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.
Keywords: Breast cancer, Driver genes, APOBEC3, Genetic susceptibility, Single nucleotide polymorphism
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
| Characteristics | Samples |
| Cases/Controls | 226/200 |
| Age at diagnosis, mean ± SD (years) | 41 ± 11 |
| Range (years) | 27 – 67 |
| Menopausal Status | No. (%) |
| Premenopausal | 162(71.68) |
| Postmenopausal | 63(27.87) |
| Missing | 1(0.44) |
| Estrogen receptor | |
| Positive | 130 (57.52) |
| Negative | 78(34.51) |
| Missing | 18 (7.96) |
| Progesterone receptor | |
| Positive | 136 (59.29) |
| Negative | 72(31.85) |
| Missing | 18 (7.96) |
| Estrogen/Progesterone receptor | |
| ER+/PR+ | 111 (49.11) |
| ER+/PR− | 25 (11.06) |
| ER−/PR+ | 19 (8.40) |
| ER−/PR− | 53 (23.45) |
| Tumor size | |
| <2 cm | 30 (13.27) |
| >2 cm | 105 (46.46) |
| >5 cm | 41(18.14) |
| Tumor of any size with extension | 37 (16.37) |
| Histological grade | |
| 1 | 8 (3.53) |
| 2 | 141 (62.38) |
| 3 | 59 (26.10) |
| Lymph node status | |
| Negative | 86(38.55) |
| Positive | 132 (58.40) |
| Distant metastases | |
| Negative | 170(75.22) |
| Positive | 38 (16.81) |
ER estrogen receptors, PR progesterone receptors
Table 2.
SNPs associated with breast cancer risk
| Breast cancer risk | |||||
|---|---|---|---|---|---|
| Gene/SNP | Genotype | Cases (%) | Controls (%) | OR (95 % CI) | P-value |
| APOBEC3B | CC | 181 (80.09) | 176 (88.00) | 1.00 | |
| rs8142462 | TC | 42 (18.58) | 24 (12.00) | 1.70 (0.99-2.93) | 0.0500 |
| TT | 3 (1.33) | 0 (0.00) | 0 (0) | 0.9839 | |
| Dom | 45 (19.91) | 24 (12.00) | 1.82 (1.07-3.12) | 0.0300 | |
| Overall | 0.1584 | ||||
| APOBEC3A | GG | 111 (49.12) | 125 (62.50) | 1.00 | |
| rs17370615 | GA | 102 (45.13) | 66 (33.00) | 1.74 (1.16-2.60) | 0.0068 |
| AA | 13 (5.75) | 9 (4.50) | 1.63 (0.67-3.95) | 0.2826 | |
| Dom | 115 (50.88) | 75 (37.50) | 1.73 (1.17-2.54) | 0.0050 | |
| Overall | 0.0217 | ||||
| APOBEC3B | CC | 95 (42.0) | 69 (34.50) | 1.00 | |
| rs28401571 | CT | 93 (41.15) | 80 (40.00) | 0.84 (0.55-1.30) | 0.4412 |
| TT | 38 (16.81) | 51 (25.50) | 0.54 (0.32-0.91) | 0.0212 | |
| Add | 0.75 (0.58-0.97) | 0.0300 | |||
| Overall | 0.0682 | ||||
| APOBEC3B | TT | 82 (36.28) | 93 (46.50) | 1.00 | |
| rs6001376 | CT | 106 (46.90) | 87 (43.50) | 1.38 (0.92-2.08) | 0.1226 |
| CC | 38 (16.81) | 20 (10.00) | 2.15 (1.16-4.00) | 0.0148 | |
| Add | 1.44 (1.09-1.91) | 0.0100 | |||
| Overall | 0.0390 | ||||
| APOBEC3B | CC | 44 (19.47) | 49 (24.50) | 1.00 | |
| rs1065184 | CT | 128 (56.64) | 119 (59.50) | 1.20 (0.74-1.93) | 0.4587 |
