# Role of Four ABC Transporter Genes in Pharmacogenetic Susceptibility to Breast Cancer in Jordanian Patients

Authors: Laith N. AL-Eitan; Doaa M. Rababa’h; Mansour A. Alghamdi; Rame H. Khasawneh
Journal: Journal of Oncology
 (2019)

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## Abstract

Breast cancer pharmacogenetics is increasingly being explored due to chemotherapy resistance among certain classes of patients. The ATP binding cassette (ABC) transporter genes have been previously implicated in breast cancer progression and drug response. In the present study, single nucleotide polymorphisms (SNPs) from theABCC1,ABCC2,ABCB1, andABCG2 genes were screened in breast cancer patients and healthy volunteers from the Jordanian-Arab population. Only theABCB1SNPs showed a significant association with BC in Jordanian-Arab patients, and theABCB1 SNP rs2032582 exhibited a strong genotypic association with BC. With regard to the clinical characteristics of BC, theABCC2SNPs rs2273697 and rs717620 were found to be significantly associated with age at breast cancer diagnosis and breastfeeding status, while theABCB1 SNP rs1045642 was significantly associated with age at breast cancer diagnosis. In terms of pathological characteristics, theABCC1SNP rs35628 and theABCB1 SNP rs2032582 were significantly associated with tumor size, theABCC2SNP rs2273697 was significantly associated with estrogen receptor status, and theABCG2SNP rs2231142 was significantly associated with axillary lymph node status. In this current study, we assume that significant genetic variants within the ABC superfamily may increase the risk of breast cancer among Jordanian women. Furthermore, these variants might be responsible for worse BC prognosis.


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## Body

## 1. Introduction

Breast cancer (BC) is the most common female malignancy in the majority of countries [1]. Arab populations suffer from lower but steadily rising BC incidence rates compared to their American and European counterparts, and the clinical characteristics of the disease also differ between the aforementioned populations [2]. Such population-level differences in BC predisposition have been attributed to genetics and have been widely investigated, with different mutations having different levels of association with BC [3]. Compounding this issue is the fact that Arab BC genetics are not well researched, and much less is known about the genes involved in BC progression and drug response in Arab patients [4].The ATP binding cassette (ABC) transporters comprise seven subfamilies of membrane proteins that facilitate the transport and modulate the effects of a wide range of drugs and their metabolites [5, 6]. Remarkably, an overexpression of certain ABC transporters in cancer cell lines resulted in multidrug resistance (MDR) and a potential failure of chemotherapy [7, 8]. For example, theABCC1 gene, also known as multidrug resistance-associated protein 1 (MRP1), is associated with worsened prognoses in a wide range of tumors, while theABCC2 gene was found to contribute to drug resistance [9, 10]. Likewise, theABCB1gene is highly polymorphic and induces chemoresistance by preventing drug accumulation in cancer cells [7]. In addition, theABCG2 gene, also known as the breast cancer resistance protein (BCRP), is responsible for the transport of many conventional chemotherapeutics and causes MDR in various cancer cells [11].In the present study, four SNPs of ABC transporter genes, namelyABCC1,ABCC2,ABCB1, andABCG2, were screened in Jordanian Arabs with and without breast cancer. Previous reports have indicated that these genes play a critical role in increasing tumor risk, especially in breast cancer [9, 11]. The aim of this study is to determine whether the aforementioned genes play a significant role in Jordanian breast cancer patients.


## 2. Materials and Methods

### 2.1. Ethical Approval and Conduct

The present study was given ethical approval by the Institutional Review Board (IRB) at Jordan University of Science and Technology. Written informed consent was obtained from all participants in this study before blood sample withdrawal.


### 2.2. Study Population and Design

The study cohort consisted of 222 women diagnosed with breast cancer as well as 218 healthy matched volunteers. All participants were recruited from the Jordanian population and were of Arab descent. 5 ml of blood were withdrawn from each participant into EDTA tubes and refrigerated until DNA extraction.


### 2.3. Genomic Extraction and Genotyping

Genomic DNA was extracted from a total of 440 blood samples using the Wizard® Genomic DNA Purification Kit (Promega, USA). Extracted DNA was evaluated in terms of concentration (ng/μl) and purity (A260/280) quantity using the Nano-Drop ND-1000 UV-Vis Spectrophotometer (BioDrop, UK). DNA samples were then loaded onto an agarose gel to confirm product quality. Samples that met our requirements were diluted using nuclease-free water for a final concentration of 20 ng/μl and a final volume of 30 μl. Genotyping was carried out by the Melbourne node of the Australian Genome Research Facility (AGRF) using the Sequenom MassARRAY® system (iPLEX GOLD) (Sequenom, San Diego, CA, USA).


### 2.4. Denomination of Genotypic-Phenotypic Correlation

In this study, several clinical and pathological features of BC were investigated in correlation with the studied variants. Clinical and pathological information for patients was collected from their medical records. P values were selected to estimate the association between SNPs and risk of BC. The analyses were done per genotype.


