Open Access

E-cadherin genetic variants predict survival outcome in breast cancer patients

  • Hager Memni1, 2,
  • Yosra Macherki1, 3,
  • Zahra Klayech1, 3,
  • Ahlem Ben-Haj-Ayed1, 2,
  • Karim Farhat4,
  • Yassmine Remadi1,
  • Sallouha Gabbouj1,
  • Wijden Mahfoudh1,
  • Nadia Bouzid1, 5,
  • Noureddine Bouaouina1, 5,
  • Lotfi Chouchane6,
  • Abdelfattah Zakhama1, 7 and
  • Elham Hassen1, 3Email author
Journal of Translational Medicine201614:320

https://doi.org/10.1186/s12967-016-1077-4

Received: 29 August 2016

Accepted: 8 November 2016

Published: 16 November 2016

Abstract

Background

E-cadherin is a major component of adherens junctions that regulates cell shape and maintains tissue integrity. A complete loss or any decrease in cell surface expression of E-cadherin will interfere with the cell-to-cell junctions’ strength and leads to cell detachment and escape from the primary tumor site. In this prospective study, three functional single nucleotide polymorphisms (−347G/GA, rs5030625; −160C/A, rs16260; +54C/T, rs1801026), were found to modulate E-cadherin expression.

Methods

577 DNA samples from breast cancer (BC) cases were genotyped by polymerase chain reaction-restriction fragment length polymorphism (PCR–RFLP).

Results

We detected no significant correlations between each polymorphism and the clinical parameters of the patients whereas the GACC haplotype was significantly associated with low SBR grading. Overall survival analysis showed that both −347G/G and +54C/C wild (wt) genotypes had a significantly worse effect compared to the other genotypes (non-wt). Moreover, carrying simultaneously both the −347 and +54 wt genotypes confers a significantly higher risk of death. However, with metastatic recurrence, the death-rate was null in patients carrying the non-wt genotypes, and attained 37% in those carrying the wt genotype. A multivariate analysis showed that these two polymorphisms are independent prognostic factors for overall survival in BC patients.

Conclusions

Our results support the fact that E-cadherin genetic variants control disease severity and progression and could be a marker of disease outcome. These findings could be useful in selecting patients that should be monitored differently.

Keywords

E-cadherin SNP Prognosis Breast cancer

Background

In 2012, one out of four cancer cases is a breast cancer (BC) case [1]. In North African countries BC is also the commonest cancer among women, and the incidence rates are widely lower than in European Mediterranean countries [2]. The incidence is probably underestimated in these developing countries because of the difficulties related to screening and diagnostic programs. However, considering death rates, BC is the second cause of cancer death in more developed regions (198,000 deaths) and remains the most frequent in less developed regions (324,000 deaths). Yet, whatever socioeconomic status, it is well known that BC is a multifactorial disease, and that besides environment and lifestyle, the genetic background contributes to the increase of the risk of having breast cancer. While a huge amount of studies reported the involvement of genes, genetic loci or genetic polymorphisms in breast cancer susceptibility, studies of the genetic influence on disease progression and severity are less frequent [3]. Defining in gene-candidates, single nucleotide polymorphisms (SNPs) associated with increased severity or worsening progression of BC will potentially allow a better individualized handling of patients and as well as a better understanding of the mechanisms of cancer progression [46].

BC recurrence or metastasis represents the main cause of breast cancer-related deaths. It has been shown that epithelial-mesenchymal transition (EMT), a process by which epithelial cells acquire mesenchymal stem cell proprieties, plays a critical role in promoting metastasis in carcinomas [7]. One of the initiating steps of EMT involves downregulation and relocation of the main epithelial cell adhesion protein, the epithelial-cadherin (E-cadherin) [8]. In normal epithelial tissues, E-cadherin is a major component of adherens junctions that regulates cell shape and maintains tissue integrity [9]. A complete loss of E-cadherin expression/function or any decrease in cell surface expression, caused by mutation of the CDH1 gene, or other mechanisms that decrease E-cadherin expression will interfere with the cell-to-cell junctions’ strength and leads, inter alia, to cell detachment and escape from the primary tumor site. The CDH1 gene (OMIM 192090) is located on chromosome 16q22, several single nucleotide polymorphisms (SNP) localized in non-coding sequences and affecting protein expression were described. The −347G/GA (rs5030625) and −160C/A (rs16260) SNPs within the promoter region are the most extensively studied CDH1-SNPs in disease association studies. Both the minor alleles were previously shown to reduce E-cadherin expression by affecting the transcriptional efficiency of the CDH1 gene. The −347GA allele has weak transcriptional factor-binding strength and transcriptional activity compared with that of the G allele [10], while the −160A allele decreases the transcriptional efficiency compared with that of the C allele [11]. Additional regulatory polymorphisms outside of the promoter region that influence E-cadherin expression have also been reported. The +54C/T (rs1801026) is located at 141 bp upstream of the poly-A signal in the 3′-UTR region. The study of Jacobs et al. showed that the occurrence of the T allele is related to a lower mRNA stability and also a reduced luciferase expression by a reporter gene constructs driven by a constitutive SV40-promoter [12]. In regards to breast cancer, few investigations have been carried out on the association between CDH1 polymorphisms and cancer severity or progression. This is why in this prospective cohort study conducted on 577 sporadic BC cases, three functional SNPs from of the E-cadherin gene (CDH1) were genotyped, and the possible prognostic values of these genetic variations were investigated.