| TT | 54 (23.89) | 32 (16.00) | 1.88 (1.03-3.42) | 0.0385 | |
| Add | 1.36 (1.01-1.84) | 0.0400 | |||
| Overall | 0.1000 | ||||
| ATR | GG | 78 (34.51) | 94(47.00) | 1.00 | |
| rs2227928 | AG | 110 (48.67) | 87(43.50) | 1.52 (1.01-2.30) | 0.0448 |
| AA | 38 (16.81) | 19(9.50) | 2.41 (1.29-4.51) | 0.0060 | |
| AG + AA | 148 (65.49) | 106(53.00) | 1.68 (1.14-2.49) | 0.0090 | |
| Overall | 0.0123 | ||||
| ARID1B | CC | 50 (22.12) | 63 (31.50) | 1.00 | |
| rs73013281 | CT | 126 (55.75) | 90 (45.00) | 1.76 (1.11-2.79) | 0.0154 |
| TT | 50 (22.12) | 47 (23.50) | 1.34 (0.78-2.31) | 0.2915 | |
| CT + TT | 176 (77.88) | 137 (68.50) | 1.62 (1.05-2.50) | 0.0293 | |
| Overall | 0.0500 | ||||
| MAP3K1 | CC | 130 (57.52) | 137 (68.50) | 1.00 | |
| rs832583 | AC | 80 (35.40) | 58 (29.00) | 1.45 (0.96-2.20) | 0.0770 |
| AA | 16 (7.08) | 5 (2.50) | 3.37 (1.20-9.47) | 0.0210 | |
| AC + CC | 96 (42.48) | 63 (31.50) | 1.61 (1.08-2.39) | 0.0197 | |
| Overall | 0.0236 | ||||
| NCOR1 | CC | 102 (45.13) | 108 (54.00) | 1.00 | |
| rs178831 | CT | 103 (45.58) | 82 (41.00) | 1.33 (0.89-1.98) | 0.1589 |
| TT | 21 (9.29) | 10 (5.00) | 2.22 (1.00-4.95) | 0.0500 | |
| CT + TT | 124 (54.87) | 92 (46.00) | 1.43 (0.97-2.09) | 0.0681 | |
| Overall | 0.0908 | ||||
| RUNX1 | AA | 153 (67.70) | 165 (82.50) | 1.00 | |
| rs8130963 | AG | 70 (30.97) | 33 (16.50) | 2.29 (1.43-3.65) | 0.0005 |
| GG | 3 (1.33) | 2 (1.00) | 1.62 (0.27-9.81) | 0.6010 | |
| AG + GG | 73 (32.30) | 35 (17.50) | 2.25 (1.42-3.56) | 0.0005 | |
| Overall | 0.0024 | ||||
| RUNX1 | CC | 53 (23.45) | 71 (35.50) | 1.00 | |
| rs17227210 | CT | 123 (54.42) | 92 (46.00) | 1.79 (1.15-2.80) | 0.0106 |
| TT | 50 (22.12) | 37 (18.50) | 1.81 (1.04-3.15) | 0.0359 | |
| CT + TT | 173 (76.55) | 129 (64.50) | 1.80 (1.18-2.74) | 0.0066 | |
| Overall | 0.0249 | ||||
| SMAD4 | AA | 145 (64.16) | 157 (78.50) | 1.00 | |
| rs12456284 | AG | 72 (31.86) | 39 (19.50) | 2.00 (1.27-3.14) | 0.0026 |
| GG | 9 (3.98) | 4 (2.00) | 2.44 (0.73-8.08) | 0.1457 | |
| AG + GG | 81 (35.84) | 43 (21.50) | 2.04 (1.32-3.15) | 0.0013 | |
| Overall | 0.0053 | ||||
| TBX3 | CC | 104 (46.02) | 127 (63.50) | 1.00 | |
| rs8853 | CT | 106 (46.90) | 60 (30.00) | 2.16 (1.43-3.25) | 0.0002 |
| TT | 16 (7.08) | 13 (6.50) | 1.50 (0.69-3.27) | 0.3037 | |
| CT + TT | 122 (53.98) | 73 (36.50) | 2.04 (1.38-3.01) | 0.0003 | |
| Overall | 0.0011 | ||||
| TBX3 | TT | 118 (52.21) | 140 (70.00) | 1.00 | |
| rs1061651 | TC | 97 (42.92) | 50 (25.00) | 2.30 (1.51-3.50) | 0.0001 |
| CC | 11 (4.87) | 10 (5.00) | 1.31 (0.54-3.18) | 0.5579 | |
| TC + CC | 108 (47.79) | 60 (30.00) | 2.14 (1.43-3.18) | 0.0002 | |
