The Pharmacogenomics Journal https://doi.org/10.1038/s41397-018-0016-6

ARTICLE

Associations between genetic polymorphisms of membrane transporter and prognosis after chemotherapy: meta-analysis and finding from Seoul Breast Cancer Study (SEBCS)

1 1 1 1 2 3 4 Ji-Eun Kim ● Jaesung Choi ● JooYong Park ● Chulbum Park ● Se Mi Lee ● Seong Eun Park ● Nan Song ● 5 6 4,7 8 1,4,9 10 Seokang Chung ● Hyuna Sung ● Wonshik Han ● Jong Won Lee ● Sue K. Park ● Mi Kyung Kim ● 4,7 9,11 1,4,9,12 1,4,9 Dong-Young Noh ● Keun-Young Yoo ● Daehee Kang ● Ji-Yeob Choi

Received: 7 June 2017 / Revised: 13 October 2017 / Accepted: 4 December 2017 © Macmillan Publishers Limited, part of Springer Nature 2018

Abstract Membrane transporters can be major determinants of the pharmacokinetic profiles of anticancer drugs. The associations between genetic variations of ATP-binding cassette (ABC) and solute carrier (SLC) genes and cancer survival were investigated through a meta-analysis and an association study in the Seoul Breast Cancer Study (SEBCS). Including the SEBCS, the meta-analysis was conducted among 38 studies of genetic variations of transporters on various cancer survivors. 1234567890();,: The population of SEBCS consisted of 1 338 breast cancer patients who had been treated with adjuvant chemotherapy. A total of 7 750 SNPs were selected from 453 ABC and/or SLC genes typed by an Affymetrix 6.0 chip. ABCB1 rs1045642 was associated with poor progression-free survival in a meta-analysis (HR = 1.33, 95% CI: 1.07–1.64). ABCB1, SLC8A1, and SLC12A8 were associated with breast cancer survival in SEBCS (Pgene < 0.05). ABCB1 rs1202172 was differentially associated with survival depending on the chemotherapy (Pinteraction = 0.035). Our finding provides suggestive associations of membrane transporters on cancer survival.

Introduction chemotherapy has been demonstrated in breast cancer sur- vival, the heterogeneity of response among patients still Breast cancer is the second most common cancer in Korean exists [3–5]. Such inter-individual differences can be elu- women, and an increasing trend has been seen in the mor- cidated by genetic variations of pharmacogenes that are tality of breast cancer [1, 2]. Although the effect of adjuvant involved in drug absorption, distribution, metabolism and excretion (ADME) [6]. Application of genome-wide association studies Electronic supplementary material The online version of this article (GWAS) in pharmacogenomics has been increased since (https://doi.org/10.1038/s41397-018-0016-6) contains supplementary 2007. Pharmacogenomic information could be used for material, which is available to authorized users.

* Ji-Yeob Choi Institute, Bethesda MD USA [email protected] 7 Department of Surgery, Seoul National University College of Medicine, Seoul, Korea 1 Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, Korea 8 Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea 2 College of Pharmacy Chonnam National University, Gwangju, Korea 9 Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Korea 3 College of Pharmacy, Duksung Women’s university, Seoul, Korea 10 Division of Cancer Epidemiology and Management, National 4 Cancer Research Institute, Seoul National University, Cancer Center, Goyang, Korea Seoul, Korea 11 The Armed Forces Capital Hospital, Seongnam, Korea 5 Division for New Health Technology Assessment, National Evidence-based Healthcare Collaborating Agency, Seoul, Korea 12 Institute of Environmental Medicine, Seoul National University Medical Research Center, Seoul, Korea 6 Division of Cancer Epidemiology and Genetics, National Cancer J-E Kim et al. making practical decisions on drug prescriptions for an 346 records identified 472 records identified through PubMed searching through EMBASE searching individual patient [7, 8]. However, GWAS in pharmaco- (Jan, 2000 – Dec, 2016) (Jan, 2000 – Dec, 2016) genomics has potential limitations on sample size, replica- tion of findings and understanding of drug response 572 records remained after duplicates removed mechanisms [7, 9]. Previously, a two-stage GWAS on 274 records removed disease-free survival (DFS) in breast cancer stratified by Not original article or meta-analysis (n=107) tumor subtypes in the Seoul Breast Cancer Study (SEBCS) Unrelated topic (n=160) Not accessible (n=7) was conducted. Although two loci close to the methe- 298 records screened on the nyltetrahyfrofolate synthetase (MTHFS) and H3 histone base of abstract and title 206 records excluded (H3K27Ac) detected from GWAS were associated No chemotherapy fi or antitumor agent (n=47) with DFS in response to treatment, it is dif cult to under- Not for human study (n=35) No transporter genes (n=54) stand the mechanisms of inter-individual differences in drug Abstract without full-text (n=70) Full-text articles assessed response from the GWAS in SEBCS [10]. for eligibility (n=92) In this study, we mainly focused on drug transporters 55 full-text articles excluded involved in drug absorption and elimination. There are two No qualified data (n=55) major subfamilies of transporters: ATP-binding cassette 37 studies met (ABC) and solute carrier (SLC). The ABC transporters are the inclusion criteria efflux pumps that are involved in the movement of intra- The current study (SEBCS) cellular substrates using ATP hydrolysis energy, often included 38 studies included against the concentration gradient. The SLC transporters are in the meta-analysis involved in the uptake of substrates depending on con- centration gradient [11–13]. Impaired delivery of drug can Fig. 1 Flow chart summarizing the literature search and selection process for inclusion of studies result in poor absorption and, increased elimination of drug and can affect drug sensitivity due to genetic alterations of transporter genes [14]. (d) no chemotherapy or antitumor agent (n = 47); (e) not for The aim of this study was to investigate the association human study (n = 35); (f) no transporter (n = 54); (g) between genetic polymorphisms of ABC and SLC trans- abstract without full text (n = 70); (h) no qualified data (n porters and breast cancer survival among patients who had = 55). A total of 38 studies including SEBCS were con- been treated with adjuvant chemotherapy. First, we con- sidered eligible for the meta-analysis. All the reviewed ducted a systematic review and meta-analysis to summarize articles in the present study were limited to English lan- previously conducted genetic association studies. Second, guage reports (Fig. 1). we investigated the association between genetic variations of transporter genes and breast cancer survival among Data extraction and outcomes for meta-analysis patients who had received adjuvant chemotherapy based on GWAS in SEBCS. The following data were extracted: author, year, ethnicity, gene, variant, type of cancer, regimen of chemotherapy, type of outcome and estimates (hazard ratio (HR) and 95% Methods confidence interval (95% CI)) by three reviewers (JEK, CBP, and JYC). If one study was conducted separately by Search strategies and selection criteria for meta- the type of regimen or ethnicity, the study was separated as analysis (a) and (b) [15–19]. The outcomes were classified as overall survival (OS), A systematic literature search was performed in PubMed progression-free survival (PFS), disease-free survival and EMBASE from January 2000 to December 2016 using (DFS), and time-to-progression (TTP). OS is defined as the the following terms: “transporter” (“ABC” OR “SLC”) time from randomization to death from any cause. PFS is AND “cancer [title]” AND (“pharmacogenetics” OR defined as time from randomization until disease progres- “pharmacogenomics” OR “SNP”) AND (“therapy” OR sion or death from any cause. DFS is defined as the time “treatment” OR “drug” OR “medicine”) AND (“survival” from randomization until recurrence of tumor or death from OR “response” OR “outcome” OR “prognosis”). any cause [20]. TTP is defined as the time from treatment After removing duplicates in the electronic databases, the start to disease progression [21]. following study exclusion criteria was applied for 572 stu- The results were extracted according to the following dies: (a) not original article or meta-analysis (n = 107); (b) genetic models: additive, dominant, and codominant unrelated topic (n = 160); (c) not accessible articles (n = 7); (homozygote and heterozygote). In the additive model, the Associations between genetic polymorphisms of membrane transporter genes and prognosis after. . . risk of disease depends on increasing the number of the in the previous study [23]. SNPs were mapped to genes minor alleles. In the dominant model, genotypes including considering 10 kb upstream and downstream of the coding any minor allele contribute equally to the phenotype. In the region. We excluded the SNPs with minor allele frequency codominant model, the heterozygote is between a major (MAF)<1% and a P-value <10−4 for deviation from allele homozygote and minor allele homozygote. Hardy–Weinberg Equilibrium (HWE).