### 2.5. Statistical Analysis

Case-control analyses were carried out using different statistical software. Allelic and genotypic frequencies were calculated using the Hardy-Weinberg equilibrium (HWE) equation (Court lab - HW calculator) (http://www.oege.org/software/hwe-mr-calc.html). The Statistical Package for the Social Sciences (SPSS), version 25.0 (SPSS, Inc., Chicago, IL) was used to calculate the p values that allowed discrimination between cases and controls in association with the genotype. It also facilitated the analysis of the different genotype models. On the other hand, genotype-phenotype assessment was performed using the Chi-Square test and ANOVA tests [12]. P value denoted statistical significance if they were less than 0.05.


## 2.1. Ethical Approval and Conduct

The present study was given ethical approval by the Institutional Review Board (IRB) at Jordan University of Science and Technology. Written informed consent was obtained from all participants in this study before blood sample withdrawal.


## 2.2. Study Population and Design

The study cohort consisted of 222 women diagnosed with breast cancer as well as 218 healthy matched volunteers. All participants were recruited from the Jordanian population and were of Arab descent. 5 ml of blood were withdrawn from each participant into EDTA tubes and refrigerated until DNA extraction.


## 2.3. Genomic Extraction and Genotyping

Genomic DNA was extracted from a total of 440 blood samples using the Wizard® Genomic DNA Purification Kit (Promega, USA). Extracted DNA was evaluated in terms of concentration (ng/μl) and purity (A260/280) quantity using the Nano-Drop ND-1000 UV-Vis Spectrophotometer (BioDrop, UK). DNA samples were then loaded onto an agarose gel to confirm product quality. Samples that met our requirements were diluted using nuclease-free water for a final concentration of 20 ng/μl and a final volume of 30 μl. Genotyping was carried out by the Melbourne node of the Australian Genome Research Facility (AGRF) using the Sequenom MassARRAY® system (iPLEX GOLD) (Sequenom, San Diego, CA, USA).


## 2.4. Denomination of Genotypic-Phenotypic Correlation

In this study, several clinical and pathological features of BC were investigated in correlation with the studied variants. Clinical and pathological information for patients was collected from their medical records. P values were selected to estimate the association between SNPs and risk of BC. The analyses were done per genotype.


## 2.5. Statistical Analysis

Case-control analyses were carried out using different statistical software. Allelic and genotypic frequencies were calculated using the Hardy-Weinberg equilibrium (HWE) equation (Court lab - HW calculator) (http://www.oege.org/software/hwe-mr-calc.html). The Statistical Package for the Social Sciences (SPSS), version 25.0 (SPSS, Inc., Chicago, IL) was used to calculate the p values that allowed discrimination between cases and controls in association with the genotype. It also facilitated the analysis of the different genotype models. On the other hand, genotype-phenotype assessment was performed using the Chi-Square test and ANOVA tests [12]. P value denoted statistical significance if they were less than 0.05.


## 3. Results

### 3.1. ABC Transporter Variants and Their Minor Allele Frequencies (MAF)

Table1 displays the SNPs of theABCC1,ABCC2,ABCB1, andABCG2 candidate genes. All of the polymorphic SNPs were tested for minor allele frequencies (MAF) and HWE p values in both the cases and controls (Table 1).Table 1
Minor allele frequencies among breast cancer patients and healthy controls and the HWEc p value of ABC gene polymorphisms.
Gene SNP ID SNP positiona Cases (n = 222) Controls (n = 218) MAb MAFc HWEd MAb MAFc HWEd p-value p-value ABCC1 rs35626 16076758 T 0.38 0.3 T 0.41 0.12 rs35628 16077249 G 0.1 0.049 G 0.11 0.27 rs4148351 16076711 T 0.16 0.037 T 0.2 N/A  ABCC2 rs2273697 99804058 A 0.25 N/A A 0.24 0.089 rs3740065 99845936 G 0.23 N/A G 0.21 0.066 rs717620 99782821 T 0.12 0.75 T 0.13 0.38  ABCB1 rs1045642 87509329 T 0.35 0.025 T 0.43 0.026 rs1128503 87550285 A 0.36 0.039 A 0.44 0.074 rs2032582 87531302 T 0.03 0.4591 T 0.01 0.615  ABCG2 rs2231142 88131171 T 0.04 0.552 T 0.04 0.572 aChromosome positions are based on NCBI Human Genome Assembly Build. bMA: minor allele. cMAF: minor allele frequency. dHWE: Hardy–Weinberg equilibrium. N/A: not applicable.


