Glutathione pathway genes and lung cancer risk in young and old populations

P. Yang1,5, W.R. Bamlet2, J.O. Ebbert3, W.R. Taylor4 and M. de Andrade2

1 Division of Epidemiology, 2 Division of Biostatistics, 3 Nicotine Research Center and 4 Department of Laboratory Medicine and Cancer Genotyping Facility, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, MN 55905, USA

5 To whom correspondence should be addressed at: Department of Health Sciences Research and Cancer Center, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA. Tel: +1 507 266 5369; Fax: +1 507 266 2478; Email: yang.ping{at}mayo.edu


    Abstract
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Multiple enzymes with overlapping functions and shared substrates in the glutathione (GSH) metabolic pathway have been associated with host susceptibility to tobacco smoke carcinogens and in lung cancer etiology. However, few studies have investigated the differing and interacting roles of GSH pathway enzymes with tobacco smoke exposure on lung cancer risk in young (<50 years of age) and old (>80 years of age) populations. Between 1997 and 2001, 237 primary lung cancer patients (170 young, 67 old) and 234 controls (165 young, 69 old) were enrolled at the Mayo Clinic. Using PCR amplification of genomic DNA, polymorphic markers for {gamma}GCS, GPX1, GSTP1 (I105V and A114V), GSTM1 and GSTT1 were genotyped. Recursive partitioning and logistic regression models were used to build binary classification trees and to estimate odds ratios (OR) and 95% confidence intervals for each splitting factor. For the young age group, cigarette smoking had the greatest association with lung cancer (OR = 3.3). For never smokers, the dividing factors of recursive partitioning were GSTT1 (OR = 1.7), GPX1 (OR = 0.6) and GSTM1 (OR = 4.3). For the old age group, smoking had the greatest association with lung cancer (OR = 3.6). For smokers, the dividing factors were GPX1 (OR = 3.3) and GSTP1 (I105V) (OR = 4.1). Results from logistic regression analyses supported the results from RPART models. GSH pathway genes are associated with lung cancer development in young and old populations through differing interactions with cigarette smoking and family history. Carefully evaluating multiple levels of gene–environment and gene–gene interactions is critical in assessing lung cancer risk.

Abbreviations: CI, 95% confidence intervals; ETS, environmental tobacco smoke; {gamma}GCS, gamma glutamylcysteine synthetase; GPX, glutathione peroxidase; GPX1, gene encoding GPX; GSH, glutathione; GST, glutathione S-transferase; GSTP1, gene encoding GST{pi}; GSTM1, gene encoding GSTµ; GSTT1, gene encoding GST{theta}; OR, odds ratios; PCR, polymerase chain reaction; RPART, recursive partitioning; ROS, reactive oxygen species


    Introduction
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Around the world, the majority of lung cancer occurs in tobacco smokers and is diagnosed on average in the sixth and seventh decades of life (1). Individuals younger than 50 (young age group) or older than 80 years of age (old age group) develop lung cancer infrequently (2). Factors resulting in lung cancer development at these extreme ages cannot be explained by smoking alone. Assessing individual susceptibility or resistance to exogenous carcinogens through the study of lung cancer in young and old populations may be important in understanding the etiology of lung cancer. Multiple enzymes with overlapping functions and shared substrates in the glutathione (GSH) metabolic pathway have been associated with host susceptibility to carcinogens and toxic agents (3). This system is dependent upon the concentration of GSH and the level of activities of enzymes that catalyze the conjugation of substrates to GSH (4). Therefore, the absence or low levels of GSH-related enzyme activities could result in retention of active carcinogens or toxic DNA-damaging compounds and eventually lead to the development of cancer. Because of the strong correlation between phenotype (enzyme activity levels) and genotype assays for GSH pathway enzymes in general (4,5), the use of polymorphic genotypes to identify individuals who lack or have low activities of these enzymes is reliable.

The critical enzymes in this pathway are glutathione S-transferase mu (GSTµ), glutathione S-transferase theta (GST{theta}), glutathione S-transferase pi (GST{pi}), glutathione peroxidase (GPX) and gamma glutamylcysteine synthetase ({gamma}GCS) (3,4,6). GSTµ detoxifies tobacco-related and other carcinogens. The GSTM1 gene, which encodes GSTµ, has three identified alleles (a, b and null). Based upon reviews of meta-analyses (79), the GSTM1 null genotype confers an increased risk for lung cancer [odds ratio (OR) = 1.4–2.1]. GST{theta} detoxifies low molecular weight toxins as well as tobacco-related carcinogens (10,11). The GSTT1 gene, encoding GST{theta}, has the same allele system as GSTM1 and the GSTT1 null genotype increases lung cancer risk (OR 1.2–1.6) (1217). Individuals null for both GSTM1 and GSTT1 appear to be at substantially elevated risk (17), especially for squamous cell lung carcinoma among people with low levels of cigarette exposure (12). GSTP1, encoding GST{pi}, has two polymorphic sites: exon 5 A1578G encodes I105V and exon 6 C2508T encodes A114V (13,18). The I105V variant association with lung cancer risk has been inconsistent in the literature (8,9,19), but the A114V variant has recently been reported to increase lung cancer risk, particularly among smokers (20). GPX1 encodes cellular GPX, a selenium-dependent detoxifying enzyme that is important in the cellular defense against cytotoxic lipid peroxidation products and in CD95-triggered apoptosis to eliminate neoplastic cells (21,22). A GPX1 variant (P198L) may contribute a 2-fold increase in lung cancer risk (23). {gamma}GCS is the rate-limiting enzyme in GSH biosynthesis (24) and a trinucleotide (GAG) marker has been identified (25). Genotypes associated with lung cancer prognosis (6) have been reported, but not with lung cancer risk. A few studies have reported an association between GSH pathway genes and lung cancer risk in never smokers, but the results are inconsistent (2631).

To date, only one study of GSTM1 and GSTT1 polymorphisms and lung cancer risk has focused on young populations (32). The purpose of our study is to investigate the role of multiple GSH pathway genes in lung cancer among young and old populations and interactions with cigarette smoke exposure and family history of lung cancer.


    Materials and methods
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Study participants and data collection
The research protocol and consent form were approved by the Mayo Clinic Institutional Review Board. Details about the study design and methods have been provided elsewhere (6,3335). Cases were patients diagnosed as having and/or being treated for pathologically confirmed primary lung cancer at Mayo Clinic (Rochester, MN) and were enrolled between April 1997 and March 2001. Mayo Clinic is a tertiary medical center serving Olmsted County residents as a major primary care provider. Eligible patients were invited to participate in an interview and provided a peripheral blood sample.

