A gene-wide investigation on polymorphisms in the ABCG2/BRCP transporter and susceptibility to colorectal cancer

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Mutation Research/Fundamental and Molecular

Mechanisms of Mutagenesis

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / m o l m u t

C o m m u n i t y a d d r e s s : w w w . e l s e v i e r . c o m / l o c a t e / m u t r e s

Short communication

A gene-wide investigation on polymorphisms in the ABCG2/BRCP transporter and

susceptibility to colorectal cancer

Daniele Campa

a

,

b

_{, Barbara Pardini}

c

_{, Alessio Naccarati}

c

_{, Ludmila Vodickova}

c

_{, Jan Novotny}

d

_{, Asta Försti}

a

,

e

, Kari Hemminki

a

,

e

_{, Roberto Barale}

b

_{, Pavel Vodicka}

c

_{, Federico Canzian}

a

,

∗

a_{German Cancer Research Center (DKFZ), Heidelberg, Germany} b_{Department of Biology, University of Pisa, Pisa, Italy}

c_{Institute of Experimental Medicine, Academy of Sciences of the Czech Republic, Prague, Czech Republic} d_{Department of Oncology, First Faculty of Medicine, Charles University, Prague, Czech Republic} e_{Center for Family and Community Medicine, Karolinska Institute, Huddinge, Sweden}

a r t i c l e i n f o

Article history:

Received 3 July 2008

Received in revised form 31 July 2008 Accepted 1 August 2008

Available online 19 August 2008

Keywords: ABCG2 BRCP Transporter Colorectal cancer Polymorphisms Susceptibility

a b s t r a c t

ATP-binding cassette (ABC) transporters actively export a wide variety of molecules from cells, contribut-ing to reduce the local cellular burden of toxic compounds. ABCG2/BCRP is abundantly expressed in epithelial cells of the intestine and colon. The expression and activity of this transporter in the gut differ between individuals, due at least in part to genetic polymorphisms, which may thus affect the risk of col-orectal cancer (CRC). We selected 15 tagging SNPs, covering all the known genetic variation of the gene, and typed them in 680 CRC cases and 593 controls. We found that heterozygous carriers of the minor alleles of SNPs rs2622621 and rs1481012 had a decreased risk of CRC, respectively, with odds ratios of 0.73 (95% conﬁdence interval 0.56–0.94; Pvalue= 0.017), and 0.72 (95% CI 0.53–0.97; Pvalue= 0.03). Thus, we found no strong and clearcut association between ABCG2 polymorphisms and CRC risk. To our knowledge this is the ﬁrst report on ABCG2 and CRC risk.

1. Introduction

The ATP-binding cassette (ABC) transporter superfamily is

among the largest and most broadly expressed protein

superfam-ilies known. The vast majority of its members are responsible

for the active transport of a wide variety of compounds

across biological membranes, including phospholipids, ions,

pep-tides, steroids, polysaccharides, amino acids, organic anions, bile

acids, drugs, and other xenobiotics

[1–3]

. In humans, 48 ABC

genes that are organized into seven subfamilies (A–G) have

been described, several of which are involved in well-deﬁned

genetic disorders

[1,3,4]

(

http://nutrigene.4t.com/humanabc.htm

,

http://www.gene.ucl.ac.uk/nomenclature/genefamily/abc.htm

).

The major role of ABC transporters is to reduce the local cellular

burden of toxic compounds, giving the individual cell a protection

against toxic effects. These export pumps are primarily expressed

in the apical membrane of epithelial cells, such as enterocytes,

∗ Corresponding author at: Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, D-69120 Heidelberg, Germany. Tel.: +49 6221 421791; fax: +49 6221 421810.