### 3.2. Association between ABC Transporter SNPs and Breast Cancer (BC)

The allelic and genotypic frequencies of the ABC transporter SNPs were determined for both cases and controls (Table2). All threeABCB1 SNPs were found to be significantly associated with BC in Jordanian patients, with rs1045642, rs1128503, and rs2032582 having p values of 0.01164587, 0.01610842, and 0.03565022, respectively. Figure 1 shows a representative scatter pattern for rs1045642 ofABCB1. In contrast, only the rs2032582 SNP ofABCB1 showed a strong genotypic association with BC (p value = 1e-8, OR =6.72, 95% CI = 4.27 to 10.57). rs2032582 is a triallelic polymorphism comprising the A, C, and T (minor) alleles (the homozygous TT variant was not estimated in the current study population). None of the other investigated SNPs showed any significant correlation with BC, as all the allelic and genotypic frequencies were greater than 0.05 (Table 2).Table 2
Association of the investigated ABCC1, ABCC2, ABCB1, and ABCG2 SNPs and breast cancer (BC).
Gene SNP ID Allelic and Genotypic Frequencies in Cases and Controls Allele/Genotype Cases Controls P-value∗ Chi-square (n = 222) (n = 218) ABCC1 rs35626 G 283(0.65) 256 (0.59) 0.073 3.214 T 155 (0.35) 180 (0.41) GG 95 (0.43) 81(0.37) 0.216 3.063 GT 93 (0.42) 94(0.43) TT 31 (0.14) 43 (0.2) rs35628 A 394 (0.9) 388 (0.89) 0.788 0.072 G 44 (0.1) 46 (0.11) AA 180(0.82) 175(0.81) 0.820 0.395 AG 34(0.16) 38(0.18) GG 5(0.02) 4(0.02) rs4148351 C 369(0.84) 346(0.8) 0.082 3.021 T 69 (0.16) 88 (0.2) CC 160 (0.73) 138 (0.64) 0.068 5.374 CT 49 (0.22) 70(0.32) TT 10 (0.05) 9(0.04)  ABCC2 rs2273697 G 332(0.75) 331(0.76) 0.778 0.079 A 108 (0.25) 103 (0.24) AA 13 (0.06) 17(0.08) 0.412 1.773 GA 82 (0.37) 69(0.32) GG 125 (0.57) 131(0.6) rs3740065 A 341(0.77) 345(0.79) 0.478 0.503 G 101(0.23) 91(0.21) AA 131(0.59) 141(0.65) 0.285 2.51 AG 79(0.36) 63(0.29) GG 11(0.05 14(0.06) s717620 C 387(0.88) 377(0.87) 0.285 2.51 T 55(0.12) 57 (0.13) CC 170(0.77) 165(0.76) 0.928 0.149 CT 47(0.21) 47(0.22) TT 4(0.02) 5(0.02)  ABCB1 rs1045642 C 288(0.65) 248(0.57) 0.012 6.364 T 152(0.35) 186(0.43) CC 102(0.46) 79(0.36) 0.063 5.499 CT 84(0.38) 90(0.41) TT 34(0.15) 48(0.22) rs1128503 A 278(0.64) 242(0.56) 0.016 5.791 G 158(0.36) 192(0.44) AA 36(0.17) 49(0.23) 0.074 5.189 GA 86(0.39) 94(0.43) GG 96(0.44) 74(0.34) rs2032582 A 144(0.33) 174(0.43) 0.035 6.668 C 284(0.65) 252(0.58) T 12(0.03) 6(0.01) AA 29(0.13) 41(0.19) 1e-8 44.386 CA 82(0.37) 90(0.42) CC 97(0.44) 79(0.37) TA 49(0.02) 2(0.0093) TC 8(0.04) 4(0.02)  ABCG2 rs2231142 T 17(0.04) 16(0.04) 0.902 0.015 G 425 (0.96) 418 (0.96) GG 204(0.92) 201(0.93) 0.899 0.016 GT 17(0.08) 16(0.07) P value <0.05 was considered as significant.Figure 1
Scatter plot for rs1045642 withinABCB1 gene. Each Dot represents a sample while different genotypes are indicated with different colors.Further genetic analyses were carried out to test for the association of different genetic models with BC. Table3 summarizes three different genetic models and the chi-squared value for each. TheABCG2 gene was excluded from the analysis because it expressed only two genotypes. For theABCC1SNP rs4148351, Het (CT) versus Common Hz (CC) was found to be associated with BC in Jordanian Arabs (χ2 = 5.33; p value <0.05). Similarly, for theABCB1 SNP rs1128503, the Rare Hz (AA) versus Common Hz (GG) model was related to BC in Jordanian Arabs (χ2 =4.52; p value <0.05). No such association was found for any of theABCC2 SNPs (Table 3).Table 3
Genetic association analysis for the ABCC1, ABCC2, ABCB1, and ABCG2 SNPs using different genetic models.
Gene SNP ID Category Test Odds Ratio 95% CI Chi square∗ ABCC1 rs35626 Het (GT) vs. Common Hz (GG) 0.84 0.56-1.27 0.65 Rare Hz (TT) vs. Het (GT) 0.73 0.42-1.25 1.31 Rare Hz (TT) vs. Common Hz (GG) 0.61 0.36-1.06 3.04 rs35628 Het (AG) vs. Common Hz (AA) 0.87 0.52-1.44 0.29 Rare Hz (GG) vs. Het (AG) 1.4 0.35-5.63 0.22 Rare Hz (GG) vs. Common Hz (AA) 1.22 0.32-4.6 0.08 rs4148351 Het (CT) vs. Common Hz (CC) 0.6 0.39-0.93 5.33 Rare Hz (TT) vs. Het (AG) 1.58 0.6-4.19 0.88 Rare Hz (TT) vs. Common Hz (CC) 0.96 0.38-2.43 0.01  ABCC2 rs2273697 Het (GA) vs. Common Hz (GG) 1.55 0.71-3.42 1.21 Rare Hz (AA) vs. Het (GA) 0.8 0.54-1.2 1.14 Rare Hz (AA) vs. Common Hz (GG) 1.25 0.58-2.68 0.32 rs3740065 Het (GA) vs. Common Hz (AA) 1.35 0.9-2.03 2.08 Rare Hz (GG) vs. Het (GA) 0.63 0.27-1.48 1.16 Rare Hz (GG) vs. Common Hz (AA) 0.85 0.37-1.93 0.16 rs717620 Het (CT) vs. Common Hz (CC) 0.97 0.61-1.53 0.02 Rare Hz (TT) vs. Het (CT) 0.8 0.2-3.17 0.1 Rare Hz (TT) vs. Common Hz (CC) 0.78 0.2-2.94 0.14  ABCB1 rs1045642 Het (CT) vs. Common Hz (CC) 0.72 0.48-1.1 2.32 Rare Hz (TT) vs. Het (CT) 0.85 0.5-1.43 0.39 Rare Hz (TT) vs. Common Hz (CC) 0.61 0.37-1.03 3.46 rs1128503 Het (GA) vs. Common Hz (AA) 1.25 0.74-2.1 0.68 Rare Hz (GG) vs. Het (GA) 1.42 0.93-2.16 2.65 Rare Hz (GG) vs. Common Hz (AA) 1.77 1.04-2.99 4.52 ∗ For significant association χ2 should be >3.84 with P<0.025. CI indicates confidence interval.