Methods

Study population

The study was approved by the National Ethical Committee and a written informed consent was obtained from all enrolled individuals prior to their participation. Both patients and controls were collected between 2000 and 2012 from the Farhat Hached University Hospital of Sousse (Tunisia). During blood sampling, all participants (patients and controls) were interviewed using a questionnaire to collect demographic characteristics, personal and family medical history, contraceptive methods, sexual and reproductive behavior information and lifestyle (smoking, alcohol consumption). In this study, unrelated participants without family history of breast cancer or any cancer were included. A total of 577 breast cancer patients were recruited from the Department of Cancerology and Radiotherapy (Farhat Hached University Hospital, Sousse, Tunisia). A detailed description of the clinico-pathological characteristics of this cohort is presented in Table 1. The patients had a mean age of 48.7 ± 11 years (range 23–81). The median follow-up was 70 months (range 1–144 months). At the time of this study, 145 patients relapsed after treatment (local or distant recurrence). Among them, 31 patients died from breast carcinoma. To estimate linkage disequilibrium (LD), 300 Tunisian healthy blood donors without a personal history of cancer were recruited as control group from the Regional Center of Blood Transfusion (Farhat Hached University Hospital, Sousse, Tunisia).
Table 1

Clinical characteristics of breast cancer patients

Characteristics

Patients (%)

Total

577

Age at diagnosis

 ≤40

133 (23.1)

 >40

444 (76.9)

Menopausal status

 Non menopausal

298 (51.6)

 Menopausal

279 (48.4)

Tumor size

 T1–T2

381 (66)

 T3–T4

167 (29)

 Unknown

29 (5)

Lymph node involvement

 Negative

230 (39.9)

 Positive

328 (56.8)

 Unknown

19 (3.3)

Metastasis

 Negative

362 (62.7)

 Positive

10 (1.7)

 Unknown

205 (35.5)

SBR grade

 SBR 1–2

329 (57)

 SBR 3

192 (33.3)

 Unknown

56 (9.7)

Histology

 Ductual

513 (88.9)

 Lobular

23 (4)

 Other

25 (4.3)

 Unknown

16 (2.8)

Estrogen receptor

 Negative

195 (33.8)

 Positive

255 (44.2)

 Unknown

127 (22)

Progesteron receptor

 Negative

240 (41.6)

 Positive

210 (36.4)

 Unknown

127 (22)

Her-2 status

 Negative

75 (13)

 Positive

39 (6.8)

 Unknown

463 (80.2)

Genomic DNA extraction and SNP genotyping analysis

Genomic DNA was extracted from peripheral blood leukocytes by a “salting out” procedure [13]. Briefly, 10 ml of blood was mixed with Triton lysis buffer (0.32 M sucrose, 1% Triton X-100, 5 mM MgCl2, 10 mM Tris–HCl, pH 7.5). The pellet was incubated with proteinase K at 56 °C and subsequently salted out using a saturated NaCl solution. Precipitated proteins were removed by centrifugation. The DNA in supernatant fluid was precipitated with ethanol. Finally, the DNA pellet was conserved in Tris–EDTA buffer. DNA concentration and quality were analyzed by thermo-scientific NanoDrop 2000™.

Genotype analysis of the CDH1 gene SNP −347 G/GA (rs5030625), −160C/A (rs16260) and +54 C/T (rs1801026) polymorphisms were performed by polymerase chain reaction-restriction fragment length polymorphism (PCR–RFLP) using previously described primers (Table 2) [14, 15]. Each PCR was performed under the standard conditions and the amplification was carried out in a final volume of 30 μl containing 25–100 ng of genomic DNA samples, 0.6 μM of each primer for the −160C/A and −347G/GA SNPs and 0.3 μM of each primer for the +54C/T SNP, 0.2 mM desoxy-nucleotide tryphosphate (dNTP), 1.5 mM MgCl2, 3 μl of 10 X PCR buffer and 0.5U of SuperTaq DNA polymerase (Amersham, Paris, France). Reaction conditions used with thermal cycler (Biometra, Göttinger, Germany)were as follows: For the promoter SNPs −160C/A and −347G/GA the DNA was initially denatured for 5 min at 94 °C for 1 cycle, and incubated for 30 cycles: denaturing for 30 s at 94 °C, annealing for 30 s at 61 °C and extending for 60 s at 72 °C. The +54C/T PCR reaction was carried out in the same conditions described previously except for the annealing temperature, that was at 58 °C. A final extension of 7 min at 72 °C was performed at the end of each reaction. After amplification, PCR products were digested at 37 °C over night with 5U of the restriction enzyme HphI for the −160C/A SNP, for 3 h with 3U of the restriction enzyme BanII for the −347G/GA and over night with 2U of the restriction enzyme PmlI for the +54C/T SNP. Digestion products were, then, separated on a 3% agarose gel stained with ethidium bromide and visualized with ultraviolet light.
Table 2

Primers and restriction enzymes used for polymorphism genotyping

SNPs

Primer sequences

Restriction enzymes

Fragments size (bp)

−347 G/GA

rs5030625

−160 C/A

rs16260

Forword: 5′-GCCCCGACTTGTCTCTCTAC-3′

Reverse: 5′-GGCCACAGCCAATCAGCA-3′

BanII

G: 263 + 116 + 68

GA: 332 + 116

HphI

C: 181 + 177 + 89

A: 266 + 181

+54 C/T

rs1801026

Forword: 5′-CAGACAAAGACCAGGACTAT-3′

Reverse: 5′-AAGGGAGCTGAAAAACCACCAGCCAC-3′

PmlI

C: 146 + 26

T: 172

Statistical analysis

To evaluate if our study population (patients and controls) is in the Hardy–Weinberg equilibrium we used the Chi square test to compare between observed and expected genotype frequencies of CDH1 gene polymorphisms. The same test was used to evaluate any significant association between the three CDH1 polymorphisms and the clinicopathological characteristics of the disease. The differences were considered significant if the p value did not exceed 0.05. Odd ratios (ORs) and 95% confidence intervals (CIs) were calculated by unconditional logistic regression. When expected values in contingency tables were under 5, Fisher’s exact test was used. The LD between SNPs pairs was quantified using the standardized linkage disequilibrium coefficient (D’) [16]. The haplotypes and their frequencies were estimated using the Phase program [17].

