A Case-Control Study

Cancer Investigation

ISSN: 0735-7907 (Print) 1532-4192 (Online) Journal homepage: http://www.tandfonline.com/loi/icnv20

Candidate Gene Analysis of Breast Cancer in the Jordanian Population of Arab Descent: A CaseControl Study Laith N. AL-Eitan, Reem I. Jamous & Rame H. Khasawneh To cite this article: Laith N. AL-Eitan, Reem I. Jamous & Rame H. Khasawneh (2017): Candidate Gene Analysis of Breast Cancer in the Jordanian Population of Arab Descent: A Case-Control Study , Cancer Investigation, DOI: 10.1080/07357907.2017.1289217 To link to this article: http://dx.doi.org/10.1080/07357907.2017.1289217

Published online: 08 Mar 2017.

Submit your article to this journal

View related articles

View Crossmark data

Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=icnv20 Download by: [46.32.122.53]

Date: 08 March 2017, At: 13:02

CANCER INVESTIGATION http://dx.doi.org/./..

Candidate Gene Analysis of Breast Cancer in the Jordanian Population of Arab Descent: A Case-Control Study Laith N. AL-Eitana,b , Reem I. Jamousa,b , and Rame H. Khasawnehc a Department of Applied Biological Sciences, Jordan University of Science and Technology, Irbid, Jordan; b Department of Biotechnology and Genetic Engineering, Jordan University of Science and Technology, Irbid, Jordan; c Department of Hematopathology, King Hussein Medical Center (KHMC), Jordan Royal Medical Services (RMS), Amman, Jordan

ABSTRACT

ARTICLE HISTORY

This study aimed to investigate whether there are specific polymorphisms within six genes (BRCA1, BRCA2, TP53, DAPK1, MMP9 promoter, and TOX3) that are associated with breast cancer among the Jordanian population. Sequenom MassARRAY system was used to genotype 17 single nucleotide polymorphisms (SNPs) within these genes in 230 Jordanian breast cancer patients and 225 healthy individuals. Three SNPs (MMP9 (rs6065912), TOX3 (rs1420546), and DAPK1 (rs11141901) were found to be significantly associated with an increased risk of breast cancer (p < .05). This study is the first to provide evidence that genetic variation in MMP9, TOX3, and DAPK1 genes contribute to the development of breast cancer in the Jordanian population.

Received  August  Accepted  January 

Background Breast cancer refers to heterogeneous tumors that develop from breast tissue with inconstant prognosis and various degrees of aggressiveness, and these tumors have the capacity to attack adjacent tissues and metastasize to other organs (1, 2). Breast cancer is the most common female malignancy in Jordan with more than 1230 women diagnosed with the disease in 2012 (3). Although the exact causes of breast cancer are unknown, it is believed that the disease is induced by environmental cues as well was an individual’s genetic profile (4). With regard to genetics, an individual’s family history is one of the most important prognostic indicators of breast cancer. Such a connection was made early in human history and the familial clustering of breast cancer was first observed by the Ancient Romans several thousand years ago (5). However, Le Dran and Broca, two French surgeons, were the first to publish formal reports about the relationship between breast cancer and family history (6). Subsequent epidemiological studies showed that women who have first-degree relatives with breast cancer are having an increased susceptibility to developing the disease. The establishment of this direct relationship demonstrates the existence of certain genetic components that are

KEYWORDS

Breast cancer; Jordanian Arabs; genetic association; case-control study

associated with a higher risk of breast cancer. Although family history is considered to be the main known risk factor, hereditary breast cancer is estimated at only 5–10% of breast cancer cases (7, 8). As a result, the large majority of breast cancer cases are sporadic events. It has been found that women with breast cancer 1 (BRCA1) and breast cancer 2 (BRCA2) mutations are at a higher risk of developing the disease than others (9, 10). TP53 is another high-penetrance breast cancer susceptibility gene (11). Furthermore, a large portion of breast cancer patients were reported to have a mixture of low-penetrance risk alleles that were associated with an increased risk of breast cancer (12). The BRCA1 gene is positioned on chromosome 17q21 and comprises 24 exons that cover 70 kb of genomic DNA (9). In contrast, the BRCA2 gene is positioned on chromosome 13 (q12-13) and comprises 27 coding exons that span 84 kb of genome (10). BRCA1 and BRCA2 are considered to be tumor suppressor genes (13). In addition, these genes are involved in the repair of DNA damage caused by homologous recombination (14, 15), thus acting to prevent the development of tumors and to preserve genome integrity. In addition, it has been found that females with mutations in BRCA1 or BRCA2 genes have a higher susceptibility

CONTACT Laith N. AL-Eitan [email protected] Department of Applied Biological Sciences, Jordan University of Science and Technology, Irbid, Jordan. Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/icnv. ©  Taylor & Francis Group, LLC

2

L. N. AL-EITAN ET AL.

for breast and ovarian cancers, with rates as varied as 84% and 39% respectively (16–19). It has been reported by Ellisen and Haber (20) as well as Antoniou et al. (21) that the carrier frequency of BRCA1 and BRAC2 mutations is about 0.05 to 0.1% in general population. However, some BRCA1 mutations occur at relatively higher incidences in certain ethnicities. For example, although the founder mutation 185delAG (in exon 2 of the BRCA1 gene) was first found in Canadian kindreds, it has an incidence of 20% in Ashkenazi Jews and results in early onset of breast cancer. Similarly, mutation 5382insC (in exon 20 of the BRCA1 gene) accounted for approximately 22% of all reported mutations, and it was the most common mutation among Europeans, especially in Hungarian families with an increased incidence of breast cancer among their members (16). Other mutations within the BRCA1 gene have been reported as founder mutations in the Netherlands (2804delAA), Sweden (317ins5), and Italy (5083del19) (22). Similarly, the prevalence of BRCA2 mutations also differs between ethnic groups. For example, the 6174delT mutation is higher in Ashkenazi Jews but 999del5 is higher in Icelanders (23–26). The tumor protein 53 (TP53) gene is located on chromosome 17 (p13.1) and encompasses approximately 20 kb of genome containing 11 exons (27). In general, different molecular genetic studies showed that TP53 mutations can be found in both upstream and downstream regions of TP53 gene. The upstream region acts to prevent TP53 activation by blocking DNA damage and overexpression of oncogenes, while the downstream region acts to prevent cell cycle regulation by blocking the function of TP53 target genes (28). Many studies reported that there is an association between rs1042522 single nucleotide polymorphism (SNP) in exon 4 of the TP53 gene and breast cancer (29). This SNP has been repeatedly studied and is considered to be a prognostic marker associated with a low tumor grade in breast cancer (30–32). Death-associated protein kinase 1 (DAPK1) was first discovered in 1995 during experiments that aimed to recognize which genes contributed to apoptosis, or programmed cell death (33). The DAPK1 gene is positioned on chromosome 9 (q21.33), and is transcribed into 6.3 kb of mRNA (34). DAPK1 was first recognized as a tumor suppressor gene due to its capacity of inducing cell death (35, 36). Although DAPK1 has a primarily protective role in suppressing tumor activity, DAPK1 -associated cell death can occur due to a wide range