The controls were drawn from a pool of 2335 Olmsted County residents enrolled as general controls in the Mayo Clinic Cancer Center's Population Science Program between 1997 and 2001 (33). This design was based on findings from the Rochester Epidemiology Project (36) showing that in a 3 year period, >90% of Olmsted County residents will visit the Mayo Clinic at least once for a blood test. Control subjects had no current or previously diagnosed malignancy (except non-melanoma skin cancer) as of the date of phlebotomy. Eligible controls received a self-administered questionnaire (equivalent to the case interview) and a request for permission to use their peripheral blood sample obtained during clinic visits. Of the respondents, 78% gave permission to use their blood sample, of whom 84% completed the questionnaire; 11% declined to participate and 1% were dead or could not be located. Ethnic background of the cases and controls were very similar (93% of the young cases, 92% young controls and 99% old cases and controls), mostly Caucasians of non-Hispanic origin. Minority groups included African-Americans, Native Americans and Inuits, Asians and Pacific Islanders, Hispanic and other ethnicities.

Data collected from case interviews or control questionnaires regarding each first degree relative included vital status and health history of malignant and non-malignant diseases with age at diagnosis and cause of death. Ancestral background of each subject's paternal and maternal grandparents was also obtained (37). Cigarette smoking data collected from ever smokers (former and current) included age of smoking initiation, years of smoking (duration), cigarettes smoked per day (intensity) and date of smoking cessation if applicable. A detailed environmental tobacco smoke (ETS) history was obtained (34). Never smokers were defined as those who smoked <100 cigarettes during their lifetime.

Genotyping analysis
Each subject's blood sample was assigned a blind identification number and tested systematically at the Mayo Clinic Cancer Center's Genotyping Laboratory. Specific quality control procedures included: isolated DNA was stored at 4°C until analysis was performed; positive and negative control samples were used to confirm proper system operation and the absence of cross contamination; and amplified products were directly sequenced with the primers used for the PCR to confirm reaction fidelity, as needed. The contrasting genotypes for the six polymorphic markers used in our analysis were {gamma}GCS (repeat 77 versus other), GPX1 (CC versus other), GSTP1-I105V (II versus other), GSTP1A114V (CC versus other), GSTM1 (null versus present) and GSTT1 (null versus present).

Determination of GSTM1, GSTT1 and {gamma}GCS genetic polymorphisms
Patient genomic DNA, extracted from a peripheral blood sample, was amplified with the primer sets listed in Table I. Samples were amplified in 25 µl reactions containing 50 ng genomic DNA, 0.5 µM forward and reverse primers, 200 µM deoxynucleotide triphosphates (Applied Biosystems, Foster City, CA), 1.5 mM MgCl2, 50 mM KCl, 10 mM Tris–HCl (pH 8.3) and 0.625 U Taq polymerase (AmpliTaq Gold; Applied Biosystems). GSTM1 and GSTT1 were amplified in a multiplex format along with CYP1A1 as a positive control (38). Reaction conditions were 35 cycles of 95°C for 30 s, 59°C for 30 s, 72°C for 30 s following a10 min 95°C ‘hot start’ activation and followed by a 7 min 72°C extension. The reaction conditions for {gamma}GCS were similar with a 65°C annealing temperature. Upon completion, amplicons from both PCRs were mixed and 1 µl aliquots loaded onto DNA500 microcapillary chips and run on a model 2100 bioanalyzer (Agilent, Palo Alto, CA). The GSTT1 and GSTM1 polymorphisms are characterized by a homozygous deletion of the respective gene, easily distinguishable from the bioanalyzer electropherograms (6). The {gamma}GCS polymorphism involves a GAG trinucleotide repeat in clusters of either 7, 8 or 9 subunits, each of which is able to be discriminated from the ordering of potential 132, 135 and/or 138 bp electropherogram peaks (6).


View this table:
[in this window]
[in a new window]
 
Table I. Primer sequences for polymorphic markers in a case–control study of 237 lung cancer patients and 234 community-based controls

 
Determination of GSTP1, CYP1A1 and GPX1 genetic polymorphisms
Patient genomic DNA was amplified under standard PCR conditions, as above, with the exception of using 5' to 3' biotinylated primers. Primer sequences and specific annealing temperatures are also shown in Table I. The 72°C extension step was increased from 30 s to 2 min for the longer GSTP1 amplicon containing both the I105V and A114V polymorphisms. Samples were then purified using nucleic acid filtration plates (MultiScreen-PCR; Millipore, Bedford, MA) and resuspended in ddH2O. Histidine (Sigma, St Louis, MO) was added to each of the samples to a final concentration of 50 mM and a final volume of 80 µl.

The NanoChip Molecular Biology Workstation (Nanogen, San Diego, CA) was used to determine patient sample genotypes in a two-step procedure. First, purified controls and patient samples (5–40 nM) were electronically addressed to discreet microarray test sites on NanoChip cartridges. Second, 5'-Cy5 and 5'-Cy3 labeled probes with sequences specific to the individual polymorphisms were hybridized to the arrays. Adjacent unlabeled probes were added to stabilize shorter labeled probes. The concentration of the probes varied from 250 to 500 nM in 50 mM NaPO4 (pH 7.0) and 500 mM NaCl. The cartridges were washed with 50 mM NaPO4 (pH 7.0) at discriminating temperatures [30°C for CYP1A1 and GSTP1 (I105V); 42°C for GSTP1 (A114V) and GPX1], scanned and analyzed. Heterozygote controls were used to effect proper equilibration of fluorescent signals and determination was completed based upon signal ratios.

Statistical analysis
Demographic information stratified by case/control status within each age group was tabulated as a mean ± standard deviation for continuous variables and a number (and percentage) for categorical variables. Pearson's {chi}2 test was used to assess group differences on categorical variables and a two-sample t-test was used to assess group differences for continuous variables. In all cases two-tailed P values ≤ 0.05 were considered statistically significant. We employed recursive partitioning, a method of tree-based classification, to investigate the following risk factors: tobacco smoke exposure (cigarette smoking and/or ETS exposure), GSH-pathway genes, gene x gene interactions and cigarette smoking x genes x family history interactions in lung cancer risk. We applied the RPART function written in the S-Plus software to construct a classification tree accounting for potential confounders (39,40). As an alternative to logistic models, tree-based models rectify classification analysis, and they are useful for non-linear dependent and for visualizing complex interactions.