Disease-free survival (DFS), metastasis-free survival (MFS), and overall survival (OS) were calculated using the Kaplan–Meier method for ten years and compared with the log-rank test. DFS was defined as the date of diagnosis until first recurrence, metastasis, death due to breast cancer or the last date of follow-up. MFS was defined as the date of diagnosis until first metastasis or last date of follow-up and OS was defined as the date of diagnosis until death due to breast cancer or last date of follow-up. Variables with a p-value less than 0.1 in the univariate Cox regression model were evaluated in a multivariate Cox regression model using the enter method. Because of the low number of the cases with metastasis at diagnosis (n = 10) we choose to exclude the metastasis parameter from the haplotypes and multivariate analysis. All statistics were carried out using Software Package for Social Sciences (SPSS) version 20.0 (SPSS, Chicago, IL, USA).

Results

CDH1 SNPs and their association with clinicopathological characteristics of breast cancer patients

All DNA samples were successfully genotyped for the three CHD1 SNPs (577 patients and 300 controls). For both patient and control groups all genotype distributions did not diverge significantly from Hardy–Weinberg equilibrium. Moreover, the minor allele frequencies (MAF) of these variants in the control population were close to those reported in Europeans (HapMap consortium) (−347GA: 0.150; −160A: 0.322 and +54T: 0.206). Among the patients, the −347G/GA genotype frequencies were 72.4% for GG, 24.8% for GGA and 2.8% for GAGA. For the −160C/A SNP, genotype frequencies were 45.9% for CC, 43.7% for CA and 10.4% for AA. The +54C/T genotype frequencies were 35.6% for CC, 47.1% for CT and 17.3% for TT.

We then investigated the association between CHD1 SNP genotype distributions and clinicopathological characteristics at diagnosis of patients with breast cancer. For each SNP genotype analysis, the patients were divided into homozygous wild-type carriers (wt) and non-carriers (non-wt, heterozygous and homozygous mutant). The relationships between CHD1 SNP genotypes and clinicopathological characteristics are shown in Table 3. Overall, no statistically significant association was observed with any of the three SNPs and clinical characteristics, including age, menopausal status, tumor size, lymph node involvement, metastasis, SBR grading and histologic type.
Table 3

Correlation between CDH1 SNPs and clinicopathological characteristics of breast cancer patients

Characteristics

347 G/GA

160 C/A

+54 C/T

GG b

GGA + GAGA

P

OR (95% CI)

CC b

CA + AA

P

OR (95% CI)

CC b

CT + TT

P

OR (95% CI)

Age at diagnosis

 ≤40

96

37

  

61

72

  

48

85

  

 >40

322

122

0.93

0.98 (0.63–1.51)

204

240

0.98

0.99 (0.67–1.47)

157

287

0.87

1.03 (0.68–1.54)

Menopausal status

 Non menopausal

225

73

  

145

153

  

107

191

  

 Menopausal

193

86

0.08

1.37 (0.95–1.98)

120

159

0.17

1.25 (0.90–1.74)

98

181

0.84

1.03 (0.73–1.45)

Tumor size

 T1–T2

279

102

  

166

215

  

134

247

  

 T3–T4

119

48

0.63

1.1 (0.73–1.65)

81

86

0.28

0.82 (0.56–1.18)

61

106

0.76

0.94 (0.64–1.37)

Lymph node involvement

 Negative

167

63

  

116

114

  

73

157

  

 Positive

239

89

0.94

0.98 (0.67–1.44)

141

187

0.08

1.34 (0.96–1.80)

127

201

0.09

0.73 (0.51–1.05)

Metastasis

 Negative

264

98

  

166

196

  

121

241

  

 Positive

8

2

0.46a

0.67 (0.14–3.20)

3

7

0.25a

1.97 (0.50–7.70)

2

8

0.37a

2 (0.42–9.60)

SBR grade

 SBR 1–2

232

97

  

150

179

  

112

217

  

 SBR 3

148

44

0.10

0.71 (0.47–1.07)

91

101

0.69

0.93 (0.65–1.32)

74

118

0.30

0.82 (0.56–1.19)

Histology

 Ductual

376

137

  

240

273

  

183

330

  

 Lobular

18

5

0.59

0.76 (0.27–2.09)

11

12

0.92

0.95 (0.41–2.21)

6

17

0.35

1.57 (0.60–4.05)

CI confidence interval, OR odds ratio

aFisher’s exact test

bReference group

To analyze the association of the combined effects of the CDH1 SNPs and clinicopathological characteristics of patients with breast cancer, CDH1 haplotype frequencies and linkage disequilibrium coefficient were estimated. The haplotype frequencies among both patients and controls are summarized in Table 4. Eight different haplotypes were observed among both controls and patients with breast cancer. The −347G-160C + 54C haplotype was the most frequent among both controls and patients. The −347G − 160A + 54T haplotype was significantly more frequently observed in patients than controls, and seemed to be a risk haplotype for BC occurrence (p < 10−4, OR = 4.03). However, further larger population-based case–control studies are necessary to validate these findings. The LD analysis results showed different patterns between cases and controls. Both −347G/GA and −160C/A loci, and −160C/A and +54C/T loci showed a stronger LD in controls compared to patients (D′ = 0.484 versus D′ = 0.279 and D′ = 0.473 versus D′ = 0.070). Moreover, the −347G/GA and +54C/T loci showed a higher LD in controls compared to patients (D′ = 0.083 versus D′ = 0.015). The relationships between CHD1 SNP haplotypes and clinicopathological characteristics are shown in Table 5. The most frequent haplotype was used as a reference in the correlation analysis. The results showed that the −347GA − 160C + 54T haplotype was significantly more frequent among the younger women (age ≤ 40) (p = 0.01, OR = 0.37). However, the −347GA − 160C + 54C haplotype was significantly more frequent among patients aged more than 40 years (p = 0.015, OR = 3.74). The −347GA − 160C + 54C haplotype was also significantly more frequent among women with low SBR grading compared to high SBR grading (p = 0.049, OR = 0.44). There was no more significant correlation of CDH1 haplotypes with clinical characteristics, including menopausal status, tumor size and lymph node involvement and histological type.
Table 4

