The Pharmacogenomics Journal (2010) 10, 513–523 & 2010 Macmillan Publishers Limited. All rights reserved 1470-269X/10 www.nature.com/tpj ORIGINAL ARTICLE

SNPs in coding for ROS metabolism and signalling in association with docetaxel clearance

H Edvardsen1,2, PF Brunsvig3, The dose of docetaxel is currently calculated based on body surface area 1,4 5 and does not reflect the pharmacokinetic, metabolic potential or genetic H Solvang , A Tsalenko , background of the patients. The influence of genetic variation on the 6 7 A Andersen , A-C Syvanen , clearance of docetaxel was analysed in a two-stage analysis. In step one, 583 Z Yakhini5, A-L Børresen-Dale1,2, single-nucleotide polymorphisms (SNPs) in 203 genes were genotyped on H Olsen6, S Aamdal3 and samples from 24 patients with locally advanced non-small cell lung cancer. 1,2 We found that many of the genes harbour several SNPs associated with VN Kristensen clearance of docetaxel. Most notably these were four SNPs in EGF, three SNPs 1Department of Genetics, Institute of Cancer in PRDX4 and XPC, and two SNPs in GSTA4, TGFBR2, TNFAIP2, BCL2, DPYD Research, Oslo University Hospital Radiumhospitalet, and EGFR. The multiple SNPs per suggested the existence of common Oslo, Norway; 2Institute of Clinical Medicine, haplotypes associated with clearance. These were confirmed with detailed 3 University of Oslo, Oslo, Norway; Cancer Clinic, haplotype analysis. On the basis of analysis of variance (ANOVA), quantitative Oslo University Hospital Radiumhospitalet, Oslo, Norway; 4Institute of Basic Medical Research, mutual information score (QMIS) and Kruskal–Wallis (KW) analysis SNPs Faculty of Medicine, University of Oslo, Oslo, significantly associated with clearance of docetaxel were confirmed for Norway; 5Agilent Technologies, Santa Clara, CA, GSTA4, PRDX4, TGFBR2 and XPC and additional putative markers were found 6 USA; Department of Pharmacology, Oslo University in CYP2C8, EPHX1, IGF2, IL1R2, MAPK7, NDUFB4, TGFBR3, TPMT (2 SNPs), Hospital Radiumhospitalet, Oslo, Norway and 7Department of Medical Sciences, Uppsala (Po0.05 or borderline significant for all three methods, 14 SNPs in total). In University, Uppsala, Sweden step two, these 14 SNPs were genotyped in additional 9 samples and the results combined with the genotyping results from the first step. For 7 of the Correspondence: 14 SNPs, the results are still significant/borderline significant by all three Professor VN Kristensen, Institute for Cancer methods: ANOVA, QMIS and KW analysis strengthening our hypothesis that Research, Oslo University Hospital Radiumhospitalet, Montebello 0310, Oslo, they are associated with the clearance of docetaxel. Norway. The Pharmacogenomics Journal (2010) 10, 513–523; doi:10.1038/tpj.2010.6; E-mail: [email protected] published online 16 February 2010

Keywords: SNPs; docetaxel; clearance; pharmacogenetics; cytochrome P450 system

Introduction

Multiple modes of cell death generate the overall tumour response to treatment. The resulting mechanisms of cell death are determined by the mode of action of the drug, the dose regimen used and the genetic background of the cells within the tumour, with apoptosis being considered to be the prevailing mechanism in response to chemotherapy. Docetaxel, a semi-synthetic taxane with antineo- plastic effect, functions primarily through inducing stability on microtubule structures. Docetaxel promotes tubulin assembly and inhibits depolymerisation, thereby disrupting the balance of the microtubular network.1,2 As a result, a mitotic catastrophe occurs, cells undergo arrest in the G2-M phase and if irreversible, the changes lead to cell death.1 Docetaxel is used in the clinic, either alone or in combination with other chemotherapeutic agents, for the Received 12 November 2006; revised 11 December 2009; accepted 24 December 2009; treatment of breast, gastric, prostate, head and neck, ovarian as well as lung published online 16 February 2010 cancer.3 Chemotherapeutic drugs such as docetaxel administered at subtoxic Genetic variation influencing docetaxel clearance H Edvardsen et al 514

