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Carcinogenesis Advance Access originally published online on January 27, 2007
Carcinogenesis 2007 28(6):1287-1293; doi:10.1093/carcin/bgm021
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© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Development of lung cancer before the age of 50: the role of xenobiotic metabolizing genes

Federica Gemignani1,2,{dagger}, Stefano Landi1,{dagger}, Neonilia Szeszenia-Dabrowska3, David Zaridze4, Jolanta Lissowska5, Peter Rudnai6, Eleonora Fabianova7, Dana Mates8, Lenka Foretova9, Vladimir Janout10, Vladimir Bencko11, Valérie Gaborieau2, Lydie Gioia-Patricola2, Ilaria Bellini1, Roberto Barale1, Federico Canzian12, Janet Hall13, Paolo Boffetta2, Rayjean J. Hung2,14 and Paul Brennan2,*

1 Genetics, Department of Biology, University of Pisa, 56100 Pisa, Italy
2 International Agency for Research on Cancer, 150 cours Albert Thomas, F-69372 Lyon, France
3 Department of Epidemiology, Institute of Occupational Medicine, 92-348 Lodz, Lodz, Poland
4 Institute of Carcinogenesis, Cancer Research Centre, 115478 Moscow, Moscow, Russia
5 Department of Cancer Epidemiology and Prevention, Cancer Center and Maria Sklodowska-Curie Institute of Oncology, 02-784 Warsaw, Warsaw, Poland
6 National Institute of Environmental Health, Fodor József National Center for Public Health, 1097 Budapest, Budapest, Hungary
7 Specialized Institute of Hygiene and Epidemiology, 97556 Banska-Brystica, Banska Bystrica, Slovakia
8 Department of Hygiene, Institute of Public Health, 76256 Bucharest, Romania
9 Department of Cancer Epidemiology and Genetics, Masaryk Memorial Cancer Institute, 65653 Brno, Brno, Czech Republic
10 Department of Preventive Medicine, Faculty of Medicine, Palacky University, 77180 Olomouc, Olomouc, Czech Republic
11 Institute of Hygiene and Epidemiology, First Faculty of Medicine, Charles University of Prague, 11636 Prague 1, Prague, Czech Republic
12 Genomic Epidemiology Group, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
13 Inserm U612 Institut Curie, 91405 Orsay, Paris, France and
14 Division of Epidemiology, Department School of Public Health, University of California, Berkeley, CA 94720 USA

* To whom correspondence should be addressed. Tel +33 472738391; Fax: +33 472738342; Email: brennan{at}iarc.fr


   Abstract
Top
 Abstract
Introduction
 Materials and methods
 Results
 Discussion
 References
 
The role of genes coding for xenobiotic metabolizing enzymes (XMEs) and the risk of lung cancer is unclear. Under the assumption that these genes may be more important among people having a diagnosis of lung cancer at younger ages, we have investigated the role of single-nucleotide polymorphisms (SNPs) within phase I and phase II XME genes, and also genes involved in the metabolism of nucleic acids in a series of young onset patients and matched controls. We genotyped 299 lung cancer cases diagnosed before the age of 50 and 317 controls, from six countries of Central and Eastern Europe, by use of an oligonucleotide microarray and arrayed primer extension technique for 45 SNPs in 15 phase I XME genes, 46 SNPs in 17 phase II genes and 9 SNPs in 4 genes related to metabolism of nucleic acids. Heterozygote carriers of SNPs in CYP1A2 1545T>C, –164C>A and –740T>G; CYP2A6 –47A>C; MDR1 3435T>C; NAT1 1088T>A and 1095A>C; GSTA2 S112T; GSTM3 V224I and MTHFR A222V had altered risk of developing lung cancer. Phenotypes reconstructed after haplotype analyses showed that the carriers of the combined NAT1 fast+ NAT2 fast phenotypes were at lower risk when compared with those with the combined NAT1 slow + NAT2 slow acetylator phenotypes. Finally, extensive EPHX1 metabolizers showed an increased risk as compared with the poor metabolizers.