### 3.3. Association between ABC Transporter SNPs and Major Prognostic Factors of Breast Cancer (BC)

Certain clinical and pathological characteristics of BC serve as major prognostic factors for the disease that are exploited in the process of treatment selection. None of theABCC1SNPs showed any significant association with the clinical characteristics of BC, but theABCC2SNPs rs2273697 and rs717620 were found to be significantly associated with age at breast cancer diagnosis (p value = 0.042) and breastfeeding status (p value = 0.05), respectively (Table 4). Meanwhile, theABCC1SNP rs35628 was associated with the pathological characteristic of tumor size (p value = 0.014), while theABCC2SNP rs2273697 was significantly associated with estrogen receptor status (p value = 0.013) (Table 4).Table 4
Association between different ABCC1 and ABCC2 SNP genotypes and the clinicopathological characteristics of breast cancer (BC).
Clinical characteristics ABCC1 ABCC2 rs35626GG vs GT vs TT rs35628AA vs AG vs GG rs4148351CC vs CT vs TT rs2273697AA vs AG vs GG rs3740065AA vs AG vs GG rs717620CC vs CT vs TT Body mass index∗∗ 0.535 0.116 0.068 0.813 0.461 0.084  Age at first pregnancy∗∗ 0.990 0.624 0.358 0.381 0.921 0.458  Age at BC diagnosis∗∗ 0.311 0.352 0.198 0.042 0.194 0.104  Allergy∗ 0.808 0.824 0.867 0.501 0.324 0.065  Age at menarche∗∗ 0.219 0.824 0.373 0.820 0.747 0.611  Breastfeeding status∗ 0.284 0.117 0.761 0.439 0.340 0.005  Age at menopause∗∗ 0.437 0.665 0.373 0.115 0.155 0.251  Family history∗ 0.669 0.605 0.762 0.472 0.891 0.415  Comorbidity∗ 0.764 0.967 0.976 0.130 0.741 0.140  Smoking∗ 0.237 0.287 0.163 0.320 0.406 0.362  Pathological characteristics  Progesterone receptor status∗ 0.292 0.516 0.244 0.610 0.823 0.423  Estrogen receptor status∗ 0.730 0.550 0.562 0.013 0.839 0.125  HER2∗ 0.146 0.500 0.330 0.441 0.226 0.842  IHC profile∗ 0.013 0.838 0.260 0.381 0.775 0.270  Tumor differentiation∗ 0.754 0.940 0.963 0.768 0.718 0.431  Axillary lymph nodes∗ 0.113 0.184 0.817 0.138 0.989 0.213  Tumor stage∗ 0.491 0.751 0.665 0.748 0.999 0.357  Histology classification∗ 0.963 0.502 0.348 0.301 0.294 0.661  Tumor size∗∗ 0.888 0.014 0.968 0.720 0.576 0.922  Lymph node involvement∗ 0.694 0.944 0.794 0.165 0.339 0.528 ∗ Pearson’s chi-squared test was used to determine genotype-phenotype association. ∗ ∗ Analysis of variance (ANOVA) test was used to determine genotype-phenotype association. P value <0.05 was considered as significant.Likewise, rs1045642 was the onlyABCB1 SNP to be significantly associated with a clinical characteristic of BC, namely, age at breast cancer diagnosis (p value = 0.029) (Table 5). In contrast, rs2032582 was the onlyABCB1 SNP to show significant association with a pathological characteristic of BC, namely, tumor size (p value = 0.03) (Table 5). TheABCG2SNP rs2231142 was found to be significantly associated with axillary lymph node status (p value = 0.001) but not with any clinical characteristic (Table 5).Table 5
Association between different ABCB1 and ABCG2 SNP genotypes and the clinicopathological characteristics of breast cancer (BC).
Clinical characteristics ABCB1 ABCG2 rs2032582A vs C vs T rs1128503AA vs AG vs GG rs1045642CC vs CT vs TT rs2231142GG vs GT Body mass index∗∗ 0.298 0.383 0.180 0.164  Age at first pregnancy∗∗ 0.212 0.326 0.815 0.490  Age at BC diagnosis∗∗ 0.931 0.924 0.029 0.592  Allergy∗ 0.310∗ 0.331 0.169 0.511  Age at menarche∗∗ 0.508 0.525 0.115 0.947  Breastfeeding status∗ 0.708 0.291 0.665 0.553  Age at menopause∗∗ 0.746 0.258 0.676 0.563  Family history∗ 0.585 0.626 0.469 0.481  Comorbidity∗ 0.350 0.347 0.751 0.341  Smoking∗ 0.462 0.365 .303 0.429  Pathological characteristics  Progesterone receptor status∗ 0.375 0.555 0.268 0.244  Estrogen receptor status∗ 0.470 0.480 0.299 0.312  HER2∗ 0.712 0.886 0.835 0.560  IHC profile∗ 0.186 0.645 0.160 0.606  Tumor differentiation∗ 0.429 0.632 0.595 0.926  Axillary lymph nodes∗ 0..373 0.718 0.847 0.001  Tumor stage∗ 0.700 0.705 0.723 0.722  Histology classification∗ 0.488 0.498 0.602 0.648  Tumor size∗∗ 0.030 0.032 0.556 0.249  Lymph node involvement∗ 0.021 0..056 0.417 0.381 ∗ Pearson’s chi-squared test was used to determine genotype-phenotype association. ∗ ∗ Analysis of variance (ANOVA) test was used to determine genotype-phenotype association. P value <0.05 was considered as significant.