| Overall | 0.0005 | ||||
| TBX3 | GG | 89 (39.38) | 106 (53.00) | 1.00 | |
| rs2242442 | AG | 104 (46.02) | 84 (42.00) | 1.47 (0.99-2.21) | 0.0500 |
| AA | 33 (14.60) | 10 (5.00) | 3.93 (1.84-8.42) | 0.0004 | |
| AG + AA | 137 (60.62) | 94 (47.00) | 1.74 (1.18-2.55) | 0.0050 | |
| Overall | 0.0012 | ||||
| TTN | AA | 131 (57.96) | 139(69.50) | 1.00 | |
| rs12463674 | AG | 85 (37.61) | 53(26.50) | 1.70 (1.12-2.58) | 0.0127 |
| GG | 10 (4.42) | 8(4.00) | 1.33 (0.51-3.46) | 0.5641 | |
| AG + GG | 95 (42.04) | 61(30.50) | 1.65 (1.11-2.47) | 0.0140 | |
| Overall | 0.0436 | ||||
| TTN | CC | 135 (59.73) | 150 (75.00) | 1.00 | |
| rs12465459 | CT | 84 (37.17) | 46 (23.00) | 2.03 (1.32-3.11) | 0.0012 |
| TT | 7 (3.10) | 4 (2.00) | 1.94 (0.56-6.79) | 0.2972 | |
| CT + TT | 91 (40.27) | 50 (25.00) | 2.02 (1.33-3.07) | 0.0009 | |
| Overall | 0.0041 | ||||
OR odds ratio, CI confidence interval, SNP single nucleotide polymorphism
Table 3.
SNPs associated with clinico-pathological features
| Gene/SNP | Genotype | Significant association | No. of patients Group 1(%) | No. of patients Group 2(%) | OR (95 % CI) | P-value | Significant association | No. of patients Group 1(%) | No. of patients Group 2(%) | OR (95 % CI) | P-value |
|---|---|---|---|---|---|---|---|---|---|---|---|
| APOBEC3B | Tumor size | ≤2 cm | >2 cm | ||||||||
| rs8142462 | CC | 68 (87.18) | 105 (76.09) | 1.00 | |||||||
| TC | 8 (10.26) | 32 (23.19) | 2.59 (1.13-5.96) | 0.0300 | |||||||
| TT | 2 (2.56) | 1 (0.72) | 0.32 (0.03-3.64) | 0.3600 | |||||||
| TC + TT | 10 (12.82) | 33 (23.91) | 2.14 (0.99-4.62) | 0.0500 | |||||||
| Overall | 0.0500 | ||||||||||
| APOBEC3B | Estrogen receptor/Progesterone receptors | ER+/PR+ | ER-/PR- | Estrogen receptor | ER+ | ER- | |||||
| rs28401571 | CC | 48 (43.24) | 21 (39.62) | 1.00 | 59 (43.38) | 30 (41.67) | 1.00 | ||||
| CT | 49 (44.14) | 16 (30.19) | 0.75 (0.35-1.60) | 0.4500 | 62 (45.59) | 22 (30.56) | 0.70 (0.36-1.34) | 0.2800 | |||
| TT | 14 (12.61) | 16 (30.19) | 2.61 (1.08-6.31) | 0.0300 | 15 (11.03) | 20 (27.78) | 2.62 (1.18-5.84) | 0.0200 | |||
| CT + TT | 63 (56.76) | 32 (60.38) | 1.16 (0.60-2.26) | 0.6600 | 77 (56.62) | 42 (58.33) | 1.07 (0.60-1.91) | 0.8100 | |||
| Overall | 0.0200 | 0.0100 | |||||||||
| APOBEC3B | Estrogen receptor/Progesterone receptors | ER+/PR+ | ER-/PR- | ||||||||
| rs2076111 | CC | 40 (36.04) | 11 (20.75) | 1.00 | |||||||
| CT | 67 (60.36) | 41 (77.36) | 2.23 (1.03-4.82) | 0.0400 | |||||||
| TT | 4 (3.60) | 1 (1.89) | 0.91 (0.09-8.98) | 0.9300 | |||||||
| CT + TT | 71 (63.96) | 42 (79.25) | 2.15(1.00-4.64) | 0.0500 | |||||||