Study population of SEBCS Statistical analysis

As a multicenter case-control study, the SEBCS included A meta-analysis of reviewed studies was conducted in 4 040 incident breast cancer patients who were recruited various cancer patients with chemotherapy. The associa- from Seoul National University Hospital (SNUH), ASAN tions of genetic variants with each outcome (PFS and OS) Medical Center (AMC) and the National Cancer Center were estimated by summary hazard ratios (HRs) and cor- (NCC) from 2001 to 2007. Detailed information has been responding 95% confidence interval (95% CI). Hetero- presented in the previous studies [10, 22]. All patients geneity was evaluated by Q-test and I2 statistic. We used the provided written informed consent for the study. The study fixed-effect model if I2 was <30% and P-value by Q-test design was approved by the Committee on Human Research was >0.05; otherwise, the random-effect model was used. If of Seoul National University Hospital (IRB No. H-0503- the SNPs included in the meta-analysis does not exist in the 144-004). SEBCS, the highest correlated SNPs based on linkage dis- After the exclusion of subjects with no or insufficient equilibrium (LD) on the Han Chinese from Beijing and DNA samples, 2 342 breast cancer patients with sufficient Japanese from Tokyo (CHB + JPT) from the HapMap DNA samples were suitable to be genotyped. We excluded phase II database (release 22) were considered. If possible, 198 patients who had a history of breast or other cancers, subgroup analysis was performed by ethnicity, cancer site, had benign breast cancers, or had no clinical pathological study design, and regimen. The funnel plot and Egger’s test information. A total 421 patients who had missing follow- were carried out to detect the publication bias. up duration and no information of chemotherapy were For SEBCS, we conducted SNP-based and gene-based additionally excluded for the survival analysis. A final total analyses to investigate the association of transporter genes of 1 721 incident breast cancer patients with genotypes on breast cancer survival (DFS and OS). As the first step, remained. Among them, 1 338 patients received adjuvant SNP-based analysis was performed using multivariate chemotherapy, and 383 patients did not receive adjuvant Cox proportional hazard models in the additive genetic chemotherapy. model. Violations of the proportional hazard assumption were assessed by Schoenfeld residuals test. We selected Outcomes for breast cancer survival SNPs that had association at P-values < 0.001 among the patients who received chemotherapy. For the second step, According to the retrospective medical record review, the genes that contain significant SNPs from the SNP-based outcomes in the cohort were defined as follows: DFS is results were further investigated by gene-based method defined as the time from the date of first breast cancer based on an adaptive rank truncated product (ARTP) in a surgery to the date of recurrence, such as loco-regional nested case-control study design, because the ARTP was recurrence, distant metastasis, contralateral recurrence, and not available for the survival analysis [24]. Patients who any cause of death; OS is the time from the date of first had no event (DFS and OS) were selected by frequency- breast cancer surgery to the date of death from any cause or matching based on follow-up duration (in 3 year strata) last follow-up until December 2011 for censored patients. and age (in 10 year strata) as a 1:1 ratio. The ARTP method summarizes gene-level P-values as combining Genotyping significant SNP-level P-values. SNPs with correlation r2 > 0.3 were removed in the gene-based analysis to avoid A total of 453 ABC and SLC transporter genes were any associated signals for the genes caused by their top included in this study. We selected 49 ABC and 404 SLC SNPs. P-values for gene-based analysis were estimated by transporter genes from the Kyoto Encyclopedia of Genes 10,000 parametric permutation steps [25]. The P-values and Genomes (KEGG) pathway and the Pharmacogenomics were corrected by the false discovery rate (FDR) based on Knowledgebase (PharmGKB). the number of tested genes. A total of 7,750 SNPs in the 453 ABC and SLC trans- From the SEBCS results, LD structure of 34 SNPs in porter genes were extracted from 555,117 genotyped SNPs ABCB1 was determined by using the Haploview 4.2 soft- in the Affymetrix Genome-Wide Human SNP array 6.0 chip ware (Broad Institute of MIT and Harvard, USA) [26]. (Affymetrix Inc.). Quality control measures were described Among 5 haplotype blocks, rs1045642 was correlated with Table 1 Characteristics of studies included through a systematic review Site First author, year Clinical trial Regimen No. of patients Country Gene (No of SNP) Endpoints

Breast Bray, 2010 [59] N Cyclophosphamide, doxorubicin 230 UK ABCB1 (1) OS and PFS Ji, 2012 [60] Y TA/TAC, FAC 153 China ABCB1 (3) DFS Lee, 2014 [61] Y Gemcitabine, paclitaxel 85 Korea SLC28A3 (1) OS Kim, 2015 [62] Y Docetaxel, doxorubicin 216 Korea ABCB1 (3) OS Colorectal Huang, 2013 [36] N FOLFIRI, mCapeIRI 137 China SLC19A1 (1) OS and PFS SLCO1B1 (2) Kap, 2016 (a) [15] N Oxaliplatin, irinotecan, bevacizumab, cetuximab 201 Germany ABCA9 (1) OS ABCB11 (3) ABCC1 (1) Kap, 2016 (b) [15] N Fluoropyrimidine, irinotecan, bevacizumab 422 Germany ABCA9 (1) OS ABCB11 (3) ABCC1 (1) Ulrich, 2014 [37] Y Leucovorin, 5FU 754 Mixed SLC19A1 (1) OS Wu, 2013 (a) [16] Y Oxaliplatin 831 China ABCB1 (3) OS and PFS Wu, 2013 (b) [16] Y Lv5-Fu2, FUP 116 China ABCB1 (3) OS and PFS Yue, 2013 [63] N Folfox, Xelox, LV5-FU2, fluorouracil 428 China ABCB1 (2) OS and PFS Lung Campa, 2012 (a) [17] N Platinum 127 Germany ABCB1 (1) OS and PFS ABCC2 (1) ABCG2 (1) Campa, 2012 (b) [17] N Etoposide 167 Germany ABCB1 (1) OS and PFS ABCC2 (1) ABCG2 (1) Chen, 2015 [64] Y EGFR-TKIs (erlotinib, gefitinib) 100 China ABCG2 (3) OS Corrigan, 2014 [38] Y Pemetrexed, platinum 136 Mixed SLC19A1 (3) OS Cuffe, 2016 (a) [18] N Cisplatin, carboplatin 170 Canada ABCC2 (10) OS Cuffe, 2016 (b) [18] Y Carboplatin, paclitaxel, cediranib maleate 219 UK ABCC2 (2) OS Dogu, 2012 [65] N Cisplatin, carboplatin, gemstabine, paclitaxel, 79 Turkey ABCB1 (1) OS and PFS docetaxel, vinorelbine Fukudo, 2013 [28] Y EGFR-TKIs (erlotinib) 88 Japan ABCB1 (3) PFS ABCG2 (1) Gandara, 2009 [29] Y Carboplatin, paclitaxel 526 Japan/USA ABCB1 (1) OS and PFS Knez, 2012 [30] N CEV, EP 177 Slovenia ABCB1 (2) OS and PFS Lee, 2013 [66] N Platinum, EFGR-TKIs, gemcitabine, taxane 348 Korea SLC35D2 (1) OS Li, 2012 [39] N Gemcitabine 394 USA SLC29A1 (2) OS

