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Carcinogenesis Advance Access originally published online on February 16, 2007
Carcinogenesis 2007 28(9):1902-1905; doi:10.1093/carcin/bgm039
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© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Evidence of gene–gene interactions in lung carcinogenesis in a large pooled analysis

Paolo Vineis1,2, Sisko Anttila3, Simone Benhamou4, Monica Spinola5, Ari Hirvonen3, Chikako Kiyohara6, Seymour J. Garte7, Riccardo Puntoni8, Agneta Rannug9, Richard C. Strange10 and Emanuela Taioli7,*

1 Epidemiology Department, Univerisitá degli Studi di Torino, 10126 Torino, Italy
2 Department of Epidemiology and Public health, Imperial College London, Norfolk Place W2 1PG, London, UK
3 Orion Pharma, Research Centre, Orionintie 1, FI-02200 Espoo P.O. Box 65, FI-02101 Espoo, Finland
4 INSERM, U794, Evry, France
5 Department of Experimenta; Oncology, Istituto Nazionale Tumori, Via G. Venezian 1, 20133 Milan, Italy
6 Department of Preventive Medicine, Graduate School of Medical sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
7 Cancer Institute, University of Pittsburgh, 5150 Centre Avenue, Pittsburgh, PA 15232, USA
8 Department of Epidemiology and Biostatics, National Cancer Research Institute, 16100 Genoa, Italy
9 Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden, and Division of Cellular and Genetic Toxicology, Stockholm University, SE-171 77 Stockholm, Sweden
10 Keele Multiple Sclerosis Research, Human Genomics Research Group, Institute of Science and Technology in Medicine, Keele University Medical School, Hartshill Campus, University Hospital of North Staffordshire, Stoke on Trent, Staffordshire ST4 7LN, UK

* To whom correspondence should be addressed. Tel: +412 623 2217; Fax: +412 623 1382; Email: taiolien{at}upmc.edu


   Abstract
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 Abstract
Introduction
 Material and methods
 Results
 Discussion
 References
 
To test the hypothesis of interaction among genetic variants in increasing the individual risk of cancer, we have studied the cumulative effect on lung cancer risk of variants in three metabolic genes, CYP1A1, GSTM1 and GSTT1, which are involved in the metabolism of the tobacco smoke constituents and environmental contaminants, polycyclic aromatic hydrocarbons and of other lung carcinogens. We have selected from the Genetic Susceptibility to Environmental Carcinogens pooled analysis all the studies on lung cancer conducted after 1991 in which all variants were available. The data set includes 611 cases and 870 controls. We found a cumulative effect of the combination of the a priori ‘at-risk’ alleles for these genes (P for trend 0.004). The risk of lung cancer was increased with the combination of CYP1A1*2B or CYP1A1*4 alleles and the double deletion of both GSTM1 and GSTT1 up to an odds ratio (OR) of 8.25 (95% confidence interval 2.29–29.77) for the combination including CYP1A1*4; among never smokers, the latter combination was associated with an OR of 16.19 (1.90–137). Estimates did not change after adjustment by the number of cigarettes smoked and duration of smoking were consistent across ethnicities and were approximately the same for adenocarcinomas and squamous cell carcinomas. These observations from a large pooled analysis strongly suggest the existence of gene–gene interactions in lung carcinogenesis. People with rare combinations of common gene variants have a high risk of cancer and can be assimilated to subjects with highly penetrant mutations.

Abbreviations: CI, confidence interval; GSEC, Genetic Susceptibility to Environmental Carcinogens; OR, odds ratio


   Introduction
Top
 Abstract
 Introduction
Material and methods
 Results
 Discussion
 References
 
Although the role of gene–gene interaction in the risk of human lung cancer is not clear, animal models make plausible such role, suggesting that several gene polymorphisms and/or haplotypes may cooperate in increasing the individual risk of cancer (1). According to a model of gene–gene interaction, we have shown previously that subjects with an increasing number of ‘at-risk’ variant alleles for DNA repair genes have increasing levels of DNA damage, with a dose–response relationship (2). Since the study of gene–gene interactions may be important in the identification of high-susceptibility subgroups, in the present paper we consider the cumulative effect of polymorphisms in three metabolic genes, CYP1A1, GSTM1 and GSTT1, on the pathway to tobacco smoke metabolism.

