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Analysis of functional germline variants in APOBEC3 and driver genes on breast cancer risk in Moroccan study population

BMC Cancer volume 16, Article number: 165 (2016) Cite this article

Abstract

Background

Breast cancer (BC) is the most prevalent cancer in women and a major public health problem in Morocco. Several Moroccan studies have focused on studying this disease, but more are needed, especially at the genetic and molecular levels. Therefore, we investigated the potential association of several functional germline variants in the genes commonly mutated in sporadic breast cancer.

Methods

In this case–control study, we examined 36 single nucleotide polymorphisms (SNPs) in 13 genes (APOBEC3A, APOBEC3B, ARID1B, ATR, MAP3K1, MLL2, MLL3, NCOR1, RUNX1, SF3B1, SMAD4, TBX3, TTN), which were located in the core promoter, 5’-and 3’UTR or which were nonsynonymous SNPs to assess their potential association with inherited predisposition to breast cancer development. Additionally, we identified a ~29.5-kb deletion polymorphism between APOBEC3A and APOBEC3B and explored possible associations with BC. A total of 226 Moroccan breast cancer cases and 200 matched healthy controls were included in this study.

Results

The analysis showed that12 SNPs in 8 driver genes, 4 SNPs in APOBEC3B gene and 1 SNP in APOBEC3A gene were associated with BC risk and/or clinical outcome at P ≤ 0.05 level. RUNX1_rs8130963 (odds ratio (OR) = 2.25; 95 % CI 1.42-3.56; P = 0.0005; dominant model), TBX3_rs8853 (OR = 2.04; 95 % CI 1.38-3.01; P = 0.0003; dominant model), TBX3_rs1061651 (OR = 2.14; 95 % CI1.43-3.18; P = 0.0002; dominant model), TTN_rs12465459 (OR = 2.02; 95 % confidence interval 1.33-3.07; P = 0.0009; dominant model), were the most significantly associated SNPs with BC risk. A strong association with clinical outcome were detected for the genes SMAD4 _rs3819122 with tumor size (OR = 0.45; 95 % CI 0.25-0.82; P = 0.009) and TTN_rs2244492 with estrogen receptor (OR = 0.45; 95 % CI 0.25-0.82; P = 0.009).

Conclusion

Our results suggest that genetic variations in driver and APOBEC3 genes were associated with the risk of BC and may have impact on clinical outcome. However, the reported association between the deletion polymorphism and BC risk was not confirmed in the Moroccan population. These preliminary findings require replication in larger studies.

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Background

Breast Cancer (BC) is one of the most frequent malignant disease and primary cause of death in women worldwide. Approximately 522,000 women died on BC in 2012 and 1.67 million new cancer cases were diagnosed worldwide [1, 2].

The vast majority of sporadic and familial breast cancer cases arise due to lifelong accumulation of genetic factors in the breast tissue. Recent genome-wide association studies (GWASs) focusing on evaluating common single nucleotide polymorphisms (SNPs) have identified more than 70 genetic susceptibility loci for breast cancer [3–25]. Partial and full tumor genome sequences have revealed the existence of hundreds to thousands of mutations in most cancers [26–32]. However, genome sequencing has revealed that many cancers, including breast cancer, have somatic mutation spectra dominated by C-to-T transitions [27–32]. Recently, the International Cancer Genome Consortium (ICGC) was launched to identify those somatic mutations and consequently to determine those genes which are required for human cancer development [29, 33]. Approximately 10 % of those are driver mutations, which initiate the carcinogenic process [34].

Additionally, recent studies have shown that copy number variations (CNVs), another type of genetic variation, occur frequently in the genome and account for more nucleotide sequence variation than single-nucleotide polymorphisms [35]. This variation accounts for roughly 12 % of human genomic DNA, and each variation may range from about 1 kb to several megabases in size [36]. Recently, through CNV GWAS, Long et al. [37] discovered a common CNV locus for breast cancer in Chinese women, which was located between exon 5 of APOBEC3A and exon 8 of APOBEC3B, resulting in a fusion gene with a protein sequence identical to APOBEC3A, but with a 3’-UTR of APOBEC3B. This deletion has been associated with increased BC risk in both Chinese and a Caucasian population with a population frequency of around 37 and 6 % respectively [37–39]. In addition to decreased expression of APOBEC3B, the deletion may lead to alteration in APOBEC3A RNA stability.

Considering the potential function of driver and APOBEC3 gene in the process of tumorigenesis in BC, it is possible that germline variations and CNV in those genes could influence the risk of BC. For this reason, we conducted this case–control study in a sample of Moroccan women.