Moyer, 2010 [67] N Platinum 973 USA ABCC1 (3) OS al. et Kim J-E ABCC2 (1) ABCC4 (6) soitosbtengntcplmrhsso ebaetasotrgnsadponssatr . . after. prognosis and genes transporter membrane of polymorphisms genetic between Associations Table 1 (continued) Site First author, year Clinical trial Regimen No. of patients Country Gene (No of SNP) Endpoints

Muller, 2009 [68] N Etoposide, gemcitabine, platinum 348 Germany ABCC3 (3) OS and PFS ABCG2 (2) SLC28A1 (1) Soo, 2009 [69] N Gemcitabine 53 Singapore SLC28A1 (6) OS SLC28A2 (2) SLC28A3 (1) Szczyrek, 2016 [70] N Platinum, docetaxel 58 NR ABCB1 (1) OS ABCC2 (1) Qiao, 2016 [71] Y Paclitaxel, cisplatin, carboplatin 64 China ABCB1 (2) OS and PFS ABCC2 (2) ABCG2 (1) Wu, 2014 [40] Y Gemcitabine, platinum 225 China SLC29A1 (1) OS Ovarian Bergmann, 2011 [72] N Carboplatin, paclitaxel 182 Denmark ABCB1 (3) OS Johnatty, 2008 [35] N Carboplatin, paclitaxel 309 Australia ABCB1 (3) OS and PFS Peethambaram, 2011 [73] N Taxane 365 USA ABCB1 (33) DFS Tian, 2012 [31] Y Cisplatin, paclitaxel 511 USA ABCB1 (2) OS and PFS ABCC2 (1) ABCG2 (1) Pancreas Tanaka, 2011 [74] N Cisplatin, gemcitabine 176 USA ABCB1 (1) OS ABCC1 (1) ABCC2 (2) ABCC4 (1) ABCG2 (1) Woo, 2012 [21] N Gemstabine, erlotinib, fluoropyrimidine 102 Korea ABCB1 (2) OS and TTP SLC28A1 (6) SLC28A3 (2) SLC29A1 (4) Zeng, 2011 [41] N Capacitabine, cisplatin, erlotinib, 211 USA ABCB1 (1) OS gemcitabine, oxaliplatin, 5FU ABCG2 (1) SLC29A1 (1) Stomach (Gastric) Chang, 2010 [32] Y Leucovorin, paclitaxel, 5FU 43 Korea ABCB1 (2) PFS Li, 2011 [33] Y Docetaxel, leucovorin, oxaliplatin, paclitaxel, 5FU 102 China ABCB1 (1) OS and PFS Li, 2016 [75] N Platinum, 5FU 103 China ABCC2 (1) OS Shim, 2010 [76] Y Cisplatin, docetaxel, paclitaxel 207 Korea ABCB1 (1) OS and PFS Shitara, 2010 (a) [19] N Taxane 61 Japan ABCB1 (1) OS and PFS Shitara 2010 (b) [19] N Irinotecan 39 Japan ABCB1 (1) OS and PFS CEV cyclophosphamide/epirubicin/vincristine, DFS disease-free survival, EGFR-TKIs epidermal growth factor receptor-tyrosine kinase inhibitors, EP etoposide/cisplatin, FAC 5-fluorouracil/ doxorubicin/cyclophosphamide, FEC 5-fluorouracil/epirubicin/cyclophosphamide, FOLFIRI irinotecan/5-fluorouracil/leucovorin, FUP uracil/cisplatin, GI gastric intestinal, Lv5-Fu2 leucovorin/ 5-fluorouracil, mCapeIRI irinotecan/capecitabine, OS overall survival, PFS progression-free survival, RECIST Response Evaluation Criteria in Solid Tumor, TAC taxanes/anthracycline/ cyclophosphamide, TKIs tyrosine kinase inhibitors, 5FU 5-Fluorouracil J-E Kim et al.

Table 2 Summary of meta-analysis of survival after chemotherapy by genetic variation of ABCB1, ABCC2 and ABCG2 Gene SNP Type of Genetic model No. of Summary HRs (95% CI)a P for I2 survival studies heterogeneity

ABCB1 rs1045642, C>Tb PFS Dominant 9 1.33 (1.07–1.64) 0.046 49.3% Heterozygote 5 1.04 (0.89–1.21) 0.448 0.0% Homozygote 5 0.94 (0.76–1.16) 0.783 0.0% OS Dominant 8 1.11 (0.89–1.40) 0.070 46.6% Heterozygote 5 0.96 (0.81–1.15) 0.668 0.0% Homozygote 5 0.98 (0.77–1.23) 0.282 20.8% Additive 3 1.07 (0.90–1.28) 0.540 0.0% rs1045642, T>C PFS Heterozygote 4 1.00 (0.80–1.24) 0.020 15.3% Homozygote 4 1.08 (0.95–1.24) 0.139 49.3% OS Heterozygote 2 1.13 (0.71–1.82) 0.698 0.0% Homozygote 2 1.30 (0.92–1.84) 0.792 0.0% rs1128503, C>Tb PFS Heterozygote 4 0.96 (0.74–1.24) 0.194 36.3% Homozygote 4 1.06 (0.82–1.36) 0.143 44.8% OS Heterozygote 3 0.71 (0.41–1.23) 0.041 68.6% Homozygote 3 0.74 (0.57–0.97) 0.305 15.7% rs2032582, G>T/A PFS Dominant 6 1.17 (0.84–1.63) 0.002 74.4% Heterozygote 5 0.96 (0.84–1.09) 0.558 0.0% Homozygote 5 0.95 (0.83–1.08) 0.676 0.0% OS Dominant 5 1.17 (0.76–1.79) 0.090 50.2% Heterozygote 5 0.91 (0.75–1.11) 0.591 0.0% Homozygote 5 1.06 (0.93–1.21) 0.328 13.5% ABCC2 rs717620, C>Tb OS Additive 4 0.84 (0.69–1.02) 0.477 0.0% ABCG2 rs2231137, G>Ab OS Dominant 3 1.19 (0.77–1.84) 0.058 64.9% rs2231142, C>Ab PFS Dominant 3 0.98 (0.73–1.32) 0.040 69.0% OS Dominant 4 1.00 (0.85–1.18) 0.405 0.0% aSummary HRs (95% CI) were estimated by fixed-effect models if I2 was <30% and P for heterogeneity was >0.05; otherwise, the summary HRs (95% CIs) were estimated by random-effect models bMeta-analysis was conducted to include the SEBCS

6 SNPs, rs1128503 was correlated with 11 SNPs and estrogen receptor (ER), progesterone receptor (PR) and rs1202172 was correlated with 8 SNPs (r2 > 0.3). Three human epidermal growth factor receptor 2 (HER2). Patients SNPs in block 4 of ABCB1 were considered to construct with TNM stage IV were excluded for the association of haplotypes, with two SNPs (rs4148738 (T>C, LD with DFS. rs1045642 (r2 = 0.516)), and rs10276036 (C>T, LD with We evaluated the interaction of the SNPs and che- rs1128503 (r2 = 1)) shown significant results from meta- motherapy by the likelihood ratio test comparing models analysis and one SNP (rs1202172 (A>C)) shown significant with and without an interaction terms. Among patients who results from SEBCS (Supplementary Figure 1). After received chemotherapy, a subgroup analysis was performed excluding the missing genotype with at least one of three by prescribed regimen (anthracycline, CMF (cyclopho- polymorphic sites, individual haplotypes were estimated by sphamide, methotrexate and fluorouracil), and taxane) and using the PHASE program version 2.1 [27]. The hazard of additionally adjusted for other regimen types that were not breast cancer survival was estimated by each haplotype considered as a stratification factor. compared with the haplotype containing all the major alleles Statistical analysis was performed by PLINK software as a reference. Based on the haplotype results, the diplotype version 1.07 (http://pngu.mgh.harvard.edu/), “ARTP2”, (combination of haplotype) analysis was conducted to “GenABEL” and “meta” packages in R version 3.3.2 (the R assess effects by the increased number of risk haplotype. All Foundation for Statistical Computing) and SAS statistical analyses of SEBCS were adjusted for age, tumor size, node software package version 9.4 (SAS Institute., Cary, NC, status and metastasis (TNM) stage, and subtype based on USA). Associations between genetic polymorphisms of membrane transporter genes and prognosis after. . .