Recursive partitioning (RPART) in this study involved the following steps. Each variable under consideration was examined and dividing rules were used to examine all possible binary splits for the full group of subjects (‘root node’) to select a dichotomization of the variable that maximally discriminated disease status. Each binary split yielded two subgroups (‘descendant nodes’), one that contained a relatively high proportion of cases (splitting to the right) and the other that contained a relatively high proportion of controls (splitting to the left) (Figures 1 and 2). The variable producing the best split (greatest discrimination) was then used to partition the root node into two descendant nodes. The process was applied to each descendant node to produce further splits. The combination of these binary splits provided a set of prediction rules that was used to classify subjects according to the probability of being a case or a control. At the end of the recursive partitioning process, the initial tree was pruned using the technique of cross-validation (39,40). RPART can minimize the impact of missing data by using all of the non-missing data available for the variables at each split. Since recursive partitioning is exploratory and not hypothesis testing, multiple comparison tests were not applied. The end results of RPART were terminal nodes representing combinations of risk factors associated with an increased likelihood of lung cancer.



View larger version (23K):
[in this window]
[in a new window]
 
Fig. 1. RPART analysis: young age group. The classification tree illustrates complex interactions of risk factors in the populations. Each node shows the number of controls (left) and cases (right). Branches to the right are more likely to contain cases, whereas branches to the left are more likely to contain controls. Terminal nodes are indicated by square boxes. The values 0 or 1 at the top of each box indicate whether there are more cases (1) or more or the same number of controls (0) in a particular branch. The four terminal nodes on the left correspond to never smokers, and the five terminal nodes on the right correspond to ever smokers. The shaded area indicates branches dropped in the cross-validation pruning process of the RPART analysis. HX means history.

 


View larger version (15K):
[in this window]
[in a new window]
 
Fig. 2. RPART analysis: old age group. The classification tree illustrates complex interactions of risk factors in the populations. Each node shows the number of controls (left) and cases (right). Branches to the right are more likely to contain cases, whereas branches to the left are more likely to contain controls. Terminal nodes are indicated by square boxes. The values 0 or 1 at the top of each box indicate whether there are more cases (1) or more or the same number of controls (0) in a particular branch. There are three terminal nodes each for never and ever smokers. The shaded area indicates branches dropped in the cross-validation pruning process of the RPART analysis.

 
In addition, we incorporated the results of the exploratory analyses using RPART into a series of logistic regression models and applied the Akaike information criterion (36) to identify the model that best fitted our data using SAS software (version 8.12). The variables considered included age (at diagnosis for cases or blood sample collection for controls), gender, smoking status, ETS, family history of lung cancer and genotypes of the six GSH-related genes.

Missing values
Individuals with a missing value on any of the variables or factors included in the model were omitted in the RPART models. On the other hand, the SAS procedure logistic regression included the factors of interest for each univariate analysis and omitted any individual who was missing information for a univariate variable. Therefore, the odds ratios (OR) with 95% confidence intervals (CI) calculated using SAS may theoretically differ slightly from those obtained from the RPART analysis due to the impact of missing values in the different modeling techniques. In our analysis, missing values were minimal for all variables, and this difference was negligible.


    Results
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Among the young population, 170 cases and 165 controls were identified. Among the old population, 67 cases and 69 controls were identified. When comparing cases and controls within an age group, there was no difference in gender ratio, mean age or ethnic background. For the young age group, reported cigarette smoking history, ETS exposure and family history of lung cancer were significantly different between cases and controls. For the old age group, only smoking and ETS exposure history was significantly different. Between the young and old age groups, as shown in Table II, the male:female ratio, region of residence, smoking status at diagnosis, family history of lung cancer, stage of NSCLC and histology were significantly different.


View this table:
[in this window]
[in a new window]
 
Table II. Selected characteristics of 237 primary lung cancer patients, Mayo Clinic, MN, 1997–2002

 
Allele distribution of the six polymorphic markers for cases and controls are presented in Table III. The GSTT1 present genotype was more prevalent, and the GPX1 TT genotype was underrepresented in cases compared with controls for the young age group. No significant difference was found for the other four markers in either age group. Table IV provides overall ORs and 95% CIs for all six genes from univariate analysis. Genotype other is a combined group of homozygous alternative allele and heterozygous genotypes that are not informative (e.g. too rare) as separate groups.


View this table:
[in this window]
[in a new window]
 
Table III. Allele distribution of genetic markers in a case–control study of 237 lung cancer patients and 234 community-based controls

 

View this table:
[in this window]
[in a new window]
 
Table IV. Unadjusted odds ratios for six genes associated with lung cancer

 
RPART analysis was carried out separately for the young and old groups (Figures 1 and 2). The order and the level of each split represented the relative importance of each factor and the interactions among factors. Logistic regression models were applied to calculate ORs and 95% CIs for the splitting factors identified by the recursive partitioning models (Table V). For the young age group RPART models produced a classification tree with eight splits and nine terminal nodes (Figure 1 and upper panel of Table V). A positive cigarette smoking history, the first split, had the most important effect on lung cancer risk (OR = 3.29). For young smokers, the first dividing factor was family history of lung cancer (OR = 3.06). Young smokers with no such family history were further divided on {gamma}GCS, GSTP1 (Il05V) and GSTP1 (A114V). If {gamma}GCS is not homozygous 77, then being homozygous CC at GSTP1 (A114V) increases the lung cancer risk by >4-fold. However, if {gamma}GCS is homozygous 77, then possessing the GSTP1 (I105V) II genotype decreases the risk of lung cancer by >2-fold. For young never smokers, the first dividing factor was GSTT1 (OR = 1.65); the next dividing factor was GPX1, and then GSTM1. For young never smokers who have GSTT1 present, possessing at least one T allele at GPX1 increases the lung cancer risk by almost 2-fold; for those with GSTM1 present possessing at least one T allele on GPX1 could modify the risk by >4-fold. For the old age group, RPART models produced a classification tree with five splits and six terminal nodes (Figure 2 and lower panel of Table V). We observed an interaction between smoking and GPX1 genotype, i.e. GPX1 CC was associated with an increased lung cancer risk among smokers while the same genotype was associated with a reduced risk among never smokers. Furthermore, old smokers with the GSTP1 II genotype were at an increased risk only when they carried a T allele at the GPX1 locus.