Methods

Study population

The present case–control study was performed involving 226 cases, recruited from the Department of Oncology of the Littoral Clinic of Casablanca during 2013. The control group included a total of 200 healthy women with no personal history of cancer diseases selected from DNA bank volunteers of the Genetics and Molecular Pathology Laboratory. Clinico-pathological parameters including age at diagnosis, menopausal status, histology type, tumor size, Scarff-Bloom-Richardson (SBR) grade, lymph nodes status, and hormone receptors status were obtained from patients’ medical records. The study protocols have been approved by the Ethic Committee for Biomedical Research in Casablanca (CERBC) of the Faculty of Medicine and Pharmacy and written informed consent was obtained from each subject.

Gene/SNP selection

Regarding driver genes, we focused on genes described to carry BC driver mutations in at least two of the following publications: Stephens et al. 2012; Banerji et al. 2012; Ellis et al. 2012; Shah et al. 2012 [32, 40–42]. The well-known and intensively studied genes such as BRCA1 or PTEN were excluded from this study. A total of 36 SNPs across 11 driver genes (ARID1B, ATR, MAP3K1, MLL2, MLL3, NCOR1, RUNX1, SF3B1, SMAD4, TBX3, TTN) and 2 genes of APOBEC3 family (APOBEC3A, APOBEC3B) were selected to the study based on data obtained from Ensembl Genome browser (http://www.ensembl.org/index.html) for the CEU (Utah residents with Northern and Western European ancestry from the CEPH collection). The SNPs selection was based on these criteria: (1) minor allele frequency (MAF) value over 10 %; (2) location within the coding region (non synonymous SNPs), core promoter regions and 5’- and 3’-untranslated regions (UTRs), (3) Haploview was used to select SNPs on the basis of linkage disequilibrium (LD; r2 ≥ 0.80)) to minimize the number of SNPs to be genotyped. RegulomeDB (http://www.regulomedb.org/) was used to explore the potential function of the associated SNPs.

Genotyping

Genomic DNA was extracted from peripheral blood leukocytes using the salting out procedure [31]. Genomic DNA was dissolved in TE (10 mM Tris–HCl and 0.1 mM EDTA, pH8.0). Spectrophotometry was used to quantify DNA using the Nanovue TM Plus spectrophotometer.

Genotyping was performed using TaqMan SNP Genotyping Assay from Life Technologies (Darmstadt, Germany) or KASPar SNP Genotyping system from KBioscience (Hoddesdon, Great Britain) in a 384-well plate format. Master Mix for the the KASPar assay was prepared according to the KBioscience’s conditions and products, whereas 5× HOT FIREPol Probe qPCR Mix Plus from Solis BioDyne (Tartu, Estonia) for TaqMan SNP Genotyping Assay was used. The Polymerase chain reactions (PCR) were performed in a final reaction volume of 5 μl per well. The PCR poducts were analyzed using ViiA7 Real-Time PCR System from Applied Biosystems (Weiterstadt, Germany).

Screening for APOBEC3 deletion

Polymerase chain reaction (PCR) was carried out to amplify APOBEC3 gene in a final volume of 10 μl containing 10× reaction buffer, 50 mM MgCl2, 10 mM dNTPs, 10 μM primers, 5U Taq DNA polymerase, and 10 ng genomic DNA. The PCR amplification parameters were 40 cycles of 1 min of denaturing at 95 °C, 1 min of annealing at 60 °C, and 1 min of extension at 72 °C.

The insertion and deletion alleles were detected by amplifying genomic DNA with the following oligonucleotide sequences:

Deletion_F:TAGGTGCCACCCCGAT;Deletion_R:TTGAGCATAATCTTACTCTTGTAC; Insertion1_F: TTGGTGCTGCCCCCTC; Insertion1_R: TAGAGACTGAGGCCCAT; and Insertion2_F: TGTCCCTTTTCAGAGTTTGAGTA; Insertion2_R: TGGAGCCAATTAATCACTTCAT. Deletion alleles resulted in 700 bp fragment, Insertion1alleles resulted in 490 bp fragment and Insertion2 alleles resulted in 705 bp fragment. Insertion and deletion PCR assays were performed separately, the products pooled, and visualized by ethidium bromide staining on a standard 1.5 % agarose gel.

Statistical analysis

The Hardy Weinberg equilibrium (HWE) was tested by comparing observed and expected genotype frequencies in both cases and controls using χ2 test. Odds ratio with a confidence intervals (CIs) of 95 % were calculated using multiple logistic regression (PROC LOGISTIC, SAS Version 9.2; SAS Institute, Cary, NC) to assess the strength of the association between genotypes and breast cancer risk. The P value ≤ 0.05 was considered statistically significant.