### 3.4. Association between ABC Transporter SNPs and Immunohistochemistry (IHC) Profiles of Breast Cancer (BC)

Different combinations of the progesterone receptor, estrogen receptor, and Her2/neu expression molecular markers gives rise to three different immunohistochemistry profiles: Luminal A, Luminal B, and Triple Negative. These profiles and their correlation with the investigated SNPs are displayed in Tables4 and 5. Only theABCC1SNP rs35626 was found to be significantly correlated with the different IHC profiles (p value = 0.013).


## 3.1. ABC Transporter Variants and Their Minor Allele Frequencies (MAF)

Table1 displays the SNPs of theABCC1,ABCC2,ABCB1, andABCG2 candidate genes. All of the polymorphic SNPs were tested for minor allele frequencies (MAF) and HWE p values in both the cases and controls (Table 1).Table 1
Minor allele frequencies among breast cancer patients and healthy controls and the HWEc p value of ABC gene polymorphisms.
Gene SNP ID SNP positiona Cases (n = 222) Controls (n = 218) MAb MAFc HWEd MAb MAFc HWEd p-value p-value ABCC1 rs35626 16076758 T 0.38 0.3 T 0.41 0.12 rs35628 16077249 G 0.1 0.049 G 0.11 0.27 rs4148351 16076711 T 0.16 0.037 T 0.2 N/A  ABCC2 rs2273697 99804058 A 0.25 N/A A 0.24 0.089 rs3740065 99845936 G 0.23 N/A G 0.21 0.066 rs717620 99782821 T 0.12 0.75 T 0.13 0.38  ABCB1 rs1045642 87509329 T 0.35 0.025 T 0.43 0.026 rs1128503 87550285 A 0.36 0.039 A 0.44 0.074 rs2032582 87531302 T 0.03 0.4591 T 0.01 0.615  ABCG2 rs2231142 88131171 T 0.04 0.552 T 0.04 0.572 aChromosome positions are based on NCBI Human Genome Assembly Build. bMA: minor allele. cMAF: minor allele frequency. dHWE: Hardy–Weinberg equilibrium. N/A: not applicable.


