Retention of wild-type p53 in tumors from p53 heterozygous mice: reduction of p53 dosage can promote cancer formation. EMBO J. 1998, 17: 4657-4667. 10.1093/emboj/17.16.4657.PubMedPubMed CentralView ArticleGoogle Scholar
- Yeung AT, Patel BB, Li X-M, Seeholzer SH, Coudry RA, Cooper HS, Bellacosa A, Boman BM, Zhang T, Litwin S, Ross EA, Conrad P, Crowell JA, Kopelovich L, Knudson A: One-hit effects in cancer: altered proteome of morphologically normal colon crypts in familial adenomatous polyposis. Cancer Res. 2008, 68: 7579-7586. 10.1158/0008-5472.CAN-08-0856.PubMedPubMed CentralView ArticleGoogle Scholar
- Matys V, Kel-Margoulis OV, Fricke E, Liebich I, Land S, Barre-Dirrie A, Reuter I, Chekmenev D, Krull M, Hornischer K, Voss N, Stegmaier P, Lewicki-Potapov B, Saxel H, Kel AE, Wingender E: TRANSFAC and its module TRANSCompel: transcriptional gene regulation in eukaryotes. Nucleic Acids Res. 2006, 34: D108-110. 10.1093/nar/gkj143.PubMedPubMed CentralView ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.