In Silico prediction

To investigate how the SNPs can influence the gene expression and their consequences on protein binding sites, chromatin structure and promoter and enhancer strength, we used HaploReg (http://www.broadinstitute.org/mammals/haploreg/haploreg.php). To identify the possible effects on histone modification we used RegulomeDB (http://regulome.stanford.edu/). These effects were proofed for data in MCF7 (Michigan Cancer Foundation-7 breast cancer cell line), T-47D (epithelial cell line derived from mammary ductal carcinoma), HMEC (human mammary epithelial cells) or MCF10A-ER-SRc (breast epithelial cell line -estrogen receptor –src) cell lines. SIFT and PolyPhen predictions were used to determine the possible effect of amino acid substitutions on protein function and structure (Ensemble release 75, http://www.ensembl.org/index.html). The MicroSNiPer was used to predict the impact of all the significant SNPs of this study located in 3’UTR on micro-RNA binding using microSNiPer (http://epicenter.ie-freiburg.mpg.de/services/microsniper/).

Results

The baseline characteristics of the population sample analyzed in our study are listed in Table 1. In total, 226 BC cases and 200 controls were successfully genotyped for 36 selected SNPs in 13 potential genes. Altogether 12 SNPs in 8 driver genes, 4 SNPs in APOBEC3B gene and 1 SNP in APOBEC3A gene were associated with BC risk and/or clinical outcome at P ≤ 0.05 level (Tables 2 and 3).

Table 1 Characteristics of breast tumors at time of diagnosis
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Table 2 SNPs associated with breast cancer risk
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Table 3 SNPs associated with clinico-pathological features
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The most significant associations with BC risk were observed for RUNX1_rs8130963 (OR = 2.25; 95 % CI 1.42-3.56; P = 0.0005; dominant model), TBX3_rs8853 (OR = 2.04; 95 % CI 1.38-3.01; P = 0.0003; dominant model), TBX3_rs1061651 (OR = 2.14; 95 % CI 1.43-3.18; P = 0.0002; dominant model), TTN_rs12465459 (OR = 2.02; 95 % CI 1.33-3.07; P = 0.0009; dominant model). However, the strongest significant associations were observed for TBX3_rs2242442, ATR_rs2227928, RUNX1_rs17227210; both heterozygous and homozygous carriers of the minor allele were at increased risk of BC (Table 2). Considering driver gene, only the SNP rs2227928 in ATR was associated both with risk (OR 1.68, 95 % CI 1.14-2.49 dominant model), tumor size and hormone receptor status (Table 3).

An increased risk was observed for homozygous carriers of the minor allele for rs178831 in NCOR1 (OR 2.22, 95%CI 1.00-4.95) (Table 2), however no association with clinical tumor characteristics was observed. Two of the six genotyped SNPs in TTN were associated with less aggressive tumor features: rs12463674 with low histological grade and rs2244492 with low hormone receptor status (Table 3). Additionally, the minor allele carriers of the SNPs rs6001376 in APOBEC3B and rs832583 in MAP3K1 had an increased risk of BC (OR 2.15, 95 % CI 1.16-4.00; OR and OR 3.37, 95 % CI 1.20-9.47, respectively) (Table 2). Three additional SNPs in APOBEC3B showed associations with clinic-pathological features: large tumor size and hormone receptor status (Table 3). An increased risk was observed for rs12456284 in SMAD4(OR 2.04, 95%CI 1.32-3.15). The SNP was also associated with histologic grade. No correlation was observed between APOBEC3 deletion and clinic-pathological parameters of breast cancer either in the hormone receptor status, tumor size, histological grade, lymph node status and distant metastases (Table 4). In addition, no statistically significant association was observed between APOBEC3 deletion and breast cancer risk (Table 5).

Table 4 Frequencies of APOBEC3 deletion according to clinic-pathological features
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Table 5 Genotype of APOBEC3 deletion polymorphism in breast cancer patients and healthy controls
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Discussion

In this population-based case–control study, we investigated for the first time the influence of the germline variation and CNVs in the potential driver genes and APOBEC3 genes on breast cancer susceptibility in a North African population.

The APOBEC3 genes family, including APOBEC3A, APOBEC3B, APOBEC3C, APOBEC3D, APOBEC3E, APOBEC3F, APOBEC3G, and APOBEC3H, plays pivotal roles in intracellular defense against viral infections [43]. The APOBEC3 genes family encodes cytosine deaminases that have been implicated in innate immune responses by restricting retroviruses, mobile genetic elements like retro-transposons and endogenous retroviruses [44]. Furthermore, the APOBEC3 genes may play a role in carcinogenesis by triggering DNA mutation through dC deamination [45]. Moreover, expression of the APOBEC3 genes is regulated by estrogen [46], a hormone that plays a central role in the etiology of breast cancer. Very recently, Burns et al. provided evidence that APOBEC3B is overexpressed in breast cancer tumors and cell lines and that the APOBEC3B mutation signature is statistically more prevalent in the breast tumor database of The Cancer Genome Atlas (TCGA) than is expected [47]. Interestingly, the APOBEC3B mutation signature was detectable in colorectal and prostate cancers only when whole- genome, but not whole-exome, data were used, suggesting a tissue-specific bias against enrichment of mutations by APOBEC3B in coding regions. Both studies from Burns et al. and Roberts et al. reached the same conclusion that the APOBEC3B mutation signature is specifically enriched in six types of cancers, including those of the cervix, bladder, lung (adeno and squamous cell), head and neck, and breast [47, 48].