Most studies included Asian patients (n = 19). Most of the studies were conducted for lung cancer (n = 16), and 4 studies were conducted among breast cancer patients. The number of patients varied within a range from 53 to 973 patients. Most studies included ABCB1 (n = 22), with 8 and 7 studies including ABCC2 and ABCG2, respectively. The summary of the included studies from a systematic review is identified in Supplementary Table 1. Considering the number of studies that can be analyzed based on outcomes and genetic models, the meta-analysis was conducted on three ABC genes. Table 2 shows that the meta-analysis in both PFS and OS was conducted by genetic variations of ABCB1, ABCC2, and ABCG2. The SEBCS was included as a study in the meta-analysis of ABCB1 (rs6949448 in LD with rs1045642 (r2 = 0.618), rs10276036 in LD with rs1128503 (r2 = 1)), ABCG2 (rs4148152 in LD with 2231137 (r2 = 1), rs1481012 in LD with rs23231142 (r2 = 1)) and ABCC2 (rs717620). The results of the meta-analysis in the dominant model showed that rs1045642 (C>T) in ABCB1 (HR = 1.33, 95% CI: 1.07–1.64) was associated with poor PFS, especially in Asian patients (HR = 1.56, 95% CI: 1.07–2.27) and stomach cancer (HR = 1.88, 95% CI: 1.14–3.10). When stratified by study design, the association of rs1045642 with PFS was significant in observational studies (HR = 1.28, 95% CI: 1.05–1.56), but not significant in clinical trials (HR = 1.47, 95% CI: 0.96–2.27) (Fig. 2). The funnel plot and Egger’s test (P = 0.142) suggested no evidence of publication bias of included studies (Supple- mentary Figure 2). In the codominant model, homozygotes of rs1128503 (C > T) in ABCB1 was associated with longer overall survival (HR = 0.74, 95% CI: 0.57–0.97). However, there was no significant association between ethnicity. The funnel plot and Egger’s test (P = 0.520) suggested no evi- dence of publication bias of included studies. There was no significant association with survival in either ABCC2 or ABCG2. The result of meta-analysis limited to breast cancer indicated that ABCB1 rs2032582 (G>T/A) was not sig- nificant association with OS (HR = 2.03, 95% CI: 0.53–7.84). In SEBCS, 195 patients relapsed, and 108 patients died Fig. 2 Meta-analysis of rs1045642 (C>T) in ABCB1 and progression- during follow-up duration among 1 338 patients who free survival in the dominant model. a subgroup by ethnicity, b sub- group by cancer site, and c subgroup by study design received chemotherapy. Both the characteristics of patients in the total cohort study for SNP-based analysis and in the nested case-control study for gene-based analysis showed Results similar patterns of significant association of TNM stage and tumor subtype on DFS and OS (Table 3 and Supplementary Table 1 shows the characteristics of 37 studies included Table 2). In the 383 patients who did not receive che- through a systematic review. A total of 9 ABC (ABCA9, motherapy, 23 patients relapsed, and 9 patients died during ABCB1, ABCB11, ABCC1, ABCC2, ABCC3, ABCC4, follow-up duration (Supplementary Table 3). ABCC10, and ABCG2) and 7 SLC (SLC19A1, SLC28A1, Table 4 shows the associations between transporter genes SLC28A2, SLC28A3, SLC29A1, SLC35D2,andSLCO1B1) and both of DFS and OS in the SEBCS by chemotherapy transporter genes were identified from a systematic review. status. According to the SNP-based results in the patients J-E Kim et al.

Table 3 Baseline characteristics of patients who received chemotherapy in SEBCS

a a Characteristics All (N = 1338) EventDFS HR (95% CI) EventOS (N = 108, 8.1%) HR (95% CI) (N = 195, 14.7%) N (%) N (%) N (%)

Age (years) <40 254 (19.0) 35 (18.0) 0.92 (0.62–1.36) 21 (19.4) 0.86 (0.51–1.46) 40–49 604 (45.1) 85 (43.6) Reference 48 (44.4) Reference 50–59 326 (24.4) 51 (26.2) 1.08 (0.76–1.53) 24 (22.2) 0.86 (0.52–1.43) ≥60 154 (11.5) 24 (12.3) 1.19 (0.75–1.87) 15 (13.9) 1.32 (0.73–2.37) Education

c who received chemotherapy (P < 0.001), four genes gene P

( (ABCB1, SLC9A9, SLC28A3, and SLC35F2) in the DFS interaction P 0.035 0.160 0.763 0.151 0.177 0.392 0.781 0.680 0.292 0.212 0.345 and seven genes (ABCC13, SLC7A7, SLC8A1, SLC12A8, SLC22A10, SLC30A10, and SLC35F1) in the OS were SLC8A1 selected. From the SNP-based and gene-based results in the 1 1 2 1 1 1 1 3 1 1 1 − − − − − − − − − − − group that received chemotherapy, rs1202172 in ABCB1 was associated with increased risk of recurrence and the -value

P interaction terms of rs1202172 and with or without che- 383) 0.005) on DFS, motherapy was significant (HR = 1.81, 95% CI: 1.34–2.43, = = −5 N P = 8.96 × 10 , Pgene = 0.005, Pinteraction = 0.035). Both gene

P SLC8A1 = P =

( rs2373895 in (HR 1.58, 95% CI: 1.20-2.08, b

2.07)1.71)3.67) 3.361.65) × 10 9.21 × 10 8.544.08) × 10 7.812.87) × 10 5.08) 6.9716.15) × 10 9.4814.63) × 10 5.525.37) × 10 8.75 × 10 4.57) 5.76 × 10 6.75 × 10 4.88 × 10 -4 – – – – – – – – – – – 9.90 × 10 , Pgene = 0.011) and rs1767087 in SLC12A8 (HR -4 = 1.88, 95% CI: 1.34–2.62, P = 2.28 × 10 , Pgene = 0.010) ABCB1 were associated with shorter OS. None of significant genes

ed in were observed after FDR based on the number of tested fi genes. Haplotype of TTC was significantly associated with hazard of recurrence compared with TCA haplotype among the patients who received chemotherapy (HR = 1.90, 95% CI: 1.34–2.69, Pinteraction = 0.043). In the diplotype analysis,