Abbreviations: CI, confidence interval; FPRP, false positive report probability; HWE, Hardy–Weinberg equilibrium; OR, odds ratio; SNP, single-nucleotide polymorphisms; UDP, uridine diphosphate; XME, xenobiotic-metabolizing enzyme


   Introduction
Top
 Abstract
 Introduction
Materials and methods
 Results
 Discussion
 References
 
Lung cancer is one of the most common cancers worldwide (1). While it is apparent that it is predominantly caused by exposure to tobacco smoke, only a minority of heavy smokers will develop a tobacco-related cancer. It has been calculated that only 15% of lifelong smokers develop a lung cancer by the age of 75 (2). It is thought that genetic factors may lead to large differences in the level of internal carcinogen dose which an individual is subjected to, leading to differences in risk for individuals with similar exposures (3). This could, in part, be due to the differences in the rate of metabolism and detoxification of xenobiotic compounds inhaled in the tobacco smoke and a modification of the balance between these two processes. Inhaled pro-carcinogens are bioactivated into DNA-reactive metabolites by cytochromes P450s but are also detoxicated through ‘phase II’ enzymes such as, for example, the glutathione S-transferases, the N-acetyltransferases and the UDP-glucoronosyltransferases (4).

Polymorphisms functionally important in determining the rate of biotransformation have been described (5), and several studies have shown associations between increased risk of lung cancer and polymorphisms within these xenobiotic-metabolizing enzymes (XMEs) (6–13).

Previous epidemiological studies have suggested that a large proportion of lung cancers occurring before the age of 50 years have a more pronounced genetic component and the risks due to genetic factors are further amplified by cigarette smoking (14,15). Thus, the role of polymorphisms in XMEs should be even more prominent among young lung cancer patients. However, in spite of numerous studies on these genes, the role of individual genes and variants is still unclear. This is partly due to the difficulties in recruiting a sufficiently large study cohort with lung cancer at a young age to have the statistical power to detect altered risk. In the present report, we describe an association study performed in a case–control setting that explored single-nucleotide polymorphisms (SNPs) in 36 genes of phase I and II XME genes in a series of 299 patients diagnosed of lung cancer under 50 years old and 317 controls.


   Materials and methods
Top
 Abstract
 Introduction
 Materials and methods
Results
 Discussion
 References
 
Study population
This study was conducted in 15 centers in six Central and Eastern European countries: the Czech Republic (Prague, Olomouc and Brno), Hungary (Borsod, Heves, Szabolcs, Szolnok and Budapest), Poland (Warsaw and Lodz), Romania (Bucharest), Russia (Moscow) and Slovakia (Banska Bystrica, Bratislava and Nitra). Each center followed an identical protocol and was responsible for recruiting a consecutive group of patients who were newly diagnosed with lung cancer and a comparable group of hospital-based control subjects without lung cancer from February 1998 to October 2002. All cancer diagnoses were confirmed histologically or cytologically. Eligible subjects (case patients and control subjects) must have resided in the study area for at least 1 year before recruitment. Lung cancer case patients were identified through an active search of the records of clinical and pathology departments at the participating hospitals. All centers attempted to recruit all eligible patients as soon as possible after the patient had received an initial diagnosis of lung cancer. The maximum time interval between diagnosis and recruitment was 3 months. All study subjects (case patients and control subjects) and their physicians provided written informed consent. This study was approved by the institutions at all study centers, and ethical approval was obtained from the International Agency for Research on Cancer (Lyon, France), the coordinating center. At all centers, except the Warsaw center, control subjects were chosen from among in-patients and out-patients admitted to the same hospitals as the case patients or hospitals serving the same population; case patients and control subjects from each hospital were frequency matched by sex, age (±3 years), center and referral (or residence) area. Control subjects were eligible for this study if they had been diagnosed with non-tobacco-related diseases or had undergone minor surgical procedures or had benign disorders, common infections, eye conditions (except cataract or diabetic retinopathy) or common orthopedic diseases (except osteoporosis). At the Warsaw center, control subjects were selected by random sampling of the general population using the Electronic List of Residents. Overall, the average participation rate was 91.0% among case patients and 91.2% among control subjects. Case patients and control subjects underwent an identical in-person interview during which they completed a detailed questionnaire and provided blood samples. The questionnaire collected information about demographic variables, such as sex, date of birth and education level, medical history, family history of cancer, history of tobacco consumption, including frequency, intensity, duration and status, history of alcohol consumption, diet history (using a general food frequency questionnaire) and occupational history. Blood samples were stored in liquid nitrogen until further processing. Of the 2633 case patients and 2884 control subjects who agreed to participate in this study, 2188 case patients (83%) and 2198 control subjects (76%) provided blood samples during the interview.