| Overall | 0.1000 | ||||||||||
| ATR | Tumor Size | ≤2 cm | >2 cm | Estrogen receptor/Progesterone receptors | ER+/PR+ | ER+/PR- | |||||
| rs2227928 | GG | 33 (42.31) | 40 (28.99) | 1.00 | 33 (29.73) | 13 (52.00) | 1.00 | ||||
| AG | 34 (43.59) | 71 (51.45) | 1.72 (0.93-3.19) | 0.0800 | 58 (52.25) | 10 (40.00) | 0.44 (0.17-1.11) | 0.0800 | |||
| AA | 11 (14.10) | 27 (19.57) | 2.02 (0.88-4.69) | 0.0900 | 20 (18.02) | 2 (8.00) | 0.25 (0.05-1.24) | 0.0900 | |||
| AG + AA | 45 (57.69) | 98 (71.01) | 1.80 (1.01-3.21) | 0.0400 | 78 (70.27) | 12 (48.00) | 0.39 (0.16-0.95) | 0.0300 | |||
| Overall | 0.1300 | 0.0900 | |||||||||
| MLL2 | Tumor Size | ≤2 cm | >2 cm | Histologic grade | 1 + 2 | 3 | |||||
| rs11614738 | GG | 26 (33.33) | 61 (44.20) | 1.00 | 18 (30.51) | 69 (46.31) | 1.00 | ||||
| CG | 37 (47.44) | 64 (46.38) | 0.74 (0.40-1.36) | 0.3200 | 35 (59.32) | 59 (39.60) | 0.44 (0.23-0.86) | 0.0100 | |||
| CC | 15 (19.23) | 13 (9.42) | 0.37 (0.15-0.88) | 0.0200 | 6 (10.17) | 21 (14.09) | 0.91 (0.32-2.60) | 0.8600 | |||
| CG + CC | 52 (66.67) | 77 (55.80) | 0.63 (0.35-1.13) | 0.1100 | 41 (69.49) | 80 (53.69) | 0.51 (0.27-0.97) | 0.0300 | |||
| Overall | 0.0800 | 0.0300 | |||||||||
| SMAD4 | Histologic grade | 1 + 2 | 3 | ||||||||
| rs12456284 | AA | 36 (61.02) | 99 (66.44) | 1.00 | |||||||
| AG | 18 (30.51) | 47 (31.54) | 0.95 (0.49-1.84) | 0.8700 | |||||||
| GG | 5 (8.47) | 3 (2.01) | 0.22 (0.05-0.96) | 0.0400 | |||||||
| AG + GG | 23 (38.98) | 50 (33.56) | 0.79 (0.42-1.48) | 0.4600 | |||||||
| Overall | 0.1300 | ||||||||||
| SMAD4 | Tumor Size | ≤2 cm | >2 cm | Estrogen receptor/Progesterone receptors | ER+/PR+ | ER+/PR- | |||||
| rs3819122 | AA | 22 (28.21) | 64 (46.38) | 1.00 | 43 (38.74) | 15 (60.00) | 1.00 | ||||
| AC | 45 (57.69) | 52 (37.68) | 0.40 (0.21-0.74) | 0.0030 | 48 (43.24) | 7 (28.00) | 0.42 (0.16-1.12) | 0.0800 | |||
| CC | 11 (14.10) | 22 (15.94) | 0.69 (0.29-1.64) | 0.3900 | 20 (18.02) | 3 (12.00) | 0.43 (0.11-1.66) | 0.2100 | |||
| AC + CC | 56 (71.79) | 74 (53.62) | 0.45 (0.25-0.82) | 0.0090 | 68 (61.26) | 10 (40.00) | 0.42 (0.17-1.02) | 0.0500 | |||
| Overall | 0.0100 | 0.1600 | |||||||||
| TBX3 | Histologic grade | 1 + 2 | 3 | ||||||||
| rs3759173 | GG | 11 (18.64) | 47 (31.54) | 1.00 | |||||||
| GT | 34 (57.63) | 69 (46.31) | 0.47 (0.22-1.03) | 0.0500 | |||||||
| TT | 14 (23.73) | 33 (22.15) | 0.55 (0.22-1.37) | 0.1900 | |||||||
| GT + TT | 48 (81.36) | 102 (68.46) | 0.50 (0.24-1.04) | 0.0600 | |||||||
| Overall | 0.1600 | ||||||||||
| TBX3 | Regional lymph node met | N- | N+ | ||||||||