Furthermore, the APOBEC3 deletion is 29.5 kb in length, located between exon 5 of APOBEC3A gene and exon 8 of APOBEC3B gene resulting in complete removal of the coding region of the APOBEC3B gene. This deletion is associated with decreased expression of the APOBEC3B gene in breast cancer cells [46]. Somatic deletion of this 29.5 kb has also been observed in breast and oral cancer tumor tissue [39, 46]. In the present study, our results did not reveal significant association between APOBEC3 deletion polymorphism and breast cancer risk (Table 5). This result is in agreement with a Japanese case–control study of 50 cases and 50 controls reporting a non-statistically significant risk of breast cancer associated with homozygous deletion of this region (OR = 3.91, 95 % CI = 0.77 to 19.83) [49]. Nevertheless, there are some studies showing an important role of this CNVs in breast cancer and provide additional evidence to implicate APOBEC3 deletion as a novel susceptibility factor for breast cancer risk [37, 39].

In addition, our genetic data pointed to the possible involvement of genetic variants within the studied genes NCOR1, RUNX1, SMAD4, TBX3, TTN, ATR, ARID1B and MAP3K1. The most significant association with breast cancer risk was identified by RUNX1_rs8130963, RUNX1_ rs17227210, TBX3_rs8853, TBX3_ rs1061651, TBX3_2242442, TTN_rs12463674, and ATR_rs2227928. The other driver gene did not reveal an important role in breast cancer risk.

RUNX1 (Run-Related Transcription Factor 1) also known as AML1 (acute myeloid leukemia 1 gene) is a tumor suppressor gene with a length of 1,196,949 bp and was original identified in acute myeloid leukemia (AML). Previously, several studies have suggested that the RUNX1 gene is highly expressed in breast epithelial cells and it is frequently mutated in breast cancer [50]. Down regulation of RUNX1 is part of a 17-gene signature that has been suggested to predict breast cancer metastasis [51]. In the present study, 2 of 3 genotyped SNPs (rs8130963 and rs17227210) were associated with breast cancer risk. Rs8130963 shows a strong genetic differentiation between the European and African population (Fst = 0.346), which is an indication for positive selection. Interestingly rs17227231 which is linked with an r2 = 92 to rs17227210 could change the protein binding of GATA3 (GATA binding protein3) as well as the transcription factor binding site of GATA. GATA3 was already classified as a high confident driver gene for breast [52]. On the other hand, rs17227210 has an effect in splicing. The variant C do not bind SF2/ASF which is involved in alternative mRNA splicing. It is a member of the serine/arginine rich protein family and was found to be up regulated in diverse tumors [49].

The T-box transcription factor 3 (13,910 bp) is expressed in mammary tissues and plays therefore a context-dependent role in mammary gland development as well as in mammary tumor genesis [53]. In addition, The TBX3 is overexpressed in a number of breast cancer cell lines [54] and could serve as a biomarker [55]. Our results reveal that one of genotyped SNPs in TBX3 was associated both with breast cancer risk and clinical outcome. Rs8853 apparently has an impact on the transcription factor binding site STAT (signal transducer and activator of transcription). Gene expression of TBX3 could be influenced by the SNP rs8853 and its impact on miR-3189. However an association to breast cancer could not be discovered. Furthermore Douglas and Papaioannou observed TBX3 overexpression in estrogen-receptor-positive breast cancer cell lines [53]. However, other publications describe an effect of TBX3 overexpression results in a pool of estrogen receptor negative cancer stem-like cells [56].

TTN (Titin or connectin) is the largest polypeptide encoded by the human genome [57] and it has been intensely studied as a component of the muscle contractile machinery [27]. However, TTN is expressed in many cell types and has other functions that are compatible with a role in oncogenesis [58–60]. The role of TTN as a cancer gene is currently a mathematically based prediction and will require direct biological evaluation. During the present study, 2 out of 6 genotyped SNPs show significant association with increased risk and 4 out of 6 genotyped SNPs with clinical outcome. In addition, more than 50 % of the statistical significant SNPs show an association with negative estrogen or progesterone receptor status. A link between hormones and calcium, which plays a major role in the muscle contractile machinery were Titin is located, could be seen in the estrogen signaling pathway, where the Calcium signaling pathway is a part of. Furthermore, a relation of Calcium signaling pathways and breast cancer is proofed [61, 62].