## 3.2. Association between ABC Transporter SNPs and Breast Cancer (BC)

The allelic and genotypic frequencies of the ABC transporter SNPs were determined for both cases and controls (Table2). All threeABCB1 SNPs were found to be significantly associated with BC in Jordanian patients, with rs1045642, rs1128503, and rs2032582 having p values of 0.01164587, 0.01610842, and 0.03565022, respectively. Figure 1 shows a representative scatter pattern for rs1045642 ofABCB1. In contrast, only the rs2032582 SNP ofABCB1 showed a strong genotypic association with BC (p value = 1e-8, OR =6.72, 95% CI = 4.27 to 10.57). rs2032582 is a triallelic polymorphism comprising the A, C, and T (minor) alleles (the homozygous TT variant was not estimated in the current study population). None of the other investigated SNPs showed any significant correlation with BC, as all the allelic and genotypic frequencies were greater than 0.05 (Table 2).Table 2
Association of the investigated ABCC1, ABCC2, ABCB1, and ABCG2 SNPs and breast cancer (BC).
Gene SNP ID Allelic and Genotypic Frequencies in Cases and Controls Allele/Genotype Cases Controls P-value∗ Chi-square (n = 222) (n = 218) ABCC1 rs35626 G 283(0.65) 256 (0.59) 0.073 3.214 T 155 (0.35) 180 (0.41) GG 95 (0.43) 81(0.37) 0.216 3.063 GT 93 (0.42) 94(0.43) TT 31 (0.14) 43 (0.2) rs35628 A 394 (0.9) 388 (0.89) 0.788 0.072 G 44 (0.1) 46 (0.11) AA 180(0.82) 175(0.81) 0.820 0.395 AG 34(0.16) 38(0.18) GG 5(0.02) 4(0.02) rs4148351 C 369(0.84) 346(0.8) 0.082 3.021 T 69 (0.16) 88 (0.2) CC 160 (0.73) 138 (0.64) 0.068 5.374 CT 49 (0.22) 70(0.32) TT 10 (0.05) 9(0.04)  ABCC2 rs2273697 G 332(0.75) 331(0.76) 0.778 0.079 A 108 (0.25) 103 (0.24) AA 13 (0.06) 17(0.08) 0.412 1.773 GA 82 (0.37) 69(0.32) GG 125 (0.57) 131(0.6) rs3740065 A 341(0.77) 345(0.79) 0.478 0.503 G 101(0.23) 91(0.21) AA 131(0.59) 141(0.65) 0.285 2.51 AG 79(0.36) 63(0.29) GG 11(0.05 14(0.06) s717620 C 387(0.88) 377(0.87) 0.285 2.51 T 55(0.12) 57 (0.13) CC 170(0.77) 165(0.76) 0.928 0.149 CT 47(0.21) 47(0.22) TT 4(0.02) 5(0.02)  ABCB1 rs1045642 C 288(0.65) 248(0.57) 0.012 6.364 T 152(0.35) 186(0.43) CC 102(0.46) 79(0.36) 0.063 5.499 CT 84(0.38) 90(0.41) TT 34(0.15) 48(0.22) rs1128503 A 278(0.64) 242(0.56) 0.016 5.791 G 158(0.36) 192(0.44) AA 36(0.17) 49(0.23) 0.074 5.189 GA 86(0.39) 94(0.43) GG 96(0.44) 74(0.34) rs2032582 A 144(0.33) 174(0.43) 0.035 6.668 C 284(0.65) 252(0.58) T 12(0.03) 6(0.01) AA 29(0.13) 41(0.19) 1e-8 44.386 CA 82(0.37) 90(0.42) CC 97(0.44) 79(0.37) TA 49(0.02) 2(0.0093) TC 8(0.04) 4(0.02)  ABCG2 rs2231142 T 17(0.04) 16(0.04) 0.902 0.015 G 425 (0.96) 418 (0.96) GG 204(0.92) 201(0.93) 0.899 0.016 GT 17(0.08) 16(0.07) P value <0.05 was considered as significant.Figure 1
Scatter plot for rs1045642 withinABCB1 gene. Each Dot represents a sample while different genotypes are indicated with different colors.Further genetic analyses were carried out to test for the association of different genetic models with BC. Table3 summarizes three different genetic models and the chi-squared value for each. TheABCG2 gene was excluded from the analysis because it expressed only two genotypes. For theABCC1SNP rs4148351, Het (CT) versus Common Hz (CC) was found to be associated with BC in Jordanian Arabs (χ2 = 5.33; p value <0.05). Similarly, for theABCB1 SNP rs1128503, the Rare Hz (AA) versus Common Hz (GG) model was related to BC in Jordanian Arabs (χ2 =4.52; p value <0.05). No such association was found for any of theABCC2 SNPs (Table 3).Table 3
Genetic association analysis for the ABCC1, ABCC2, ABCB1, and ABCG2 SNPs using different genetic models.
Gene SNP ID Category Test Odds Ratio 95% CI Chi square∗ ABCC1 rs35626 Het (GT) vs. Common Hz (GG) 0.84 0.56-1.27 0.65 Rare Hz (TT) vs. Het (GT) 0.73 0.42-1.25 1.31 Rare Hz (TT) vs. Common Hz (GG) 0.61 0.36-1.06 3.04 rs35628 Het (AG) vs. Common Hz (AA) 0.87 0.52-1.44 0.29 Rare Hz (GG) vs. Het (AG) 1.4 0.35-5.63 0.22 Rare Hz (GG) vs. Common Hz (AA) 1.22 0.32-4.6 0.08 rs4148351 Het (CT) vs. Common Hz (CC) 0.6 0.39-0.93 5.33 Rare Hz (TT) vs. Het (AG) 1.58 0.6-4.19 0.88 Rare Hz (TT) vs. Common Hz (CC) 0.96 0.38-2.43 0.01  ABCC2 rs2273697 Het (GA) vs. Common Hz (GG) 1.55 0.71-3.42 1.21 Rare Hz (AA) vs. Het (GA) 0.8 0.54-1.2 1.14 Rare Hz (AA) vs. Common Hz (GG) 1.25 0.58-2.68 0.32 rs3740065 Het (GA) vs. Common Hz (AA) 1.35 0.9-2.03 2.08 Rare Hz (GG) vs. Het (GA) 0.63 0.27-1.48 1.16 Rare Hz (GG) vs. Common Hz (AA) 0.85 0.37-1.93 0.16 rs717620 Het (CT) vs. Common Hz (CC) 0.97 0.61-1.53 0.02 Rare Hz (TT) vs. Het (CT) 0.8 0.2-3.17 0.1 Rare Hz (TT) vs. Common Hz (CC) 0.78 0.2-2.94 0.14  ABCB1 rs1045642 Het (CT) vs. Common Hz (CC) 0.72 0.48-1.1 2.32 Rare Hz (TT) vs. Het (CT) 0.85 0.5-1.43 0.39 Rare Hz (TT) vs. Common Hz (CC) 0.61 0.37-1.03 3.46 rs1128503 Het (GA) vs. Common Hz (AA) 1.25 0.74-2.1 0.68 Rare Hz (GG) vs. Het (GA) 1.42 0.93-2.16 2.65 Rare Hz (GG) vs. Common Hz (AA) 1.77 1.04-2.99 4.52 ∗ For significant association χ2 should be >3.84 with P<0.025. CI indicates confidence interval.