| rs8853 | CC | 67 (50.76) | 33 (38.37) | 1.00 | |||||||
| CT | 53 (40.15) | 49 (56.98) | 1.88 (1.06-3.32) | 0.0300 | |||||||
| TT | 12 (9.09) | 4 (4.65) | 0.68 (0.20-2.26) | 0.5200 | |||||||
| CT + TT | 65 (49.24) | 53 (61.63) | 1.66 (0.95-2.88) | 0.0700 | |||||||
| Overall | 0.0400 | ||||||||||
| TTN | Regional lymph node met | N- | N+ | ||||||||
| rs2303838 | CC | 87 (65.91) | 50 (58.14) | 1.00 | |||||||
| CT | 42 (31.82) | 29 (33.72) | 1.20 (0.67-2.16) | 0.5400 | |||||||
| TT | 3 (2.27) | 7 (8.14) | 4.06 (1.00-16.4) | 0.0400 | |||||||
| CT + TT | 45 (34.09) | 36 (41.86) | 1.39 (0.80-2.44) | 0.2400 | |||||||
| Overall | 0.1300 | ||||||||||
| TTN | Estrogen receptor | ER+ | ER- | Estrogen receptor/Progesterone receptors | ER+/PR+ | ER-/PR- | |||||
| rs2244492 | CC | 36 (26.47) | 32 (44.44) | 1.00 | 31 (27.93) | 23 (43.40) | 1.00 | ||||
| CT | 77 (56.62) | 32 (44.44) | 0.47 (0.25-0.88) | 0.0100 | 63 (56.76) | 25 (47.17) | 0.53 (0.26-1.09) | 0.0800 | |||
| TT | 23 (16.91) | 8 (11.11) | 0.39 (0.15-1.00) | 0.0400 | 17 (15.32) | 5 (9.43) | 0.40 (0.13-1.23) | 0.1000 | |||
| CT + TT | 100 (73.53) | 40 (55.56) | 0.45 (0.25-0.82) | 0.0090 | 80 (72.07) | 30 (56.60) | 0.51 (0.26-1.00) | 0.0500 | |||
| Overall | 0.0300 | 0.1300 | |||||||||
| TTN | Progesterone receptor | PR+ | PR- | Estrogen receptor/Progesterone receptors | ER+/PR+ | ER-/PR- | |||||
| rs12465459 | CC | 87 (66.92) | 40 (51.28) | 1.00 | 74 (66.67) | 27 (50.94) | 1.00 | ||||
| CT | 39 (30.00) | 36 (46.15) | 2.01 (1.12-3.61) | 0.0200 | 34 (30.63) | 24 (45.28) | 1.93 (0.98-3.83) | 0.0500 | |||
| TT | 4 (3.08) | 2 (2.56) | 1.09 (0.19-6.18) | 0.9200 | 3 (2.70) | 2 (3.77) | 1.83 (0.29-11.54) | 0.5200 | |||
| CT + TT | 43 (33.08) | 38 (48.72) | 1.92 (1.08-3.42) | 0.0200 | 37 (33.33) | 26 (49.06) | 1.93 (0.99-3.75) | 0.0500 | |||
| Overall | 0.0600 | 0.1500 | |||||||||
| TTN | Progesterone receptor | PR+ | PR- | Regional lymph node met | N- | N+ | |||||
| rs12463674 | AA | 70 (53.85) | 51 (65.38) | 1.00 | 71 (53.79) | 56 (65.12) | 1.00 | ||||
| AG | 56 (43.08) | 22 (28.21) | 0.54 (0.29-0.99) | 0.0400 | 56 (42.42) | 25 (29.07) | 0.57 (0.31-1.02) | 0.0500 | |||
| GG | 4 (3.08) | 5 (6.41) | 1.72 (0.44-6.71) | 0.4300 | 5 (3.79) | 5 (5.81) | 1.27 (0.35-4.60) | 0.7100 | |||
| AG + GG | 60 (46.15) | 27 (34.62) | 0.62 (0.35-1.10) | 0.1000 | 61 (46.21) | 30 (34.88) | 0.62 (0.36-1.09) | 0.0900 | |||
| Overall | 0.0700 | 0.1300 | |||||||||
| Histologic grade | 1 + 2 | 3 | Estrogen receptor/Progesterone receptors | ER+/PR+ | ER-/PR+ | ||||||
| 34 (57.63) | 88 (59.06) | 1.00 | 64 (57.66) | 6 (31.58) | 1.00 | ||||||