CYP1A1 is a phase I, predominantly extrahepatic, microsomal enzyme involved in the bioactivation of carcinogenic polycyclic aromatic hydrocarbons including benzo(a)pyrene. Early work (3,4) suggested that the risk of lung cancer could be modulated by high aryl hydrocarbon hydroxylase activity. Following the identification of CYP1A1 gene polymorphisms, subsequent work focused on the association of these polymorphisms and CYP1A1 inducibility, whereas analyses of the association between CYP1A1 polymorphisms and lung cancer were published in the 1990s (5–10). More recently, meta-analyses and pooled analyses of this association have been published (11,12). The CYP1A1*2A allele has a T to C mutation in the 3' region. An A to G transition in exon 7 creates a second allelic variant, CYP1A1*2B (also known as exon 7 polymorphism), which leads to an amino acid substitution of Val for Ile in the heme-binding region and results in an increase in microsomal enzyme activity (13). The variant CYP1A1*3 has a polymorphism in intron 7 and is African-American specific. Studies on the association between lung cancer and these CYP1A1 polymorphisms have been published, with conflicting results (11,12,14). Another exon 7 restriction fragment length polymorphism giving a mutation in codon 461, and an amino acid change of Thr to Asn, exists 2 bp away from the Ile/Val polymorphism and is referred to as *4.

Cytosolic glutathione-S-transferases are a large family of isozymes involved in detoxification of many electrophilic substrates by their conjugation with reduced glutathione. The class mu contains a specific isozyme, present in ~50% of Caucasians. The absence of the isozyme is due to an inherited deletion of both paternal and maternal alleles of the GSTM1 gene, transmitted in an autosomic dominant way (15). GSTM1 has been shown to play a role in the metabolism of organic epoxides and peroxides and in particular to conjugate known carcinogens as epoxides of polycyclic aromatic hydrocarbons (16), suggesting that people lacking the functional gene are at greater risk of developing cancers associated with exposure to polycyclic aromatic hydrocarbons (17). GSTT1 seems to act through a different pathway (18,19), since smokers lacking GSTT1 cannot conjugate monohalomethanes found in tobacco smoke.

In previous papers we have reported on the association between the CYP1A1*2A (12), GSTM1 (20) and CYP1A1 exon 7 (*2B) (21) polymorphisms in relation to lung cancer using data from the Genetic Susceptibility to Environmental Carcinogens (GSEC) study, a cooperative pooled analysis of studies on metabolic gene polymorphisms and cancer. The association between the combination of CYP1A1 and GSTM1 (22–24), and CYP1A1, GSTM1 and GSTT1 (25–27) and lung cancer have been investigated before with positive results in subgroup analyses only, possibly because of the small numbers of subjects included in the studies. Here we analyze interactions between two polymorphisms in CYP1A1, GSTM1 and GSTT1 deletion in a large pooled analysis data set.


   Material and methods
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 Abstract
 Introduction
 Material and methods
Results
 Discussion
 References
 
GSEC (28,29) is an initiative started in 1997, aimed at pooling available original data sets on metabolic polymorphisms and cancer risk at several sites. Authors of published papers and abstracts were contacted and invited to provide data sets. The majority have participated in this collaborative effort; details are given elsewhere (28). None of the data included any personal identifiers. Non-informative consecutive ID numbers were assigned to each subject at the time of receipt of the data. It is therefore not possible to trace any particular subject in the database back to his/her actual identity through the identification number. All data on genotype were converted to a standard nomenclature (30). Data were received from the database in an Excel file, and all analyses were performed using SAS statistical software (version 8.0) and STATA package (version 8.0).

The present analyses refer to the gene–gene interactions of the CYP1A1*2A, CYP1A1*2B (exon 7 Ile/Val), CYP1A1*4 (exon 7 Thr/Asn), GSTM1 and GSTT1 polymorphisms from studies on lung cancer conducted from 1991 to 2005 and that included information on smoking. The GSEC database includes some data using older methodology in which the *2B and *4 alleles, representing different unlinked polymorphisms in exon 7, were not well differentiated. Here we only used data for which there was evidence that one or the other polymorphism was present. The 2B allele was determined as the combination of the two polymorphisms exon 7 Ile/Val. Cases were defined as incident (newly diagnosed) cases of lung cancer with any histology. Recurrences have been excluded. All the original studies were case–control designs. In addition to genotypes, the studies include information on smoking habits and on histology, and most of them on occupational exposure to asbestos. In the present paper, we have used subsets of 611 cases and 870 controls in which information on combinations of the above-mentioned polymorphisms was available. Quality and logical controls on the data are usually performed by the Research Assistant when entering the data in the main GSEC database. In addition, a questionnaire was provided to each participating laboratory at the time of enrollment in the study, containing information on the study design, the selection and the source of controls, the laboratory methods used for genotyping subjects, the source of DNA for genotype analysis and the response rates for both cases and controls.