of damaging effects resulting from other diseases. It has been reported that the c.1-6531A>G SNP enhances the binding affinity of HOXB7 during the transcription process, leading to an altered rate of protein-binding and causing an increased affinity for HOXB7, which in turn downregulates DAPK1 transcription (37). The Matrix Metalloproteinases (MMPs) family consists of zinc-dependent endoproteinases and includes 26 members (38). MMP9 is a candidate metastasis-associated gene (39), and both its activity and expression level increase in malignant breast tumors, indicating that MMP9 acts as a useful marker for prognosis and may even be used as a diagnostic marker for breast cancer patients (40). It has been suggested that upregulation of this gene’s expression is also related to a poorer prognosis in breast cancer, and its expression has been associated with an infiltrating lobular breast cancer phenotype (41, 42). It has been shown that the rs3918242 SNP within the MMP9 promoter is linked to the severity of many diseases, including coronary atherosclerosis (43). In addition to tumor growth and invasion, MMP9 is also involved in carcinogenesis and angiogenesis (42). The TOX high-mobility group box family member 3 (TOX3) gene is located at chromosome 16q12.1 (44). The TOX3 gene is a non-histone chromatin and a member of the HMG-box protein family. The TOX3 gene plays an essential function in chromatin structure alteration, which, in turn, can be summarized as the twisting and unwinding of DNA to modulate its architecture. It has been shown that bone metastasis is associated with the amplified expression of TOX3 (45). Smid et al. clarified that TOX3, along with other genes, was expressed at various levels in breast cancer patients with bone metastasis compared with those who had suffered from relapses in other organs of their body (46). A genetic epidemiological study that used a population-based series of breast cancer patients suggested that a number of low-penetrance genes (e.g. TOX3) with additive effects may account for familial breast cancer (47). To the best of our knowledge, no genetic studies have investigated the association between breast cancer and various candidate genes (e.g., BRCA1, BRCA2, TP53, DAPK1, MMP9 promoter, and TOX3) in Jordanian females of Arab descent. Therefore, it is an essential step in the process of genetic analysis to examine the significance of reported breast cancer susceptibility genes among different populations. This study aimed

CANCER INVESTIGATION

to examine the genetic association of the chosen polymorphisms in the selected genes with breast cancer susceptibility and to establish a genetic profile of different candidate genes associated with breast cancer susceptibility in Jordan.

3

DNA genotyping

Samples that passed quantitative and quality control requirements were shipped to be genotyped in collaboration with the Australian Genome Research Facility (AGRF, Melbourne Node, Australia). DNA samples were genotyped using the Sequenom MassARRAY system (iPLEX GOLD) (Sequenom, San Diego, CA, USA) by the AGRF SNP group.

®

Material and methods Subjects

Ethical approval was obtained for this research from the Intuitional Review Board (IRB) at Jordan University of Science and Technology. Approval for collecting blood samples and clinical data from patients was also obtained from the Human Ethics Committee at the Jordanian Royal Medical Services (JRMS). All participants in this study signed a written informed consent form. A total of 5 mL of peripheral blood was aseptically withdrawn into ethylenediaminetetraacetic acid (EDTA)-coated blood collection tubes from 230 patients diagnosed with breast cancer. These patients were randomly recruited from the Breast Surgery Clinic at the JRMS. In addition, 225 unrelated healthy females, who hailed from an ethnically homogenous Jordanian population and had no breast cancer family history, were used as controls. The control group was recruited from the blood bank at JRMS. Both breast cancer patients and healthy individuals were ethnic-, gender-, and aged-matched. Clinical, demographic, and lifestyle data as well as diagnostic and drug information were collected for each patient using their medical records. These medical records were obtained by JRMS as part of their routine screening and testing process for all of their blood donors. Sample analysis

Genomic DNA was extracted from 455 blood samples using the commercially available Wizard Genomic DNA Purification Kit (Promega Corp., Madison, WI, USA) according to the manufacturer’s protocol. The Nano-Drop ND-1000 UV-Vis Spectrophotometer (BioDrop Ltd., Cambridge, UK) was used to measure the DNA concentration (as ng/µL) and purity (A260/280). In addition, 1.5 µl of DNA rehydration solution was used as a blank. Nuclease-free water was used to prepare diluted DNA samples with a concentration of 20 ng/μL (50–500 μL) and a final volume of 30 μL for each DNA sample.

®

Statistical and haplotype analysis

The Hardy–Weinberg Equilibrium (HWE) equation (p² + 2pq + q² = 1) was examined for all genetic markers in both groups using the Chi-square (χ ²) goodnessof-fit test. The genetic differences in allele frequencies and genotype distribution for each polymorphism of interest were compared between the two groups (breast cancer patients and healthy individuals) using the Chisquare test or the Fisher’s Exact test when appropriate. For markers that showed significant association at the single-marker level, the following three genetic models were considered for genotypic analysis: co-dominant, dominant, and recessive. The odds ratio (OR) was also calculated using binary logistic regression with 95% confidence intervals (CI). Statistical significance was set at a p value of .05. All analyses were carried out using the Statistical Package for Social Sciences, version 20 (SPSS Inc., Chicago, IL, USA) software. Finally, haplotype analysis was utilized using the Haploview program (version 4.2) to test for linkage disequilibrium (LD) and to allow for the analysis of LD blocks and haplotype. The number in each box corresponds to r2 values (multiplied by 100) between SNPs. The permutation test (adjusted p value) with 100,000 fold permutations (N = 455) was also computed using the Haploview program (version 4.2) for each SNP that showed significant association.

Results Sample characteristics

The study involved 230 Jordanian Arab patients diagnosed with breast cancer who were randomly chosen depending on their pathological records. All patients were females, and the average age (±SD) of the cohort was 53.906 ± 12.777 years with a median of

4


Table . Demographic and clinical characteristics for selected breast cancer patients and healthy individuals. Mean ± SD (%) Category Demographic details

Age (years) Gender Marital status

Level of education

Income

Age at first pregnancy Breast feeding Allergy Comorbidity

Clinical details

Alcohol Body mass index Family history of BC Contraceptive use Age of BC diagnosis Age at menarche (years) Age at menopause (years)

Subcategory Female Male Single Married Widowed Divorced Primary school High school Undergraduate degree Postgraduate degree  JDs Yes No Yes No None Hypertension Coronary artery disease Asthma Diabetes Yes No Yes No Yes No —

Breast Cancer Patients (cases)

Healthy individuals (controls)

. ± .  . . . . . . . . . . . . . . ± . . . . . . . . . . . . . ± . . . . . . ± . . ± . . ± .

. ± .  . . . . . . . . . . . . . . ± . . . . . . . . . . . . .±. .  . . ∗ N/A . ± . . ± .

∗ N/A: not applicable.