## 3.3. Association between ABC Transporter SNPs and Major Prognostic Factors of Breast Cancer (BC)

Certain clinical and pathological characteristics of BC serve as major prognostic factors for the disease that are exploited in the process of treatment selection. None of theABCC1SNPs showed any significant association with the clinical characteristics of BC, but theABCC2SNPs rs2273697 and rs717620 were found to be significantly associated with age at breast cancer diagnosis (p value = 0.042) and breastfeeding status (p value = 0.05), respectively (Table 4). Meanwhile, theABCC1SNP rs35628 was associated with the pathological characteristic of tumor size (p value = 0.014), while theABCC2SNP rs2273697 was significantly associated with estrogen receptor status (p value = 0.013) (Table 4).Table 4
Association between different ABCC1 and ABCC2 SNP genotypes and the clinicopathological characteristics of breast cancer (BC).
Clinical characteristics ABCC1 ABCC2 rs35626GG vs GT vs TT rs35628AA vs AG vs GG rs4148351CC vs CT vs TT rs2273697AA vs AG vs GG rs3740065AA vs AG vs GG rs717620CC vs CT vs TT Body mass index∗∗ 0.535 0.116 0.068 0.813 0.461 0.084  Age at first pregnancy∗∗ 0.990 0.624 0.358 0.381 0.921 0.458  Age at BC diagnosis∗∗ 0.311 0.352 0.198 0.042 0.194 0.104  Allergy∗ 0.808 0.824 0.867 0.501 0.324 0.065  Age at menarche∗∗ 0.219 0.824 0.373 0.820 0.747 0.611  Breastfeeding status∗ 0.284 0.117 0.761 0.439 0.340 0.005  Age at menopause∗∗ 0.437 0.665 0.373 0.115 0.155 0.251  Family history∗ 0.669 0.605 0.762 0.472 0.891 0.415  Comorbidity∗ 0.764 0.967 0.976 0.130 0.741 0.140  Smoking∗ 0.237 0.287 0.163 0.320 0.406 0.362  Pathological characteristics  Progesterone receptor status∗ 0.292 0.516 0.244 0.610 0.823 0.423  Estrogen receptor status∗ 0.730 0.550 0.562 0.013 0.839 0.125  HER2∗ 0.146 0.500 0.330 0.441 0.226 0.842  IHC profile∗ 0.013 0.838 0.260 0.381 0.775 0.270  Tumor differentiation∗ 0.754 0.940 0.963 0.768 0.718 0.431  Axillary lymph nodes∗ 0.113 0.184 0.817 0.138 0.989 0.213  Tumor stage∗ 0.491 0.751 0.665 0.748 0.999 0.357  Histology classification∗ 0.963 0.502 0.348 0.301 0.294 0.661  Tumor size∗∗ 0.888 0.014 0.968 0.720 0.576 0.922  Lymph node involvement∗ 0.694 0.944 0.794 0.165 0.339 0.528 ∗ Pearson’s chi-squared test was used to determine genotype-phenotype association. ∗ ∗ Analysis of variance (ANOVA) test was used to determine genotype-phenotype association. P value <0.05 was considered as significant.Likewise, rs1045642 was the onlyABCB1 SNP to be significantly associated with a clinical characteristic of BC, namely, age at breast cancer diagnosis (p value = 0.029) (Table 5). In contrast, rs2032582 was the onlyABCB1 SNP to show significant association with a pathological characteristic of BC, namely, tumor size (p value = 0.03) (Table 5). TheABCG2SNP rs2231142 was found to be significantly associated with axillary lymph node status (p value = 0.001) but not with any clinical characteristic (Table 5).Table 5
Association between different ABCB1 and ABCG2 SNP genotypes and the clinicopathological characteristics of breast cancer (BC).
Clinical characteristics ABCB1 ABCG2 rs2032582A vs C vs T rs1128503AA vs AG vs GG rs1045642CC vs CT vs TT rs2231142GG vs GT Body mass index∗∗ 0.298 0.383 0.180 0.164  Age at first pregnancy∗∗ 0.212 0.326 0.815 0.490  Age at BC diagnosis∗∗ 0.931 0.924 0.029 0.592  Allergy∗ 0.310∗ 0.331 0.169 0.511  Age at menarche∗∗ 0.508 0.525 0.115 0.947  Breastfeeding status∗ 0.708 0.291 0.665 0.553  Age at menopause∗∗ 0.746 0.258 0.676 0.563  Family history∗ 0.585 0.626 0.469 0.481  Comorbidity∗ 0.350 0.347 0.751 0.341  Smoking∗ 0.462 0.365 .303 0.429  Pathological characteristics  Progesterone receptor status∗ 0.375 0.555 0.268 0.244  Estrogen receptor status∗ 0.470 0.480 0.299 0.312  HER2∗ 0.712 0.886 0.835 0.560  IHC profile∗ 0.186 0.645 0.160 0.606  Tumor differentiation∗ 0.429 0.632 0.595 0.926  Axillary lymph nodes∗ 0..373 0.718 0.847 0.001  Tumor stage∗ 0.700 0.705 0.723 0.722  Histology classification∗ 0.488 0.498 0.602 0.648  Tumor size∗∗ 0.030 0.032 0.556 0.249  Lymph node involvement∗ 0.021 0..056 0.417 0.381 ∗ Pearson’s chi-squared test was used to determine genotype-phenotype association. ∗ ∗ Analysis of variance (ANOVA) test was used to determine genotype-phenotype association. P value <0.05 was considered as significant.