Selection of polymorphisms
Details on the selection of SNPs within genes of phase I and phase II of the xenobiotic metabolism were described in detail elsewhere (16–18). Briefly, we tried to investigate all the polymorphisms with known or inferred impact on the function of the XME genes. We enriched our set with SNPs with high allele frequency in Caucasians and, whenever possible, with information on the haplotype structure and on the haplotype–phenotype relationship.

Laboratory techniques
Genomic DNA was extracted from blood samples using Puregene chemistry (Gentra Systems, Minneapolis, MN). DNA concentrations were measured by using PicoGreen dsDNA quantification kits (Molecular Probes, Leiden, The Netherlands). All polymorphisms were analyzed together for a given sample by a microarray technique based on the arrayed primer extension principle using a technique described previously (17).

To ensure quality control, we followed several strategies:

(i) DNA samples from case patients and control subjects were randomly distributed, and all genotyping was conducted by personnel who were blinded to the case–control status of the subjects.
(ii) Each arrayed primer extension oligonucleotide was spotted in replicate.
(iii) Each SNP was analyzed independently, by genotyping both the sense and the anti-sense strands of the DNA (in case of disagreement the base call was discarded).
(iv) Internal positive controls allowed to verify that the intensities of the four channels (A, C, T, G) were equilibrated.
(v) The automatically called bases were visually inspected by three independent trained operators; discordant results were re-checked, and, in case of disagreement, were discarded.
(vi) DNA samples from individuals of known genotypes were added to check periodically the validity of the genotyping.
(vii) Ten percent of the samples, randomly selected, were re-genotyped blindly (both case patients and control subjects).

Haplotype–phenotype reconstruction
Haplotypes were reconstructed using the software PHASE (19), and a global test of hypothesis for the gene was carried out, followed by contrasts for specific haplotypes. Haplotypes with a frequency <5% were pooled, to reduce the degrees of freedom. Moreover, when information on the haplotype–phenotype relationship was known (for the genes NAT1, NAT2, CYP2A6, CYP2D6, CYP2E1 and EPHX1), we grouped subjects according to their possible phenotype, in order to extend the statistical analysis beyond that of the haplotypes alone. N-acetyltransferases 1 and 2 haplotypes were grouped based on the Wikman et al. (20) and the database at http://atlasgeneticsoncology.org/. According to this classification, the NAT1*10 haplotype is associated with a ‘fast’ acetylator phenotype, NAT1*4 and NAT1*11 are considered to give a ‘normal’ acetylator phenotype and NAT1*14 and NAT1*15 are the ones associated with a ‘slow’ acetylator phenotype. Subjects carrying one fast and one slow allele were grouped as normal acetylators. For NAT2, the haplotypes *4, *12 and *13 are all considered associated with a fast acetylator phenotype, whereas the haplotypes *5, *6 and *7 are considered as slow acetylators. Thus, subjects were grouped according to their status as ‘homozygous fast acetylators’ when they carried two fast alleles, as ‘heterozygous intermediate acetylators’ when they carried one fast and one slow allele and ‘homozygous slow acetylators’ when they carried two slow alleles.

For CYP2A6, the alleles *1A and *1B are considered to give a normal activity, the allele *9 a ‘poor metabolizer’ phenotype and *2 to lack any activity [http://www.cypalleles.ki.se/ and (21)]. Heterozygous carriers of the *1A or *1B haplotypes with either *2 or *9 were classified as poor metabolizers. CYP2D6 haplotypes were grouped according to Gaedigk (22) and Marez (23). According to these criteria, the presence of one allele of high enzymatic activity is sufficient to produce an ‘extensive metabolizer’ phenotype, whereas the carriers of two ‘low-activity’ alleles were classified as poor metabolizers. Haplotype CYP2D6*5 corresponds to the deletion of the locus, i.e. a complete lack of biological activity. Our method of genotyping does not allow us to discriminate a heterozygous carrier of a ‘null’ allele. However, the null allele has biological relevance only when it is present in the homozygous state resulting in no enzymatic activity. When it is in heterozygosity, the resulting enzymatic activity is due to that of the remaining functional allele (22). Thus, the genotyping should give a reliable assessment of the phenotype status also for the heterozygotes of the *5 allele. The null/null genotype would not give any polymerase chain reaction product, therefore resulting in a lack of signal on the microarray; however, it should be noted that as the frequency of the *5 allele in Caucasians is ~5% (22), the expected number of homozygotes would be less than one for both cases and controls.