51 and a range of 24 to 95 years. Two hundred twentyfive matched females were also selected to act as controls, and they had an average age (±SD) of 50.806 ± 12.607 years, a median of 49, and their ages ranged from 24 to 90 years. There were no significant differences in age and gender between the breast cancer patients and the controls. A complete demographic and clinical data set for both patients (cases) and healthy individuals (controls) was obtained from their medical records as shown in Table 1. In addition, the clinical pathological data of all the selected patients is presented in Table 2. The candidate genes and the SNPs

In order to detect SNPs that could be associated with a high risk of breast cancer susceptibility in the Jordanian population, six candidate genes were selected to be tested in this study. These were mainly selected for their biological effects and involvement in breast cancer

pathogenesis, which included their role in the development and progression of this type of cancer. Depending on the information obtained from different accredited genome databases (NCBI, dbSNP, and HapMap), 17 SNPs were identified and recognized within these genes. The SNPs were selected according to their functional relevance, biological significance, and their locations within the selected genes of interest to achieve confirmed and broad coverage of each entire gene. Hardy—Weinberg equilibrium test

After performing the HWE test for the examined polymorphisms in both case and control sample sets, all the polymorphisms that met the HWE criteria were included in the genetic analysis for this study except for one SNP (rs80359688) in the BRCA2 gene, which was non-polymorphic. The minor alleles of the studied SNPs (for both cases and controls) and their frequencies are shown in Table 3.


Table . Histopathological characteristics of selected breast cancer patients. Mean ± SD (%)

Category Histologic type

Ductal carcinoma in-situ Invasive ductal carcinoma Lobular carcinoma in-situ Invasive lobular carcinoma Invasive tubular carcinoma Invasive mammary carcinoma Invasive mucinous carcinoma Pathological Histologic grade Grade  details Grade  Grade  Tumour size  cm Lymph nodes Yes involvement No ER status Positive Negative PR status Positive Negative HerNeu status Positive Negative

. . . . . . . . . . . . . . . . . . . . .

Quality control (QC)

The genotypes detected by the Sequenom MassARRAY system (iPLEX GOLD) for all 17 SNPs were very precise, with an average success rate of 100%. Water controls were clean, SNPs were in HWE, and no Mendelian errors were observed. All duplicated DNA samples and water controls had identical genotyping assignments. Genotype call rates ranged from 96 to 97%, and duplicate concordance rates were higher than

®

5

99%. The genotype discrepancy average (±SD) rate across the 17 loci was only 0.06% (±0.039%) in the whole cohort (455 subjects) as shown in Table 4. Association of SNP candidate genes with breast cancer

Genetic analyses of the alleles and frequencies among markers of the studied genes (BRCA1, BRCA2, TP53, DAPK1, MMP9, and TOX3) between breast cancer patients and healthy controls were conducted. Table 4 demonstrates the genotypic and allelic frequencies for all 455 samples. The significance was determined according to the p value, and the level of significance was taken as p < .05. The genotype frequencies of the rs1420546 SNP C/T in intron 4 of the TOX3 gene in chromosome 16 shows the highest association with breast cancer susceptibility. Moreover, its genotype frequency in the breast cancer patient group was significantly different from the genotype frequency in the control group, with the overall estimate of effects returning an OR of 2.64 (χ 2 (2, N = 455) = 30.463 p = .00000024). However, no significant differences were detected in the allele frequencies between breast cancer patients and controls (p = .263 for allele frequency) as shown in Table 5. The second SNP found to be associated with breast cancer susceptibility was rs6065912 G/A, which is located within the promoter region in the MMP9 gene on chromosome 20, with the overall estimate of effects returning an OR of 0.42 (χ ² (2, N = 455) = 17.669, p = .0001). The genotype and allele frequencies of the rs6065912 SNP in the patient group

Table . The minor allele frequencies and HWE p values for BRCA, BRAC, TP, DAPK, MMP promoter, and TOX SNPs in cases and controls. Cases (N =) Gene

SNP _ID

BRCA

rs rs rs rs rs rs rs rs rs rs rs rs rs rs rs rs rs

BRCA TP DAPK MMP promoter TOX3

Controls (N = )

MA

MAF (%)

HWE p value

MA

MAF (%)

HWE p value

A T T G C C G G C C A G T A G A C

. . . . . . N/A . . . . . . . . . .

. . . . . . N/A . . . . . . . . . .

A T T G C C G G C C A G T A G A C

. . . . . . N/A . . . . . . . . . .

. . . . . . N/A . . . . . . . . . .

MA: minor allele. MAF: minor allele frequency. HWE: Hardy—Weinberg equilibrium. N/A: not applicable.

6


Table . List of genes, their SNPs, positions, and genotyping data based on the whole cohort ( cases). Gene

Gene location

BRCA

q.

BRCA

q.

TP q. DAPK

q.

TOX MMP

q. q.

a

SNP_ID

Position

rs rs rs rs rs rs rs rs rs rs rs rs rs rs rs rs rs

                

b

c

SNP

SNP location

Discrepancy rate (%)

Call rate (%)

C>A C>T C>T A>G G>C Del G A>G A>C A>C G>C G>A A>G C>T A>G C>T G>C G>A

’ UTR Intron  Intron  Exon  Intron  Exon  Exon  Intron  Intron  Intron  Promoter Intron  Intron  Intron  Intron  Intron  Promoter

. . . . . . . . . . . . . . . . .

                

a Chromosome positions are based on NCBI Human Genome Assembly Build. b Ratio of the number of discordant genotypes to the number of duplicates. c Ratio of

the number of valid genotypes to the number of subjects genotyped. N =  at each locus.

were significantly different from those of the control group (p = .0001 for genotype and .001 for allele). Finally, the rs11141901 SNP A/G in the intron 2 region of DAPK1 on chromosome 9 shows a significant association with breast cancer susceptibility, with an overall estimate of effects returning an OR of 13.860 (χ 2 (2, N = 455) = 0.21, p = .001). On the other hand, no significant association was found (p = .112) in the allele frequencies between the case and control groups. Representative scatter plots of rs1420546, rs6065912, and rs11141901 SNPs were constructed using Sequenom data, and are shown in Figure 1. These three SNPs showed the strongest statistical evidence in terms of their association with breast cancer in the Jordanian population. However, after adjusting for multiple testing for the associated SNPs using the Haploview program with 100,000-fold permutation (N = 455) (version 4.2), only two SNPs (rs1420546 in TOX3 and rs6065912 in MMP9) showed significant p values (0.003 and 0.018 respectively). Genetic association analysis of the three SNP genotypes among breast cancer patients and controls was further carried out using different genetic and statistical methods, which included dominant, additive, and recessive genetic models for the three associated SNPs. Table 6 summarizes the genetic association analysis of the associated SNPs using these models. For the rs11141901 SNP in DAPK1 gene, the rare homozygous GG versus heterozygous GA category was found to be significantly associated with breast cancer susceptibility (p < .05). In contrast, no genetic

associations were found within the other two categories, which were the rare homozygous GG versus common homozygous AA and the heterozygous GA versus common homozygous AA (p > .05). Another rs6065912 SNP within MMP9 gene was not significantly associated with breast cancer susceptibility using both the rare homozygous AA versus heterozygous GA and the rare homozygous AA versus common homozygous GG categories, as they had p > .05. However, the heterozygous AG versus common homozygous AA category was significant, with p < .0001. Similarly, the heterozygous TC versus common homozygous TT category for the rs1420546 SNP within the TOX3 gene was found to be significant, with p < .0001. However, the rare homozygous CC versus heterozygous TC and rare homozygous AA versus common homozygous GG categories were not statistically significant (p > .05). Linkage disequilibrium and haplotype analysis