View this table:
[in this window]
[in a new window]
 
Table V. Odds ratios estimation for risk factors identified from recursive partitioning (RPART) models in a case–control study of 237 lung cancer patients and 234 community-based controls

 
To refine or prune the classification models, a cross-validation procedure yielded final trees that were similar to but had fewer terminal nodes than the original trees (unshaded areas of Figures 1 and 2). Specifically, the cross-validated tree for the young age group follows the tree discussed above for never smokers but does not further divide the smokers. The cross-validated tree for the old age group follows the tree discussed above for smokers but does not further divide the never smokers.

In addition, logistic regression models were fitted to explore several hypotheses, from univariate models to models adjusting for smoking status and hierarchical models that incorporated second order interactions with smoking status. The Akaike information criterion (41) was used to identify the models that best fitted our data: model 1 included an intercept and a risk factor; model 2 included an intercept, a risk factor and smoking status; model 3 included an intercept, a risk factor, smoking status and their interaction. We found that for both age groups smoking status is the most important main effect. For the old age group, the model including GPX1, smoking status and their interaction provided a considerably better fit than all the other models. For the young age group, models incorporating smoking status, family history and GSTT1 provided the best fit, although we did not find one clearly superior model. Results from logistic regression models supported the results from our RPART models.

In this analysis, ETS was not identified as an independent nor significant factor in any of the models while cigarette smoking had the most important effect on lung cancer risk. We also repeated our analyses with a US White subset; the results were almost identical to the total study population for both young and old age groups.


    Discussion
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
As summarized in Table VI, we observed that significant risk factors for developing lung cancer among young smokers were family history of lung cancer and presence of the GSTP1 (A114V) CC genotype. Among young never smokers, the GSTM1 present genotype was associated with lung cancer. Among old smokers, the GPX1 CC and GSTP1 (I105V) II genotypes were associated with lung cancer. Among old never smokers, GSTP1 (I105V) II genotype was associated with lung cancer. These findings not only suggest that GSH pathway genes are important in lung cancer development among young and old populations but also elucidate the differing role of specific enzymes that interact with tobacco carcinogen exposure and family history of lung cancer in the two contrasting age groups.


View this table:
[in this window]
[in a new window]
 
Table VI. Summary of multiple risk models: significant risk factors, odds ratios and 95% confidence intervals in a case–control study of 237 lung cancer patients and 234 community-based controls

 
Our results on allele distributions of the polymorphic markers are consistent with other studies of all age groups. The frequency of homozygous deletion (null) genotypes in both GSTM1 and GSTT1 vary substantially by ethnicity (11). For GSTM1 the null genotype ranges from 36 to 67% in Caucasians, 33 to 63% in East Asians and 22 to 35% in Africans and African-Americans. Approximately 45% of US Caucasians lack a functional allele, and the null genotype is a risk factor for multiple cancers, including lung (11). For the GSTT1 null genotype, the highest frequency is observed in East Asians (38–58%), a lower frequency is observed in Africans and African-Americans (24–38%) and the lowest frequency is observed in Caucasians. Twenty percent of US Caucasians lack a functional allele, and the null genotype has been associated with a group of cancers (4,11). The reported frequency for Ile/Val (IV) or Val/Val (VV) at codon 105 of GSTP1 is 18–57% (13,20) and for Ala/Val (AV) or Val/Val (VV) at codon 114 is 14–19% (20). The {gamma}GCS polymorphic repeat site in the promoter region was only reported in a lung cancer patient cohort (6). The prevalence of the GPX1 P98L variant allele was 58% (23). These data suggest that our cases and controls are representative of the general population in terms of allele frequencies of the six polymorphic markers in the study.

In the young age group, family history of lung cancer in first degree relatives presents as a significant risk factor only among smokers, supporting a previous hypothesis that an inherited predisposition is important in early onset cancer (42,43). Because smoking as a behavior aggregates in families, which could lead to familial aggregation of smoking-related cancer, particularly lung cancer, it is not unexpected that our findings show smoking and lung cancer family history are very important risk factors even for young cases (3). Among smokers who have no family history and possess the {gamma}GCS 77 genotype, the GSTP1 (A114V) CC genotype is associated with a 4-fold increased risk for lung cancer (OR = 4.2, 95% CI 1.3–14.2). Recently, Wang et al. (20) reported that among smokers who are <62 years of age at diagnosis the TT/CT genotypes are associated with a 5-fold increased risk for lung cancer (OR = 5.1, 95% CI 2.5–10.2). The discrepant results between studies may reflect the differences in subsets of the population with regard to family history and the genotype distribution of {gamma}GCS, which were not analyzed in the study of Wang et al. (20).

Among young never smokers who have the wild-type at GSTT1 and carry at least one T allele at GPX1, the GSTM1 present genotype is associated with a 4-fold increased risk for lung cancer (OR = 4.3, 95% CI 1.5–13.0). In a review of a meta-analysis (9), among >20 published studies, the GSTM1 null genotype has been inconsistently associated with lung cancer risk, ranging from no association (three studies), a weak association (OR = 1.04–1.25), to a moderate association (OR = 1.4–2.1), an effect which is stronger in heavy smokers. London et al. (44) found a non-statistically significant association of the GSTM1 null genotype and lung cancer risk among African-Americans (OR = 1.20, 95% CI 0.72–2.00) and Caucasians (OR = 1.37, 95% CI 0.91–2.06). The association was evident among light but not heavy smokers. A large case–control study conducted in Boston also generated negative results (45), as did a case–control study in Texas that included African-Americans and Mexican-Americans (17). A similar lack of association was observed in a Portuguese population (46). A small study conducted in Spain (15) found a greater frequency of the null genotype among cases than population-based controls (OR = 1.57, 95% CI 0.99–2.51). The association appeared to be most evident for small cell carcinoma and adenocarcinoma for light rather than heavy smokers (50 pack-years as the cut-off point) and patients diagnosed at older ages (≥60 years). A Swedish study found a slightly inverse association of GSTM1 null genotype and lung cancer risk in a sample enriched for women (74.6%) and never smokers (48.2%) (27). Our finding of a seemingly opposite relation between GSTM1 and lung cancer risk could be explained by the potential role of GSTµ among never smokers being different from its role among smokers (10,11), particularly in the presence of the GSTT1 null and GPX1 TT/CT genotypes.