The method of genotyping based on polymerase chain reactions and microarray does not allow to detect the ‘ultra’ metabolizers due to multiple copies of the gene (e.g. CYP2D6*1 x 2, *2 x 2 and *4 x 2). However, according to Gaedigk (22), the sum of these three duplicated alleles is only slightly >1% in Caucasians. Thus, only about six ultra-extensive metabolizers, heterozygotes for the duplicated alleles, are expected to be found in our sample set. We do not believe that this possible misclassification would change consistently the risk assessment for the CYP2D6 gene.

For CYP2E1, the haplotypes *1A and *1B are the ‘wild type’ alleles, associated with a normal activity. All the other haplotypes are associated with an increased biological activity [http://www.cypalleles.ki.se/ and (24,25)] thus, carriers of two such haplotypes are considered extensive metabolizers, whereas carriers of one normal and one extensive haplotypes were classified as intermediate.

For EPHX1, we reconstructed the haplotypes and classified them according to the activity of the combined polymorphisms His139Arg and Tyr113His, following the classification of Benhamou (26).

Statistical analysis
We tested departure from Hardy–Weinberg equilibrium (HWE) in the controls by a chi-square test, using P = 0.01 as threshold. This threshold was chosen based on anti-conservativeness of this test, as noted by Wigginton (27). The frequency distributions of demographic variables and putative risk factors for lung cancer, including country of residence, age at recruitment (which, for case patients, was a proxy for age at diagnosis), sex, highest education level and smoking status, were examined for case patients and control subjects. Former smokers were defined as smokers who stopped smoking at least 2 years before the interview. Tobacco consumption included smoking of cigarettes, pipes and cigars. Smokers were classified as never, former and current smokers.

The minimum detectable odds ratio (OR) was calculated for each sequence variant based on its genotype frequency, our study sample size and a statistical power of 80%, as described previously (28). Our study had an 80% power to detect a minimum OR of 2.5 for relatively rare variants (5%) and a minimum OR of 1.6 for common variants (≥30%).

We used unconditional multivariate logistic regression analysis to examine associations between genetic polymorphisms and lung cancer risk by estimating ORs and 95% confidence intervals (CIs). Genotypes were categorized into three groups (major allele homozygous, heterozygous and homozygous variant) when the allele frequencies allowed or into two groups (major allele homozygous and minor allele carriers) for rare polymorphisms. We computed false positive report probabilities (FPRPs) (29) for the nominally significant associations we have observed between SNPs and lung cancer risk. Following Wacholder (29), we used a threshold of noteworthiness of FPRP ≤ 0.2. Also Bonferroni's correction was applied. All statistical analyses were conducted using SAS (version 9.1, SAS Institute, Cary, NC). All statistical tests were two-sided.


   Results
Top
 Abstract
 Introduction
 Materials and methods
 Results
Discussion
 References
 
Table I shows the frequency distribution of demographic characteristics among the 299 cases and 317 controls. As expected, the proportion of current smokers was far higher among the cases than controls (86.6 versus 53.6%, P < 0.001). The sex and age distributions were similar among cases and controls, although there was a tendency for cases to have a lower education level than the controls (P = 0.01). With respect to the histological types of the tumors in the cases, the numbers of adenocarcinoma, squamous and small cell carcinomas were similar. All SNPs (except for CDA 79A>C, EPHX1 Y113H, GSTA2 S112T, MDR1 2677G>T) were in HWE among controls. The results of 10% blindly repeated samples were at least 99% concordant each other. Because of the nature of the method of genotyping where all the SNPs are analyzed on one chip, genotyping that failed for any individual SNP could not be repeated. Genotyping success rates for individual polymorphisms averaged 92%.