doses may act by means of a radio-sensitisation mechanism4 Materials and methods and the combination of low-dose chemotherapy and thoracic radiotherapy has been shown to improve the In this study, we genotyped 24 Norwegian patients with prognosis of locally advanced non-small cell lung cancer locally advanced NSCLC. The stage of disease was IIIA or IIIB (NSCLC).5 for the patients receiving low-dose docetaxel (n ¼ 5) and IIIB The clearance of docetaxel is a result of its metabolism, a and IV for the group of patients receiving high-dose complex process involving among other different isoforms docetaxel (n ¼ 19). Of the 24 patients, pharmacokinetic data of the CYP3A and excretion, docetaxel is a substrate were available for 22, 17 receiving high-dose docetaxel for the efflux membrane-localised transporter P-gp, encoded (100 mg m–2, administered every 3 weeks) without irradia- by the gene ABCB1.6 Recent published results, however, tion and 5 receiving low-dose docetaxel (20 mg m–2, admi- imply that metabolism may be more important for elimina- nistered once every week) with irradiation to the chest wall. tion of docetaxel than transport (as reviewed in Baker To validate our findings we collected samples from addi- et al.3). A pharmacokinetic model of docetaxel clearance tional nine patients with NSCLC wherein information identifies several co-variants of drug clearance: body surface regarding clearance of docetaxel was available. The dose of area, a1-acid glycoprotein (AAG) levels and liver function (as docetaxel was determined by body surface area and liver assessed by the combined level of Alanine amino function.16 For the analysis of the genetic effect on the and Aspartate amino transferase). The model was shown to clearance of docetaxel performed here, no sub-division of be a better predictor of docetaxel clearance than a Naı¨ve the patients based on whether they received radiotherapy predictor (assuming no co-variants) only in selected subsets treatment or not, was performed. Information on level of of the population.7 AAG was available for all patients with data on clearance of Both chemotherapy and radiation therapy exert their docetaxel. The blood samples for genetic analysis were antineoplastic effect by either directly attacking cellular collected after proper informed consent was obtained and macromolecules, including DNA, or indirectly, by producing the project was approved by the local ethical committee. reactive oxygen species (ROS) and their by-products. Leukocyte DNA was isolated from thawed blood containing The generated ROS induces a stress response activating EDTA using chloroform/phenol extraction followed by multiple pathways in the cells (for example, the mitogen- ethanol precipitation using the Applied Biosystems (Foster activated kinase (MAPK) pathway) as well as City, CA, USA) 340A Nucleic Acid Extractor according to transcription factors such as Nfr2, which regulates the standard protocol. expression of genes containing the antioxidant response element. This cis-acting regulatory element has been Docetaxel clearance discovered in the sequence of important cellular antioxidant Docetaxel was administered as a 1-h infusion and blood and detoxifying (for example, the phase II drug samples were collected in EDTA tubes at time points; 0 metabolising enzymes GSTs, NQOs and UGTs) and illus- (before infusion), 30 min (midway in the infusion of trates how the pathways directly involved in the therapeutic docetaxel), 55 min (end-point of infusion, represents max- mode of action of the drug directly affect the metabolism imum dose), 90 min, 2, 3, 6, 10, 20 and 25 h. The collected and clearance of the drug.8–10 Generated ROS have also blood samples were pre-processed and stored at À70 1C until been shown to repress expression of genes such as analysed. All patients provided written informed consent. tumour necrosis factora (in certain cell types) and CYPs Docetaxel was extracted from human plasma by solid suggesting a feed-back mechanism in the transcriptional phase extraction, and detected by absorbance at 227 nm regulation of these ROS producing enzymes.11–14 Therefore, after separation by reversed phase high performance liquid the genes selected here to create the genotype profile of chromatography, described in Andersen et al.17 Individual patients treated with radiation therapy and/or chemo- pharmacokinetic parameters were calculated using the pharma- therapy are all involved in regulating the redox level in cokinetic software PK Solutions 2.0 (Summit Research Services, the cells and in signalling or in DNA damage repair caused Montrose,CO,USA).Areaunderthe concentration-time curve by ROS.15 was determined by trapezoid calculation using measured Pharmacokinetic calculations of clearance level for concentrations. Systemic docetaxel clearance (l h–1 m–2) was docetaxel were performed and correlated to the patient’s calculated as dose divided by area under the curve. genetic profile. The effect of 583 single-nucleotide poly- morphisms (SNPs) on the excretion of docetaxel was SNP mining and genotyping studied. We computed the association between each The candidate genes for SNP analysis were selected from around genotype and each measured docetaxel clearance 4000 MEDLINE entries and different databases (Online Mende- value for each patient, and searched the resulting lian Inheritance in Man and the Organisation, association matrix for statistically significant structures. John Hopkins University, Baltimore, MD, USA) as described in A selected subset of 14 SNPs identified in the analysis Edvardsen et al.15 to create the genotype profile of all genes to be associated with the clearance of docetaxel was involved in the regulation of the redox level in the cells, genotyped in an independent sample set and the data ROS-mediated signalling and repair of DNA-damage caused reanalysed with these samples included to further validate by ROS. Genes important for the metabolism of xenobiotics our findings. have been included in the original gene list such as the

The Pharmacogenomics Journal Genetic variation influencing docetaxel clearance H Edvardsen et al 515