ATR (Ataxia Telangiectasia mutated and Rad3-related), an essential regulator of genomic integrity, controls and coordinates DNA-replication origin firing, replication-fork stability, cell cycle checkpoints, and DNA repair [63]. Smith et al. showed that overexpression of the ATR gene resulted in a phenocopy of the i(3q). The genetic alteration of ATR leads to loss of differentiation as well as cell cycle abnormalities [64]. Thus ATR has been studied as a target for cancer therapy [65]. However new Inhibitors such as caffeine has been proven as fragile and nonspecific [66]. In the present study, rs2227928 was genotyped and statistical analyzed. It is predicted to be tolerated according to Ensembl release [67]. Rs2227928 could be associated with tumour size >2 cm and negative estrogen or progesterone receptor status. It has been frequently studied for an association in different populations. However, they have found no significant differences [68, 69]. These conflicting results about the relationship between rs2227928 and breast cancer could be related to some factors such as sample size and environmental factors but not genetic background. All three populations have European ancestry and can be summarized under the phylogenetic definition Caucasian. In this context, by increasing the sample size number of the French and Finish population an association of rs2227928 and breast cancer could be expected. Some SNPs which are linked with an r2 between 85 and 97 to rs2227928 are located in gene PLS1 (Plastin1). The encoded actin-binding protein has been found at high levels in small intestine [70]. However an association with breast cancer could not be discovered. Regarding signatures of selection rs2227928 shows a significant value among the European vs. African population (Fst =0.076).

Some limitations should be addressed in this study. The statistical power to perform interaction analyses between different SNPs and breast cancer risk is still limited because of our small sample size. In addition, because no data were available on SNP frequencies in any North African population, we used data on the CEU population in our selection process. As also shown by our genotyping, the genetic constitution of the Moroccan population is very similar, and it has been influenced by both European and Sub-Saharan gene flow. However, we may have missed some SNPs private to the North African populations. There may also be some rare SNPs with minor frequency allele or SNPs with still-unknown regulatory properties that were not covered by our study.

Conclusion

Our preliminary genetic analysis suggests a potential role of germline variations in driver and APOBEC3 genes in breast cancer susceptibility. These mutations can have impact on clinical outcome and/or BC risk. We could also show that there is a strong association between the polymorphisms in RUNX1, TBX3, TTN, ATR genes and the risk of BC. However to verify the results of breast cancer risk and the influence of these polymorphisms further researchers are necessary.

Abbreviations

BC:

breast cancer

OR:

odds-ratio

GWASs:

genome wide association studies

SNPs:

single nucleotide polymorphisms

CNVs:

copy number variations

ICGC:

International Cancer Genome Consortium

SBR:

Scarff-Bloom-Richardson

MAF:

minor allele frequency

LD:

linkage disequilibrium

UTR:

untranslated region

PCR:

polymerase chain reactions

HWE:

Hardy Weinberg equilibrium

CIs:

confidence intervals

ATR :

Ataxia Telangiectasia mutated and Rad3-related

STAT :

signal transducer and activator of transcription

TBX3 :

T-box transcription factor 3

References

  1. Bray F et al. Global estimates of cancer prevalence for 27 sites in the adult population in 2008. Int J Cancer. 2013;132:1133–45.

    Article  PubMed  CAS  Google Scholar 

  2. Ferlay J, S.I., Ervik M, Dikshit R, Eser S, Mathers C, Rebelo M, Parkin DM, Forman D, Bray, F. GLOBOCAN 2012 v1.0, Cancer Incidence and Mortality Worldwide: IARC CancerBase No. 11. Internet; Lyon, France: International Agency for Research on Cancer; 2013. Available from: http://globocan.iarc.fr, accessed on 05/02/2014., 2012.

  3. Ahmed S, Thomas G, Ghoussaini M, et al. Newly discovered breast cancer susceptibility loci on 3p24 and 17q23.2. Nat Genet. 2009;41:585–90.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  4. Cai Q, Long J, Lu W, et al. Genome-wide association study identifies breast cancer risk variant at 10q21.2: results from the Asia Breast Cancer Consortium. Hum Mol Genet. 2011;20:4991–9.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  5. Easton DF, Pooley KA, Dunning AM, et al. Genome-wide association study identifies novel breast cancer susceptibility loci. Nature. 2007;447:1087–93.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  6. Fletcher O, Houlston RS. Architecture of inherited susceptibility to common cancer. Nat Rev Cancer. 2010;10:353–61.

    Article  PubMed  CAS  Google Scholar 

  7. Gold B, Kirchhoff T, Stefanov S, et al. Genome-wide association study provides evidence for a breast cancer risk locus at 6q22.33. Proc Natl Acad Sci U S A. 2008;105:4340–5.