0.10/0.040.44/0.460.14/0.220.43/0.41 0.50 (0.12 0.27/0.33 0.97 (0.55 0.42/0.39 1.84 (0.92 0.30/0.33 0.92 (0.51 0.16/0.330.07/0.06 1.26 (0.39 0.08/0.06 0.96 (0.32 0.30/0.33 1.46 (0.42 4.90 (1.49 0.34 (0.01 0.63 (0.07 1.49 (0.48 poor prognosis was observed as increasing the risk haplo- cant results were identi < 0.001)

fi = – P type (TTC) (HR 1.65, 95% CI: 1.20 2.28 (with 1 TTC),

5 4 4 4 4 4 4 4 4 4 4 HR = 5.44, 95% CI: 1.71–17.29 (with 2 TTC), P =

− − − − − − − − − − − interaction 0.049) (Table 5). However, haplotype in OS was not sig- fi

-value MAF total/event HR(95% CI) ni cantly associated (data not shown). P In the subgroup analysis by regimen types, there were no

1338) Patients who did not receive chemotherapy ( interactions between each regimen and SNPs in DFS. =

N However, rs4405319 in SLC22A10 was associated with b 1.82)0.71) 1.40 × 10 2.12) 2.12 × 10 5.19 × 10 3.46) 7.88 × 10 1.77)2.13) 4.61 × 10 5.71 × 10 2.08)2.62) 9.90 × 10 3.39) 2.28 × 10 0.78) 1.58 × 10 6.23 × 10 2.43) 8.96 × 10 – – – – – – – – – – – shorter OS in the patients who received taxane treatment -2 (HR = 3.22, 95% CI: 1.04–9.96, P = 4.15 × 10 , Pinteraction = 0.016; Supplementary Table 4).

Discussion

To our knowledge, this study was the first meta-analysis to investigate the associations between genetic polymorphisms Patients who received chemotherapy ( of membrane transporter genes and various cancer survival after chemotherapy. The meta-analysis was performed on genetic variants of ABCB1, ABCC2 and ABCG2 in 38 stu- dies, which include 37 studies through a systematic review and the current study (SEBCS). We only identified the significant associations with cancer survival in ABCB1.

0.010) on OS among the patientsRs1045642 who received chemotherapy (C>T) and rs1128053 (C>T) in ABCB1 were

= associated with PFS and OS, respectively. In the SEBCS, gene

P the gene-based results suggested that ABCB1, SLC8A1 and ( SLC12A8 were associated with breast cancer survival. In consideration of significant interactions with chemotherapy, 39 rs4839628 rs13297855 T/C C/T23 0.42/0.49 rs2373895 0.13/0.181 rs17670876 T/G rs17006539 C/T 1.44 (1.18 rs283083 1.62 (1.23 C/T 0.31/0.39 0.14/0.21 T/C 0.07/0.13 1.58 0.32/0.25 (1.20 1.88 (1.34 2.23 (1.47 0.57 (0.41 7 rs1202172 A/C 0.09/0.14 1.81 (1.34 1121 rs57830014 rs2205253 C/A rs498267611 A/G G/A 0.45/0.48 rs4405319 0.26/0.17 0.40/0.50 G/T 1.48 (1.21 0.06/0.11 0.49 (0.33 1.61 (1.23 2.19 (1.39

SLC12A8 rs1202172 in ABCB1 was associated with DFS among patients who received chemotherapy, and rs4405319 in Association between transporter genes and survival of patients according to chemotherapy in SEBCS ( SLC22A10 was associated with OS among patients who

a received taxane treatment. In the SEBCS, the haplotype of 0.011) and Cox proportional hazard model adjusted for age, TNM stage and tumor subtypes Patients with TNM stage IV were excluded from the DFSInteraction terms survival between SNPs and chemotherapy (treated/untreated) by the likelihood ratio test. Gene-based signi SLC9A9 SLC28A3 SLC35F2 OS ABCC13 SLC7A7 SLC8A1 SLC12A8 SLC22A10 SLC30A10 SLC35F1 Gene Chr SNP Alleles MAF total/event HR(95% CI) Table 4 DFS ABCB1 a b c = containing risk allele of both rs10276036 and rs1202172 J-E Kim et al.

Table 5 Association between ABCB1 haplotypes and DFS in SEBCS Patients who received chemotherapy Patients who did not receive chemotherapy

b b c All EventDFS HR (95% CI) All EventDFS HR (95% CI) Pinteraction N (%) N (%) N % N (%)