| 19 (32.20) | 58 (38.93) | 1.18 (0.61-2.26) | 0.6100 | 44 (39.64) | 12 (63.16) | 2.91 (1.02-8.33) | 0.0400 | ||||
| 6 (10.17) | 3 (2.01) | 0.19 0.05-0.82) | 0.0200 | 3 (2.70) | 1 (5.26) | 3.56 (0.32-39.70) | 0.3000 | ||||
| 25 (42.37) | 61 (40.94) | 0.94 (0.51-1.74) | 0.8400 | 47 (42.34) | 13 (68.42) | 2.95 (1.04-8.33) | 0.0400 | ||||
| 0.0500 | 0.1200 |
OR odds ratio, CI confidence interval, SNP single nucleotide polymorphism, No total number
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
| APOBEC3 deletion | ||
|---|---|---|
| Variable | II | ID |
| Estrogen/Progesterone receptor | No. (%) | No. (%) |
| ER+/PR+ | 103 (45.57) | 8 (3.53) |
| ER+/PR− | 21 (9.29) | 4 (1.76) |
| ER−/PR+ | 18(7.96) | 1 (0.44) |
| ER−/PR− | 50(22.12) | 3 (1.32) |
| Tumor size | ||
| <2 cm | 26 (11.50) | 4 (1.76) |
| >2 cm | 97 (42.92) | 8 (3.53) |
| >5 cm | 39 (17.25) | 2 (0.88) |
| Tumor of any size with extension | 32 (14.15) | 5 (2.21) |
| Histological grade | ||
| 1 | 7 (3.09) | 1 (0.44) |
| 2 | 127 (56.19) | 14 (6.19) |
| 3 | 56 (24.77) | 3 (1.32) |
| Lymph node status | ||
| Negative | 64 (28.31) | 8 (3.53) |
| Positive | ||
| 122 (53.98) | ||
| 10 (4.42) | ||
| Distant metastases | ||
| Negative | 158 (69.91) | 12 (5.30) |
| Positive | ||
| 31 (13.71) | 7 (3.09) | |
II homozygous insertion, ID herozygous deletion, No total number, ER estrogen receptors, PR progesterone receptors
Table 5.
Genotype of APOBEC3 deletion polymorphism in breast cancer patients and healthy controls
| Breast cancer risk | ||||
|---|---|---|---|---|
| Genotype | Cases (%) | Controls (%) | OR (95 % CI) | P-value |
| II | 207 (91.59) | 175 (87.50) | 1.00 | |
| ID | 19 (8.41) | 25 (12.50) | 0.64 (0.34-1.21) | 0.1680 |
| DD | 0 (0) | 0 (0) | 0 (0) | |
| ID + DD | 19 (8.41) | 25 (12.50) | 0.64 (0.34-1.21) | 0.1680 |
| Overall | 0.1680 | |||
II homozygous insertion, ID herozygous deletion, DD homozygous deletion, No total number, OR odds ratio, CI confidence interval
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.
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.
Abbreviation
- 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
Footnotes
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.
Contributor Information
Chaymaa Marouf, Email: maroufchaymaa@gmail.com.
Stella Göhler, Email: s.goehler@dkfz-heidelberg.de.
Miguel Inacio Da Silva Filho, Email: m.inacio@dkfz-heidelberg.de.
Omar Hajji, Email: o.hajji@cliniquelelittoral.ma.
Kari Hemminki, Email: k.hemminki@dkfz-heidelberg.de.
Sellama Nadifi, Email: nadifisel@yahoo.com.
Asta Försti, Email: a.foersti@dkfz-heidelberg.de.