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  		Table I. Demographic characteristics of lung cancer cases and controls

 
Analysis of individual SNPs
The results of the analyses carried out for each SNP are presented in Table II (genes of phase I) and III (genes of phase II). Two SNPs in CYP1A2 (–164C>A, 1545T>C) were found associated with an increased risk of lung cancer among heterozygote carriers (P < 0.05), although not among homozygotes, whereas the allele –740T>G was found associated with a decrease of risk (Ptrend = 0.01). The allele –47A>C within CYP2A6 was associated with a reduced risk of lung cancer (Ptrend = 0.01).


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  		Table II. Associations between lung cancer risk and SNPs belonging to genes involved in the phase I of xenobiotic metabolism

 


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  		Table III. Associations between lung cancer risk and SNPs belonging to genes involved in the phase II of xenobiotic metabolism

 
Among the phase II genes (Table III), the 1088T>A and 1095A>C NAT1 alleles were associated with a reduced risk of lung cancer, whereas the GSTA2 S112T GSTA2 and the MDR13435T>C alleles appeared associated with an increased risk. However, as the variant GSTA2 S112T diverged from HWE this finding should be interpreted with caution. Finally, Table IV shows the results for SNPs within genes involved in the metabolism of nucleic acids. We found an association for homozygous carriers of A222V in MTHFR (OR = 2.32, 95% CI 1.23–4.37, P < 0.01) with an increased risk of lung cancer.


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  		Table IV. Associations between lung cancer risk and SNPs belonging to genes involved in the metabolism of nucleic acids

 
Analysis of diplotypes and phenotypes
The analyses of the diplotypes and related phenotypes, for selected genes, were consistent with the analyses for individual SNPs. In the cluster-bearing CYP1A1 and CYP1A2 on chromosome 15 (Table V), the heterozygotes carrying haplotype 4 (i.e. having both the risk alleles for CYP1A2 –164 and 1545) were at increased risk (OR = 1.82, 95% CI 1.09–3.04), compared with the homozygotes for the reference haplotype 1. From Table V it is possible to see that the two polymorphisms genotyped in CYP1A2 are in almost complete linkage disequilibrium.


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  		Table V. The most frequent haplotypes (minor frequency >1%) reconstructed for the cluster-bearing genes CYP1A1 and CYP1A2, spanning 35785 bp from rs4646903 to rs2470890 on chromosome 15

 
The phenotypes assessed for NAT1 showed that individuals carrying at least one fast acetylator allele were at reduced risk (OR = 0.70, 95% CI 0.48–1.02, P = 0.065) as compared with individuals without any of them. A similar trend was observed also for the NAT2-assessed phenotypes: heterozygotes and homozygotes fast acetylators were at reduced risk (OR = 0.76, 95% CI 0.53–1.10, P = 0.14) as compared with homozygotes slow acetylators. When we compared the acetylator status combining the two NAT genes, the fast NAT1 and fast NAT2 individuals were at reduced risk (OR = 0.48, 95% CI 0.27–0.85) as compared with slow NAT1 and slow NAT2 individuals. People having one fast and one slow phenotype were at intermediate risk (Ptrend = 0.012). Finally, when the reconstructed phenotypes for EPHX1 were divided into three classes (poor/low, normal and ultra metabolizers), the ultra metabolizers showed a statistically significant reduced risk (OR = 0.57, 95% CI 0.35–0.95), as compared with the poor metabolizers (Ptrend = 0.027). All other analyses did not show significant associations and are not reported for brevity.

Calculation of FPRP showed that none of the above associations remained noteworthy (FPRP ≤ 0.2), when a prior probability of association of 25% or lower was considered. The estimated FPRP for MTHFR C677T was 0.237 when the prior probability was 25%.


   Discussion
Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
References
 
In the present study, we have shown that several XME genes carry polymorphisms having some relevance for the risk of early onset lung cancer. At the phenotypic level, we found an association between the low activity of EPHX1 and risk. This gene has been studied in several studies, and the various meta- and pooled analyses have been inconclusive (30,31). From the biological point of view, it is unclear if this enzyme should be classified as a phase I or phase II XME gene as it is involved in the bioactivation of the powerful pro-carcinogen benzo(a)pyrene as well as in the detoxication of various epoxides (32).