E-mail address:f.canzian@dkfz.de(F. Canzian).

which are exposed to xenobiotics. In these cells the same

trans-porters function on the one hand to reduce the entrance of harmful

substances and on the other hand to eliminate their detoxiﬁcation

products. The ﬁrst function (i.e. direct elimination of xenobiotics

entering the cell) represents a ﬁrst defense line against xenobiotics

and can be called “phase 0 metabolism”, indicating the close

con-nection to the activation and conjugation steps of detoxiﬁcation

[3]

. Likewise, the latter step has been called “phase III metabolism”

[5]

. It should be taken into account that phase 0 results from the

balance of the import of substances into cells, regulated by solute

carrier transporters, and the export, regulated by ABC transporters.

In particular, ABCG2/BCRP is expressed abundantly in the

api-cal membrane of normal intestinal and colonic epithelium in

vivo

[6]

. ABCG2 is believed to function as a component of the

organism’s defense against toxicity by restricting the entrance

of genotoxins from the intestinal tract into the organism and

by facilitating the removal of toxic metabolites from the

organ-ism via bile or urine

[7–9]

. Among dietary genotoxins exported

by ABCG2 is the meat-derived heterocyclic amine

2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP)

[9]

. Thus, ABCG2

may prevent the intestinal epithelial cells from exposure to such

genotoxins and hence provide protection against chemical-induced

carcinogenesis.

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Though the role of this transporter in multidrug resistance has

been the subject of numerous studies, the pattern of differential

expression of ABCG2 in normal and cancer tissue in vivo and a

pos-sible relevance of ABCG2 to the pathophysiology of tumorigenesis

and tumor progression has not yet been elucidated.

ABCG2 may play a key role in the defense of the organism from

exogenous substrates, the majority of which are metabolized in the

gut. For this reason, a variation in the activity of the efﬂux pump

may modify the elimination rate of toxic or carcinogenic

com-pounds, modulating thus susceptibility to colorectal cancer (CRC).

It is known that the expression and activity of this transporter in the

gut may differ between individuals, due at least in part to genetic

polymorphisms

[10–12]

.

In this report we investigated the genetic variability of the ABCG2

gene. Using a tagging approach and selecting 15 SNPs we covered

all the known genetic variation of the gene. We tested the impact of

ABCG2 SNPs on CRC risk in a case–control study based on subjects

from the Czech Republic. To our knowledge this is the ﬁrst report

on ABCG2 and CRC risk.

2. Patients and methods

2.1. Study population

A hospital-based case–control study was conducted to study CRC risk. Cases were CRC patients visiting nine oncological departments (two in Prague, one each in Benesov, Brno, Liberec, Ples, Pribram, Usti nad Labem, and Zlin) distributed in all geographic regions of Czech Republic and being representative of the popula-tion of the entire country. During the study period (September 2004 to February 2006), a total of 968 cases were diagnosed with CRC in these hospitals. This study includes 680 (70.2%) patients who could be interviewed and provided biological samples of sufﬁcient quality for genetic analysis. The lost cases were similar to those enrolled with respect to age, sex, tumor location, and extent. All cases had histolog-ical conﬁrmation of their tumor diagnosis. Genetic testing for hereditary HNPCC was recommended to four patients, who belonged to families complying with the Amsterdam criteria II. These patients were excluded from our study.

Controls were selected among patients admitted to ﬁve large gastroenterolog-ical departments (Prague, Brno, Jihlava, Liberec, and Pribram) all over the Czech Republic, during the same period of the recruitment of cases. Controls were sub-jects undergoing colonoscopy for various gastrointestinal complaints. The reasons to proceed to colonoscopy for both cases and controls were (i) macroscopic bleed-ing; (ii) positive fecal occult blood test (FOBT); and (iii) abdominal pain of unknown origin. Due to the high incidence of CRC in the Czech Republic, colonoscopy is largely recommended and practiced, and it is compulsory in case of a positive FOBT. The most common ﬁndings for these subjects were hemorrhoids or idiopathic bowel dis-eases (IBD). Only subjects whose colonoscopic results were negative for malignancy, colorectal adenomas or IBD were chosen as controls. Among 739 invited controls, a total of 593 (80.2%) were analyzed in this study (lost controls were similar to those included with respect to sex distribution).