Haplotype genetic association analysis showed a strong linkage disequilibrium (D = 1) between nine SNPs in three genes that formed three blocks as shown in Table 7 and Figure 2. The first block was formed between two selected polymorphisms of BRCA2 gene (rs766173 and rs1799944), the second block was formed between five polymorphisms of BRCA1 (rs8176318, rs8176265, rs3737559, rs16940, and rs799905), and the third block was formed between two polymorphisms of DAPK1 (rs1041326 and rs1045042). The haplotype analysis showed a nominal significant


7

Table . Association of SNPs with breast cancer susceptibility. Cases vs. controls Gene BRCA

SNP ID rs

rs

rs

rs

rs

BRCA

rs

rs rs

TP

rs

rs

rs

DAPK

rs

rs

rs

MMP promoter

rs

Allele/genotype

Cases ()

Controls ()

Chi-square

p valuea

A C CC CA AA T C CC CT TT T C CC TC TT G A AA GA GG C G GG CG CC C A AA CA CC G GG G A AA GA GG C A AA CA CC C G GG CG CC A G GG AG AA G A AA GA GG T C CC CT TT A G GG GA AA G C CC

() .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () % () % () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .%

() .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () % () % () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .%

.

.

.

.

Adjusted p valueb

NS .

.

.

. NS

.

.

.

. NS

.

.

.

. NS

.

.

.

. NS

.

.

.

. NS

N/A N/A .

N/A N/A .

.

.

NS

NS .

.

.

. NS

.

.

.

. NS

.

.

.

. NS

.

.

.

.001 .

.

.

.

. NS

.

.

.

. NS

.

.

.

. (Continued on next page)

8


Table . (Continued) Cases vs. controls Gene

SNP ID

rs

TOX

rs

Allele/genotype

Cases ()

Controls ()

CG GG A G GG GA AA C T TT TC CC

() .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .%

() .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .% () .%

Chi-square

p valuea

Adjusted p valueb NS

.

.

.

.0001 .018

.

.

.

2.4e-7 .003

a Allelic and genotypic association p value was calculated using the Pearson’s Chi-square test for a × contingency table with  df for allelic and  df for genotypic comparisons; p < . is significant and presented in bold. b Permutation test (adjusted p value) with ,-fold permutations.

association between GC and the third block of DAPK1 and breast cancer susceptibility (p = .0375). Discussion Breast cancer is a complex disease that has several environmental and genetic factors involved in its etiology. Although both factors work with each other in

poorly understood mechanisms, these are implicated in the initiation, development, and progression of breast cancer. The polygenic inheritance model of breast cancer has been proposed by Antoniou et al. (21), and this model is considered to be the most descriptive model for explaining the mechanism of breast cancer’s genetic component. It is well known

Figure . Representative scatter plots from Sequenom data: each dot indicates the measurement for each sample. Panel (A) illustrates the scatter plot for the rs SNP in the TOX gene constructed from Sequenom data. TT genotype is represented by green; TC genotype is represented by yellow; CC genotype is represented by blue, and no call is indicated by red. Panel (B) illustrates the scatter plot for the rs SNP in the MMP gene constructed from Sequenom data. GG genotype is represented by green; GA genotype is represented by yellow; AA genotype is represented by blue, and no call is indicated by red. Panel (C) illustrates the scatter plot for the rs SNP in the DAPK gene constructed from Sequenom data. GG genotype is represented by green; GA genotype is represented by yellow; AA genotype is represented by blue, and no call is indicated by red.


9

Table . Genetic association analysis of the rs, rs, and rs polymorphisms in breast cancer patients and healthy individuals using different genetic models. Gene

SNP ID

Category test

DAPK

rs

MMP promoter

rs

TOX

rs

Odd ratio

% CI

Chi-square

. . . . . . . . .

.–. .–. .–. .–. .–. .–. .–. .–. .–.

. . . . . . . . .

Het (GA) vs. Common Hz (AA) Rare Hz (GG) vs. Het (GA) Rare Hz (GG) vs. Common Hz (AA) Het (GA) vs. Common Hz (GG) Rare Hz (AA) vs. Het (GA) Rare Hz (AA) vs. Common Hz (GG) Het (TC) vs. Common Hz (TT) Rare Hz (CC) vs. Het (TC) Rare Hz (CC) vs. Common Hz (TT)

a

a For significant association, Chi-square should be >. with p < ..

that the DNA repair mechanism is essential for preserving the integrity of the genome. Any defect in this DNA repair mechanism will cause genetic instability and breast cancer development. Even though there is a clear genetic structural component to breast cancer due to the fact that approximately 30% of breast cancers are caused by familial inheritance, only a fraction of the genes involved in breast cancer susceptibility have been determined. To date, it is not clear how genetic polymorphisms within these tumor suppressor and DNA repair genes could contribute to breast cancer susceptibility. With polymorphisms in the known high (BRAC1, BRAC2, and TP53) and intermediate (TOX3, DAPK1, and MMP3) penetrance susceptibility genes accounting for less than 50% of familial breast cancer patients, much remains to be clarified before our understanding can be completed. Recent genome wide association studies (GWAS) have started to classify numerous low-penetrance susceptibility alleles that may account for some of the unknown genetic susceptibility factors. Consequently, this study intended to examine the genetic association between breast cancer susceptibility and candidate SNPs in tumor suppressor and DNA repair genes. To achieve

this, 230 Jordanian women who were diagnosed with breast cancer as well as 225 age- and sex-matched healthy controls were engaged. The most promising SNPs were selected using information from relevant published literature based on their location within the gene, their clinical relevance, and their biological function. The role of TOX3 gene in breast cancer susceptibility

Although the TOX3 gene is predominantly expressed in human brain and breast tissue, it has minimal expression levels in breast tumors (48). In light of these observations, TOX3 is considered as a candidate tumor suppressor gene. It has been suggested that TOX3 is a putative high-mobility group (HMG) box motif nuclear protein of the TOX3 gene, which points toward its role as a transcription factor. Riaz et al. (49) reported that genetic polymorphisms within this gene could affect its level of expression. These types of genetic polymorphisms are involved in the regulation of TOX3 expression level and this gene has a prospective function as a tumor suppressor gene (49). On the other hand, it has been found that breast cancer patients

Table . Genetic association between the formed haplotypes and the risk of developing breast cancer. Haplotype

Frequency of block

Frequency ratio (case:control) (%)

Chi-square

p

AA CG

. .

Block : BRCA (rs and rs) ., . ., .

. .

. .

CCCAG ATCGC ATTGC CCCAC

. . . .

Block : BRCA (rs, rs, rs, rs, and rs) ., . ., . ., . ., .

. . . .

. . . .

AC GC GT

. . .