Our results are consistent with several other investigations with regards to the association between lung cancer and GSH pathway genetic polymorphisms in never smokers. In a case–control study of 198 lung cancer cases and 332 controls no difference was observed between GSTP1 (I105V) variant alleles in non-smoking cases and controls (26). No statistically significant relationship was observed among non-smokers either with or without ETS exposure and the GSTP1 (I105V) genotype in a study of 66 and 413 non-smoking cases and controls, respectively (28). In a Swedish case–control study focusing on never smokers, the GSTM1 present genotype conferred a higher risk for lung cancer in the presence of a slow acetylator genotype (OR = 3.1, 95% CI 1.1–8.6) (27). Results that were most similar to ours were reported in a clinical case–control study in Manchester, UK, where GSTM1 present was associated with a 2-fold increased risk of lung cancer (29). In the same study GSTT1 and GSTP1 (I105V) were not found to be significant genetic markers.

Our data also contrast with several other studies evaluating GSH related genes and lung cancer among never smokers. In a study of archival tumor tissues never smokers with ETS exposure who developed lung cancer were more likely to possess the GSTM1 null genotype compared with never smokers with no ETS exposure (30). However, this study included only non-smoking females with a larger percentage of adenocarcinoma than in our study. In a Japanese case–control study of 198 cases with adenocarcinoma and 152 controls the GSTM1 null genotype was associated with lung cancer (OR = 3.32, 95% CI 1.41–7.84) in non-smokers compared with non-smokers with GSTM1 present (31). However, subjects were Japanese, included adenocarcinoma cases only, had mean ages of 63 and 65 years for the cases and controls, respectively, and <10% of the cases were <50 years. Further, by the author's own admission, the risk of the GSTM1 null genotype may have been enhanced by a biased allele distribution. We could not identify any studies of the risk of lung cancer with GPX1 or {gamma}GCS polymorphisms in never smokers.

In the older age group the presence of wild-type GPX1 interacts with cigarette smoking history significantly in lung cancer risk. Among smokers the GPX1 CC genotype is associated with a 3-fold increased risk (OR = 3.3, 95% CI 1.3–8.4), while among never smokers the same genotype is associated with an 8-fold decreased risk of lung cancer (OR = 0.12, 95% CI 0.02–0.7). Our results are partly consistent with the findings of Ratnasinghe et al. (47) from 315 case–control pairs in which GPX1 CT versus GPX1 CC (OR = 1.8, 95% CI 1.2–2.8) and GPX1 TT versus GPX1 CC (OR = 2.3, 95% CI 1.3–3.8). These findings are consistent with the role that the GPX enzyme plays in lung tissue and the impact that the altered enzyme function may have on oncogenesis. Reactive oxygen species (ROS) are important in the initiation and promotion of cells to neoplastic growth, and cigarette smoking generates a high level of ROS within the human airways. However, lung cells are equipped with an integrated antioxidant defense system, which includes the antioxidants GSH and GPX1 (48), which also plays an important role in apoptosis (21). In a study of a normal human lung and six major types of human lung carcinomas immunostained for antioxidant enzymes (manganese and copper, zinc superoxide dismutases, catalase and GSH peroxidase), none of the carcinomas studied had significant levels of catalase or GPX, supporting GPX being important in antioxidant defense (49,50). Polymorphic genes for GPX1 map to loci on chromosome 3p, which is subject to frequent loss of heterozygosity in lung tumors (51), where reduced GPX1 enzyme activity may affect the prognosis of lung cancer patients due to compromised oxidative defense mechanisms.

We also observed that GSTP1 (I105V) interacts with GPX1 genotype. Among old smokers who have the GPX TT genotype, possessing GSTP1 II is associated with a 4-fold increased risk of lung cancer (OR = 4.1, 95% CI 1.1–14.8), whereas no association was observed among those who have GPX1 CC or GPX1 CT. Reviews by Kiyohara et al. (8,9) reported that five of the six studies did not show any association between GSTP1 and lung cancer risk, whereas Miller et al. (19,52) found GSTP1 II is associated with a significantly increased risk of lung cancer. Our study has provided new evidence regarding GST{pi} in lung cancer development in an elderly population.

Individuals null for multiple enzymes appear to be at elevated risk (17), especially for squamous cell carcinoma among those with low levels of cigarette exposure (12). Kelsey et al. (17) found that individuals who lacked functional genes at both the GSTP1 and GSTM1 loci were at a 2.9-fold elevated risk (P < 0.05). This result is similar to that observed by Saarikoski (12) in Finland, who observed a 2.3-fold excess risk (P < 0.05). Lung cancer patients in a Norwegian study who had the null genotype at the GSTM1 locus and GSTP1 II had higher DNA adduct levels than cases with other genotypes (14), suggesting biological plausibility for the association. However, the results are not entirely consistent with other studies (15,16). Substrate specificity could influence a person's capacity to metabolize different environmental carcinogens, which is often a complex mixture of chemicals which are possibly substrates for multiple metabolizing enzymes (1217). Therefore, additional studies with larger sample sizes and more detailed exposure histories are required to clarify these inconsistencies.

Merging data from multiple studies across the world, one recent study examined the lung cancer risk in populations younger than 45 years of age and the GSTM1 null and GSTT1 null genotypes (27). Only GSTM1 null was found to be moderately associated with lung cancer risk among never-smokers (52 cases). No studies have been published to date reporting data on populations older than 80 years of age.

There are several limitations to this study. First, the use of mostly referral-practice and self-selected patients may bias our study results. However, this bias is considered minimal because self-selection was not predetermined by genotype and, the allele distributions of the six markers in our study are in agreement with data reported in the literature.

The second limitation is that our control group was not ideal. It is always difficult to find proper controls in a tertiary referral clinic where a large number of new cases can be rapidly enrolled. Since our lung cancer patients are mainly referrals, ideal controls should be matched with cases by age, gender, race, geographic referral area and duration of care at Mayo Clinic. However, finding such ideal controls is not feasible, mainly because ~50% of our cases are from outside the tri-state area (Minnesota, Wisconsin and Iowa) where a limited number of eligible controls can be found and enrolled. The nature and seriousness of co-morbid conditions may differ between regional and long-distance referral patients. Alternative control groups include population-based samples, siblings or other relatives, neighbors, co-workers or spouses. We have chosen to use population-based community residents as controls based on Mantel and Haenszel's principle of control selection, i.e. using a group representing a more general population could be superior if the comparability of exposure is the major concern (53). Because the exposures in our study are two genetic traits, biases of preferential recall or exposure time are no longer major concerns. Although geographic distribution by residence differs between cases and controls, race and ethnic background is comparable (data not shown). Because the main exposures in our study are genetic traits, preferential recall or exposure time biases are no longer major concerns. On the other hand, our population-based control group can serve two purposes: being a reference in testing our hypotheses and providing accurate estimates of the expected allele frequencies of the candidate markers in the reference population.