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. doi: 10.1002/ijc.27711. [DOI] [PubMed] [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;41:585–590. doi: 10.1038/ng.354. [DOI] [PMC free article] [PubMed] [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;20:4991–4999. doi: 10.1093/hmg/ddr405. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Easton DF, Pooley KA, Dunning AM, et al. Genome-wide association study identifies novel breast cancer susceptibility loci. Nature. 2007;447:1087–1093. doi: 10.1038/nature05887. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Fletcher O, Houlston RS. Architecture of inherited susceptibility to common cancer. Nat Rev Cancer. 2010;10:353–361. doi: 10.1038/nrc2840. [DOI] [PubMed] [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;105:4340–4345. doi: 10.1073/pnas.0800441105. [DOI] [PMC free article] [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;39:870–874. doi: 10.1038/ng2075. [DOI] [PMC free article] [PubMed] [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 doi: 10.1371/journal.pgen.1002532. [DOI] [PMC free article] [PubMed] [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 doi: 10.1371/journal.pgen.1001002. [DOI] [PMC free article] [PubMed] [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;39:865–869. doi: 10.1038/ng2064. [DOI] [PubMed] [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;40:703–706. doi: 10.1038/ng.131. [DOI] [PubMed] [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;41:579–584. doi: 10.1038/ng.353. [DOI] [PMC free article] [PubMed] [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;42:504–507. doi: 10.1038/ng.586. [DOI] [PMC free article] [PubMed] [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;41:324–328. doi: 10.1038/ng.318. [DOI] [PMC free article] [PubMed] [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. doi: 10.1038/ng.2563. [DOI] [PMC free article] [PubMed] [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. doi: 10.1038/ng.669. [DOI] [PMC free article] [PubMed] [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. doi: 10.1093/jnci/djq563. [DOI] [PubMed] [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. doi: 10.1038/ng.985. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Ghoussaini M, et al. Genome-wide association analysis identifies three new breast cancer susceptibility loci. Nat Genet. 2012;44:312–8. doi: 10.1038/ng.1049. [DOI] [PMC free article] [PubMed] [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. doi: 10.1093/hmg/dds381. [DOI] [PMC free article] [PubMed] [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. doi: 10.1038/ng.2561. [DOI] [PMC free article] [PubMed] [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. doi: 10.1038/ng.2566. [DOI] [PMC free article] [PubMed] [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. [DOI] [PMC free article] [PubMed]
- 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. doi: 10.1038/ng.3041. [DOI] [PMC free article] [PubMed] [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;37:590–592. doi: 10.1038/ng1571. [DOI] [PubMed] [Google Scholar]
- 27.Greenman C, et al. Patterns of somatic mutation in human cancer genomes. Nature. 2007;446:153–158. doi: 10.1038/nature05610. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Jones S, et al. Frequent mutations of chromatin remodeling gene ARID1A in ovarian clear cell carcinoma. Science. 2010;330:228–231. doi: 10.1126/science.1196333. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Sjoblom T, et al. The consensus coding sequences of human breast and colorectal cancers. Science. 2006;314:268–274. doi: 10.1126/science.1133427. [DOI] [PubMed] [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;108:17087–17092. doi: 10.1073/pnas.1108745108. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Nik-Zainal S, et al. Mutational processes molding the genomes of 21 breast cancers. Cell. 2012;149:979–993. doi: 10.1016/j.cell.2012.04.024. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Stephens PJ, et al. The landscape of cancer genes and mutational processes in breast cancer. Nature. 2012;486:400–404. doi: 10.1038/nature11017. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Wood LD, et al. The genomic landscapes of human breast and colorectal cancers. Science. 2007;318:1108–13. doi: 10.1126/science.1145720. [DOI] [PubMed] [Google Scholar]
- 34.Stratton MR, Campbell PJ, Futreal PA. The cancer genome. Nature. 2009;458:719–24. doi: 10.1038/nature07943. [DOI] [PMC free article] [PubMed] [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. doi: 10.1038/ng.287. [DOI] [PMC free article] [PubMed] [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. doi: 10.1146/annurev-med-100708-204735. [DOI] [PubMed] [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. doi: 10.1093/jnci/djt018. [DOI] [PMC free article] [PubMed] [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. doi: 10.1371/journal.pgen.0030063. [DOI] [PMC free article] [PubMed] [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. [DOI] [PMC free article] [PubMed]