Haplotypesa TCA 603 (22.8) 73 (18.7) Reference 165 (21.8) 8 (17.4) Reference TTA 832 (31.4) 117 (30.0) 1.11 (0.83–1.48) 225 (29.7) 16 (34.8) 1.51 (0.64–3.56) 0.458 TTC 246 (9.3) 56 (14.4) 1.90 (1.34–2.69) 74 (9.8) 2 (4.4) 0.65 (0.14–3.05) 0.043 CCA 964 (36.4) 144 (36.9) 1.25 (0.94–1.65) 292 (38.5) 20 (43.5) 1.49 (0.65–3.38) 0.500 Number of TTC haplotype 0 1085 (81.9) 142 (72.8) Reference 308 (81.3) 21 (91.3) Reference 0.049 1 234 (17.7) 50 (25.6) 1.65 (1.20–2.28) 68 (17.9) 2 (8.7) 0.49 (0.11–2.11) 2 6 (0.5) 3 (1.5) 5.44 (1.71–17.29) 3 (0.8) 0 (0.0) — aABCB1 haplotype was composed of three SNPs in order of rs4148738 (T>C, LD with rs1045642 (r2 = 0.516)), rs10276036 (C>T, LD with rs1128503 (r2 = 1)) and rs1202172 (A>C) bCox proportional hazard model adjusted for age, TNM stage and tumor subtypes. cInteraction terms between haplotypes and chemotherapy (treated/untreated) by the likelihood ratio test. increased the hazard of recurrence comparing to the single regimen types. Rs1202172 located in the intron region of SNP. ABCB1 have been determined to be involved in the cancer In a systematic review, eight studies examined the survival differences by regulating the expression of ABCD4 association of PFS with rs1045642 (C>T) of ABCB1 in the and ABCG5 transporters [42, 43]. Although cancer drug dominant genetic model [19, 28–33]. In the meta-analysis substrates and inhibitors for ABCD4 and ABCG5 are not including SEBCS, ABCB1 rs1045642 was associated with yet identified, ABCD4 is involved in release of vitamin B12 poor PFS, especially in Asian populations and patients with which plays roles in nucleotide synthesis and deficiency of stomach cancer. Ameyaw et al[34]. investigated the dis- vitamin B12 might enhance carcinogenesis [44–47]. It is tribution of the allele frequency of rs1045642 by ethnicity reported that ABCG5 was involved in restriction of sterol and, reported no significant differences of allele frequency accumulation in the intestine and promotion of sterol between Asian and Caucasian patients. Among 4 stomach secretion from the liver, however, it has not been estab- cancer studies, 3 stomach cancer studies used taxane-based lished as ABCG2 that are involved in transporting sterol as treatment except for Shitara et al. (b) [19] that used irino- well as multiple drugs and toxins [48, 49]. Based on our tecan. Thus, the similarity of the prescribed regimen to that results, the expression regulation of ABCG5 at transcrip- of stomach cancer seems to contribute to stronger tional level may contribute to explain drug resistance as the associations. role of ABCG2. Only 2 studies reported the association between OS and Both SLC8A1 and SLC12A8 were associated with OS in the homozygote of rs1128503 (C>T) of ABCB1 in the the patients who received chemotherapy. In the regimen- codominant genetic model, and the pooling estimates specific association, rs4405319 in SLC22A10 showed sig- including SEBCS results suggested the significant associa- nificant interaction with taxane treatment and was asso- tion between rs1128503 and longer OS [16, 35]. The ciated with shorter OS in the breast cancer patients who pooling estimates of two breast cancer studies did not find received taxanes. The SLC8A1 encodes the Na+/Ca2+ significant association of rs2032582 in ABCB1 on cancer exchanger 1 (NCX1) that regulates the Ca2+ concentration survival. There may be heterogeneity such as ethnicity and [50]. Downregulation of SLC8A1 was associated with tumor prescribed regimen between two studies. Although the cell proliferation and suppressed apoptosis and it was SLC19A1 and SLC29A1 were included in more than three observed in the paclitaxel, doxorubicin and cisplatin- studies, they were not included in the meta-analysis due to resistant ovarian cell lines [51, 52]. A variant of SLC12A8 differences in SNPs and genetic models [21, 36–41]. affects the transport of polyamines, which play an important In the SEBCS, rs1202172 of ABCB1 showed significant role in cell death and proliferation by maintaining the high interaction with chemotherapy and association with an concentration in cancer cells [53–55]. Synergistic effects in increased hazard of recurrence in the breast cancer patients growth inhibition by combination of polyamines analogs who received chemotherapy only, although differences by and cytotoxic agents including taxanes were observed in regimen were not identified in the subgroup analysis by breast cancer cell lines [56]. Genetic variations of SLC12A8 Associations between genetic polymorphisms of membrane transporter genes and prognosis after. . . may be interfered in combined chemotherapeutic effects 3. Stearns V, Davidson NE, Flockhart DA. Pharmacogenetics in the with polyamines in breast cancer. SLC22A10 might be treatment of breast cancer. Pharmacogenomics J. 2004;4:143–53. observed in uptake of liver-specific substrates such as tax- 4. Chen G, Quan S, Hu Q, Wang L, Xia X, Wu J. Lack of asso- ciation between MDR1 C3435T polymorphism and chemotherapy anes, which are metabolized and eliminated in the liver [57, response in advanced breast cancer patients: evidence from current 58]. Although many portions of SLC is unclear, SLC8A1, studies. Mol Biol Rep. 2012;39:5161–8. SLC12A8, and SLC22A10 might be involved in transporting 5. Berry DA, Cronin KA, Plevritis SK, Fryback DG, Clarke L, Zelen chemotherapeutic agents, including anthracyclines and M, et al. Effect of screening and adjuvant therapy on mortality from breast cancer. N Engl J Med. 2005;353:1784–92. taxanes, which lead to a poor prognosis of breast cancer. To 6. Han SM, Park J, Lee JH, Lee SS, Kim H, Han H, et al. Targeted comprehensively understand chemotherapy resistance, the Next-Generation Sequencing for Comprehensive Genetic Profil- further studies were suggested about regulation of expres- ing of Pharmacogenes. Clin Pharmacol Ther. 2017;101:396–405. sion levels by alteration of ABC and SLC transporters [52]. 7. Daly AK. Genome-wide association studies in pharmacoge- nomics. Nat Rev Genet. 2010;11:241–6. There are several limitations of our study. The number 8. Wang L, Weinshilboum RM. Pharmacogenomics: candidate gene of studies of survival outcomes (DFS and TTP) and SLC identification, functional validation and mechanisms. Hum Mol transporter genes were insufficient to conduct the meta- Genet. 2008;17(R2):R174–9. analysis. However, the cohort study was carried out for the 9. Motsinger-Reif AA, Jorgenson E, Relling MV, Kroetz DL, Weinshilboum R, Cox NJ, et al. Genome-wide association studies lack of studies of SLC transporter genes in a meta-analysis. in pharmacogenomics: successes and lessons. Pharmacogenet Although there was heterogeneity among the studies, such Genomics. 2013;23:383–94. as ethnicity, cancer sites, treatment regimen, and the 10. Song N, Choi JY, Sung H, Jeon S, Chung S, Park SK, et al. number of patients, a stratification analysis and the use of a Prediction of breast cancer survival using clinical and genetic markers by tumor subtypes. PLoS ONE. 2015;10:e0122413. random-effect model were considered for the estimation of 11. Vasiliou V, Vasiliou K, Nebert DW. Human ATP-binding cassette the pooling effects. The SEBCS was not replicated, (ABC) transporter family. Hum Genomics. 2009;3:281–90. although the gene-based analysis was performed to com- 12. Lin L, Yee SW, Kim RB, Giacomini KM. SLC transporters as pensate for the weakness of SNP-based analysis. The therapeutic targets: emerging opportunities. Nat Rev Drug Discov. 2015;14:543–60. number of patients who did not receive chemotherapy was 13. He L, Vasiliou K, Nebert DW. Analysis and update of the human insufficient to investigate gene-level association. The solute carrier (SLC) gene superfamily. Hum Genomics. numberofpatientsstratified by regimen (anthracycline and 2009;3:195–206. CMF) was not enough to investigate regimen-specific 14. Gottesman MM, Fojo T, Bates SE. Multidrug resistance in cancer: role of ATP-dependent transporters. Nat Rev Cancer. associations with OS. Thus, further studies for replication 2002;2:48–58. and stratification by regimen are necessary in a large study 15. Kap EJ, Seibold P, Scherer D, Habermann N, Balavarca Y, Jansen population. L, et al. SNPs in transporter and metabolizing genes as predictive markers for oxaliplatin treatment in colorectal cancer patients. Int In conclusion, the meta-analysis found the associations – fi J Cancer. 2016;138:2993 3001. 10.1002/ijc.30026. Epub 2016 with cancer survival only about ABCB1. Our nding from Feb 19. SEBCS provides suggestive associations with breast cancer 16. Wu H, Kang H, Liu Y, Xiao Q, Zhang Y, Sun M, et al. Asso- survival in ABCB1, SLC8A1 and SLC12A8. These sig- ciation of ABCB1 genetic polymorphisms with susceptibility to nificant transporter genes will contribute to personalized colorectal cancer and therapeutic prognosis. Pharmacogenomics. 2013;14:897–911. treatment research. Further investigation is needed to vali- 17. Campa D, Muller P, Edler L, Knoefel L, Barale R, Heussel CP, date our findings. et al. A comprehensive study of polymorphisms in ABCB1, ABCC2 and ABCG2 and lung cancer chemotherapy response and – Acknowledgements This study was supported by grant 0320150110 prognosis. Int J Cancer. 2012;131:2920 8. (2015-1126) from the Seoul National University Hospital Research 18. Cuffe S, Azad AK, Qiu X, Qiu X, Brhane Y, Kuang Q, et al. Fund and Seoul National University Hospital (2017). ABCC2 polymorphisms and survival in the Princess Margaret cohort study and the NCIC clinical trials group BR.24 trial of platinum-treated advanced stage non-small cell lung cancer Compliance with ethical standards patients. Cancer Epidemiol. 2016;41:50–6. 10.1016/j. canep.2015.12.012. Epub 6 Jan 24. fl fl Con ict of interest The authors declare that they have no con ict of 19. Shitara K, Matsuo K, Ito S, Sawaki A, Kawai H, Yokota T, et al. interest. Effects of genetic polymorphisms in the ABCB1 gene on clinical outcomes in patients with gastric cancer treated by second-line References chemotherapy. Asian Pac J Cancer Prev. 2010;11:447–52. 20. Kogan AJ, Haren M. Translating cancer trial endpoints into the language of managed care. Biotechnol Healthc. 2008;5:22–35. 1. Youlden DR, Cramb SM, Yip CH, Baade PD. Incidence and 21. Woo HI, Kim KK, Choi H, Kim S, Jang KT, Yi JH, et al. Effect of mortality of female breast cancer in the Asia-Pacific region. genetic polymorphisms on therapeutic response and clinical out- Cancer Biol Med. 2014;11:101–15. comes in pancreatic cancer patients treated with gemcitabine. 2. Oh CM, Won YJ, Jung KW, Kong HJ, Cho H, Lee JK, et al. Pharmacogenomics. 2012;13:1023–35. https://doi.org/10.2217/ Cancer statistics in Korea: incidence, mortality, survival, and pgs.12.82 prevalence in 2013. Cancer Res Treat. 2016;48:436–50. J-E Kim et al.