Cases included in this study had a median age of 62 years (range 27–90), while controls had a median age of 56 years (range 28–91). Men were slightly more fre-quent (57.2% among cases and 53.6% of controls).

Study subjects provided information on their lifestyle habits (smoking, drink-ing, diet, etc.), and family/personal history of cancer, with the use of structured questionnaires[13].

The genetic analyses did not interfere with diagnostic or therapeutic procedures for the subjects. All participants signed an informed written consent and the design of the study was approved by the Ethical Committee of the Institute of Experimental Medicine, Prague, Czech Republic.

2.2. Selection of tagging SNPs

We aimed at surveying the entire set of common genetic variants in ABCG2. For this purpose, we used the algorithm of Carlson et al.[14]that was developed to select maximally informative sets of tagSNPs in candidate-gene association study. All poly-morphisms in the region of ABCG2 (including 5 kb upstream of the ﬁrst exon and 5 kb downstream of the last exon), with minor allele frequency (MAF)≥5% in Caucasians from the International HapMap Project (version 22;http://www.hapmap.org), were included. Tagging SNPs were selected with the use of the Tag-ger program within Haploview (http://www.broad.mit.edu/mpg/haploview/;

http://www.broad.mit.edu/mpg/tagger/)[15,16], using pairwise tagging with a min-imum r2_{of 0.8.}

This resulted in a selection of 15 tagging SNPs, with a mean r2_{of the selected}

SNPs with the SNPs they tag of 0.963, meaning that our selection captures to a very high degree the known common variability in this gene. Considering that the

genomic region of ABCG2 is characterized by high levels of linkage disequilibrium (LD), we postulate that such SNPs are also likely to tag any hitherto unidentiﬁed common SNPs in the gene. SNP rs2231142 was subsequently added to the list, in order to follow-up the association with rs1481012 (see Section3).

2.3. DNA extraction and genotyping

DNA was extracted from blood samples with standard proteinase K digestion followed by phenol/chloroform extraction and ethanol precipitation. The order of DNAs from cases and controls was randomized on PCR plates in order to ensure that an equal number of cases and controls could be analyzed simultaneously. All the genotyping was carried out using the Taqman assay. The MGB Taqman probes and primers were synthesized by Applied Biosystems (Foster City, CA). Primers and probes sequences are available upon request. The reaction mix included 5 ng genomic DNA, 10 pmol each primer, 2 pmol each probe and 2.5␮l of 2× master mix (Applied Biosystems) in a ﬁnal volume of 5␮l. The thermocycling included 40 cycles with 30 s at 95◦_{C followed by 60 s at 60}◦_{C. PCR plates were read on an ABI PRISM}

7900HT instrument (Applied Biosystems). The PCR proﬁle and reaction conditions were tested and optimized in order to contain equal amounts of template DNA, probes and primers and to be run with unique thermal conditions.

All samples that did not give a reliable result in the ﬁrst round of genotyping were resubmitted to up to two additional rounds of genotyping. Data points that were still not ﬁlled after this procedure were left blank. Repeated quality control genotypes (8% of the total) showed an average concordance of 99.1%.

2.4. Statistical analysis

The frequency distribution of genotypes was examined for the cases and the controls. Hardy–Weinberg equilibrium was tested in the cases and in the controls separately by chi square test. We used logistic regression for multivariate analyses to assess the main effects of the genetic polymorphism on CRC risk using a codominant inheritance model. The most common allele in the controls was assigned as the reference category. All analyses were adjusted for age and sex.

Additionally, we performed a logistic regression stratifying for the cancer site (colon versus rectum) and smoking (smokers versus non-smokers and heavy smok-ers vsmok-ersus light smoksmok-ers) or alcohol drinking (drinksmok-ers vsmok-ersus non-drinksmok-ers) habits. All the analyses were done with STATA software (StataCorp., College Station, TX).