The third limitation is our low statistical power to thoroughly assess the role of ETS and its interaction with GSH pathway genes in lung cancer risk, which may have been masked by the strong effect of smoking. Analyzing only never smokers could be helpful to detect ETS effects but is limited by sample size in our current study.

In this study we focused on extreme age populations to examine the interactions among selected host and environmental risk factors. We have achieved sizeable samples for the young group but a limited number of old subjects. Our results demonstrate that GSH pathway genes interacting with family history of lung cancer and cigarette smoke exposure may influence lung cancer risk in young and old populations. Although we do not know the mechanisms underlying the significant gene–age–smoking interaction from our epidemiologic observations, these results are biologically plausible. Due to the overwhelmingly strong causal effect of tobacco smoking on lung cancer risk, never smokers of any age are expected to be at minimal risk of developing lung cancer. If a never smoker does develop lung cancer, most likely the person has had previous exposure to second-hand smoke and/or other known lung carcinogens (34,54). Two obvious differences in individual susceptibility to lung cancer between smokers and never smokers are: (i) the lung carcinogenic process in never smokers does not require active smoking but may be triggered by other exogenous or endogenous carcinogens (55,56); (ii) as a consequence, metabolic pathways in reacting to carcinogens may be different depending on the specificity of physiologic substrates and the effective enzymes (57). Carefully evaluating multiple levels of gene–environment and gene–gene interactions is critical in assessing an individual's lung cancer risk.


    Acknowledgments
 
We would like to thank Ms Susan Ernst for her technical assistance with the manuscript. We also acknowledge Drs M.S.Allen, C.Deschamps, M.C.Aubry, R.Marks, S.Okuno and Z.Sun for their support and help in various stages of this project. This work was supported by grants NIH CA-77118 (P.Y.), NIH CA-80127 (P.Y.) and NIH CA92049 (J.O.E.).