Haplotype frequencies of CDH1 SNPs observed in breast cancer patients and controls

Haplotypes

Frequencies

P

347G/GA

160C/A

+54C/T

Patients

Controls

G

C

C

0.323

0.405

 

G

C

T

0.238

0.265

0.270

G

A

C

0.177

0.147

<10 −4

G

A

T

0.109

0.032

<10 −4

GA

C

C

0.068

0.101

0.380

GA

C

T

0.048

0.024

0.023

GA

A

C

0.022

0.021

0.660

GA

A

T

0.012

0.003

0.230

Table 5

Correlation between CDH1 haplotype frequencies and clinicopathological characteristics of breast cancer patients

Characteristics

GCC a

GCT

GAC

GAT

GACC

GACT

GAAC and GAAT

Age at diagnosis

 ≤40

0.358

0.213

0.194

0.084

0.027

0.094

0.030

 >40

0.312

0.248

0.173

0.117

0.081

0.032

0.036

 P

 

0.15

0.83

0.15

0.015

0.01

0.21

 OR (CI 95%)

1

1.38 (0.89–2.13)

1.06 (0.64–1.74)

1.54 (0.85–2.79)

3.74 (1.29–10.82)

0.37 (0.17–0.79)

1.37 (0.82–2.28)

Menopausal status

 Non menopausal

0.354

0.236

0.172

0.105

0.056

0.049

0.028

 Menopausal

0.291

0.245

0.182

0.114

0.081

0.043

0.044

 P

 

0.21

0.26

0.26

0.098

0.92

0.25

 OR (CI 95%)

1

1.26 (0.88–1.81)

1.29 (0.83–2.01)

1.29 (0.83–2.01)

1.69 (0.91–3.16)

1.04 (0.51–2.11)

1.73 (0.64–4.39)

Tumor size

 T1–T2

0.318

0.237

0.192

0.105

0.057

0.051

0.038

 T3–T4

0.326

0.242

0.149

0.124

0.085

0.038

0.035

 P

 

0.91

0.23

0.6

0.33

0.46

0.76

 OR (CI 95%)

1

0.98 (0.65–1.46)

0.73 (0.44–1.21)

1.13 (0.71–1.81)

1.41 (0.71–2.79)

0.74 (0.33–1.66)

0.86 (0.32–2.27)

Lymph node involvement

 Negative

0.320

0.284

0.162

0.084

0.053

0.047

0.050

 Positive

0.323

0.208

0.195

0.123

0.080

0.047

0.023

 P

 

0.19

0.34

0.24

0.29

0.91

0.19

 OR (CI 95%)

1

0.78 (0.54–1.13)

1.26 (0.78–2.04)

1.34 (0.82–2.17)

1.43 (0.74–2.77)

0.96 (0.47–1.95)

0.55 (0.22–1.81)

SBR grade

 SBR 1–2

0.314

0.225

0.167

0.131

0.086

0.047

0.030

 SBR 3

0.365

0.230

0.192

0.085

0.040

0.055

0.025

 P

 

0.46

0.96

0.053

0.049

0.97

0.84

 OR (CI 95%)

1

0.86 (0.57–1.29)

0.99 (0.61–1.61)

0.61 (0.37–1.00)

0.44 (0.19–0.99)

1.01 (0.50–2.05)

0.91 (0.34–2.42)

Histology

 Ductual

0.291

0.231

0.139

0.157

0.122

0.026

0.032

 Lobular

0.329

0.245

0.179

0.103

0.060

0.050

0.033

 P

 

0.74

0.83

0.2

0.11

0.52

0.72

 OR (CI 95%)

1.00

1.12 (0.57–2.22)

0.90 (0.35–2.34)

1.59 (0.78–3.23)

2.06 (0.86–4.94)

0.62 (0.14–2.67)

0.91 (0.54–1.52)

aReference group

CI confidence interval, OR odds ratio

CDH1 SNPs and clinicopathological characteristics associated with survival in patients with breast cancer

When the relationship between the genotype distribution of the three SNPs in overall patients and the DFS and MFS was tested, no significant differences were observed (data not shown). However, the 10-years OS curve analysis and log-rank testing showed that both the −347G/G and +54C/C wild homozygous genotypes had a significantly worse effect on OS compared to the other genotypes (survival rate: 90 versus 95% and 91 versus 99%, respectively) (Fig. 1). Moreover, when patients carrying simultaneously both the −347G/G and +54C/C genotypes were grouped and compared to the non-carrier patients, the survival rate decreased to 87% for the wt-carriers and increased to 100% for the non-wt carriers (Fig. 1).
Fig. 1

Overall survival curves of breast carcinoma patients according to the presence or absence of the wild type genotypes; a347G/GA, b160C/A, c +54C/T and d combined −347G/GA with +54C/T

Further analyses were conducted to investigate whether the −347G/GA and +54C/T SNPs were associated with overall survival, according to clinical characteristics of the breast cancer patients (tumor size, lymph node involvement, SBR grading and metastasis occurrence after treatment). Significantly worse survival rates were observed in patients carrying the −347G/G wild homozygous genotype among patients with T3–T4 tumor size (survival rates: 88 versus 100%), or positive lymph node involvement (survival rates: 88 versus 99%), or high SBR grading (survival rates: 86 versus 100%) or metastasis occurrence after treatment (survival rates: 71 versus 97%) (Fig. 2). No significant relationship was found with T1–T2 tumor size, negative lymph node involvement, or low SBR grading and non-metastasis occurrence after treatment. Moreover, no significant relationship was found when the +54C/T SNP was analyzed according to several patient subgroups. However, when we studied the combined effect of −347G/G and +54C/C genotypes on overall survival of the positive lymph node involvement (survival rates: 83 versus 100%), high SBR grading (survival rates: 80 versus 100%) and metastasis occurrence after treatment subgroups (survival rates: 63 versus 100%), as noted previously, BC patients carrying simultaneously the wt genotypes had significantly worse survival rates (Fig. 2). Although the results were not statistically significant, when the combined effect was analyzed according to T3–T4 tumor size, they indicated that patients with −347G/G and +54C/C genotypes had the worst survival rate (survival rates: 86 versus 100%) (Fig. 2).
Fig. 2