Block : DAPK (rs and rs) ., . ., . ., .

. . .

. . .

10


Figure . Genetic haplotype blocks. Linkage disequilibrium (LD) plots of genotyped polymorphisms in (A) BRCA, (B) BRCA, and (C) DAPK. Values in the boxes are D measures that indicate the extent of LD between two SNPs. Boxes without numbers have D’ = .

with TOX3 risk polymorphisms are at higher risk of developing lobular breast cancer (50, 51). It has been suggested that an increase in TOX3 mRNA expression contributes to the metastasis of breast tumors to the bone (46), and this increase is associated with an adverse outcome (50). For example, recent studies have suggested that TOX3 gene expression in breast cancer acts as a tumor promoter rather than a tumor suppressor (52), which has been previously reported as depending on the lower level of TOX3 expression (53). As a result, all of this data have suggested that the TOX3 gene may have dual and conflicting roles in the initiation, progression, and development of cancer. The genetic association between breast cancer and TOX3 was first identified using GWAS to examine 4398 European breast cancer patients and 4316 European controls (54). Another novel study using the Illumina Hap300 platform in a cohort of 1600 Icelandic patients diagnosed with breast cancer and 11,563 controls has shown that the TOX3 gene at 16q12 confirms susceptibility to estrogen-receptor-positive breast cancer (44). It has been found that the rs1420546 SNP in the TOX3 gene has a significant effect on developing estrogenreceptor-positive breast cancer and is related to distant metastasis (55). In the present study, the association between rs1420546 in the TOX3 gene and breast cancer susceptibility was confirmed (p = 2.4 e-7) and found to be consistent with earlier reports. Moreover, the permutation test indicated that this SNP is still significant (p =.003) with the application of 100,000-fold permutations. These findings indicate that the genetic variants

in TOX3 gene within the chromosome 16q12.1 could contribute to breast cancer development and progression in the Jordanian population, but such biological mechanisms need more clarification. The role of DAPK1 gene in breast cancer susceptibility

Various cell lines of human cancer, including B-cell lymphoma, breast, leukemia, bladder, and renal cell carcinoma-derived cell lines, expressed a lower expression levels of DAP-kinase mRNA and protein, both of which indicate its potential role in tumor suppression (56, 57). The association of a lower level of DAP-kinase expression with metastasis, recurrence, and prognosis of numerous cancers (e.g., B-cell malignancies, lung cancer, colon, bladder cancers, primary head and neck tumors, and multiple myeloma) were also reported (58). In this study, three SNPs within the DAPK1 gene were investigated, and only one DAPK1 SNP (rs11141901) was found to be associated with breast cancer susceptibility (p = .001). Results of a study by Natrajan et al. (59) were consistent with our findings, and they reported that the DAPK3 gene is a potential breast cancer candidate gene in the British population. The rs11141901 DAPK1 SNP is located in the intron region and may affect DNA splicing by altering its efficiency, which is one of the main steps in gene expression and important for its translation (60–63). In a study involving 99 Taiwanese patients with invasive breast cancer, a tissue microarray (TMA) was useful in


identifying the level of DAPK1 expression to investigate the status of DAPK1 and detect its role in breast cancer. No clear association was found between the various clinical variables (including the malignant tumor (TNM) stage, primary tumor staging age, histologic grading, estrogen receptor, and lymph node status) and DAPK1 expression. On the other hand, multivariate analysis revealed a significant association between TNM staging and the 5-year survival rate. These results indicate that DAPK1 expression has nothing to do with outcome prediction in breast cancer patients (64). In a French study, a highly significant correlation was found between DAP-kinase immune-staining and tumor size (p = .05), tumor grade (p = .009), PR expression (p = .001), Bcl-2 expression (p = .004), and ER expression (p = .002). Disease-free survival (DFS) was found to be significantly longer in the group of women whose tumors expressed a high level of DAP-kinase. To the contrary, DFS was shorter in women with low levels of DAP-kinase expression. In addition, it has been found that the expression of DAP-kinase protein in both primary cultures of breast epithelial cells and normal breast cells was sufficiently high (65). All of these studies indicate that DAP-kinase could be an independent and a novel prognosis marker for breast cancer. The role of MMP9 gene in breast cancer susceptibility

To date, there is insufficient information about the role of MMP9 in breast cancer risk. Among the two studied SNPs in this study (rs2250889 and rs6065912 within the MMP9 locus), we confirmed one breast cancer susceptibility loci (rs6065912) in the Jordanian population (p = .0001), and the A-rs6065912 allele exhibited significant association with breast cancer in general (p = .001). Moreover, the permutation test has indicated that this SNP is still significant (p = .018) after the application of 100,000-fold permutations. On the other hand, the other polymorphism of the MMP9 gene (rs2250889) was not significantly associated with breast cancer risk (p = .684) in the Jordanian population, nor was its minor allele (p = .166). MMP9 activity has been assessed in different studies and was found to be significantly higher in malignant breast cancers than in benign breast tumors (66). Furthermore, it has been reported that MMP9 serum levels in the circulating blood of women with benign breast tumors and healthy controls were significantly lower than MMP9 serum

11

levels in malignant breast cancer patients (44, 67). The results of this study indicate that the genetic variations that have an effect on MMP9 gene expression could contribute to breast cancer susceptibility. There are different MMP9 variants that have been determined, and several of these were found to be functional. Many studies have examined the contribution of MMP polymorphisms to carcinogenesis in general (68). Wang et al. (69) suggested that the expression of MMP9 is controlled by different biological mechanisms, including promoter methylation and microRNA inhibition. The rs6065912 SNP is located on the promoter region of MMP9 gene, which determines the expression of the gene. Alterations in this promoter’s activity can lead to a change in its expression levels (70) and may result in a variant that leads to reduction in the promoter activity of MMP9 or an increase in its expression. Nevertheless, further gene expression analysis is needed to deliver the critical evidence essential for the performance of a guided genotype association study. The role of BRCA1, BRCA2, and TP53 genes in breast cancer susceptibility

The three essential members of the high-risk breast cancer susceptibility gene family are BRCA1, BRCA2, and TP53. The incidence of early-onset breast cancer has been related to mutations in these three genes (71–73). According to different genetic studies, it has been shown that genetic polymorphisms in BRCA1 and BRCA2 are specific to each population and considered to be an ethnic marker. A wide range of founder and repeated mutations have been reported to be genetically associated with breast cancer susceptibility in diverse parts of the world, mainly in ethnically distinct and isolated populations (74, 75). In this study, none of the polymorphisms within these three genes (BRCA1, BRCA2, and TP53) differed significantly for allele or genotype frequencies between cases and controls. A possible clarification of the contradictory results between different published studies could be attributed to ethnicity, as each study involving these genes was conducted on different populations with diverse ethnic groups. Other significant factors that could lead to these conflicting results include family history and menstrual, menopausal, and reproductive status. Moreover, different tumor subtypes that are classified according to different expression levels of estrogen receptor (ER), progesterone receptor (PR), and human