    References
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 

  1. Blot,W.J. and Fraumeni,J.F.,Jr (1996) Cancers of the lung and pleura. In Schottenfeld,D. and Fraumeni,J.F.,Jr (eds) Cancer Epidemiology and Prevention. Oxford University Press, New York, NY, pp. 637–665.
  2. Weir,H.K., Thun,M.J., Hankey,B.F. et al. (2003) Annual report to the nation on the status of cancer, 1975–2000, featuring the uses of surveillance data for cancer prevention and control. J. Natl Cancer Inst., 95, 1276–1299.[Abstract/Free Full Text]
  3. Sellers,T.A. and Yang,P. (2002) Familial and genetic influences on risk of lung cancer. In King,R.A., Rotter,J.I. and Motulsky,A.G. (eds) The Genetic Basis of Common Diseases. Oxford University Press, New York, NY, pp. 700–712.
  4. Zhong,S., Howie,A.F., Ketterer,B., Taylor,J., Hayes,J.D., Beckett,G.J., Wathen,C.G., Wolf,C.R. and Spurr,N.K. (1991) Glutathione S-transferase µ locus: use of genotyping and phenotyping assays to assess association with lung cancer susceptibility. Carcinogenesis, 12, 1533–1537.[Abstract]
  5. Kempkes,M., Wiebel,F.A., Golka,K., Heitmann,P. and Bolt,H.M. (1996) Comparative genotyping and phenotyping of glutathione S-transferase GSTT1. Arch. Toxicol., 70, 306–309.[CrossRef][ISI][Medline]
  6. Yang,P., Yokomizo,A., Tazelaar,H.D. et al. (2002) Genetic determinants of lung cancer short-term survival: the role of glutathione-related genes. Lung Cancer, 35, 221–229.[CrossRef][ISI][Medline]
  7. McWilliams,J.E., Sanderson,B.J.S., Harris,E.L., Richert-Boe,K.E. and Henner,W.D. (1995) Glutathione S-transferase M1 (GSTM1) deficiency and lung cancer risk. Cancer Epidemiol. Biomarkers Prev., 4, 589–594.[Abstract]
  8. Kiyohara,C., Otsu,A., Shirakawa,T., Fukuda,S. and Hopkin,J.M. (2002) Genetic polymorphisms and lung cancer susceptibility: a review. Lung Cancer, 37, 241–256.[CrossRef][ISI][Medline]
  9. Kiyohara,C., Shirakawa,T. and Hopkin,J.M. (2002) Genetic polymorphism of enzymes involved in xenobiotic metabolism and the risk of lung cancer. Environ. Health Prev. Med., 7, 47–59.[CrossRef]
  10. Hayes,J.D. and Strange,R.C. (1995) Invited commentary: potential contribution of the glutathione S-transferase supergene family to resistance to oxidative stress. Free Radic. Res., 22, 193–207.[ISI][Medline]
  11. Rebbeck,T.R. (1997) Molecular epidemiology of the human glutathione S-transferase genotypes GSTM1 and GSTT1 in cancer susceptibility. Cancer Epidemiol. Biomarkers Prev., 6, 733–743.[Abstract]
  12. Saarikoski,S.T., Voho,A., Reinikainen,M., Anttila,S., Karjalainen,A., Malaveille,C., Vainio,H., Husgafvel-Pursiainen,K. and Hirvonen,A. (1998) Combined effect of polymorphic GST genes on individual susceptibility to lung cancer. Int. J. Cancer, 77, 516–521.[CrossRef][ISI][Medline]
  13. Harris,M.J., Coggan,M., Langton,L., Wilson,S.R. and Board,P.G. (1998) Polymorphism of the Pi class glutathione S-transferase in normal populations and cancer patients. Pharmacogenetics, 8, 27–31.[ISI][Medline]
  14. Ryberg,D., Skaug,V., Hewer,A. et al. (1997) Genotypes of glutathione transferase M1 and P1 and their significance for lung DNA adduct levels and cancer risk. Carcinogenesis, 18, 1285–1289.[Abstract]
  15. To-Figueras,J., Gene,M., Gomez-Catalan,J., Galan,M.C., Fuentes,M., Ramon,J.M., Rodamilans,M., Huguet,E. and Corbella,J. (1997) Glutathione S-transferase M1 (GSTM1) and T1 (GSTT1) polymorphisms and lung cancer risk among Northwestern Mediterraneans. Carcinogenesis, 18, 1529–1533.[Abstract]
  16. Deakin,M., Elder,J., Hendrickse,C. et al. (1996) Glutathione S-transferase GSTT1 genotypes and susceptibility to cancer: studies of interactions with GSTM1 in lung, oral, gastric and colorectal cancers. Carcinogenesis, 17, 881–884.[Abstract]
  17. Kelsey,K.T., Spitz,M.R., Zuo,Z.F. and Wiencke,J.K. (1997) Polymorphisms in the glutathione S-transferase class mu and theta genes interact and increase susceptibility to lung cancer in minority populations (Texas, United States). Cancer Causes Control, 8, 554–559.[CrossRef][ISI][Medline]
  18. Mannervik,B., Awasthi,Y.C., Board,P.G. et al. (1992) Nomenclature for human glutathione transferases. Biochem. J., 282, 305–306.[ISI][Medline]
  19. Miller,D.P., Neuberg,D., De Vivo,I., Wain,J.C., Lynch,T.J., Su,L. and Christiani,D.C. (2003) Smoking and the risk of lung cancer: susceptibility with GSTP1 polymorphisms. Epidemiology, 14, 545–551.[ISI][Medline]
  20. Wang,Y., Spitz,M.R., Schabath,M.B., Ali-Osman,F., Mata,H. and Wu,X. (2003) Association between gluthathione S-transferase p1 polymorphisms and lung cancer risk in Caucasians: a case-control study. Lung Cancer, 40, 25–32.[CrossRef][ISI][Medline]
  21. Gouaze,V., Andrieu-Abadie,N., Cuvillier,O., Malagarie-Cazenave,S., Frisach,M.F., Mirault,M.E. and Levade,T. (2002) Gluthathione peroxidase-1 protects from CD95-induced apoptosis. J. Biol. Chem., 277, 42867–42874.[Abstract/Free Full Text]
  22. Schomberg,S., Rudra,P.K., Noding,R., Skorpen,F., Bjerve,K.S. and Krokan,H.E. (1997) Evidence that changes in Se-glutathione peroxidase levels affect the sensitivity of human tumour cell lines to n-3 fatty acids. Carcinogenesis, 18, 1897–1904.[Abstract]
  23. Ratnasinghe,D., Tangrea,J.A., Forman,M.R. et al. (2000) Serum tocopherols, selenium and lung cancer risk among tin miners in China. Cancer Causes Control, 11, 129–135.[CrossRef][ISI][Medline]
  24. Walsh,A.C., Li,W., Rosen,D.R. and Lawrence,D.A. (1996) Genetic mapping of GLCLC, the human gene encoding the catalytic subunit of gamma-glutamyl-cysteine synthetase, to chromosome band 6p12 and characterization of a polymorphic trinucleotide repeat within its 5' untranslated region. Cytogenet. Cell Genet., 75, 14–16.[ISI][Medline]
  25. Liu,W., Smith,D.I., Rechtzigel,K.J., Thibodeau,S.N. and James,C.D. (1998) Denaturing high performance liquid chromatography (DHPLC) used in the detection of germline and somatic mutations. Nucleic Acids Res., 26, 1396–1400.[Abstract/Free Full Text]
  26. Lin,P., Hsueh,Y.M., Ko,J.L., Liang,Y.F., Tsai,K.J. and Chen,C.Y. (2003) Analysis of NQ01, GSTP1 and MnSOD genetic polymorphisms on lung cancer risk in Taiwan. Lung Cancer, 40, 123–129.[CrossRef][ISI][Medline]
  27. Nyberg,F., Hou,S.M., Hemminki,K., Lambert,B. and Pershagen,G. (1998) Glutathione S-transferase mu1 and N-acetyltransferase 2 genetic polymorphisms and exposure to tobacco smoke in nonsmoking and smoking lung cancer patients and population controls. Cancer Epidemiol. Biomarkers Prev., 7, 875–883.[Abstract]
  28. Miller,D.P., De Vivo,I., Neuberg,D., Wain,J.C., Lynch,T.J., Su,L. and Christiani,D.C. (2003) Association between self-reported environmental tobacco smoke exposure and lung cancer: modification by GSTP1 polymorphism. Int. J. Cancer, 104, 758–763.[CrossRef][ISI][Medline]