3. Results

The genotype frequencies among the controls and cases groups

were in Hardy–Weinberg equilibrium for all the SNPs. The

distri-bution of the genotypes and their odds ratios (ORs) for association

with CRC risk are shown in

Table 1

.

We found that, in this sample set, heterozygotes for the G allele

of rs2622621 SNP had a decreased risk of CRC, with an OR of 0.73

(95% conﬁdence interval (95% CI) 0.56–0.94; P

value

= 0.017), but not

G/G homozygote individuals with an OR of 1.17 (95% CI 0.82–1.66;

P

value

= 0.37). When we added up all carriers of the G allele, they

had an OR of 0.81 (95% CI 0.65–1.02; P

value

= 0.07), suggesting an

association with decreased risk of CRC.

Moreover we found that heterozygotes for the G allele of

rs1481012 SNP had a decreased risk of CRC, with an OR of 0.72

(95% CI 0.53–0.97; P

value

= 0.03), but not G/G homozygote

individ-uals with an OR of 1.27 (95% CI 0.47–3.42; P

value

= 0.63). When we

added up all carriers of the G allele, they had an OR of 0.77 (95% CI

0.58–1.03; P

value

= 0.07), resulting, again, in a suggestive association

with a decreased risk of CRC.

SNP rs1481012 in the Hapmap database was showed to be in LD

(D

= 1; r

2

_{= 0.92) with SNP rs2231142, a G/T polymorphism leading}

to a change from lysin to glutamin. We decided therefore to add the

rs2231142 polymorphism to the tagging set, but we did not get any

statistically signiﬁcant association with a decreased risk of CRC, as

shown in

Table 1

.

Heterozygotes for SNP rs4148157 approached statistical

signif-icance (OR 0.74; 95% CI 0.55–1.00; P

value

= 0.052). We did not ﬁnd

any statistically signiﬁcant association between the other ABCG2

SNPs and CRC.

Analyses stratiﬁed by cancer site (colon versus rectum), alcohol

and smoking habits did not show any signiﬁcant interaction with

polymorphisms (data not shown).

