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Mechanisms of Mutagenesis
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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
,
∗
aGerman Cancer Research Center (DKFZ), Heidelberg, Germany bDepartment of Biology, University of Pisa, Pisa, Italy
cInstitute of Experimental Medicine, Academy of Sciences of the Czech Republic, Prague, Czech Republic dDepartment of Oncology, First Faculty of Medicine, Charles University, Prague, Czech Republic eCenter 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.
© 2008 Elsevier B.V. All rights reserved.
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:[email protected](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.
0027-5107/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.mrfmmm.2008.08.001
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 r2of 0.8.
This resulted in a selection of 15 tagging SNPs, with a mean r2of 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 2.5l of 2× master mix (Applied Biosystems) in a final volume of 5l. 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).
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
Table 1 (Continued)
SNP Positiona Cases (%)b Controls (%)b OR (95% CI)c P
value Ptrend
rs2728124 89,225,184 (3of 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
aPosition 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.
bNumbers 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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