Overall survival curves of different subgroups of breast carcinoma patients according to the presence or absence of the wild type genotypes of −347G/GA, +54C/T and combined −347G/GA with +54C/T

Univariate and multivariate analyses were used to evaluate whether the CDH1 SNPs and the clinicopathological characteristics were independent prognostic factors of breast cancer patients. The multivariate analysis showed that tumor size (p = 0.001, HR = 1.80) and lymph node involvement (p = 0.007, HR = 1.66) were independently associated with the DFS (Table 6). Moreover, it showed that lymph node involvement (p = 0.018, HR = 3.26), SBR grade (p = 0.048, HR = 2.20), −347G/GA SNP (p = 0.039, HR = 0.12) and +54C/T SNP (p = 0.022, HR = 0.40) were independent prognostic factors for OS in breast cancer patients (Table 6).
Table 6

Clinicopathological characteristics associated with disease-free survival and overall survival in patients with breast cancer

Characteristics

Disease-free survival

Overall survival

Univariate analysis

Multivariate analysis

Univariate analysis

Multivariate analysis

HR

95% CI

P

HR

95% CI

P

HR

95% CI

P

HR

95% CI

P

Agea

0.74

0.51–1.08

0.121

ni

  

0.75

0.33–1.68

0.487

ni

  

Menopausal statusb

0.92

0.66–1.28

0.638

ni

  

0.87

0.42–1.76

0.702

ni

  

Tumor sizec

1.87

1.33–2.64

0.0003

1.80

1.27–2.56

0.001

1.44

0.69–3.02

0.326

ni

  

Lymph node involvementd

1.75

1.22–2.51

0.002

1.66

1.14–2.40

0.007

2.97

1.21–7.30

0.017

3.26

1.22–8.69

0.018

Metastasisd

3.03

1.47–6.22

0.003

ni

  

5.64

1.67–19.01

0.005

ni

  

SBR gradee

1.09

0.77–1.56

0.599

ni

  

2.29

1.08–4.84

0.030

2.20

1.00–4.81

0.048

Histologyf

1.62

0.82–3.20

0.157

ni

  

0.80

0.11–5.92

0.833

ni

  

Estrogen receptord

1.34

0.91–1.95

0.127

ni

  

0.73

0.33–1.60

0.435

ni

  

Progesteron receptord

1.16

0.80–1.67

0.419

ni

  

0.88

0.40–1.95

0.763

ni

  

Her-2 statusd

1.03

0.44–2.40

0.938

ni

  

0.67

0.06–7.44

0.746

ni

  

CDH1347 G/GA g

0.92

0.63–1.35

0.694

ni

  

0.09

0.01–0.68

0.020

0.12

0.01–0.89

0.039

CDH1160 C/A g

0.92

0.66–1.28

0.650

ni

  

1.27

0.62–2.63

0.505

ni

  

CDH1 +54 C/T g

0.98

0.69–1.40

0.951

ni

  

0.52

0.26–1.06

0.075

0.40

0.18–0.87

0.022

HR hazard ratio; ni not included in multivariate analysis

a≤40 versus >40 years

bNon menopausal versus menopausal

cT1–T2 versus T3–T4

dNegative versus positive

eSBR 1–2 versus SBR 3

fDuctal invasive carcinoma versus Lobular invasive carcinoma

gHomozygous wild allele type versus heterozygous and mutates homozygous allele types

Discussion

Previous studies have shown that the development and the progression of epithelial cancers such as BC are related to the loss or the reduced expression of the main intercellular adhesion molecule of epithelial cells, the E-cadherin. The loss of cell-to-cell adhesion is an early event in metastatic colonization, leading to the detachment of the cell from her tissue of origin to colonize other sites.

E-cadherin expression is under the control of functional SNPs. As far as we know, reports were mainly case control-studies looking for risk of developing BC, and very few studies with controversial results were investigated to identify the relationship between CDH1 genetic variants and clinicopathological features of the patients. In our study, none of the studied SNPs showed any significant correlations with patients’ epidemiological or tumor or histological features. Shabnaz et al. also did not find any correlation between −160C/A polymorphisms with clinicopathological characteristics of BC patients [18]. However, Tipirisetti et al. noted a positive correlation between the −160A allele occurrences in patients with advanced stage [19]. In a Taiwanese study conducted on hepatocellular carcinoma patients, the occurrence of the −160A allele was significantly associated with more severe clinical stages [20]. Conversely, in a recent study of pancreatic cancer cases, the −160AA genotype was found to be significantly associated with reduced risk with T stage, lymph node metastasis and pathological stage [21]. In accordance with the previous results, a Japanese study of gastric cancer cases found that the −160CC genotype was significantly associated with deep invasion and lymph node metastasis [22]. Although previous studies were conducted mainly on epithelial cancer cases, a possible explanation of these contrasting results is the occurrence of one or more other SNPs, found in strong LD with the −160C/A in some ethnic groups (e.g. rs7200690, rs9929218). Further studies should be conducted among different ethnic groups to help understanding these results.