cytochrome P450 family (CYP1B1, CYP2B6, CYP2C18, Mann–Whitney U-test. The data are assumed to come from CYP2C19, CYP2C8, CYP2C9, CYP2E1, CYP2F1, CYP3A4 continuous distributions that are identical. and CYP3A7) as well as ABCB1 and ABCC1. In addition, the list of candidate genes contained several members of Quantitative mutual information score (QMIS). For a SNP locus the GST family (GSTA2, GSTA3, GSTA4, GSTM1, GSTM5, s and a vector q of clearance values, let G be a partition of GSTP1 and GSTT2) as well as EPHX1 and EPHX2 and UDP samples induced by the genotype values at locus s. For a glycosyltransferases all of which are reported to be involved clearance level threshold p, let Cp be a partition of samples in the metabolism of xenobiotics. Briefly, each of the candidate defined by the qop and qXp. The mutual information score genes extracted from PubMed resources was matched with (MIS) is the difference between the entropy of the partition the official Human Genome Organisation gene name and Cp and the conditional entropy of Cp given G: MIS(Cp, information regarding sequence variation was obtained. The G) ¼ H(Cp)–H(Cp|G), where H is the entropy function. The annotation of each SNP was followed in several databases QMIS is defined to be the maximum possible MIS, that is, and data on its frequency in Caucasian population were QMIS(C,G) ¼ maxmin(q) pppmax(q)MIS(Cp,G).AP-value for the collected. Approximately, 4000 SNPs could be identified in mutual information score can be computed exactly by an the initial 233 candidate genes by computer annotation. efficient exhaustive approach.18,20 The null hypothesis here SNP selection was performed based on putative function and is that genotype values have the same distribution, SNP frequencies. We performed actual genotyping of 1030 of regardless of the clearance levels. these SNPs in 193 individuals (including the 24 NSCLC patients in which docetaxel clearance was studied (this Leave one out cross validation (LOOCV). LOOCV was used to paper). Of these 725 (B69%) were successfully genotyped. further confirm strong associations with level of clearance, Another 38 SNPs were not in Hardy–Weinberg equilibrium above or below median of 20.5 (l h–1 m–2) providing the (B5%) in the total cohort of 193 individuals and these SNPs possibility to assess the combined effect of sets of SNPs. We were excluded from further analysis. In the subset of the 24 performed 10 runs of LOOCV hiding two samples at each NSCLC patients studied here, further 104 (B14.3%) of the step of the analysis, using forward sequential search.18 The 725 genotyped SNPs had either a minor frequency of number of times each SNP appears as a predictor is o5% or a low success rate and were excluded from the summarised. (Similar to the methods described in analysis leaving 583 SNPs for studies of the effect of SNPs on Hedenfalk et al.21). the excretion of docetaxel. As discussed in Edvardsen et al.,15 the low success rate in this study could reflect the fact that Haplotype analyses this was a pilot experiment with no optimisation and that Linkage disequilibrium estimation was performed using the users had no previous experience with the GenomeLab Haploview (Cambridge, MA, USA). Haploview estimates the SNPstream UHT genotyping system. The genotype proce- maximum-likelihood values of the four gamete frequencies, dure includes multiplexing of assays for 12 SNPs and this from which the D0, LOD and r2 calculations are derived.22 relatively high degree of multiplexing with the expected drop-outs is also likely to have had an influence on our success rate. Pathway analyses Pathway analysis was performed using PathwayStudio v4.0 (Rockville, MD, USA), using the ResNet core 1.0 (Rockville, MD, Statistical analysis of SNP–pharmacokinetic associations USA) database that contains curated data of high confidence. An association matrix, whose entries represent the P-values of association for every SNP/clearance pair18 was computed. For each pair (s,t) of SNP and docetaxel clearance value, we Results calculated an association score and a corresponding P-value Pst using either of the three methods described below. Given Prediction of clearance above or below median a significance threshold 0oPo1, we state that s is associated The range of docetaxel clearance was 10–34 with a median of 20.5 (l h–1 m–2) for all cases combined and a median of 24 with t if Pstop. and 17 for the group of patients receiving high and low dose of docetaxel respectively. In the model by Bruno et al.,7 the Analysis of variance (ANOVA). For each SNP locus we computed covariates of docetaxel clearance include body surface area, the one-way ANOVA P-value for the clearance vector and 19 AAG levels and liver function. In this study, we did not grouping of the samples based on SNP locus genotypes. adjust for any covariates other than body surface area and The null hypothesis here is that the clearance level liver function, which was adjusted for in the dosage scheme. distributions are the same, regardless of the genotype class. Analysing a total of 547 patients, the investigators found that their model only surpassed the Naı¨ve predictor Kruskal–Wallis (KW) test. This one-way ANOVA is a non- (assuming no covariates) in extreme subset of patients such parametric rank-based method that tests the null hypothesis as patients with AAG level above the 90th percentile— that independent samples from two or more groups come 2.27 g l–1. In our study, 2 of the 22 patients had AAG level from distributions with equal medians, and returns the above the 90th percentile for our data set and for the rest of P-value for that test. The test is an extension of the the patients no adjustment would be necessary.

The Pharmacogenomics Journal Genetic variation influencing docetaxel clearance H Edvardsen et al 516

Analysing clearance above or below median the mutual An alternative LOOCV analysis revealed 20 SNPs in 19 information score revealed a distinct difference in genotype genes that were scored as predictors for docetaxel from 1 to 7 distribution for 18 SNPs in 14 genes (Table 1). A total of 9 of times out of 10, the most frequent being SNPs in EGF (7 hits these 18 SNPs were found to reside in regions reported to be out of 10), TNFAIP2 (5 hits), GSTA4 (5 hits), ALOX15B amplified or deleted in docetaxel resistant cell lines.23 In a (4 hits), PRKCABP (4 hits) and PRDX4 (3 hits), (Table 3). Two recent study, we reported the association between SNPs in different SNPs in GSTA4 were scored as predictors, rs316128 blood and intra-tumoral mRNA expression in 50 women (4 hits) and rs1032419 (1 hit), which are in complete linkage with breast cancer,24 and seven out of these nine SNPs were disequilibrium, Figure 2. found to be associated with level of transcript either in cis or trans, Table 2. The difference in genotype distribution for these 18 SNPs is shown in the heat map in Figure 1a Table 2 SNPs associated with docetaxel clearance with a (for example, PRDX4, GSTA4, EPHX1, EGF and CYP2C8) Po0.1 reported to be regulator of transcription24 with a stacked diagram of the distribution of the genotypes for these SNPs shown in Figure 1b. Several of these RS nr. Cytoband Gene Cis-trans Transcripts regulated regulator genes, indicated by red circle in the figure, are reported to have a role in the Kyoto encyclopaedia of genes and 1032419a 6p12.1 GSTA4 Cis trans GSTA4 ADD3 genomes pathway ‘metabolism of xenobiotics by 316128a 6p12.1 GSTA4 Trans GFER cytochrome P450’. 2227306a 4q13.3 IL8 Cis trans GRSF1, DCK SMS, We observed that in many cases the SNPs associated with BM-009 clearance of docetaxel reside in the same gene suggesting 314127 5q35.1 KCNMB1 Trans C5orf3, ELF3 the existence of common haplotypes associated with 2253316 1p22.1 TGFBR3 Trans ENTPD6 clearance. Most notably these are four SNPs in EGF, three 2234131a 14q32.32 TNFAIP2 Trans FLJ10290 SNPs in each of PRDX4 and XPC, and two SNPs in GSTA4, 2228001 3p25.1 XPC Cis XPC TGFBR2, TNFAIP2, BCL2, DPYD, EGFR. Analysis of linkage 2733537 3p25.1 XPC Cis KIAA0218 disequilibrium between the SNPs in these genes reveals high Abbreviation: SNPs, single-nucleotide polymorphisms. level of linkage disequilibrium as exemplified in Figure 2. aIndicates that the SNP reside in a region reported to be deleted in docetaxel Interestingly, associations of SNPs in interacting genes resistant cell lines, none of the regulatory SNPs were found to be located in 23 such as EGF and EGFR, as well as different subfamilies of amplified regions. The last column lists the transcripts associated with these SNPs and also included Cytochrome P450 and different receptors of TGFbeta were is information as to whether this regulation is in cis or in trans, (SNPs in blood and found, suggesting gene–gene interactions. expression in tumours).