12


epidermal growth factor receptor 2 (Her-2) could contribute to these diverse etiologic pathways. However, most of the studies in the Arab population are restricted by the fact that these are retrospective study designs with small sample sizes and short follow-up periods. The role of genetic haplotype in breast cancer susceptibility

In the current study, SNP haplotyping was carried out using the Haploview program (version 4.2) (76). Analysis of the results suggests that the DAPK1 haplotype GC (rs1041326 and rs1045042) is associated with an increased risk of breast cancer (p = .0375). Although there were two other blocks in linkage disequilibrium, none of the common BRCA1 (rs8176318, rs8176265, rs3737559, rs16940, and rs799905) and BRCA2 (rs766173 and rs1799944) haplotypes show an association with breast cancer. There is a probability that this haplotype is a genetic marker for a rare mutation among the Jordanian population. As a consequence, there is a potentially significant area of research for sequencing this region to detect the causative variant(s). Limitations Two hundred thirty breast cancer patients were included in the present study. Although this is a relatively large sample compared with other genetic studies among the Jordanian population, it is still relatively small compared with different population genetic studies conducted elsewhere in the world. However, it must be noted that this sample size constitutes almost a quarter of all annual breast cancer patients among Jordanians of both genders. Moreover, comparisons with the sample sizes utilized in the Western world are not feasible because of the differences in respective populations as well as the unique difficulties faced by the Arab world, which include a lack of government initiative, erroneous reporting, and reluctance to participate in academic research (77). A case-control study with a small sample number or a considerably diverse ethnicity distribution among patients or between patients and controls could lead to selection bias (78, 79). In this study, however, no bias is caused by population stratification, as the Jordanian Arab population is a relatively homogenous population. In addition, there were no statistically significant

differences between case and control groups in terms of age, outcome, level of education, marital status, and other basic demographic characteristics. Genotyping error in this study is unlikely to have occurred as the Sequenom MassARRAY system, which is characterized by being one of the most error-free, highthroughput, accurate, sensitive, and robust sequencing techniques used (80). Another potential limitation that involves gender differences was excluded in this study, as all the participants were females. Finally, the genotyping process for patients and controls was performed during the same period and under the same conditions.

®

Significance of study Although breast cancer has been the basis of much research in the Western world, the situation could not be more different in the Arab world. There is a great dearth of information on breast cancer in Arab populations because of the lack of research activity in this field. Sweileh et al. (81) reviewed all the literature on breast cancer in Arab populations and found that annual research productivity was negligible (81). Although during the past decade the Arab Republic of Egypt and the Kingdom of Saudi Arabia have begun to show academic interest in breast cancer, the rest of the Arab world still lags behind with regard to breast cancer research as less than 10% of the available studies focused on genetic factors (81). Moreover, there is evidence to indicate that the rates of breast cancer incidence have historically been lower among Arab women due to the unique reproductive patterns that result from high fertility and more breastfeeding (77, 81). However, it has been observed that these rates are changing for the worse because of the evolving lifestyles of Arab women (77). As a result, this study is novel in that it is contributing greatly to a nascent field, and the information contained in it could be used as a preventative measure in light of the increasing proportion of breast cancer in Arab women. Presently, the samples obtained over the course of this research will be the subject of future examination to ascertain association between SNPs and the phenotypic characteristics of breast cancer. Conclusions In conclusion, this study shows that genetic variants in high penetrance genes, such as DNA repair genes


(BRCA1 and BRCA2) and tumor suppressor genes (TP53), do not influence breast cancer development in a Jordanian population of Arab descent. However, it confirms the association between MMP9, TOX3, and DAPK1 and breast cancer as a result of their direct effects on the disease and statistically significant p values. Finally, it should be taken into consideration that this study was limited to a number of certain SNPs that have been reported by other studies or chosen based on their effects on increasing the risk of breast cancer. The probability of other SNPs within these genes cannot be excluded as breast cancer susceptibility genetic polymorphisms. Since it is the first study of its kind, further large-scale candidate gene studies that primarily target early-onset cases with longer observation periods should be undertaken to confirm the association between breast cancer and the candidate gene polymorphisms. Moreover, further genetic association studies could be applied to investigate other tagging SNPs associated with breast cancer susceptibility. Declaration of interest The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the article. Funding This study was funded by the Deanship of Research (RN: 20140204), Jordan University of Science and Technology.

References 1. Soini Y, Hurskainen T, Hoyhtya M, Oikarinen A, Autio-Harmainen H. 72 KD and 92 KD type IV collagenase, type IV collagen, and laminin mRNAs in breast cancer: a study by in situ hybridization. J Histochem Cytochem 1994;42:945–951. 2. Scorilas A, Karameris A, Arnogiannaki N, Ardavanis A, Bassilopoulos P, Trangas T, et al. Overexpression of matrixmetalloproteinase-9 in human breast cancer: a potential favourable indicator in node-negative patients. Br J Cancer 2001;84:1488–1496. 3. Parkin D, Bray F, Ferlay J, Pisani P. Global Cancer Statistics, 2002. Cancer J Clin 2005;55:74–108. 4. MacMahon B. A biological framework for the risk factors for breast cancer. Adv Oncol 1994;10:3–9. 5. Steel M, Thompson A, Clayton J. Genetic aspects of breast cancer. Br Med Bull 1991;47:504–518.

13

6. Eisinger F, Nogues C, Guinebretiere J, Peyrat J, Bardou V, Noguchi T, et al. Novel indications for BRCA1 screening using individual clinical and morphological features. Int J Cancer 1999;84:263–267. 7. Loman N, Johannsson O, Kristoffersson U, Olsson H, Borg A. Family history of breast and ovarian cancers and BRCA1 and BRCA2 Mutations in a population-based series of early-onset breast cancer. J Natl Cancer Inst 2001;93:1215– 1223. 8. Narod S, Foulkes W. BRCA1 and BRCA2: 1994 and beyond. Nat Rev Cancer 2004;4:665–676. 9. Miki Y, Swensen J, Shattuck-Eidens D, Futreal P, Harshman K, Tavtigian S, et al. A strong candidate for the breast and ovarian cancer susceptibility gene BRCA1. Science 1994;266:66–71. 10. Wooster R, Bignell G, Lancaster J, Swift S, Seal S, Mangion J, et al. Identification of the breast cancer susceptibility gene BRCA2. Nature 1995;378:789–792. 11. Fanale D, Amodeo V, Corsini L, Rizzo S, Bazan V, Russo A. Breast cancer genome-wide association studies: there is strength in numbers. Oncogene 2011;31:2121–2128. 12. Elematore I, Gonzalez-Hormazabal P, Reyes J, Blanco R, Bravo T, Peralta O, et al. Association of genetic variants at TOX3, 2q35 and 8q24 with the risk of familial and earlyonset breast cancer in a South-American population. Mol Biol Rep 2014;41:3715–3722. 13. Williams R. Structural consequences of a cancercausing BRCA1-BRCT missense mutation. J Biol Chem 2002;278:2630–2635. 14. Tutt A, Ashworth A. The relationship between the roles of BRCA genes in DNA repair and cancer predisposition. Trends Mol Med 2002;8:571–576. 15. Farmer H, McCabe N, Lord C, Tutt A, Johnson D, Richardson T, et al. Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy. Nature 2005;434:917–921. 16. Antoniou A, Pharoah P, Narod S, Risch H, Eyfjord J, Hopper J, et al. Average risks of breast and ovarian cancer associated with BRCA1 or BRCA2 mutations detected in case series unselected for family history: a combined analysis of 22 studies. Am J Human Genet 2003;72:1117–1130. 17. Chen X, Truong T, Weaver J, Bove B, Cattie K, Armstrong B, et al. Intronic alterations in BRCA1 and BRCA2: effect on mRNA splicing fidelity and expression. Human Mutat 2006;27:427–435. 18. Easton D, Ford D, Bishop D. Breast and ovarian cancer incidence in BRCA1-mutation carriers. Breast cancer linkage consortium. Am J Human Genet 1995;56:265. 19. Ford D, Easton D, Stratton M, Narod S, Goldgar D, Devilee P, et al. Genetic heterogeneity and penetrance analysis of the BRCA1 and BRCA2 genes in breast cancer families. Am J Human Genet 1998;62:676–689. 20. Ellisen LW, Haber DA. Hereditary breast cancer. Annu Rev Med 1998;49:425–436. 21. Antoniou A, Pharoah P, McMullan G, Day N, Stratton M, Peto J, et al. A comprehensive model for familial breast