22. Chung S, Park SK, Sung H, Song N, Han W, Noh DY, et al. 37. Ulrich CM, Rankin C, Toriola AT, Makar KW, Altug-Teber O, Association between chronological change of reproductive factors Benedetti JK, et al. Polymorphisms in folate-metabolizing and breast cancer risk defined by hormone receptor status: results enzymes and response to 5-fluorouracil among patients with from the Seoul Breast Cancer Study. Breast Cancer Res Treat. stage II or III rectal cancer (INT-0144; SWOG 9304). Cancer. 2013;140:557–65. 2014;120:3329–37. 10.1002/cncr.28830. 23. Kim HC, Lee JY, Sung H, Choi JY, Park SK, Lee KM, et al. A 38. Corrigan A, Walker JL, Wickramasinghe S, Hernandez MA, genome-wide association study identifies a breast cancer risk Newhouse SJ, Folarin AA, et al. Pharmacogenetics of pemetrexed variant in ERBB4 at 2q34: results from the Seoul Breast Cancer combination therapy in lung cancer: pathway analysis reveals Study. Breast Cancer Res. 2012;14:R56. novel toxicity associations. Pharm J. 2014;14:411–7. 10.1038/ 24. Yu K, Li Q, Bergen AW, Pfeiffer RM, Rosenberg PS, Caporaso tpj.2014.13. N, et al. Pathway analysis by adaptive combination of P-values. 39. Li L, Schaid DJ, Fridley BL, Kalari KR, Jenkins GD, Abo RP, Genet Epidemiol. 2009;33:700–9. et al. Gemcitabine metabolic pathway genetic polymorphisms and 25. Zhang HWB, Yu K, Yang Y ARTP2: Pathway and Gene-Level response in patients with non-small cell lung cancer. Pharm Association Test. https://cran.r-project.org/web/packages/ARTP2/ Genom. 2012;22:105–16. https://doi.org/10.1097/FPC. index.html2016 0b013e32834dd7e2 26. Barrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and 40. Wu F, Zhang J, Hu N, Wang H, Xu T, Liu Y, et al. Effect of visualization of LD and haplotype maps. Bioinformatics. hENT1 polymorphism G-706C on clinical outcomes of 2005;21:263–5. gemcitabine-containing chemotherapy for Chinese non-small-cell 27. Stephens M, Smith NJ, Donnelly P. A new statistical method for lung cancer patients. Cancer Epidemiol. 2014;38:728–32. haplotype reconstruction from population data. Am J Hum Genet. 10.1016/j.canep.2014.08.008. 2001;68:978–89. 41. Zeng H, Yu H, Lu L, Jain D, Kidd MS, Saif MW, et al. Genetic 28. Fukudo M, Ikemi Y, Togashi Y, Masago K, Kim YH, Mio T, effects and modifiers of radiotherapy and chemotherapy on sur- et al. Population pharmacokinetics/pharmacodynamics of erlotinib vival in pancreatic cancer. Pancreas. 2011;40:657–63. https://doi. and pharmacogenomic analysis of plasma and cerebrospinal fluid org/10.1097/MPA.0b013e31821268d1 drug concentrations in Japanese patients with non-small cell lung 42. Paik H, Lee E, Park I, Kim J, Lee D. Prediction of cancer prog- cancer. Clin Pharmacokinet. 2013;52:593–609. https://doi.org/10. nosis with the genetic basis of transcriptional variations. Geno- 1007/s40262-013-0058-5 mics. 2011;97:350–7. 29. Gandara DR, Kawaguchi T, Crowley J, Moon J, Furuse K, 43. Hlavac V, Brynychova V, Vaclavikova R, Ehrlichova M, Vrana Kawahara M, et al. Japanese-US common-arm analysis of pacli- D, Pecha V, et al. The expression profile of ATP-binding cassette taxel plus carboplatin in advanced non-small-cell lung cancer: A transporter genes in breast carcinoma. Pharmacogenomics. model for assessing population-related pharmacogenomics. J Clin 2013;14:515–29. Oncol. 2009;27:3540–6. 44. Ween MP, Armstrong MA, Oehler MK, Ricciardelli C. The role 30. Knez L, Kosnik M, Ovcaricek T, Sadikov A, Sodja E, Kern I, of ABC transporters in ovarian cancer progression and chemore- et al. Predictive value of ABCB1 polymorphisms G2677T/A, sistance. Crit Rev Oncol Hematol. 2015;96:220–56. C3435T, and their haplotype in small cell lung cancer patients 45. Coelho D, Kim JC, Miousse IR, Fung S, du Moulin M, Buers I, treated with chemotherapy. J Cancer Res Clin Oncol. et al. Mutations in ABCD4 cause a new inborn error of vitamin 2012;138:1551–60. 10.007/s00432-012-1231-1. Epub 2012 Apr B12 metabolism. Nat Genet. 2012;44:1152–5. 29. 46. Kawaguchi K, Okamoto T, Morita M, Imanaka T. Translocation 31. Tian C, Ambrosone CB, Darcy KM, Krivak TC, Armstrong DK, of the ABC transporter ABCD4 from the endoplasmic reticulum Bookman MA, et al. Common variants in ABCB1, ABCC2 and to lysosomes requires the escort protein LMBD1. Sci Rep. ABCG2 genes and clinical outcomes among women with 2016;6:30183. advanced stage ovarian cancer treated with platinum and taxane- 47. Choi SW. Vitamin B12 deficiency: a new risk factor for breast based chemotherapy: a Gynecologic Oncology Group study. cancer? Nutr Rev. 1999;57:250–3. Gynecol Oncol. 2012;124:575–81. 10.1016/j.ygyno.2011.11.022. 48. Janvilisri T, Venter H, Shahi S, Reuter G, Balakrishnan L, van Epub Nov 21. Veen HW. Sterol transport by the human breast cancer resistance 32. Chang H, Rha SY, Jeung HC, Im CK, Noh SH, Kim JJ, et al. protein (ABCG2) expressed in Lactococcus lactis. J Biol Chem. Association of the ABCB1 3435C>T polymorphism and treatment 2003;278:20645–51. outcomes in advanced gastric cancer patients treated with 49. Yu L, von Bergmann K, Lutjohann D, Hobbs HH, Cohen JC. paclitaxel-based chemotherapy. Oncol Rep. 2010;23:271–8. Selective sterol accumulation in ABCG5/ABCG8-deficient mice. 33. Li Y, Yan PW, Huang XE, Li CG. MDR1 gene C3435T poly- J Lipid Res. 2004;45:301–7. morphism is associated with clinical outcomes in gastric cancer 50. Lytton J. Na+/Ca2+exchangers: three mammalian gene families patients treated with postoperative adjuvant chemotherapy. Asian control Ca2+transport. Biochem J. 2007;406:365–82. Pac J Cancer Prev. 2011;12:2405–9. 51. Munoz JJ, Drigo SA, Barros-Filho MC, Marchi FA, 34. Ameyaw MM, Regateiro F, Li T, Liu X, Tariq M, Mobarek A, Scapulatempo-Neto C, Pessoa GS, et al. Down-regulation of et al. MDR1 pharmacogenetics: frequency of the C3435T muta- SLC8A1 as a putative apoptosis evasion mechanism by modula- tion in exon 26 is significantly influenced by ethnicity. Pharma- tion of calcium levels in penile carcinoma. J Urol. cogenetics. 2001;11:217–21. 2015;194:245–51. 35. Johnatty SE, Beesley J, Paul J, Fereday S, Spurdle AB, M.webb P, 52. Januchowski R, Zawierucha P, Rucinski M, Andrzejewska M, et al. ABCB1 (MDR 1) polymorphisms and progression-free Wojtowicz K, Nowicki M, et al. Drug transporter expression survival among women with ovarian cancer following paclitaxel/ profiling in chemoresistant variants of the A2780 ovarian cancer carboplatin chemotherapy. Clin Cancer Res. 2008;14:5594–601. cell line. Biomed Pharmacother. 2014;68:447–53. 36. Huang L, Zhang T, Xie C, Liao X, Yu Q, Feng J, et al. SLCO1B1 53. Daigle ND, Carpentier GA, Frenette-Cotton R, Simard MG, Lefoll and SLC19A1 gene variants and irinotecan-induced rapid MH, Noel M, et al. Molecular characterization of a human cation- response and survival: a prospective multicenter pharmacoge- Cl- (SLC12A8A, CCC9A) that promotes polyamine netics study of metastatic colorectal cancer. PLoS ONE. 2013;8: and amino acid transport. J Cell Physiol. 2009;220:680–9. e77223. Associations between genetic polymorphisms of membrane transporter genes and prognosis after. . .