Table 1 SNPs associated with clearance above or below median of 20.5 (l h–1 m–2) as determined by mutual information score analysis sorted by gene name (Po0.05)

Gene RS nr. Cytoband Role Mutual information P-value

CDC25C 1042124 5q31.2 G/T Promoter 0.0294 CYP2C8 1341164y 10q23.33 C/T Intron 0.0300 EGF 1860129y 4q25 C/G Intron 0.0221 EGF 2067004y 4q25 C/T Intron 0.0209 EPHX1 2234922 1q42.12 A/G Coding exon 0.0125 GSTA4 1032419y 6p12.1 A/G 30 UTR 0.0090 GSTA4 316128y 6p12.1 A/C Intron 0.0323 IGF1 1520220y 12q23.2 C/G Intron 0.0230 IL8 2227306y 4q13.3 C/T Intron 0.0496 KCNMB1 314127 5q35.1 A/G Promoter 0.0208 NDUFB4 701992 3q13.33 A/T 30 UTR 0.0391 POLE3 818694* 9q32 A/T 30 UTR 0.0208 PRDX4 518329 Xp22.11 A/G Intron 0.0110 PRDX4 795491 Xp22.11 A/T Intron 0.0045 TGFBR3 2253316 1p22.1 A/T Intron 0.0325 TNFAIP2 2234131y 14q32.32 A/G Intron (boundary) 0.0443 XPC 2228001 3p25.1 A/C 30 UTR 0.0170 XPC 2733537 3p25.1 A/G Intron 0.0300

Abbreviation: SNPs, single-nucleotide polymorphisms. Astrix (*) and paragraph mark (y) indicate SNPs located in amplified and deleted regions respectively associated with resistance to docetaxel as described by McDonald et al.23

The Pharmacogenomics Journal Genetic variation influencing docetaxel clearance H Edvardsen et al 517

Clearence ([L/h/m2] )

Clearence < 20.5 Clearence > 20.5 Rs # Gene 10 10 11 14 16 16 17 18 18 19 20 21 22 24 25 26 26 27 27 27 28 34

795491 PRDX4 2 2 1032419 GSTA4 518329 PRDX4 4 4 2234922 EPHX1 2228001 XPC 6 6 314127 KCNMB1 818694 POLE3 8 8 2067004 EGF 1860129 EGF 10 10 1520220 IGF1 1042124 CDC25C 12 12 2733537 XPC 1341164 CYP2C8 14 14 316128 GSTA4 2253316 TGFBR3 16 16 701992 NDUFB4 2234131 TNFAIP2 18 18 2227306 IL8

Figure 1 SNPs significantly associated with clearance above or below the median of 20.5 (l h–1 m–2) by mutual information score (sorted by mutual information score P-value, Po0.05). Each row indicates a SNP, blue indicates the frequency of the homozygous genotype for the most common allele, green—heterozygote and yellow—homozygous for the less common allele. The rs-numbers of the SNPs and gene symbols are listed on the side. The genes reported to have a role in the Kyoto encyclopaedia of genes and genomes pathway ‘metabolism of xenobiotics by cytochrome P450’ are circled in red. (a) Distribution of genotypes for clearance above or below median. (b) Grouped genotype distribution in the clearance groups—below or above the median.

EGF GSTA4

Chr4 Chr6

Figure 2 Level of linkage disequilibrium (LD) in EGF and GSTA4. All SNPs genotyped for these genes have been included in the analysis with SNPs significantly associated with clearance below or above the median of 20.5 (l h–1 m–2) circled in red. The LD plots are created using Haploview and the colour code on plot follows the standard colour scheme for Haploview: white ¼ (|D0|o1, LODo2) shades of pink/red ¼ (|D0|o1, LODX2), blue ¼ (|D0| ¼ 1, DODo2), bright red ¼ (|D0| ¼ 1, LODX2). Numbers on top of the plot are the reference sequence id (rs-number) of the included SNPs with the distance between them indicated on the white bar above. The numbers in the squares on the LD plot are the D’ value (only the decimals are shown)—D’ of 1 are not listed.

Significant associations by both ANOVA score and QMIS PRDX4, TGFBR2, TGFBR3, TPMT (two SNPs) and XPC (two SNPs significantly associated with clearance of docetaxel by SNPs) (Table 4). The statistical association between the both ANOVA and QMIS were found in the following genes clearance level and the SNPs identified by both ANOVA and CYP2C8, EPHX1, GSTA4, IGF2, IL1R2, MAPK7, NDUFB4, QMIS was also analysed by the KW test and 12 of the 14

The Pharmacogenomics Journal Genetic variation influencing docetaxel clearance H Edvardsen et al 518

Table 3 Genes (SNPs) predicting level of clearance above or below median, as identified by LOOCV analysis

Gene RS nr. Hits Gene description

ALOX15B 2072685 4 Arachidonate lipooxygenase 15, second type BCL2 899966 1 B-cell CLL/ lymphoma 2 DPYD 1333727 1 Dihydropyrimidine dehydrogenase EGF 1860129 7 Epidermal growth factor EPHX1 2234922 1 Epoxide 1, microsomal (xenobiotic) EPHX2 751019 1 2, cytoplasmic GCLC 634657 1 Glutamate-cysteine , catalytic subunit, GCLC GSTA3 512795 2 S-transferase alpha 3 GSTA4 316128 4 Glutathione S-transferase alpha 4 1032419 1 GSTM4 569998 2 Glutathione S-transferase mu 4 IL1A 17561 1 Interleukin 1 alpha IL1R2 2236930 2 Interleukin 1 receptor 2 MARK3 1989565 1 MAP/microtubule affinity-regulating kinase 3 (CTAK1) PPP1R9A 854518 2 Protein phosphatase 1, regulatory subunit 9A PRDX4 795489 3 Peroxiredoxin 4 PRKCABP 713729 4 Protein kinase C, alpha-binding protein TNFAIP2 2234131 5 Tumor necrosis factor, alpha-induced protein 2 TXNRD2 732262 1 Thioredoxin reductase 2 XPC 2228001 1 Gene mutated in XP group C

Abbreviations: CLL, chronic lymphocytic leukaemia; LOOCV, leave one out cross validation; MAP, mitogen-activated protein; SNPs, single-nucleotide polymorphisms. Column 3 (‘hits’) lists the number of times the individual SNPs appears in the list of predictors out of 10 LOOCV runs.