14

22. 23.

24.

25.

26.

27.

28. 29.

30.

31.

32.

33.

34.

35.

36.

37.


cancer incorporating BRCA1, BRCA2 and other genes. Br J Cancer 2002;86:76–83. Cipollini G. Genetic alterations in hereditary breast cancer. Ann Oncol 2004;15:i7–i13. Roa B, Boyd A, Volcik K, Richards C. Ashkenazi Jewish population frequencies for common mutations in BRCA1 and BRCA2. Nat Genet 1996;14:185–187. Struewing J, Hartge P, Wacholder S, Baker S, Berlin M, McAdams M, et al. The risk of cancer associated with specific mutations of BRCA1 and BRCA2 among Ashkenazi Jews. New Engl J Med 1997;336:1401–1408. Fodor F, Weston A, Bleiweiss I, McCurdy L, Walsh M, Tartter P, et al. Frequency and carrier risk associated with common BRCA1 and BRCA2 mutations in Ashkenazi Jewish breast cancer patients. Am J Human Genet 1998;63: 45–51. Grzybowska E, Zientek H, Jasinska A, Rusin M, Kozlowski P, Sobczak K, et al. High frequency of recurrent mutations inBRCA1 andBRCA2 genes in Polish families with breast and ovarian cancer. Human Mutat 2000;16:482–490. Miller C, Mohandas T, Wolf D, Prokocimer M, Rotter V, Phillip Koeffler H. Human p53 gene localized to short arm of chromosome 17. Nature 1986;319:783–784. Guimaraes D, Hainaut P. TP53: a key gene in human cancer. Biochimie 2002;84:83–93. Dunning A, Healey C, Pharoah P, Teare M, Ponder B, Easton D. A systematic review of genetic polymorphisms and breast cancer risk. Cancer Epidemiol Biomarkers Prev 1999;8:843–854. Sjalander A, Birgander R, Hallmans G, Cajander S, Lenner P, Athlin L, et al. p53 polymorphisms and haplotypes in breast cancer. Carcinogenesis 1996;17:1313–1316. Tommiska J. Breast cancer patients with p53 Pro72 homozygous genotype have a poorer survival. Clin Cancer Res 2005;11:5098–5103. Toyama T, Zhang Z, Nishio M, Hamaguchi M, Kondo N, Iwase H, et al. Association of TP53 codon 72 polymorphism and the outcome of adjuvant therapy in breast cancer patients. Breast Cancer Res 2007;9:R34. Deiss L, Feinstein E, Berissi H, Cohen O, Kimchi A. Identification of a novel serine/threonine kinase and a novel 15kD protein as potential mediators of the gamma interferoninduced cell death. Genes Dev 1995;9:15–30. Feinstein E. The death domain: a module shared by proteins with diverse cellular functions. Trends Biochem Sci 1995;20:342–344. Bialik S, Kimchi A. The death-associated protein kinases: structure, function, and beyond. Annu Rev Biochem 2006;75:189–210. Eisenberg-Lerner A, Bialik S, Simon H, Kimchi A. Life and death partners: apoptosis, autophagy and the cross-talk between them. Cell Death Differ 2009;16:966–975. Raval A, Tanner S, Byrd J, Angerman E, Perko J, Chen S, et al. Downregulation of death-associated protein kinase 1 (DAPK1) in chronic lymphocytic leukemia. Cell 2007;129:879–890.

38. Mason J, Yancy H, Lashley K, Jett M, Day A. Comparative study of matrix metalloproteinase expression between African American and Caucasian Women. J Carcinog 2004;3:15. 39. Saleem M, Kweon M, Johnson J, Adhami V, Elcheva I, Khan N, et al. S100A4 accelerates tumorigenesis and invasion of human prostate cancer through the transcriptional regulation of matrix metalloproteinase 9. Proc Nat Acad Sci 2006;103:14825–14830. 40. Ranuncolo S, Armanasco E, Cresta C, Bal de Kier Joffe E, Puricelli L. Plasma MMP9 (92 kDa-MMP) activity is useful in the follow-up and in the assessment of prognosis in breast cancer patients. Int J Cancer 2003;106:745–751. 41. Jones J, Glynn P, Walker R. Expression of MMP-2 and MMP-9, their inhibitors, and the activator MT1-MMP in primary breast carcinomas. J Pathol 1999;189:161–168. 42. Grieu F, Li W, Iacopetta B. Genetic polymorphisms in the MMP-2 and MMP9 genes and breast cancer phenotype. Breast Cancer Res Treat 2004;88:197–204. 43. Zhang B, Ye S, Herrmann S, Eriksson P, de Maat M, Evans A, et al. Functional polymorphism in the regulatory region of gelatinase B gene in relation to severity of coronary atherosclerosis. Circulation 1999;99:1788–1794. 44. Stacey S, Manolescu A, Sulem P, Rafnar T, Gudmundsson J, Gudjonsson S, et al. Common variants on chromosomes 2q35 and 16q12 confer susceptibility to estrogen receptor– positive breast cancer. Nat Genet 2007;39:865–869. 45. James J, Evans A, Pinder S, Gutteridge E, Cheung K, Chan S, et al. Bone metastases from breast carcinoma: histopathological—radiological correlations and prognostic features. Br J Cancer 2003;89:660–665. 46. Smid M, Wang Y, Klijn J, Sieuwerts A, Zhang Y, Atkins D, et al. Genes associated with breast cancer metastatic to bone. J Clin Oncol 2006;24:2261–2267. 47. Antoniou A, Pharoah P, McMullan G, Day N, Ponder B, Easton D. Evidence for further breast cancer susceptibility genes in addition to BRCA1 and BRCA2 in a populationbased study. Genet Epidemiol 2001;21:1–18. 48. Dittmer S, Kovacs Z, Yuan S, Siszler G, Kogl M, Summer H, et al. TOX3 is a neuronal survival factor that induces transcription depending on the presence of CITED1 or phosphorylated CREB in the transcriptionally active complex. J Cell Sci 2010;124:252–260. 49. Riaz M, Berns E, Sieuwerts A, Ruigrok-Ritstier K, de Weerd V, Groenewoud A, et al. Correlation of breast cancer susceptibility loci with patient characteristics, metastasis-free survival, and mRNA expression of the nearest genes. Breast Cancer Res Treat 2011;133:843–851. 50. Gudmundsdottir E, Barkardottir R, Arason A, Gunnarsson H, Amundadottir L, Agnarsson B, et al. The risk allele of SNP rs3803662 and the mRNA level of its closest genes TOX3 and LOC643714 predict adverse outcome for breast cancer patients. BMC Cancer 2012;12:621. 51. Jones J, Chin S, Wong-Taylor L, Leaford D, Ponder B, Caldas C, et al. TOX3 mutations in breast cancer. PLoS One 2013;8:e74102.