To see the combined effects of the three functional SNPs the haplotype analysis was investigated. Weak LD values, generally associated with higher recombination rates, were observed among patients [23]. Conversely, stronger LD values were observed among controls, suggesting a possible protective effect of the strong LD against BC. However, further investigations with a larger control sample size are needed to explain this result. To the best of our knowledge, this is the only study that explored the relationship between CDH1 haplotype frequencies and tumor severity. The −347GA − 160C + 54C haplotype may have a protective effect against high SBR grading. This haplotype has only one mutated allele at the −347 position, whilst having protective effect, however, the GA allelic analysis did not show significant differences with the G allele when patients were compared according to SBR grading (p = 0.12, OR = 0.75). Although each SNP had a functional impact on E-cadherin expression, the effects of their interactions and combinations are unknown, hence there is a need for further studies to describe the functionality of haplotypes including these three SNPs.

Moreover, very few studies investigated the associations of the CDH1 SNPs with patient survival. In a recent study conducted on a Chinese population, Jia et al. showed that BC patients with low clinical tumor stages and carrying the minor allele genotype of an SNP (rs7200690) located in intron 2 in strong LD with the −160C/A SNP, had unfavorable disease-free survival [24]. However, in a British population-based study no effect of the −160C/A SNP was seen on BC survival [25]. In the present study the −347G/GA and +54C/T SNPs, but not the −160C/A SNP, were shown to be associated with BC overall survival. When considering the whole patient group, both wild genotypes were shown to be associated with worse BC overall survival. Moreover, patients carrying simultaneously the wild genotypes, −347G/G and +54C/C, had worse survival rates than those carrying one of each genotype. Interestingly, when considering more aggressive tumor subgroups (T3–T4, lymph node positive, high grade SBR and metastasis occurrence after treatment) we observed significantly worse survival rates with the −347G/G genotype and a decrease in survival when patients carry simultaneously the −347G/G and +54C/C genotypes. Herein we found that the CDH1347G/G genotype confer risk of death in patients with more aggressive BC progression.

It is well known that in normal epithelial tissues, E-cadherin expression has suppressive effects on tumor progression, invasion and metastasis and thus any deregulation of E-cadherin expression could have critical pathological consequences. In a pathological context, E-cadherin expression could be modulated by several mechanisms, loss of heterozygosity, mutations of the CDH1 gene, epigenetic modulation, proteolytic processing and also cadherin switching [9]. In BC, studies on E-cadherin tissue expression were conflicting. Most of them showed that reduced or loss of E-cadherin expression correlates with high histological grade, larger tumor size, nodal metastasis, development of distant metastasis, and a reduced disease-free and overall survival [2628]. However, in recent reports the involvement of E-cadherin in breast cancer severity and progression has increasingly been suggested [29, 30]. The ambiguous role of E-cadherin could be partially due to the existence of at least two different and functional forms of E-cadherin, a full-length membrane form and an extracellular proteolytic soluble form (sE-cad). During the oncogenic process the first consequence of the E-cadherin proteolysis is the cell detachment and the release of a functional sE-cad with cancer promoting functions. Inside the tumor microenvironment, sE-cad inhibits cell-to-cell adhesion through an efficient competitive manner and by stimulating the activity of multiple matrix metalloproteinases (MMPs) [31]. Then, when sE-cad diffuse and spread into blood circulation, multiple oncogenic signaling pathways are activated [32, 33]. Recently, Liang et al., assessed the clinical significance of serum sE-cad levels in BC patients. They observed a significant correlation of sE-cad levels with tumor stage, grade, lymph node metastasis and also survival [34]. Moreover, Hofmann et al., found that serum sE-cad levels might be a marker predicting response to preoperative chemotherapy for patients with locally advanced breast cancer [35]. Functional SNPs modulate the expression of both the membrane and the soluble E-cadherin forms in the same way. Since the two forms play opposite functions, the membrane form expressed in normal tissue acts as a tumor suppressor, while the soluble form associated with disease severity promotes tumor progression, this could explain why patients expected to have reduced E-cadherin expression have better survival whatever the severity of the disease.

Conclusions

This study provides novel information about the relationship between E-cadherin (CDH1) genetic variants and clinicopathological features and progression of BC. The main finding of this study is the association of CDH1 functional SNPs with overall survival in BC, particularly in patients with a more aggressive tumor at onset or with recurrent metastatic BC. Our results support the fact that the CDH1 SNPs control disease severity and progression and could be a marker of disease outcome. These findings could be useful in selecting patients who should be monitored differently. Additional investigations on functional evaluation of CDH1 SNPs should be carried out to support our findings.

Abbreviations

BC: 

breast cancer

PCR-RFLP: 

polymerase chain reaction-restriction fragment length polymorphism

wt: 

wild genotype

SNP: 

single nucleotide polymorphism

EMT: 

epithelial-mesenchymal transition

E-cadherin: 

epithelial-cadherin

CDH1

cadherin1 gene

LD: 

linkage disequilibrium

ORs: 

odd ratios

CIs: 

95% confidence intervals

D’: 

disequilibrium

DFS: 

disease-free survival

MFS: 

metastasis-free survival

OS: 

overall survival

sE-cad: 

soluble E-cadherin

MMPs: 

multiple matrix metalloproteinases

Declarations

Authors’ contributions

HE, CL and ZA designed the study. MH, KZ, MY, RY, GS, MW, BN and BN helped in sample collection. MH assessed the molecular genotyping. KZ, MY, RY, HE, NB and NB generated the data. HE, MY, FK and B-H-AA analyzed the data. HE wrote the manuscript. All authors read and approved the final manuscript.

Acknowledgements

This work was supported by the Ministry of Higher Education and Scientific Research and by the Ministry of Health of the Republic of Tunisia. We thank Mr Adel Rdissi for English revision.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

All the data presented is available upon request.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the National Ethical Committee.

Funding

The Ministry of Higher Education and Scientific Research have supported this work.

Informed consent

A written informed consent was obtained from all enrolled individuals prior to their participation.