Table 4 (a) SNPs significantly associated with docetaxel clearance by both ANOVA and QMIS, Po0.05 as well as the results of the KW test; (b) results from ANOVA, QMIS and KW analysis of the combined sample sets

RS nr. Gene Functional Allele Cytoband (a) (b) class P-value in set of Clearance P-value in set of Clearance samples from 24 threshold samples from 34 threshold patients (1 step) (n ¼ 24, patients (2 step) (n ¼ 34, 22 with 31 with clearence clearance data) data) ANOVA KW QMIS ANOVA QMIS KW

2228001 XPC 30 UTR A/C 3p25.1 0.0019 0.0041 0.0195 27 0.0045 0.0520 0.0004 22 2236930 IL1R2 Intron A/T 2q11.2 0.0050 0.0093 0.0344 16 0.0019 0.0045 0.0119 15 1078985 TGFBR2 Intron T/C 3p24.1 0.0057 0.0179 0.0208 20 0.0355 0.1156 0.0830 24 2230949 IGF2 Exon C/T 11p15.5 0.0132 0.0242 0.0351 28 0.0043 0.0261 0.0622 25 701992 NDUFB4 30 UTR A/T 3q13.33 0.0137 0.0255 0.0172 19 0.0114 0.0341 0.0294 19 2607737 XPC Intron T/C 3p25.1 0.0188 0.0171 0.0379 27 0.3743 0.6323 o0.000001 28 795491 PRDX4 Intron T/A Xp22.11 0.0209 0.0313 0.0126 20 0.0203 0.0552 0.0003 20 2253316 TGFBR3 Intron A/T 1p22.1 0.0223 0.0286 0.0361 22 0.0998 0.2400 0.0021 22 1032419 GSTA4 30 UTR A/G 6p12.1 0.0288 0.0201 0.0197 20 0.1495 0.2805 0.0126 20 2234922 EPHX1 Coding exon A/G 1q42.12 0.0310 0.0387 0.0043 22 0.2749 0.4983 0.2111 15 7886 TPMT Promoter A/T 6p22.3 0.0389 0.0422 0.0473 18 0.0817 0.0744 0.0572 17 1341164 CYP2C8 Intron T/C 10q23.33 0.0417 0.0214 0.0162 18 0.0097 0.0013 0.0040 18 1042865 MAPK7 30 UTR C/T 17p11.2 0.0462 0.0594 0.0184 28 0.0240 0.0084 0.0534 28 2842951 TPMT Intron C/T 6p22.3 0.0471 0.0527 0.0128 17 0.1013 0.0159 0.0178 17

Abbreviations: ANOVA, analysis of variance; KW, Kruskal–Wallis; QMIS, quantitative mutual information score; SNPs, single-nucleotide polymorphisms. QMIS seeks to establish the threshold of the quantitative parameter such that the difference in distribution of genotypes between the resulting partitions will be of maximal value, the last column indicates this threshold for each given SNP. Bold numbers indicate a significant association by that given method, italic numbers indicate a borderline significant association.

The Pharmacogenomics Journal Genetic variation influencing docetaxel clearance H Edvardsen et al 519

identified SNPs were also found significant by this test. The allele was sufficient to exert phenotype. In the case of two SNPs with conflicting results were located in TPMT and rs528329 of PRDX4, even heterozygous individuals had a MAPK7 (Table 4, panel a). A heat map shows the distribution different phenotype manifested as higher clearance in which of the patients with most different combinations of genotypes the levels for the heterozygotes and the homozygote minor into the groups of above and below median (Figure 3). In this allele frequency were similar (Figure 5c) and lower clearance study, the SNPs are sorted according to ANOVA P-value with for the heterozygotes and homozygote of the minor allele the most significant listed first. For these 14 SNPs, the criteria frequency allele in rs2234922 in EPHX1 (Figure 5d). were set such that the association will be found significant by To validate our results, samples from additional nine both QMIS and ANOVA and the highest P-value o0.05. NSCLC patients were collected and analysed. As this repre- We further studied the genes harbouring SNPs positively sents a small sample set, the statistical analysis were associated with clearance to see how they are functionally performed with the combined set of 22 samples from the related. An overview of the shortest path between the genes first round and these 9 new samples. We see that for 7 of the associated with clearance was composed by Pathway Studio 14 SNPs, the results are still significant/borderline significant v4.0 (Figure 4). This pathway analysis revealed the direct by all three analysis: ANOVA, QMIS and KW, 1 SNP is interactions between the clearance-associated genes (framed significant by 2 of the selected analysis, 5 SNPs are still with blue ovals on figure) and between the clearance- significant/borderline significant by 1 of the methods, while associated genes and other genes not included in this study only 1 SNP are no longer significant by any of the three (TP53, Sp1 and MAPK1), upstream off our candidate genes. methods (Table 4, panel b). Our statistical method QMIS seeks to establish the threshold of the quantitative parameter such that the difference in distribution of genotypes between the resulting partitions will Discussion be of maximal value. This results in somewhat different thresholds for each SNP (Table 4, column ‘clearance thresh- Docetaxel is one of the most effective chemotherapeutic old’). Nevertheless, these different thresholds oscillated agents in the treatment of cancer and works through around the median (20.5 l h–1 m–2). Observing the ANOVA destabilising the balance of the microtubule network. In distribution of the clearance levels for all patients with a given addition to this primary function taxanes, docetaxel genotype distinct differences can be found (Figure 5). included, have other lines of action. We have adapted the Individuals homozygous for the variant allele of rs1382368 illustration on the effect of antimicrotubule agents on signal in XRCC4 (Figure 5a) had high clearance as well as those transduction from Wang et al.2 and added information on homozygous carriers of variant rs780995 in the in PIK3CA gene–gene families involved in the metabolism of xenobio- (Figure 5b) compared with the individual homozygous for the tics to highlight the genes found by us to be associated with more prevalent allele and the individuals with a heterozygous docetaxel clearance (Figure 6). The inclusion has been based genotype. In other instances, only the presence of the variant on information on involvement in defined Kyoto encyclo-