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Table 1

Associations of ABCG2 polymorphisms with CRC risk

SNP Positiona _{Cases (%)}b _{Controls (%)}b _{OR (95% CI)}c _P

value Ptrend rs9999111 89,292,221 (intron 1) A/A 560 (85.8) 511 (88.9) 1 0.09 A/C 89 (13.6) 62 (10.8) 1.33 (0.94–1.91) 0.11 C/C 4 (0.6) 2 (0.3) 1.72 (0.30–9.70) 0.54 rs17731799 89,287,479 (intron 1) G/G 203 (31.3) 180 (30.2) 1 0.15 G/T 296 (45.6) 294 (49.3) 0.89 (0.68–1.16) 0.39 T/T 150 (23.1) 122 (20.5) 1.14 (0.82–1.57) 0.82 rs2725248 89,287,031 (intron 1) T/T 363 (55.3) 313 (54.1) 1 0.85 T/G 239 (36.4) 221 (38.2) 0.89 (0.69–1.14) 0.37 G/G 55 (8.4) 45 (7.8) 0.98 (0.63–1.53) 0.94 rs6857600 89,285,099 (intron 1) C/C 434 (65.2) 367 (65.2) 1 0.97 C/T 206 (30.9) 173 (30.7) 1.04 (0.81–1.35) 0.78 T/T 26 (3.9) 23 (4.1) 0.90 (0.49–1.66) 0.75 rs3109823 89,283,626 (intron 1) T/T 371 (57.9) 311 (54.1) 1 0.23 C/T 217 (33.9) 213 (37.0) 0.80 (0.62–1.03) 0.09 C/C 53 (8.3) 51 (8.9) 0.82 (0.53–1.27) 0.39 rs3114018 89,283,605 (intron 1) C/C 197 (29.8) 164 (27.8) 1 0.81 A/C/ 330 (49.8) 295 (50.1) 0.97 (0.71–1.32) 0.82 A/A 135 (20.4) 130 (22.1) 0.84 (0.61–1.76) 0.32 rs2231142 89,271,347 (exon 5) C/C 472 (81.1) 409 (79.1) 1 0.54 C/A 103 (17.7) 104 (20.1) 0.84 (0.62–1.16) 0.28 A/A 7 (1.2) 4 (0.8) 1.51 (0.43–5.27) 0.52 rs2725256 89,270,022 (intron 5) T/T 273 (42.6) 217 (39.0) 1 0.97 C/T 262 (40.9) 266 (47.8) 0.79 (0.61–1.02) 0.08 C/C 106 (16.5) 73 (13.1) 1.21 (0.83–1.72) 0.31 rs1481012 89,258,106 (intron 7) A/A 547 (82.6) 459 (78.6) 1 0.12 A/G 105 (15.9) 118 (20.2) 0.72 (0.53–0.97) 0.03 G/G 10 (1.5) 7 (1.2) 1.27 (0.47–3.42) 0.63 rs13120400 89,252,551 (intron 9) T/T 326 (51.9) 289 (50.7) 1 0.97 C/T 255 (40.6) 244 (42.8) 0.83 (0.51–1.35) 0.46 C/C 47 (7.5) 37 (6.5) 0.91 (0.56–1.47) 0.71 rs2622621 89,249,944 (intron 9) C/C 288 (44.6) 224 (39.5) 1 0.60 C/G 250 (38.7) 265 (46.7) 0.73 (0.56–0.94) 0.02 G/G 108 (16.7) 78 (13.8) 1.17 (0.82–1.66) 0.37 rs12505410 89,249,865 (intron 9) T/T 236 (35.1) 192 (33.0) 1 0.48 T/G 280 (41.6) 248 (42.7) 0.90 (0.69–1.17) 0.44 G/G 157 (23.3) 141 (24.3) 0.84 (0.62–1.14) 0.27 rs2054576 89,247,799 (intron 9) T/T 560 (83.2) 480 (80.8) 1 0.42 C/T 105 (15.6) 111 (18.7) 0.79 (0.60–1.07) 0.12 C/C 8 (1.2) 3 (0.5) 2.19 (0.56–8.51) 0.26 rs2231148 89,247,502 (intron 9) A/A 245 (38.7) 224 (39.6) 1 0.13 A/T 268 (42.3) 264 (46.7) 0.88 (0.68–1.14) 0.37 T/T 120 (19.0) 77 (13.6) 1.35 (0.95–1.93) 0.09 rs4148157 89,239,958 (intron 9) C/C 543 (81.9) 457 (78.5) 1 0.43 C/T 110 (16.6) 121 (20.8) 0.74 (0.55–1.00) 0.05 T/T 10 (1.5) 4 (0.7) 2.23 (0.68–7.29) 0.19

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Table 1 (Continued)

SNP Positiona _{Cases (%)}b _{Controls (%)}b _{OR (95% CI)}c _P

value Ptrend

rs2728124 89,225,184 (3_{of exon 16)}

A/A 182 (27.7) 169 (29.0) 1 0.43

A/T 316 (48.0) 283 (48.6) 0.95 (0.70–1.27) 0.71

T/T 160 (24.3) 130 (22.3) 0.95 (0.68–1.31) 0.74

a_{Position of SNP on chromosome 4, in base pairs (referred to NCBI build 36.1 of human genome and dbSNP build 128). In parentheses we report the position of the}

polymorphism with respect to the gene.

b_{Numbers may not add up to 100% of subjects due to genotyping failure. All samples that did not give a reliable result in the ﬁrst round of genotyping were resubmitted}

to up to two additional rounds of genotyping. Data points that were still not ﬁlled after this procedure were left blank.

c _{OR: odds ratio; CI: conﬁdence interval. Adjusted for age and gender. Values in bold are statistically signiﬁcant (P < 0.05).}