- 40.Banerji S, et al. Sequence analysis of mutations and translocations across breast cancer subtypes. Nature. 2012;486(7403):405–9. doi: 10.1038/nature11154. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Ellis MJ, et al. Whole-genome analysis informs breast cancer response to aromatase inhibition. Nature. 2012;486(7403):353–60. doi: 10.1038/nature11143. [DOI] [PMC free article] [PubMed] [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. doi: 10.1038/nature10933. [DOI] [PMC free article] [PubMed] [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. doi: 10.1016/S0168-9525(03)00054-4. [DOI] [PubMed] [Google Scholar]
- 44.Conticello SG. The AID/APOBEC family of nucleic acid mutators. Genome Biol. 2008;9:229. doi: 10.1186/gb-2008-9-6-229. [DOI] [PMC free article] [PubMed] [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;108:4858–4863. doi: 10.1073/pnas.1009687108. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Komatsu A, et al. Identification of novel deletion polymorphisms in breast cancer. Int J Oncol. 2008;33:261–270. [PubMed] [Google Scholar]
- 47.Burns MB, et al. Nature. 2013;494:366–370. doi: 10.1038/nature11881. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Roberts SA, Getz G, Gordenin DA. Nat Genet. 2013;45:970–976. doi: 10.1038/ng.2702. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Fang HY, et al. Proteomic identification of differentially expressed proteins in curcumin-treated MCF-7 cells. Phytomedicine. 2011;18:697–703. doi: 10.1016/j.phymed.2010.11.012. [DOI] [PubMed] [Google Scholar]
- 50.Chimge NO, Frenkel B. The RUNX family in breast cancer: relationships with estrogen signaling. Oncogene. 2013;32:2121–30. doi: 10.1038/onc.2012.328. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Janes KA. RUNX1 and its understudied role in breast cancer. Cell Cycle. 2011;10(20):3461–5. doi: 10.4161/cc.10.20.18029. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Tamborero D, et al. Comprehensive identification of mutational cancer driver genes across 12 tumor types. Sci Rep. 2013;3:2650. doi: 10.1038/srep02650. [DOI] [PMC free article] [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. doi: 10.1007/s10911-013-9282-8. [DOI] [PMC free article] [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. doi: 10.1158/0008-5472.CAN-04-0615. [DOI] [PubMed] [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. doi: 10.1002/ijc.21332. [DOI] [PubMed] [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. doi: 10.1002/wsbm.1162. [DOI] [PMC free article] [PubMed] [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. doi: 10.1016/S0065-3233(04)71003-7. [DOI] [PubMed] [Google Scholar]
- 58.Machado C, Andrew DJ. D-Titin: a giant protein with dual roles in chromosomes and muscles. J Cell Biol. 2000;151:639–652. doi: 10.1083/jcb.151.3.639. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 59.Machado C, Sunkel CE, Andrew DJ. Human autoantibodies reveal Titin as a chromosomal protein. J Cell Biol. 1998;141:321–333. doi: 10.1083/jcb.141.2.321. [DOI] [PMC free article] [PubMed] [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;119:239–249. doi: 10.1242/jcs.02728. [DOI] [PubMed] [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. doi: 10.1371/journal.pone.0098229. [DOI] [PMC free article] [PubMed] [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. [DOI] [PMC free article] [PubMed]
- 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. [DOI] [PMC free article] [PubMed]
- 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. [DOI] [PubMed]
- 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. doi: 10.1158/1078-0432.CCR-09-1029. [DOI] [PMC free article] [PubMed] [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. doi: 10.1038/bjc.2011.243. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 67.Flicek P, et al. Ensembl 2013. Nucleic Acids Res. 2013;41(Database issue):D48–55. doi: 10.1093/nar/gks1236. [DOI] [PMC free article] [PubMed] [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. doi: 10.1186/bcr1037. [DOI] [PMC free article] [PubMed] [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. doi: 10.1186/1471-2407-6-230. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 70.Pruitt KD, et al. RefSeq: an update on mammalian reference sequences. Nucleic Acids Res. 2014;42(Database issue):D756–63. doi: 10.1093/nar/gkt1114. [DOI] [PMC free article] [PubMed] [Google Scholar]