Docetaxel clearence

10 10 11 14 16 16 17 18 18 19 20 21 22 24 25 26 26 27 27 27 28 34 RS-number Gene 2228001 XPC 2 2236930 IL1R2 1078985 TGFBR2 4 2230949 IGF2 701992 NDUFB4 6 2607737 XPC 795491 PRDX4 8 2253316 TGFBR3 1032419 GSTA4 10 2234922 EPHX1 7886 TPMT 12 1341164 CYP2C8 1042865 MAPK7 14 2842951 TPMT

Figure 3 SNPs significant associated with level of docetaxel clearance by both ANOVA and QMIS analysis (Po0.05). The SNPs are sorted according to ANOVA P-value. Each row indicates a SNP, blue indicates the frequency of the homozygous genotype for the most common allele, green— heterozygote and yellow—homozygous for the less common allele.

The Pharmacogenomics Journal Genetic variation influencing docetaxel clearance H Edvardsen et al 520

Small molecule

Cell Process

Cell Object

Treatment

Functional Class

Complex

Nuclear receptors

Kinases

Phosphatases

Extracellular

Ligands

Transcription factors

Receptors

Pathway Expression Regulation Mol. Transport Prot. Modification Binding Promotor Binding Mol. Synthesis Chemical Reaction Direct regulation

Figure 4 Shortest path between genes predicted to be associated with docetaxel clearance by both ANOVA and QMIS analysis (highest P-value 0.100). Genes circled in blue are input genes. The figure is generated using Pathway Studio 4.0 with the Resnet Core database containing high confidence curated data based on the ResNet 4 database for mammals.

1382368 (G/A) XRCC4 870995 (A/C) PIK3CA A (GG) 0.4 WT / WT 0.8 B (GA) WT / MT C (AA) MT / MT 0.3 0.6 0.2 0.4 0.1 0.2 0 0 AAAA AAA A AA A A BB B BBB BB B B BBBBBB −0.1 −0.2 CCC CCC

−20 −100 1020304050 60 −100 1020304050 Clearence (L/h/m2) level Clearence (L/h/m2) level

518329 (A/G) PRDX4 2234922 (A/G) EPHX1 A (AA) 0.14 B (AG) 0.1 A (GG) C (GG) B (AG) 0.12 0.08 C (AA) 0.1 0.08 0.06 0.06 0.04 0.04 0.02 0.02 0 AAAA A 0 −0.02 A B BBB B −0.04 −0.02 BBB B BBBB C CC −0.06 CCC CC CCCC −0.04 −5 0 5 10 15 20 25 30 35 40 45 0 1020304050 Clearence (L/h/m2) level Clearence (L/h/m2) level

Figure 5 Normal distribution of clearance levels corresponding to each genotype for rs1382368 (a), rs870995 (b), rs518329 (c) and rs2234922 (d) in XRCC4, PIK3CA, PRDX4 and EPHX1, respectively, together with linkage disequilibrium (LD) plots for all SNPs genotyped in these genes. The LD plots are created using Haploview and the colour code on plot follows the standard colour scheme for Haploview: white ¼ (|D0|o1, LODo2) shades of pink/red ¼ (|D0|o1, LODX2), blue ¼ (|D0| ¼ 1, DODo2), bright red ¼ (|D0| ¼ 1, LODX2). Numbers on top of the plot are the reference sequence id (rs-number) of the included SNPs with the distance between them indicated on the white bar above. The numbers in the squares on the LD plot are the D’ value (only the decimals are shown)—D’ of 1 are not listed.

The Pharmacogenomics Journal Genetic variation influencing docetaxel clearance H Edvardsen et al 521

paedia of genes and genomes pathways retrieved from vectors. These genes are clearly involved in the clearance GeneCard, (http://www.genecards.org/index.shtml). The coupled metabolism of the drug indicating an association SNPs found associated with docetaxel clearance in this between ROS and clearance of docetaxel in line with our study are in genes that target both the metabolism and findings and may explain the resistance with lower modes of action of docetaxel at multiple levels. therapeutic load in the nonresponders. Iwao-Koizumi et al.25 have analysed the link between High-level, docetaxel-resistant human breast cancer expression levels in tumour biopsies from breast cancer cell lines (MCF-7 and MDA-MB-231) have been created patients before treatment and the response to docetaxel and studied by comparative genomic hybridisation. Geno- therapy. The most prominent characteristic in nonrespon- mic regions associated with resistance to docetaxel in ders was found to be elevated expression of genes control- MCF-7 were identified as amplification of ling the cellular redox environment, that is, those of the 7q21.11-q22.1, 17q23-q24.3, 18, and deletion of chromo- glutathione and thioredoxin systems. These genes included somes 6p, 10q11.2-qter and 12p. In MDA-MB-231 cells on GSTP1, GPX1, TXN and PRDX1 as well as GPX4 and were all the other hand, a gain of chromosomes 5p, 7q11.1-q35, 9, subsequently confirmed in functional studies using reporter and loss of chromosomes 4, 8q24.1-qter, 10, 11q23.1-qter,