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)
Laboratory of Molecular Immuno-Oncology, Faculty of Medicine of Monastir, Monastir University
(2)
Faculty of Sciences of Bizerte, Carthage University
(3)
Higher Institute of Biotechnology of Monastir, Monastir University
(4)
Cancer Research Chair, College of Medicine, King Saud University
(5)
Department of Cancerology and Radiotherapy, Farhat Hached University Hospital, Sousse University
(6)
Laboratory of Genetic Medicine and Immunology, Weill Cornell Medicine-Qatar, Education City, Qatar Foundation
(7)
Department of Anatomy and Pathologic Cytology, Fattouma Bourguiba University Hospital, Monastir University

References

  1. Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, Parkin DM, Forman D, Bray F. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 2015;136:E359–86.View ArticlePubMedGoogle Scholar
  2. Giordano L, Bisanti L, Salamina G, Ancelle Park R, Sancho-Garnier H, Espinas J, Berling C, Rennert G, Castagno R, Dotti M, Jaramillo L, Segnan N, Euromed Cancer Working Group. The EUROMED CANCER network: state-of-art of cancer screening programmes in non-EU Mediterranean countries. Eur J Public Health. 2016;26:83–9.View ArticlePubMedGoogle Scholar
  3. Shan J, Mahfoudh W, Dsouza SP, Hassen E, Bouaouina N, Abdelhak S, Benhadjayed A, Memmi H, Mathew RA, Aigha II, Gabbouj S, Remadi Y, Chouchane L. Genome-Wide Association Studies (GWAS) breast cancer susceptibility loci in Arabs:susceptibility and prognostic implications in Tunisians. Breast Cancer Res Treat. 2012;135:715–24.View ArticlePubMedPubMed CentralGoogle Scholar
  4. Mestiri S, Bouaouina N, Ahmed SB, Khedhaier A, Jrad BB, Remadi S, Chouchane L. Genetic variation in the tumor necrosis factor-alpha promoter region and in the stress protein hsp70-2: susceptibility and prognostic implications in breast carcinoma. Cancer. 2001;91:672–8.View ArticlePubMedGoogle Scholar
  5. Khedhaier A, Hassen E, Bouaouina N, Gabbouj S, Ahmed SB, Chouchane L. Implication of xenobiotic metabolizing enzyme gene (CYP2E1, CYP2C19, CYP2D6, mEH and NAT2) polymorphisms in breast carcinoma. BMC Cancer. 2008;8:109.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Snoussi K, Mahfoudh W, Bouaouina N, Fekih M, Khairi H, Helal AN, Chouchane L. Combined effects of IL-8 and CXCR2 gene polymorphisms on breast cancer susceptibility and aggressiveness. BMC Cancer. 2010;10:283.View ArticlePubMedPubMed CentralGoogle Scholar
  7. Tsai JH, Yang J. Epithelial-mesenchymal plasticity in carcinoma metastasis. Genes Dev. 2013;27:2192–206.View ArticlePubMedPubMed CentralGoogle Scholar
  8. Thiery JP. Epithelial-mesenchymal transitions in tumour progression. Nat Rev Cancer. 2002;2:442–54.View ArticlePubMedGoogle Scholar
  9. Van Roy F, Berx G. The cell–cell adhesion molecule E-cadherin. Cell Mol Life Sci. 2008;65:3756–88.View ArticlePubMedGoogle Scholar
  10. Shin Y, Kim IJ, Kang HC, Park JH, Park HR, Park HW, Park MA, Lee JS, Yoon KA, Ku JL, Park JG. The E-cadherin −347G−>GA promoter polymorphism and its effect on transcriptional regulation. Carcinogenesis. 2004;25:895–9.View ArticlePubMedGoogle Scholar
  11. Li LC, Chui RM, Sasaki M, Nakajima K, Perinchery G, Au HC, Nojima D, Carroll P, Dahiya R. A single nucleotide polymorphism in the E-cadherin gene promoteralters transcriptional activities. Cancer Res. 2000;60:873–6.PubMedGoogle Scholar
  12. Jacobs G, Hellmig S, Huse K, Titz A, Franke A, Kwiatkowski R, Ott S, Kosmahl M, Fischbach W, Lucius R, Klapper W, Fölsch UR, Hampe J, Schreiber S, Rosenstiel P. Polymorphisms in the 3′-untranslated region of the CDH1 gene are a risk factor for primary gastric diffuse large B-cell lymphoma. Haematologica. 2011;96:987–95.View ArticlePubMedPubMed CentralGoogle Scholar
  13. Miller SA, Dykes DD, Polesky HF. A simple salting out procedure for extracting DNA from human nucleated cells. Nucleic Acids Res. 1988;16:1215.View ArticlePubMedPubMed CentralGoogle Scholar
  14. Kiemeney LA, van Houwelingen KP, Bogaerts M, Witjes JA, Swinkels DW, denHeijer M, Franke B, Schalken JA, Verhaegh GW. Polymorphisms in the E-cadherin(CDH1) gene promoter and the risk of bladder cancer. Eur J Cancer. 2006;42:3219–27.View ArticlePubMedGoogle Scholar
  15. Zhang XF, Wang YM, Ge H, Cao YY, Chen ZF, Wen DG, Guo W, Wang N, Li Y, ZhangJ H. Association of CDH1 single nucleotide polymorphisms with susceptibility toesophageal squamous cell carcinomas and gastric cardia carcinomas. Dis Esophagus. 2008;21:21–9.View ArticlePubMedGoogle Scholar
  16. Shi YY, He L. SHEsis, a powerful software platform for analyses of linkage disequilibrium, haplotype construction, and genetic association at polymorphism loci. Cell Res. 2005;15:97–8.View ArticlePubMedGoogle Scholar