54. Gagnon KB, Delpire E. Physiology of SLC12 transporters: les- cancer: A genome-wide association study. Carcinogenesis. sons from inherited human genetic mutations and genetically 2013;34:307–13. engineered mouse knockouts. Am J Physiol Cell Physiol. 67. Moyer AM, Sun Z, Batzler AJ, Li A, Schaid DJ, Yang P, et al. 2013;304:C693–714. Glutathione pathway genetic polymorphisms and lung cancer 55. Minois N, Carmona-Gutierrez D, Madeo F. Polyamines in aging survival after platinum-based chemotherapy. Cancer Epidemiol and disease. Aging. 2011;3:716–32. Biomark Prev. 2010;19:811–21. 56. Hahm HA, Dunn VR, Butash KA, Deveraux WL, Woster PM, 68. Müller PJ, Dally H, Klappenecker CN, Edler L, Jäger B, Gerst M, Casero RA Jr., et al. Combination of standard cytotoxic agents et al. Polymorphisms in ABCG2, ABCC3 and CNT1 genes and with polyamine analogues in the treatment of breast cancer cell their possible impact on chemotherapy outcome of lung cancer lines. Clin Cancer Res. 2001;7:391–9. patients. Int J Cancer. 2009;124:1669–74. 57. Sun W, Wu RR, van Poelje PD, Erion MD. Isolation of a family 69. Soo RA, Wang LZ, Ng SS, Chong PY, Yong WP, Lee SC, et al. of organic anion transporters from human liver and kidney. Bio- Distribution of gemcitabine pathway genotypes in ethnic Asians and chem Biophys Res Commun. 2001;283:417–22. their association with outcome in non-small cell lung cancer patients. 58. Gligorov J, Lotz JP. Preclinical pharmacology of the taxanes: Lung Cancer. 2009;63:121–7. 10.1016/j.lungcan.2008.04.010. implications of the differences. Oncologist. 2004;9(Suppl 2):3–8. 70. Szczyrek M, Mlak R, Krawczyk P, Wojas-Krawczyk K, Pow- 59. Bray J, Sludden J, Griffin MJ, Cole M, Verrill M, Jamieson D, rozek T, Szudy-Szczyrek A, et al. Polymorphisms of genes et al. Influence of pharmacogenetics on response and toxicity in encoding multidrug resistance as a predictive factor for breast cancer patients treated with doxorubicin and cyclopho- second-line docetaxel therapy in advanced non-small cell lung sphamide. Br J Cancer. 2010;102:1003–9. 10.38/sj.bjc.6605587. cancer. Pathol Oncol Res. 2016;17:17. 60. Ji M, Tang J, Zhao J, Xu B, Qin J, Lu J. Polymorphisms in genes 71. Qiao R, Wu W, Lu D, Han B. Influence of single nucleotide involved in drug detoxification and clinical outcomes of polymorphisms in ABCB1, ABCG2 and ABCC2 on clinical anthracycline-based neoadjuvant chemotherapy in Chinese Han outcomes to paclitaxel-platinum chemotherapy in patients with breast cancer patients. Cancer Biol Ther. 2012;13:264–71. non-small-cell lung cancer. International. J Clin Exp Med. 61. Lee SY, Im SA, Park YH, Woo SY, Kim S, Choi MK, et al. 2016;9:298–307. Genetic polymorphisms of SLC28A3, SLC29A1 and RRM1 72. Bergmann TK, Gréen H, Brasch-Andersen C, Mirza MR, Herr- predict clinical outcome in patients with metastatic breast cancer stedt J, Hølund B, et al. Retrospective study of the impact of receiving gemcitabine plus paclitaxel chemotherapy. Eur J Cancer. pharmacogenetic variants on paclitaxel toxicity and survival in 2014;50:698–705. patients with ovarian cancer. Eur J Clin Pharmacol. 62. Kim HJ, Im SA, Keam B, Ham HS, Lee KH, Kim TY, et al. 2011;67:693–700. ABCB1 polymorphism as prognostic factor in breast cancer 73. Peethambaram P, Fridley BL, Vierkant RA, Larson MC, Kalli patients treated with docetaxel and doxorubicin neoadjuvant KR, Elliott EA, et al. Polymorphisms in ABCB1 and ERCC2 chemotherapy. Cancer Sci. 2015;106:86–93. associated with ovarian cancer outcome. Int J Mol Epidemiol 63. Yue AM, Xie ZB, Zhao HF, Guo SP, Shen YH, Wang HP. Genet. 2011;2:185–95. Associations of ABCB1 and XPC genetic polymorphisms with 74. Tanaka M, Okazaki T, Suzuki H, Abbruzzese JL, Li D. Asso- susceptibility to colorectal cancer and therapeutic prognosis in a ciation of multi-drug resistance gene polymorphisms with pan- Chinese population. Asian Pac J Cancer Prev. 2013;14:3085–91. creatic cancer outcome. Cancer . 2011;117:744–51. 10.1002/ 64. Chen X, Chen D, Yang S, Ma R, Pan Y, Li X, et al. Impact of cncr.25510. ABCG2 polymorphisms on the clinical outcome of TKIs therapy 75. Li Z, Xing X, Shan F, Li S, Li Z, Xiao A, et al. ABCC2-24C>T in Chinese advanced non-small-cell lung cancer patients. Cancer polymorphism is associated with the response to platinum/5-Fu- Cell Int. 2015;15:43. 10.1186/s12935-015-0191-3. based neoadjuvant chemotherapy and better clinical outcomes in 65. Dogu GG, Kargi A, Turgut S, Ayada C, Taskoylu BY, Demiray advanced gastric cancer patients. Oncotarget. 2016;7:55449–57. G, et al. MDR1 single nucleotide polymorphism C3435T in 10.18632/oncotarget.0961. Turkish patients with non-small-cell lung cancer. Gene. 76. Shim HJ, Yun JY, Hwang JE, Bae WK, Cho SH, Lee JH, et al. 2012;506:404–7. 10.1016/j.gene.2012.06.057. BRCA1 and XRCC1 polymorphisms associated with survival in 66. Lee Y, Yoon KA, Joo J, Lee D, Bae K, Han JY, et al. Prognostic advanced gastric cancer treated with taxane and cisplatin. Cancer implications of genetic variants in advanced non-small cell lung Sci. 2010;101:1247–54. 10.111/j.349-7006.2010.01514.x.