    Article  PubMed Central  PubMed  Google Scholar 

  8. Hunter DJ, Kraft P, Jacobs KB, et al. A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer. Nat Genet. 2007;39:870–4.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  9. Long J, Cai QY, Sung H, et al. Genome-wide association study in East Asians identifies novel susceptibility loci for breast cancer. PLoS Genet. 2012;8, e1002532.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  10. Long J, Cai Q, Shu XO, et al. Identification of a functional genetic variant at 16q12.1 for breast cancer risk: results from the Asia Breast Cancer Consortium. PLoS Genet. 2010;6, e1001002.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  11. Stacey SN, Manolescu A, Sulem P, et al. Common variants on chromosomes 2q35 and 16q12 confer susceptibility to estrogen receptor-positive breast cancer. Nat Genet. 2007;39:865–9.

    Article  PubMed  CAS  Google Scholar 

  12. Stacey SN, Manolescu A, Sulem P, et al. Common variants on chromosome 5p12 confer susceptibility to estrogen receptor-positive breast cancer. Nat Genet. 2008;40:703–6.

    Article  PubMed  CAS  Google Scholar 

  13. Thomas G, Jacobs KB, Kraft P, Yeager M, Wacholder S, Cox DG, et al. A multistage genome-wide association study in breast cancer identifies two new risk alleles at 1p11.2 and 14q24.1 (RAD51L1). Nat Genet. 2009;41:579–84.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  14. Turnbull C, Ahmed S, Morrison J, et al. Genome-wide association study identifies five new breast cancer susceptibility loci. Nat Genet. 2010;42:504–7.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  15. Zheng W, Long J, Gao YT, et al. Genome-wide association study identifies a new breast cancer susceptibility locus at 6q25.1. Nat Genet. 2009;41:324–8.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  16. Michcailidou K, Hall P, Gonzalez-Neira A, et al. Large-scale genotyping identifies 41 new loci associated with breast cancer risk. Nat Genet. 2013;45:353–61.

    Article  CAS  Google Scholar 

  17. Antoniou AC et al. A locus on 19p13 modifies risk of breast cancer in BRCA1 mutation carriers and is associated with hormone receptor-negative breast cancer in the general population. Nat Genet. 2010;42:885–92.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  18. Fletcher O et al. Novel breast cancer susceptibility locus at 9q31.2: results of a genome-wide association study. J Natl Cancer Inst. 2011;103:425–35.

    Article  PubMed  CAS  Google Scholar 

  19. Haiman CA et al. A common variant at the TERT-CLPTM1L locus is associated with estrogen receptor-negative breast cancer. Nat Genet. 2011;43:1210–4.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  20. Ghoussaini M et al. Genome-wide association analysis identifies three new breast cancer susceptibility loci. Nat Genet. 2012;44:312–8.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  21. Siddiq A et al. A meta-analysis of genome-wide association studies of breast cancer identifies two novel susceptibility loci at 6q14 and 20q11. Hum Mol Genet. 2012;21:5373–84.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  22. Garcia-Closas M et al. Genome-wide association studies identify four ER negative-specific breast cancer risk loci. Nat Genet. 2013;45:392–8.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  23. Bojesen SE et al. Multiple independent variants at the TERT locus are associated with telomere length and risks of breast and ovarian cancer. Nat Genet. 2013;45:371–84.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  24. Milne RL et al. Common non-synonymous SNPs associated with breast cancer susceptibility: findings from the Breast Cancer Association Consortium. Hum Mol Genet. 2014.

  25. Cai Q et al. Genome-wide association analysis in East Asians identifies breast cancer susceptibility loci at 1q32.1, 5q14.3 and 15q26.1. Nat Genet. 2014;46:886–90.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  26. Stephens P et al. A screen of the complete protein kinase gene family identifies diverse patterns of somatic mutations in human breast cancer. Nat Genet. 2005;37:590–2.

    Article  PubMed  CAS  Google Scholar 

  27. Greenman C et al. Patterns of somatic mutation in human cancer genomes. Nature. 2007;446:153–8.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  28. Jones S et al. Frequent mutations of chromatin remodeling gene ARID1A in ovarian clear cell carcinoma. Science. 2010;330:228–31.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  29. Sjoblom T et al. The consensus coding sequences of human breast and colorectal cancers. Science. 2006;314:268–74.

    Article  PubMed  CAS  Google Scholar 

  30. Kumar A et al. Exome sequencing identifies a spectrum of mutation frequencies in advanced and lethal prostate cancers. Proc Natl Acad Sci U S A. 2011;108:17087–92.

    Article  PubMed Central  PubMed  Google Scholar 

  31. Nik-Zainal S et al. Mutational processes molding the genomes of 21 breast cancers. Cell. 2012;149:979–93.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  32. Stephens PJ et al. The landscape of cancer genes and mutational processes in breast cancer. Nature. 2012;486:400–4.