We have computed odds ratios (ORs) and their 95% confidence intervals (CIs). Multivariate logistic regression was used to assess the independent contribution of each factor to lung cancer risk and to control for confounding. Covariates include gender, age, ethnicity and smoking behavior (ever/never smoker). Also, the study identity was included in some models as a dummy variable. We have tested the deviation from the Hardy–Weinberg equilibrium in the control group by chi-square (31). Potential effect modification by smoking was examined. Heterogeneity among the studies has been evaluated by the Breslow–Day test (32) and the Q statistics (33, with P values <0.05, indicating the presence of heterogeneity among studies). A fixed-effects model was used when the test for heterogeneity was not statistically significant, whereas a random-effects model was performed when heterogeneity across studies was statistically observed (34).

The Egger test (35) was performed to test for publication bias; funnel plots were used for a graphical representation of publication bias.


   Results
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 Abstract
 Introduction
 Material and methods
 Results
Discussion
 References
 
Overall, we pooled six studies that fulfilled the requirements (refs 36–42 and T.Dragani, unpublished data) for a total of 611 cases (417 males and 194 females) and 870 controls (725 males and 145 females). Among the cases, 107 were never smokers and 504 ever smokers, and among the controls, 287 were never smokers and 583 ever smokers. The average age of the cases was 61, of the controls 48 years. Table I shows the absolute numbers of cases and controls by combinations of genotypes and the corresponding ORs and CIs. Genotypes in controls were in Hardy–Weinberg equilibrium for all the genes tested. An association with lung cancer was found for combinations that included CYP1A1*2B or *4 alleles and deletions of GSTM1 and GSTT1. In particular, the risk of lung cancer rose to 8.25 (95% CI: 2.29–29.77) when the combination of CYP1A1*4 and the double deletion of both GSTM1 and GSTT1 was present. Also, a dose–response relationship was found by simply summing up the number of polymorphic alleles (P for trend = 0.004). All estimates were adjusted by gender, age, smoking (yes/no) and center. Further adjustment by ethnicity (Caucasians, Asians and others), number of cigarettes smoked and duration of smoking did not change the estimates substantially. The analysis stratified by the two main ethnicities (Caucasians and Asians), presented in Table I, confirms what observed in the whole population, although CP1A1*B seems to play a more important role among Asians.


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  		Table I. Association between lung cancer and the combination of metabolic genes on the pathway of tobacco metabolism

 
The effect of the gene variants was apparently higher among non-smokers, with OR of 3.71 (1.70–8.07), 3.99 (0.49–32.20), 4.51 (0.72–28.35) and 16.19 (1.90–137.65) for the combination of GSTM1–GSTT1 double deletions with CYP1A1*1, *2A, *2B and *4, respectively (P for trend = 0.13) (reference *1 and no deletion). No statistically significant heterogeneity among studies was found with both the Breslow–Day test and a random-effects model. No evidence of publication bias was observed.

Table II shows the estimates separately for the two main histological types. As expected on the basis of previous observations, the association with smoking is stronger for squamous cell carcinomas than for adenocarcinomas. The association with gene variants is substantially the same in the two histotypes.


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  		Table II. Association between lung cancer and the combination of metabolic genes on the pathway of tobacco metabolism, by main histologic types

 

   Discussion
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 Material and methods
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 Discussion
References
 
The vast majority of epidemiological studies on the role of low-penetrant genes in cancer etiology have considered main effects of single-nucleotide polymorphisms, or gene–environment interactions and rarely gene–gene interactions, mainly due to the lack of statistical power. Most observed associations between cancer and low-penetrant gene variants have been weak or very weak (with 20–50% increases in cancer risk) (43,44). This, in fact, is inherent in the concept of ‘low penetrance’. However, penetrance of a gene variant depends on events such as the interaction with external exposures, with the internal environment or with other genes. Therefore, the strength of association is a relative and not an absolute concept and requires the study of interactions. In addition, consistency in such studies has usually been poor, with few associations confirmed in more than one study (43,44). The reasons for inconsistency are not well understood. One possibility is that studies were biased (e.g. due to incorrect choice of control subjects) and cannot be replicated. A second explanation is that the role of genes is so dependent on the surrounding environmental circumstances that the gene variant's main effect is difficult to capture in different studies conducted under different conditions. A third explanation is that the effect of a single single-nucleotide polymorphism can only be detected against the background of other gene variants, i.e. through the study of gene–gene interactions or the cumulative effect of different gene variants. This third hypothesis was at the basis of the present study.