For the other genes, we observed that genotypes corresponding to fast phenotypes in phase I XME genes and/or slow phenotypes in phase II are associated with an increased risk of early onset lung cancer. For example, CYP1A2 –164C>A, related to a high level of gene expression (33), was associated with a higher risk, in our study. The polymorphism –47A>C in CYP2A6 is known to affect the TATA box of the promoter thereby halving the expression of the gene (21,34), and was associated with a reduced risk in our study. It has been shown that the hepatic expression of the GSTA Ser112 variant was ~4-fold higher than that of the Thr112 variant, paralleling the capacity to conjugate the active compounds with glutathione (35) and we found that the Thr112 allele is associated with a higher risk in our study. This result should, however, be taken with caution as the polymorphism diverged from the HWE. Finally the NAT1 1088T>A and 1095A>C polymorphisms, typically associated with a fast acetylator phenotype (20), were associated with a reduced risk. It should be noted that we found also a cooperative effect between NAT1 and NAT2 acetylator phenotypes. Other studies, not restricted to early onset lung cancer, showed that NAT1 phenotypes were associated with the risk (8). One of the strongest associations noted was that between the functional variant A222V in MTHFR and an increased risk of lung cancer. The common Val222 variant in the methylenetetrahydrofolate reductase gene leads to a disturbed folate metabolism. It causes a decrease of the activity of the enzyme by 70% in homozygotes for the Val allele (36) and is associated with elevated plasma homocysteine levels (37) as well as decreased genomic DNA methylation (38). This variant was reported to be associated with increased cancer risk (39), including lung cancer (40). It has been thought that this polymorphism may play a role in situations where dietary folate intake is low (41,42). The population investigated in our study, located in Central and Eastern Europe, is reported to have a low consumption of dietary folate (World Health Organization, 2005; European Health for all statistical database: http://www.euro.who.int/hfadb). Thus, the polymorphism Alanine-to-Valine at codon 222 in the MTHFR gene may be an important risk factor for lung cancer at young age in situations where the diet is poor in folate. More studies are clearly warranted on the role of this polymorphism in lung and other type of cancers occurring in young age, when dietary folate is low.

The effect of polymorphisms may be modified by smoking exposures, and may differentiate by histology subtype. In our study population of lung cancer patients with young onset, adenocarcinoma is slightly over-represented compared with the whole population (23%). To address the concern of disease heterogeneity and potential effect modification by smoking status, we have conducted stratified analysis but the results did not reveal evidence of effect modification by smoking status or histology (data not shown). Nevertheless, our study power for stratified analysis is very limited thus no conclusion can be drawn.

In summary, our data seem to corroborate the hypothesis that inhaled pro-carcinogenic compounds undergo bioactivation into more reactive molecules. The extent of this metabolism seems to affect the risk of lung cancer also in young subjects, paralleling some of the mechanisms already hypothesized and confirmed when lung cancer is studied in older patients. Thus, each individual finding seems to reinforce the same a priori hypothesis. However, the main limitation of this study is limited sample size, thus it is possible that most of the associations we have described are due to chance. When each result was corrected for multiple testing, the associations were no longer noteworthy.


   Footnotes
 
{dagger} These authors contributed equally to this work. Back


   Acknowledgments
 
This work was supported by a grant from the European Commission (DG-XII) (contract IC15-CT96-0313) and a National Cancer Institute R01 grant (contract CA 092039-01A2), an Association for International Cancer Research grant (contract 03-281), a ‘Marie-Curie Reintegration Grant’ (contract MERG-CT-2004-506373) and Associazione Italiana Ricerca sul Cancro principal investigator grant 2005. The Warsaw part of the study was supported by a local grant from The Polish State Committee for Scientific Research (grant SPUB-M-COPERNICUS/P-05/DZ-30/99/2000). F.G. is a recipient of a fellowship from the International Association for the Study on Lung Cancer.

Conflict of Interest Statement: None declared.


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 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 

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Received October 3, 2006; revised January 12, 2007; accepted January 18, 2007.


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