  17. Stephens M, Smith NJ, Donnelly P. A new statistical method for haplotype reconstruction from population data. Am J Hum Genet. 2001;68:978–89.View ArticlePubMedPubMed CentralGoogle Scholar
  18. Shabnaz S, Ahmed MU, Islam MS, Islam MR, Al-Mamun MM, Islam MS, Hasnat A. Breast cancer risk in relation to TP53 codon 72 and CDH1 gene polymorphisms in the Bangladeshi women. Tumour Biol. 2016;37:7229–37.View ArticlePubMedGoogle Scholar
  19. Tipirisetti NR, Govatati S, Govatati S, Kandukuri LR, Cingeetham A, Singh L, Digumarti RR, Bhanoori M, Satti V. Association of E-cadherin single-nucleotide polymorphisms with the increased risk of breast cancer: a study in South Indian women. Genet Test Mol Biomarkers. 2013;17:494–500.View ArticlePubMedGoogle Scholar
  20. Chien MH, Yeh KT, Li YC, Hsieh YH, Lin CH, Weng MS, Kuo WH, Yang SF. Effects of E-cadherin (CDH1) gene promoter polymorphisms on the risk and clinicopathological development of hepatocellular carcinoma. J Surg Oncol. 2011;104:299–304.View ArticlePubMedGoogle Scholar
  21. Zhao L, Wang YX, Xi M, Liu SL, Zhang P, Luo LL, Liu MZ. Association between E-cadherin (CDH1) polymorphisms and pancreatic cancer risk in Han Chinese population. Int J Clin Exp Pathol. 2015;8:5753–60.PubMedPubMed CentralGoogle Scholar
  22. Kuraoka K, Oue N, Yokozaki H, Kitadai Y, Ito R, Nakayama H, Yasui W. Correlation of a single nucleotide polymorphism in the E-cadherin gene promoter with tumorigenesis and progression of gastric carcinoma in Japan. Int J Oncol. 2003;23:421–7.PubMedGoogle Scholar
  23. Ardlie KG, Kruglyak L, Seielstad M. Patterns of linkage disequilibrium in the human genome. Nat Rev Genet. 2002;3:299–309.View ArticlePubMedGoogle Scholar
  24. Jia YM, Xie YT, Wang YJ, Han JY, Tian XX, Fang WG. Association of genetic polymorphisms in CDH1 and CTNNB1 with breast cancer susceptibility and patients’ prognosis among Chinese han women. PLoS ONE. 2015;10:e0135865.View ArticlePubMedPubMed CentralGoogle Scholar
  25. Goode EL, Dunning AM, Kuschel B, Healey CS, Day NE, Ponder BA, Easton DF, Pharoah PP. Effect of germ-line genetic variation on breast cancer survival in a population-based study. Cancer Res. 2002;62:3052–7.PubMedGoogle Scholar
  26. Oka H, Shiozaki H, Kobayashi K, Inoue M, Tahara H, Kobayashi T, Takatsuka Y, Matsuyoshi N, Hirano S, Takeichi M, et al. Expression of E-cadherin cell adhesion molecules in human breast cancer tissues and its relationship to metastasis. Cancer Res. 1993;53:1696–701.PubMedGoogle Scholar
  27. Siitonen SM, Kononen JT, Helin HJ, Rantala IS, Holli KA, Isola JJ. ReducedE-cadherin expression is associated with invasiveness and unfavorable prognosis in breast cancer. Am J Clin Pathol. 1996;105:394–402.View ArticlePubMedGoogle Scholar
  28. Hunt NC, Douglas-Jones AG, Jasani B, Morgan JM, Pignatelli M. Loss of E-cadherin expression associated with lymph node metastases in small breast carcinomas. Virchows Arch. 1997;430:285–9.View ArticlePubMedGoogle Scholar
  29. Kowalski PJ, Rubin MA, Kleer CG. E-cadherin expression in primary carcinomas of the breast and its distant metastases. Breast Cancer Res. 2003;5:R217–22.View ArticlePubMedPubMed CentralGoogle Scholar
  30. Rodriguez FJ, Lewis-Tuffin LJ, Anastasiadis PZ. E-cadherin’s dark side: possible role in tumor progression. Biochim Biophys Acta. 2012;1826:23–31.PubMedPubMed CentralGoogle Scholar
  31. Nawrocki-Raby B, Gilles C, Polette M, Bruyneel E, Laronze JY, Bonnet N, Foidart JM, Mareel M, Birembaut P. Upregulation of MMPs by soluble E-cadherin in human lung tumor cells. Int J Cancer. 2003;105:790–5.View ArticlePubMedGoogle Scholar
  32. Brouxhon SM, Kyrkanides S, Teng X, Raja V, O’Banion MK, Clarke R, Byers S, Silberfeld A, Tornos C, Ma L. Monoclonal antibody against the ectodomain of E-cadherin (DECMA-1) suppresses breast carcinogenesis: involvement of theHER/PI3K/Akt/mTOR and IAP pathways. Clin Cancer Res. 2013;19:3234–46.View ArticlePubMedPubMed CentralGoogle Scholar
  33. Brouxhon SM, Kyrkanides S, Teng X, Athar M, Ghazizadeh S, Simon M, O’Banion MK, Ma L. Soluble E-cadherin: a critical oncogene modulating receptor tyrosinekinases, MAPK and PI3K/Akt/mTOR signaling. Oncogene. 2014;33:225–35.View ArticlePubMedGoogle Scholar
  34. Liang Z, Sun XY, Xu LC, Fu RZ. Abnormal expression of serum soluble E-cadherin is correlated with clinicopathological features and prognosis of breast cancer. Med Sci Monit. 2014;20:2776–82.PubMedPubMed CentralGoogle Scholar
  35. Hofmann G, Balic M, Dandachi N, Resel M, Schippinger W, Regitnig P, Samonigg H, Bauernhofer T. The predictive value of serum soluble E-cadherin levels in breast cancer patients undergoing preoperative systemic chemotherapy. Clin Biochem. 2013;46:1585–9.View ArticlePubMedGoogle Scholar

Copyright

© The Author(s) 2016

Advertisement