In addition, we performed analyses stratiﬁed by age (using the

median age = 59 as cutpoint), and we found that two

polymor-phisms were associated with CRC in the younger subgroup, with

borderline statistical support. We found that subjects homozygous

for the G allele of rs2622621 had an increased risk of CRC, with

an OR of 1.63 (95% CI 1.01–2.63; P

value

= 0.044). Subjects

homozy-gous for G allele of rs12505410 SNP had a decreased risk, with

an OR of 0.63 (95% CI 0.41–0.98; P

value

= 0.044). Finally,

strat-ifying by gender we did not found any statistically signiﬁcant

association.

4. Discussion

ABCG2 is a key player in the protection of the intestine from

outside offence, due for example to dietary carcinogens such as

PhIP. In this study we investigated the genetic variability of ABCG2

using a tagging approach and selecting 15 SNPs. Using this method

we covered all the known common genetic variation of this gene.

The previously published functional data and the association

with other type of tumors

[10,17]

made the polymorphisms of this

gene attractive candidates for affecting CRC risk. In our case–control

study we found two associations between two ABCG2 variants and

a decreased risk of CRC.

The main ﬁnding of this work is that heterozygous carriers of

the G allele of rs2622621 SNP and of the G allele of rs1481012

SNP had a deceased risk of CRC. Using the dominant model

(i.e. by combining heterozygotes with homozygotes for the rare

allele), we found a borderline, not statistically signiﬁcant

associ-ation with a decreased risk of CRC OR of 0.77 (95% CI 0.58–1.03;

P

value

= 0.07) for SNP rs1481012. To check whether this ﬁnding could

be explained by LD with an untyped functional SNP, we typed

additionally SNP rs2231142, which is in high LD with rs1481012

and whose alleles result in a change from lysine to glutamine.

This change from a strongly basic amino acid to one that is

not basic is predicted not to be deleterious by PolyPhen

analy-sis (

http://genetics.bwh.harvard.edu/pph/

). On the other hand, this

polymorphism plays a role in pharmacogenetics of many drugs such

as mitoxantrone, topotecan, and doxorubicin, and is also associated

with altered risk of neoplastic diseases

[10,17–20]

, which suggests

that it is functionally relevant. However, we did not ﬁnd any

statis-tically signiﬁcant association with a risk of CRC for rs2231142.

Thus, either our ﬁrst ﬁnding was due to random chance, or one

of rs2622621 and rs1481012 SNP is a real hit. In the latter case,

either SNP could be the causal variant or in LD with an unknown

causative variant. This hypothesis could be tested by resequencing

of the region and genotyping all novel variants in a larger set of

cases and controls.

SNP rs2622621 is in the middle of intron 9, and rs1481012 is in

the seventh intron. There is no indication that either SNP has a

bio-logical function. Moreover, the fact that we found associations only

with the heterozygote individuals corroborates the hypothesis that

our ﬁndings are due to random chance. Applying a multiple testing

correction, due to the many comparisons we have performed, leads

to non-signiﬁcant results.

In addition to the data on cancer risk, our study provides

information potentially relevant for pharmacogenetics. Most drugs

are developed based on data from European-derived ‘reference’

populations, but clinically relevant DNA polymorphisms often

demonstrate population-speciﬁc patterns of allele frequencies.

The knowledge of the frequency distribution of functional

poly-morphisms in a population may contribute to national planning

for selection of therapeutic options. To this end, data on allelic

frequencies provided by our study should be complemented by

pharmacokinetic characterization of the polymorphisms. Here we

present, to our knowledge, the largest existing study on common

polymorphisms in a key gene such as ABCG2, which is involved in

determining bioavailability of many drugs.

Conﬂict of interest

The authors declare that there are no conﬂicts of interest.

Acknowledgements

The study was partially supported by grants GACR 310/07/1430,

AVOZ 50390703 and 50390512 of the Czech Republic.

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