    PubMed Central  PubMed  CAS  Google Scholar 

  33. Wood LD et al. The genomic landscapes of human breast and colorectal cancers. Science. 2007;318:1108–13.

    Article  PubMed  CAS  Google Scholar 

  34. Stratton MR, Campbell PJ, Futreal PA. The cancer genome. Nature. 2009;458:719–24.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  35. Willer CJ, Speliotes EK, Loos RJ, et al. Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nat Genet. 2009;41:25–34.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  36. Stankiewicz P, Lupski JR. Structural variation in the human genome and its role in disease. Annu Rev Med. 2010;61:437–55.

    Article  PubMed  CAS  Google Scholar 

  37. Long J, Delahanty RJ, Li G, Gao YT, Lu W, Cai Q, et al. A common deletion in the APOBEC3 genes and breast cancer risk. J Natl Cancer Inst. 2013;105:573–9.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  38. Kidd JM, Newman TL, Tuzun E, Kaul R, Eichler EE. Population stratification of a common APOBEC gene deletion polymorphism. PLoS Genet. 2007;3(4):63.

    Article  CAS  Google Scholar 

  39. Xuan D, Li G, Cai Q, Deming-Halverson S, Shrubsole MJ, Shu XO, et al. APOBEC3 deletion polymorphism is associated with breast cancer risk among women of European ancestry. Carcinogenesis. 2013;34:2240–3.

  40. Banerji S et al. Sequence analysis of mutations and translocations across breast cancer subtypes. Nature. 2012;486(7403):405–9.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  41. Ellis MJ et al. Whole-genome analysis informs breast cancer response to aromatase inhibition. Nature. 2012;486(7403):353–60.

    PubMed Central  PubMed  CAS  Google Scholar 

  42. Shah SP et al. The clonal and mutational evolution spectrum of primary triple-negative breast cancers. Nature. 2012;486(7403):395–9.

    PubMed  CAS  Google Scholar 

  43. Wedekind JE, Dance GS, Sowden MP, Smith HC. Messenger RNA editing in mammals: new members of the apobec family seeking roles in the family business. Trends Genet. 2003;19:207–16.

    Article  PubMed  CAS  Google Scholar 

  44. Conticello SG. The AID/APOBEC family of nucleic acid mutators. Genome Biol. 2008;9:229.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  45. Suspene R et al. Somatic hypermutation of human mitochondrial and nuclear DNA by APOBEC3 cytidine deaminases, a pathway for DNA catabolism. Proc Natl Acad Sci U S A. 2011;108:4858–63.

    Article  PubMed Central  PubMed  Google Scholar 

  46. Komatsu A et al. Identification of novel deletion polymorphisms in breast cancer. Int J Oncol. 2008;33:261–70.

    PubMed  CAS  Google Scholar 

  47. Burns MB et al. Nature. 2013;494:366–70.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  48. Roberts SA, Getz G, Gordenin DA. Nat Genet. 2013;45:970–6.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  49. Fang HY et al. Proteomic identification of differentially expressed proteins in curcumin-treated MCF-7 cells. Phytomedicine. 2011;18:697–703.

    Article  PubMed  CAS  Google Scholar 

  50. Chimge NO, Frenkel B. The RUNX family in breast cancer: relationships with estrogen signaling. Oncogene. 2013;32:2121–30.

    Article  PubMed  CAS  Google Scholar 

  51. Janes KA. RUNX1 and its understudied role in breast cancer. Cell Cycle. 2011;10(20):3461–5.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  52. Tamborero D et al. Comprehensive identification of mutational cancer driver genes across 12 tumor types. Sci Rep. 2013;3:2650.

    PubMed Central  PubMed  Google Scholar 

  53. Douglas NC, Papaioannou VE. The T-box transcription factors TBX2 and TBX3 in mammary gland development and breast cancer. J Mammary Gland Biol Neoplasia. 2013;18:143–7.

    Article  PubMed Central  PubMed  Google Scholar 

  54. Fan W, Huang X, Chen C, Gray J, Huang T. TBX3 and its isoform TBX3 + 2a are functionally distinctive in inhibition of senescence and are overexpressed in a subset of breast cancer cell lines. Cancer Res. 2004;64:5132–9.

    Article  PubMed  CAS  Google Scholar 

  55. Lomnytska M, Dubrovska A, Hellman U, Volodko N, Souchelnytskyi S. Increased expression of cSHMT, Tbx3 and utrophin in plasma of ovarian and breast cancer patients. Int J Cancer. 2006;118:412–21.

    Article  PubMed  CAS  Google Scholar 

  56. Washkowitz AJ et al. Diverse functional networks of Tbx3 in development and disease. Wiley Interdiscip Rev SystBiol Med. 2012;4:273–83.

    Article  CAS  Google Scholar 

  57. Granzier HL, Labeit S. Titin and its associated proteins: the third myofilament system of the sarcomere. Adv Protein Chem. 2005;71:89–119.