Paclitaxel, docetaxel

Modes of action Metabolism

TGFB EGF IGF1/2 CYPs CYP2C8 TGFBR CYP3A7 EGFR TGFBR2/3

EPHX1 GSTs PKA I & II/PRKCA Ras/Raf MAP/CDC2 Metabolism of EPHX2 GSTA4 MAPK7 Xenobiotics by GSTA3 Cytochrome P450 GSTM4 PRKCABP p53/p21/Bax/Bcl-2

UGTs UGT2A1 Caspases Nfr2

Microtubule Mitosis disorganisation Substrate Cleavage regulation PARP, DFF-45, Lamins (KIF2C, CDC25C)

Microtubule Chromatin Condensation disorganisation Cross-communication DNA Fragmentation Cytoplasmic Baling

PIK3CA ALOX12 Apoptosis ALOX15B

Figure 6 The metabolism of xenobiotics and effect of antimicrotubule agents on signal transduction adapted from Wang et al.2. Activation of signalling pathways induces the expression of drug metabolising genes containing the antioxidant response element such as GSTs and UGTs. The increased ROS level after drug metabolism affects the expression level/function of members of the signalling pathways such as Bcl-2 as well as drug metabolising enzymes themselves. Genes given in bold harbour SNPs found associated with clearance level in our study.

The Pharmacogenomics Journal Genetic variation influencing docetaxel clearance H Edvardsen et al 522

12q15-q24.31, 14q and 18 were showed to be associated with Acknowledgments docetaxel resistance. Amplification of 7q21 and loss of 10q are suggested to represent a common mechanism of acquired This project was supported by the Norwegian Cancer Association docetaxel resistance in breast cancer cells.23 We observed that (project number D-03067 and D-99061) and the National Pro- many of the SNPs associated with docetaxel clearance by gramme for Research in Functional Genomics in Norway (FUGE, project numbers 151924/150 and 15204/150), funded by the mutual information score reside in genes located in these Research Council of Norway. Genotyping was partly performed at areas reported to be amplified or deleted in docetaxel resistant the Uppsala SNP technology platform, which is funded by the cells, such as CYP2C8 (10q23.33), CYP3A7 (7q22.1), EGF Swedish Wallenberg Consortium North (WCN). (4q25), IL8 (4q13.3), TNFAIP2 (14q32.32) and GSTA4 (6p12.1) several of which are also selected by LOOCV analysis to harbour SNPs predictive of docetaxel clearance together with References PRKCABP, ALOX15B and PRDX4, and shown by us previously 24 1 Morse DL, Gray H, Payne CM, Gillies RJ. Docetaxel induces cell death to influence transcription. CYP3A7 is involved in the through mitotic catastrophe in human breast cancer cells. Mol Cancer hydroxylation of retinoic acid and 16 alpha-hydroxylation Ther 2005; 4: 1495–1504. of steroids, which implicates it both in normal development 2 Wang LG, Liu XM, Kreis W, Budman DR. The effect of antimicrotubule and carcinogenesis.26,27 GSTA4 on the other hand is highly agents on signal transduction pathways of apoptosis: a review. Cancer Chemother Pharmacol 1999; 44: 355–361. effective in catalysing 4-hydroxynonenal, a commonly used 3 Baker SD, Sparreboom A, Verweij J. Clinical pharmacokinetics biomarker for oxidative damage in tissues.28 Both these of docetaxel: recent developments. Clin Pharmacokinet 2006; 45: proteins are together with CYP2C8 involved in the metabo- 235–252. lism of xenobiotics by cytochrome P450. TNFAIP2 is induced 4 Caffo O. Radiosensitization with chemotherapeutic agents. Lung Cancer 2001; 34(Suppl 4): S81–S90. in response to the proinflammatory molecules TNF and IL1B 5 Brunsvig PF, Hatlevoll R, Berg R, Lauvvang G, Owre K, Wang M et al. while PRKCABP is involved in modulating the activity of Weekly docetaxel with concurrent radiotherapy in locally advanced protein kinase C alpha (PRKCA) and anchoring the protein to non-small cell lung cancer: a phase I/II study with 5 years0 follow-up. mitochondria.29 Allele-specific alterations have been de- Lung Cancer 2005; 50: 97–105. 6 Baker SD, Verweij J, Cusatis GA, van Schaik RH, Marsh S, Orwick SJ et al. scribed enforcing the effect of low-penetrance variants in Pharmacogenetic pathway analysis of docetaxel elimination. Clin genes with oncogenic or tumour suppressor potential30 Pharmacol Ther 2009; 85: 155–163. indicating that a SNP that confers an alteration in the 7 Bruno R, Vivler N, Vergniol JC, De Phillips SL, Montay G, Sheiner LB. A function or level of a gene product in a deleted or amplified population pharmacokinetic model for docetaxel (Taxotere): model building and validation. J Pharmacokinet Biopharm 1996; 24: 153–172. area may lead to loss of heterozygosity either through deletion 8 Yu R, Chen C, Mo YY, Hebbar V, Owuor ED, Tan TH et al. Activation of of the ancestral or specific amplification of the variant allele. mitogen-activated protein kinase pathways induces antioxidant re- Our study indicates that it is possible to extract a limited sponse element-mediated via a Nrf2-dependent set of SNPs that will successfully predict an individual mechanism. J Biol Chem 2000; 275: 39907–39913. 9 Itoh K, Chiba T, Takahashi S, Ishii T, Igarashi K, Katoh Y et al. An Nrf2/ patient’s clearance level. Ultimately, drug concentration and small Maf heterodimer mediates the induction of phase II detoxifying clearance will be used in the clinic to adjust dosage of enzyme genes through antioxidant response elements. Biochem Biophys docetaxel administered to the patients. Therefore, being Res Commun 1997; 236: 313–322. able to predict beforehand whether a patient will have a 10 Venugopal R, Jaiswal AK. Nrf1 and Nrf2 positively and c-Fos and Fra1 negatively regulate the human antioxidant response element-mediated high or low clearance, based on genotyping performed on expression of NAD(P)H:quinone oxidoreductase1 gene. Proc Natl Acad germline DNA, will be of importance for clinical practise. Sci USA 1996; 93: 14960–14965. 11 Dai Y, Rashba-Step J, Cederbaum AI. Stable expression of human Conflict of interest cytochrome P4502E1 in HepG2 cells: characterization of catalytic activities and production of reactive oxygen intermediates. Biochemistry 1993; 32: 6928–6937. The authors declare no conflict of interest. 12 Morel Y, Barouki R. Repression of gene expression by oxidative stress. Biochem J 1999; 342(Pt 3): 481–496. 13 Raza H, John A. 4-hydroxynonenal induces mitochondrial oxidative Abbreviations stress, apoptosis and expression of glutathione S-transferase A4-4 and cytochrome P450 2E1 in PC12 cells. Toxicol Appl Pharmacol 2006; 216: 309–318. AAG a1-acid glycoprotein AUC area under the curve 14 Park JY, Shigenaga MK, Ames BN. Induction of cytochrome P4501A1 BSA body surface area by 2,3,7,8-tetrachlorodibenzo-p-dioxin or indolo(3,2-b)carbazole is HPLC high performance liquid chromatography associated with oxidative DNA damage. Proc Natl Acad Sci USA 1996; KW Kruskal–Wallis 93: 2322–2327. LD linkage disequilibrium 15 Edvardsen H, Irene Grenaker AG, Tsalenko A, Mulcahy T, Yuryev A, LOOCV leave one out cross validation Lindersson M et al. Experimental validation of data mined single MAF minor allele frequency nucleotide polymorphisms from several databases and consecutive MIS mutual information score dbSNP builds. Pharmacogenet Genomics 2006; 16: 207–217. NSCLC non-small cell lung cancer 16 Gurney H. Dose calculation of anticancer drugs: a review of the OMIM online Mendelian inheritance in man current practice and introduction of an alternative. J Clin Oncol 1996; QMIS quantitative mutual information score 14: 2590–2611. ROS reactive oxygen species 17 Andersen A, Warren DJ, Brunsvig PF, Aamdal S, Kristensen GB, Olsen H. SNPs single-nucleotide polymorphisms High sensitivity assays for docetaxel and paclitaxel in plasma using solid- UHT ultra high throughput phase extraction and high-performance liquid chromatography with UV detection. BMC Clin Pharmacol 2006; 6:2.

