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  • Research article
  • Open Access
  • Open Peer Review

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

  • 1, 2, 3Email author,
  • 1,
  • 1,
  • 4,
  • 1, 5,
  • 2, 3 and
  • 1, 5
BMC Cancer201616:165

https://doi.org/10.1186/s12885-016-2210-8

  • Received: 15 October 2015
  • Accepted: 21 February 2016
  • Published:
Open Peer Review reports

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 [325]. Partial and full tumor genome sequences have revealed the existence of hundreds to thousands of mutations in most cancers [2632]. However, genome sequencing has revealed that many cancers, including breast cancer, have somatic mutation spectra dominated by C-to-T transitions [2732]. 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 [3739]. 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, 4042]. 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 [5860]. 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.

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

Declarations

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.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
(2)
Laboratory of Genetics and Molecular Pathology–Medical School of Casablanca, Casablanca, Morocco
(3)
University Hassan II Ain Chock, Center Of Doctoral Sciences “In Health Sciences”, Casablanca, Morocco
(4)
Department of Oncology, Littoral Clinic, Casablanca, Morocco
(5)
Center for Primary Health Care Research, Clinical Research Center, Lund University, Malmö, Sweden

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