    Article  PubMed  CAS  Google Scholar 

  58. Machado C, Andrew DJ. D-Titin: a giant protein with dual roles in chromosomes and muscles. J Cell Biol. 2000;151:639–52.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  59. Machado C, Sunkel CE, Andrew DJ. Human autoantibodies reveal Titin as a chromosomal protein. J Cell Biol. 1998;141:321–33.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  60. Zastrow MS, Flaherty DB, Benian GM, Wilson KL. Nuclear Titin interacts with A- and B-type lamins in vitro and in vivo. J Cell Sci. 2006;119:239–49.

    Article  PubMed  CAS  Google Scholar 

  61. Woltmann A et al. Systematic pathway enrichment analysis of a genome-wide association study on breast cancer survival reveals an influence of genes involved in cell adhesion and calcium signaling on the patients’ clinical outcome. PLoS One. 2014;9:98229.

    Article  CAS  Google Scholar 

  62. Davis FM, et al. Induction of epithelial-mesenchymal transition (EMT) in breast cancer cells is calcium signal dependent. Oncogene. 2013;33:2307-16.

  63. Tanaka A, Weinel S, Nagy N, O'Driscoll M, Lai-Cheong JE, Kulp-Shorten CL, et al. Germline mutation in ATR in autosomal-dominant oropharyngeal cancer syndrome. Am J Hum Genet. 2012;90:511–7.

  64. Smith L, Liu SJ, Goodrich L, Jacobson D, Degnin C, Bentley N, et al. Duplication of ATR inhibits MyoD, induces aneuploidy and eliminates radiation-induced G1 arrest. Nature Genet. 1998;19:39–46.

  65. Dai Y, Grant S. New insights into checkpoint kinase 1 in the DNA damage response signaling network. Clin Cancer Res. 2010;16:376–83.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  66. Peasland A et al. Identification and evaluation of a potent novel ATR inhibitor, NU6027, in breast and ovarian cancer cell lines. Br J Cancer. 2011;105:372–81.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  67. Flicek P et al. Ensembl 2013. Nucleic Acids Res. 2013;41(Database issue):D48–55.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  68. Heikkinen K et al. Mutation analysis of the ATR gene in breast and ovarian cancer families. Breast Cancer Res. 2005;7:R495–501.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  69. Durocher F et al. Mutation analysis and characterization of ATR sequence variants in breast cancer cases from high-risk French Canadian breast/ovarian cancer families. BMC Cancer. 2006;6:230.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  70. Pruitt KD et al. RefSeq: an update on mammalian reference sequences. Nucleic Acids Res. 2014;42(Database issue):D756–63.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

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Acknowledgements

We would like to thank all the staff of the Department of Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ) and the Genetic and Molecular Pathology Laboratory for their collaboration. We also thank all the patients and their families for their participation in this study. Our gratitude go also to Dr. Omar Hajji and all the staff of Oncology department of Littoral Clinic for their assistance in data and sample collection. We gratefully acknowledge Dr. Yassine Naasse for his excellent collaboration.

This study was funded by EU FP7/2007-2013 grant 260715 from EUNAM project (EU and North African Migrants: Health and Health Systems), Germany.

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Authors and Affiliations

  1. Department of Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany

    Chaymaa Marouf, Stella Göhler, Miguel Inacio Da Silva Filho, Kari Hemminki & Asta Försti

  2. Laboratory of Genetics and Molecular Pathology–Medical School of Casablanca, Casablanca, Morocco

    Chaymaa Marouf & Sellama Nadifi

  3. University Hassan II Ain Chock, Center Of Doctoral Sciences “In Health Sciences”, Casablanca, Morocco

    Chaymaa Marouf & Sellama Nadifi

  4. Department of Oncology, Littoral Clinic, Casablanca, Morocco

    Omar Hajji

  5. Center for Primary Health Care Research, Clinical Research Center, Lund University, Malmö, Sweden

    Kari Hemminki & Asta Försti

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  1. Chaymaa Marouf
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  2. Stella Göhler
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  3. Miguel Inacio Da Silva Filho
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  4. Omar Hajji
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  6. Sellama Nadifi
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  7. Asta Försti
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Correspondence to Chaymaa Marouf.

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Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

CM carried out the molecular genetic studies, recruited the patients and drafted the manuscript. SG assisted in the sequencing experiment and helped analyze the sequencing result. MD performed statistical analysis and participated in the analysis of the result. OH coordinated the patient’s recruitment and provided the clinical data. KH conceived the study, participated in its design and coordination. SN revised the manuscript. AF helped to draft the manuscript and supervised the sequencing experiment. All authors read and approved the final manuscript.

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Marouf, C., Göhler, S., Filho, M.I.D.S. et al. Analysis of functional germline variants in APOBEC3 and driver genes on breast cancer risk in Moroccan study population. BMC Cancer 16, 165 (2016). https://doi.org/10.1186/s12885-016-2210-8

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Keywords

BMC Cancer

ISSN: 1471-2407

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