  29. Lewis,S.J., Cherry,N.M., Niven,R.M., Barber,P.V. and Povey,A.C. (2002) GSTM1, GSTT1 and GSTP1 polymorphisms and lung cancer risk. Cancer Lett., 180, 165–171.[CrossRef][ISI][Medline]
  30. Bennett,W.P., Alavanja,M.C., Blomeke,G. et al. (1999) Environmental tobacco smoke, genetic susceptibility and risk of lung cancer in never-smoking women. J. Natl Cancer Inst., 91, 2009–2014.[Abstract/Free Full Text]
  31. Sunaga,N., Kohno,T., Yanagitani,N. et al. (2002) Contribution of the NQ01 and GSTT1 polymorphisms to lung adenocarcinoma susceptibility. Cancer Epidemiol. Biomarkers Prev., 11, 730–738.[Abstract/Free Full Text]
  32. Taioli,E., Gaspari,L., Benhamou,S. et al. (2003) Polymorphisms in CYP1A1, GSTM1, GSTT1 and lung cancer below the age of 45 years. Int. J. Epidemiol., 32, 60–63.[CrossRef][ISI][Medline]
  33. Taniguchi,K., Yang,P., Jett,J., Bass,E., Meyer,R., Wang,Y., Deschamps,C. and Liu,W. (2002) Polymorphisms in the promoter region of the neutrophil elastase gene are associated with lung cancer development. Clin. Cancer Res., 8, 1115–1120.[Abstract/Free Full Text]
  34. de Andrade,M., Ebbert,J.O., Wampfler,J.A. et al. (2004) Environmental tobacco smoke exposure in women with lung cancer. Lung Cancer, 43, 127–134.[CrossRef][ISI][Medline]
  35. Visbal,A.L., Williams,B.A., Nichols,F.C. et al. (2004) Gender differences in non-small cell lung cancer survival: an analysis of 4,618 patients diagnosed between 1997–2002. Ann. Thoracic Surg., in press.
  36. Melton,L.J.,III (1996) History of the Rochester Epidemiology Project. Mayo Clin. Proc., 71, 266–274.[ISI][Medline]
  37. Yang,P., Wentzlaff,K.A., Katzmann,J.A. et al. (1999) Alpha1-antitrypsin deficiency allele carriers in lung cancer patients. Cancer Epidemiol. Biomarkers Prev., 8, 461–465.[Abstract/Free Full Text]
  38. Abdel-Rahman,S.Z., El-Zein,R.A., Anwar,W.A. and Au,W.W. (1996) A multiplex PCR procedure for polymorphic analysis of GSTM1 and GSTT1 genes in population studies. Cancer Lett., 107, 229–233.[CrossRef][ISI][Medline]
  39. Therneau,T.M. and Atkinson,E.J. (1997) An Introduction to Recursive Partitioning Using the RPART Routines, Technical Report no. 61. Mayo Clinic, Rochester, MN.
  40. Zhang,H. and Bonney,G. (2000) Use of classification trees for association studies. Genet. Epidemiol., 19, 323–332.[CrossRef][ISI][Medline]
  41. Akaike,H. (1974) A new look at the statistical model identification. IEEE Trans. Automatic Control, 19, 716–723.
  42. Sellers,T.A., Potter,J.D. and Folsom,A.R. (1991) Association of incident lung cancer with family history of female reproductive cancers: the Iowa Women's Health Study. Genet. Epidemiol., 8, 199–208.[ISI][Medline]
  43. Yang,P., Schwartz,A.G., McAllister,A.E., Swanson,G.M. and Aston,C.E. (1999) Lung cancer risk in families of nonsmoking probands: heterogeneity by age at diagnosis. Genet. Epidemiol., 17, 253–273.[CrossRef][ISI][Medline]
  44. London,S.J., Daly,A.K., Cooper,J., Navidi,W.C., Carpenter,C.L. and Idle,J.R. (1995) Polymorphism of glutathione S-transferase M1 and lung cancer risk among African-Americans and Caucasians in Los Angeles County, California. J. Natl Cancer Inst., 87, 1246–1253.[Abstract]
  45. Garcia-Closas,M., Kelsey,K.T., Wiencke,J.K., Xu,X., Wain,J.C. and Christiani,D.C. (1997) A case-control study of cytochrome P450 1A1, glutathione S-transferase M1, cigarette smoking and lung cancer susceptibility. Cancer Causes Control, 8, 544–553.[CrossRef][ISI][Medline]
  46. Moreira,A., Martins,G., Monteiro,M.J. et al. (1996) Glutathione S-transferase mu polymorphism and susceptibility to lung cancer in the Portuguese population. Teratog. Carcinog. Mutagen., 16, 269–274.[CrossRef][ISI][Medline]
  47. Ratnasinghe,D., Tangrea,J.A., Andersen,M.R., Barrett,M.J., Virtamo,J., Taylor,P.R. and Albanes,D. (2000) Glutathione peroxidase codon 198 polymorphism variant increases lung cancer risk. Cancer Res., 60, 6381–6383.[Abstract/Free Full Text]
  48. Moscow,J.A., Schmidt,L., Ingram,D.T., Gnarra,J., Johnson,B. and Cowan,K.H. (1994) Loss of heterozygosity of the human cystosolic glutathione peroxidase I gene in lung cancer. Carcinogenesis, 15, 2769–2773.[Abstract]
  49. Coursin,D.B., Cihla,H.P., Sempf,J., Oberley,T.D. and Oberley,L.W. (1996) An immunohistochemical analysis of antioxidant and glutathione S-transferase enzyme levels in normal and neoplasti human lung. Histol. Histopathol., 11, 851–860.[ISI][Medline]
  50. Jaruga,P., Zastawny,T.H., Skokowski,J., Dizdaroglu,M. and Olinski,R. (1994) Oxidative DNA base damage and antioxidant enzyme activities in human lung cancer. FEBS Lett., 341, 59–64.[CrossRef][ISI][Medline]
  51. Hardie,L.J., Briggs,J.A., Davidson,L.A., Allan,J.M., King,R.F.G.J., Williams,G.I. and Wild,C.P. (2000) The effect of hOGG1 and glutathione peroxidase I genotypes and 3p chromosmal loss on 8-hydroxydeoxyguasonsine levels in lung cancer. Carcinogenesis, 21, 167–172.[Abstract/Free Full Text]
  52. Miller,D.P., Liu,G., De Vivo,I., Lynch,T.J., Wain,J.C., Su,L. and Christiani,D.C. (2002) Combinations of the variant genotypes GSTP1, GSTM1 and P53 are associated with an increased lung cancer risk. Cancer Res., 62, 2819–2823.[Abstract/Free Full Text]
  53. Mantel,N. and Haenszel,W. (1959) Statistical aspects of the analysis of data from retrospective studies of disease. J. Natl Cancer Inst., 22, 719–748.[ISI][Medline]
  54. National Cancer Institute (1999) Carcinogenic effects. In Health Effects of Exposure to Environmental Tobacco Smoke. The Report of the California Environmental Protection Agency. Smoking and Tobacco Control Monograph no. 10. National Cancer Institute, Bethesda, MD, Ch. 7, pp. 265–357.
  55. Lang,M. and Pelkonen,O. (1999) Metabolism of xenobiotics and chemical carcinogens. In Vineis,P., Malats,N., Lang,M., d'Errico,A., Caporaso,N.,E., Cuzick,J. and Boffetta,P. (eds) Metabolic Polymorphisms and Susceptibility to Cancer. IARC, Lyon, Ch. 3, pp. 13–22.
  56. Pelkonen,O., Raunio,H., Rautio,A. and Lang,M. (1999) Xenobiotic-metabolizing enzymes and cancer risk: correspondence between genotype and phenotype. In Vineis,P., Malats,N., Lang,M., d'Errico,A., Caporaso,N.,E., Cuzick,J. and Boffetta,P. (eds) Metabolic Polymorphisms and Susceptibility to Cancer. IARC, Lyon, Ch. 8, pp. 77–88.
  57. Strange,R.C. and Fryer,A.A. (1999) The glutathione S-transferases: influence of polymorphism on cancer susceptibility. In Vineis,P., Malats,N., Lang,M., d'Errico,A., Caporaso,N.,E., Cuzick,J. and Boffetta,P. (eds) Metabolic Polymorphisms and Susceptibility to Cancer. IARC, Lyon, Ch. 19, pp. 231–249.
Received March 9, 2004; revised April 26, 2004; accepted May 27, 2004.