Few studies have investigated gene–gene interactions in lung cancer. An early investigation from Japan (22) reported that individuals with the susceptible CYP1A1 MspI or Ile–Val genotype combined with deficient GSTM1 were at high risk (ORs: 16.00 or 41.00, respectively), but only at a low-dose level of cigarette smoking. A previous analysis on a small set of data from the GSEC database in non-smokers (23) found a similar result, with an OR for the combination of the CYP1A1 Ile(462)Val variant and GSTM1 null genotype of 4.67 (95% CI: 2.00–10.9), whereas another analysis of the same data set indicated a possible interaction between the CYP1A1*2A allele and GSTM1 deletion on lung cancer risk in Caucasians (25).

In a recent large investigation, myeloperoxidase and CYP1A1 risk genotypes interacted to increase the overall risk of non-small cell carcinoma (OR = 2.88; 95% CI: 1.70–5.00) (45), whereas other studies found a possible interaction between CYP1A1 and GSTM1 (24–27). Similarly, the interaction of gene variants with specific histologic types is still uncertain, although two studies found that CYP1A1 and GSTM1 were more strongly associated with squamous cell carcinoma (24,26).

The main reason why few studies previously looked at gene–gene interactions is that such investigation requires considerable study power (as a rule of thumb, the study of the interaction between two genes needs a study size four times higher than for the study of the main effect of a single gene). Our pooled analysis was based on a large data set that contained originally >15 000 cases of lung cancer, although the selection of studies with information on all the variant combinations we were interested in reduced the number of cases to 611.

Consistently with a previous investigation showing a dose–response relationship in the levels of DNA adducts with an increasing number of allelic variants in DNA repair genes (2), we have found in the present investigation a dose–response relationship between lung cancer and an increasing number of at-risk alleles for metabolic genes involved in the metabolism of tobacco carcinogens. It is unlikely that our results are explained by bias because the pooled analysis was based on investigations that met quality standards at selection. Biological plausibility is related to the fact that we selected genes that are involved in the metabolism of tobacco-related carcinogens and environmental contaminants, and we defined a priori the unfavorable combination of genetic polymorphisms that should define a category of subjects at an increased lung cancer risk. The effect of the gene variants was apparently higher among non-smokers, similar to what was already reported in a previous analysis of the current data set (23). The biological interpretation of this finding is not straightforward; it is possible that the effects of some genetic susceptibility profiles are more easily observed in subjects with lower levels of exposure, as reported by others (22,27).

Our study therefore suggests that the combined effect of multiple variant alleles may be more important than the investigation of a single-nucleotide polymorphism in modulating cancer risk. A theoretical model has been proposed elsewhere (44) to explain why gene–environment interactions might be particularly important through the combination of genes. In fact, the population can be described according to their sensitivity to the action of a toxic agent, which is likely to follow a normal or log-normal distribution. Outside the distribution we will have out-of-scale ‘over-reactors’, people with extreme responses due to highly penetrant mutations (typically, ataxia-teleangectasia patients). At one tail of the Gaussian distribution we find people who, within a ‘normal’ range of response, are over-sensitive, i.e. develop the same response at a much lower dose level than other people, whereas the opposite tail includes less susceptible individuals. Apart from over-reactors, the Gaussian distribution is likely to originate from the combined effect of several (or many) low-penetrant alleles (44). Our data support this theoretical model of a continuous distribution of genetic susceptibility related to the combination of gene variants. Since in our population people with the least common combination of variants had a risk of cancer eight times greater compared with no variant, they can be assimilated to subjects with highly penetrant mutations. However, the public health implications of this finding for primary prevention actions should be considered, since high-risk allele combinations are rare in the general population.


   Acknowledgments
 
This study has been made possible by a grant of the Compagnia di San Paolo to the ISI Foundation, Torino, by a grant from the European Commission to E.T. (CAN/96/33919), by a grant from the Associazione and Fondazione Italiana Ricerca Cancro (AIRC and FIRC), and to the Environmental Cancer Risk, Nutrition, Individual Suscriptibility (ECNIS) Network of Excellence (grant FOOD-CT-2005-513943) (WP8) and NIH P50CA090440-07.

Conflict of Interest Statement: None declared.


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 Introduction
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 References
 

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Received November 18, 2006; revised February 10, 2007; accepted February 11, 2007.


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