## 3.4. Association between ABC Transporter SNPs and Immunohistochemistry (IHC) Profiles of Breast Cancer (BC)

Different combinations of the progesterone receptor, estrogen receptor, and Her2/neu expression molecular markers gives rise to three different immunohistochemistry profiles: Luminal A, Luminal B, and Triple Negative. These profiles and their correlation with the investigated SNPs are displayed in Tables4 and 5. Only theABCC1SNP rs35626 was found to be significantly correlated with the different IHC profiles (p value = 0.013).


## 4. Discussion

In the present study, four ABC transporter genes were screened in female BC patients and healthy volunteers from Jordan. Three SNPs from each of theABCC1, ABCC2,andABCB1genes and one SNP from theABCG2 gene were investigated for their association with BC in patients of Jordanian-Arab descent.TheABCC1 (MRP1)gene has been previously reported as being a predictor of hematological toxicity in BC patients undergoing certain chemotherapy regimens [13]. It has also been found to be involved in MDR development in cases of neuroblastoma [14]. Moreover,ABCC1 expression was found to be increased in children with acute lymphoblastic leukemia, andABCC1gene induction resulted in worsened disease-free and overall survival rates [15, 16]. Our results show that none of the three investigatedABCC1 SNPs showed any significant association with the clinical and pathological characteristics of BC. However, we found that theABCC1SNP rs35626 was significantly associated with different immunohistochemistry (IHC) profiles in Jordanian-Arab patients.Similar toABCC1, theABCC2gene is involved in decreased recurrence-free survival in BC patients receiving tamoxifen [17]. Nuclear expression ofABCC2 in BC cells was also found to be associated with worsened clinical outcome [18]. Our findings showed that theABCC2SNP rs2273697 was significantly associated with age at breast cancer diagnosis. Furthermore, rs2273697 was in correlation with estrogen receptor status for genotype association, patients were categorized according to the expression of estrogen receptor (positive versus negative) and tested with regard to their genotypes. However, in this study only gender was matched for the analysis. In addition, rs717620 was associated with breastfeeding status.ThreeABCB1 SNPs rs1045642, rs1128503, and rs2032582 have been suggested to play a role in altered doxorubicin pharmacokinetics in Asian BC patients [19]. In the present study, all three aforementionedABCB1SNPs were significantly associated with BC in Jordanian Arabs. Moreover, theABCB1 SNPs rs1045642 and rs2032582 were significantly associated with age at breast cancer diagnosis and tumor size, respectively.Overexpression of theABCG2gene was implicated in developing flavopiridol resistance in BC cells [20]. The homozygous genotype (CC) of theABCG2 SNP rs2231142 of theABCG2gene resulted in significantly reduced intestinal transport activity compared to the wildtype (AA) [21]. In Kurdish BC patients, the A allele of the rs2231142 SNP may be a risk factor for BC progression, while the C allele was associated with poorer responses to anthracyclines and paclitaxel [22]. In contrast, the homozygous (CC) genotype of theABCG2 SNP rs2231142 was significantly associated with longer progression-free survival in Han Chinese BC patients [23]. In the present study, theABCG2SNP rs2231142 was found to be significantly associated with axillary lymph node status in Jordanian BC patients.Conclusively, screening certain ABC transporter genes in BC patients and healthy volunteers from the Jordanian-Arab population revealed a number of interesting observations. Perhaps the most important finding was that theABCB1SNPs were the only variants to be significantly associated with BC in Jordanian Arabs.



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*Source: 6425708-2019-07-17.xml*