52. Seksenyan A, Kadavallore A, Walts A, de la Torre B, Berel D, Strom S, et al. TOX3 is expressed in mammary ER+ epithelial cells and regulates ER target genes in luminal breast cancer. BMC Cancer 2015;15:22. 53. Cowper-Sal·lari R, Zhang X, Wright J, Bailey S, Cole M, Eeckhoute J, et al. Breast cancer risk-associated SNPs modulate the affinity of chromatin for FOXA1 and alter gene expression. Nat Genet 2012;44:1191–1198. 54. Baynes C, Healey C, Pooley K, Scollen S, Luben R, Thompson D, et al. Common variants in the ATM, BRCA1, BRCA2, CHEK2 and TP53 cancer susceptibility genes are unlikely to increase breast cancer risk. Breast Cancer Res 2007;9:R27. 55. Tapper W, Hammond V, Gerty S, Ennis S, Simmonds P, Collins A, et al. The influence of genetic variation in thirty selected genes on the clinical characteristics of early onset breast cancer. Breast Cancer Res 2008;10:R108. 56. Cohen O. DAP-kinase is a Ca2+/calmodulin-dependent, cytoskeletal-associated protein kinase, with cell deathinducing functions that depend on its catalytic activity. EMBO J 1997;16:998–1008. 57. Kissil J, Feinstein E, Cohen O, Jones P, Tsai Y, Knowles M, et al. DAP-kinase loss of expression in various carcinoma and B-cell lymphoma cell lines: possible implications for role as tumor suppressor gene. Oncogene 1997;15: 403–407. 58. Cohen O, Kimchi A. DAP-kinase: from functional gene cloning to establishment of its role in apoptosis and cancer. Cell Death Differ 2001;8:6–15. 59. Natrajan R, Mackay A, Lambros M, Weigelt B, Wilkerson P, Manie E, et al. A whole-genome massively parallel sequencing analysis of BRCA1 mutant oestrogen receptor-negative and -positive breast cancers. J Pathol 2012;227:29–41. 60. Alberts B, Johnson A, Lewis J, Raff M, Roberts K, Walters P. Molecular biology of the cell (4th ed.). New York and London: Garland Science; 2002. 61. Court M. A pharmacogenomics primer. J Clin Pharmacol 2007;47:1087–1103. 62. Guttmacher A, Collins F, Guttmacher A, Collins F. Genomic medicine—a primer. New Engl J Med 2002;347:1512–1520. 63. Wolfram S. Principles of nucleic acid structure. New York: Springer; 1984. 64. Baquero M, Lostritto K, Gustavson M, Bassi K, Appia F, Camp R, et al. Evaluation of prognostic and predictive value of microtubule associated protein tau in two independent cohorts. Breast Cancer Res 2011;13:R85. 65. Levy D. Death-associated protein kinase loss of expression is a new marker for breast cancer prognosis. Clin Cancer Res 2004;10:3124–3130. 66. Easton F, Pooley A, Dunning A, Pharoah P, Thompson D, Ballinger D, et al. Genome-wide association study identifies novel breast cancer susceptibility loci. Nature 2007;447:1087–1093.

15

67. Hunter D, Kraft P, Jacobs K, Cox D, Yeager M, Hankinson S, 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. 68. Lei H, Hemminki K, Altieri A, Johansson R, Enquist K, Hallmans G, et al. Promoter polymorphisms in matrix metalloproteinases and their inhibitors: few associations with breast cancer susceptibility and progression. Breast Cancer Res Treat 2006;103:61–69. 69. Wang Z, Lu S, Liu C, Zhao B, Pei K, Tian L, et al. Expressional and epigenetic alterations of placental matrix metalloproteinase 9 in preeclampsia. Gynecol Endocrinol 2010;26:96–102. 70. Zhang B, Henney A, Eriksson P, Hamsten A, Watkins H, Ye S. Genetic variation at the matrix metalloproteinase9 locus on chromosome 20q12.2-13.1. Human Genet 1999;105:418–423. 71. Langston A, Malone K, Thompson J, Daling J, Ostrander E. BRCA1 mutations in a population-based sample of young women with breast cancer. New Engl J Med 1996;334:137– 142. 72. Krainer M, Silva-Arrieta S, FitzGerald M, Shimada A, Ishioka C, Kanamaru R, et al. Differential contributions of BRCA1 and BRCA2 to early-onset breast cancer. New Engl J Med 1997;336:1416–1422. 73. Børresen-Dale A. TP53 and breast cancer. Human Mutat 2003;21:292–300. 74. Neuhausen S. Ethnic differences in cancer risk resulting from genetic variation. Cancer 1999;86:2575–2582. 75. Weihrauch M, Bader M, Lehnert G, Wittekind C, Tannapfel A, Wrbitzky R. Carcinogen-specific mutation pattern in the p53 tumour suppressor gene in UV radiationinduced basal cell carcinoma. Int Arch Occup Environ Health 2002;75:272–276. 76. Barrett J, Fry B, Maller J, Daly M. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 2004;21:263–265. 77. Corbex M, Harford J. Perspectives on breast cancer in Arab populations. Lancet 2013;14:e582. 78. Rothman N, Wacholder S, Caporaso N, Garcia-Closas M, Buetow K, Fraumeni J. The use of common genetic polymorphisms to enhance the epidemiologic study of environmental carcinogens. Biochimica et Biophysica Acta Rev Cancer 2001;1471:C1–C10. 79. Wacholder S. Population stratification in epidemiologic studies of common genetic variants and cancer: quantification of bias. J Natl Cancer Inst 2000;92:1151–1158. 80. Jurinke C, Van Den Boom D, Cantor C, Köster H. Automated genotyping using the DNA MassArrayTM technology. PCR Mutat Detect Protocols 2002:179–192. 81. Sweileh W, Zyoud S, Al-Jabi S, Sawalha A. Contribution of Arab countries to breast cancer research: comparison with non-Arab Middle Eastern countries. BMC Women’s Health 2015;15:1–7.