The Pharmacogenomics Journal Genetic variation influencing docetaxel clearance H Edvardsen et al 523

18 Tsalenko A, Ben-Dor A, Cox N, Yakhini Z. Methods for analysis and 25 Iwao-Koizumi K, Matoba R, Ueno N, Kim SJ, Ando A, Miyoshi Y et al. visualization of SNP genotype data for complex diseases. Pac Symp Prediction of docetaxel response in human breast cancer by gene Biocomput 2003: 548–561. expression profiling. J Clin Oncol 2005; 23: 422–431. 19 Rice JA. Mathematical Statistics and Data Analysis, 3rd edn. Australia; 26 Lee AJ, Conney AH, Zhu BT. Human cytochrome P450 3A7 has a distinct Belmont, CA: Thompson/Brooks/Cole, c2007. high catalytic activity for the 16alpha-hydroxylation of estrone but not 20 Tsalenko A, Sharan R, Edvardsen H, Kristensen VN, Borresen-Dale AL, 17beta-estradiol. Cancer Res 2003; 63: 6532–6536. Ben-Dor A et al. Analysis of SNP-expression association matrices. 27 Chen H, Fantel AG, Juchau MR. Catalysis of the 4-hydroxylation of J BioComput Bioinformat 2006; 4: 259–274. retinoic acids by cyp3a7 in human fetal hepatic tissues. Drug Metab 21 Hedenfalk I, Ringner M, Ben-Dor A, Yakhini Z, Chen Y, Chebil G et al. Dispos 2000; 28: 1051–1057. Molecular classification of familial non-BRCA1/BRCA2 breast cancer. 28 Hubatsch I, Ridderstrom M, Mannervik B. Human glutathione Proc Natl Acad Sci USA 2003; 100: 2532–2537. transferase A4-4: an alpha class enzyme with high catalytic efficiency 22 Barrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and visualization in the conjugation of 4-hydroxynonenal and other genotoxic products of LD and haplotype maps. Bioinformatics 2005; 21: 263–265. of lipid peroxidation. Biochem J 1998; 330: 175–179. 23 McDonald SL, Stevenson DA, Moir SE, Hutcheon AW, Haites NE, Heys SD et 29 Wang WL, Yeh SF, Chang YI, Hsiao SF, Lian WN, Lin CH et al. PICK1, an al. Genomic changes identified by comparative genomic hybridisation in anchoring protein that specifically targets protein kinase Calpha to docetaxel-resistant breast cancer cell lines. Eur J Cancer 2005; 41: 1086–1094. mitochondria selectively upon serum stimulation in NIH 3T3 cells. J Biol 24 Kristensen VN, Edvardsen H, Tsalenko A, Nordgard SH, Sorlie T, Chem 2003; 278: 37705–37712. Sharan R et al. Genetic variation in putative regulatory loci controlling 30 Nagase H, Mao JH, Balmain A. Allele-specific Hras mutations and gene expression in breast cancer. Proc Natl Acad Sci USA 2006; 103: genetic alterations at tumor susceptibility loci in skin carcinomas from 7735–7740. interspecific hybrid mice. Cancer Res 2003; 63: 4849–4853.

The Pharmacogenomics Journal