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Department of Epidemiology, School of Public Health [A. F. O.] and Division of Otolaryngology/Head and Neck Surgery, Department of Surgery, School of Medicine [A. F. O., M. C. W.], University of North Carolina, Chapel Hill, North Carolina 27599, and Laboratory of Computational Biology and Risk Assessment, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina 27709 [M. A. W., D. A. B.]
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Abstract |
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 Top Abstract IntroductionMaterials and MethodsResultsDiscussionReferences |
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Introduction |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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Despite the strong biological plausibility for the role of metabolizing enzymes in the etiology of SCCHN, there have been a relatively small number of epidemiological studies that have evaluated these polymorphisms. The published studies have not provided evidence for a clear pattern of association with several enzyme polymorphisms, although few have directly evaluated the potential interaction between tobacco exposure and the presence of an at-risk genotype. We report the results of a case-control study of SCCHN, including the assessment of GSTM1, GSTT1, GSTP1, CYP1A1, and NAT1 genotypes.
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Materials and Methods |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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Control subjects included patients seen in the same clinic with conditions requiring surgery. These major conditions included chronic sinusitis, nasal obstruction, and obstructive sleep apnea. Eligibility criteria for controls were the same as for cases. In addition, patients with the aspirin triad (nasal polyposis, asthma, and aspirin sensitivity) were also excluded. Controls were frequency-matched with cases on age (20–44, 45–49, 50–54, 55–59, 60–64, 65–69, 70+ years) and gender. A total of 236 eligible controls were identified, and 202 participated (86%) in the study.
An in-person interview was conducted with each subject in the hospital
clinic by a trained interviewer. The interview consisted of questions
related to lifetime tobacco and alcohol consumption (not including the
year before diagnosis), occupation, medical history, family history of
cancer, demographics, and diet. In addition, a blood sample and buccal
swab sample of exfoliated oral cells were obtained with informed
consent at that time. Smokers were defined as those subjects that
reported that they had smoked a total of 
100 cigarettes over their
lifetime. In addition, subjects were asked about the use of cigars,
pipes, chewing tobacco, and snuff. Cigarette smokers were asked the
number of cigarettes smoked per day, on average, the age they began
smoking and the number of years of smoking, and whether the cigarettes
were filter or nonfilter. In the analysis, tobacco use was represented
in several forms, including average number of cigarettes smoked per
day, years of smoking, and pack-years. These variables were also
separately constructed for filter and nonfilter cigarettes. Subjects
using only other tobacco products were included in the analysis of
tobacco use (ever/never) but not in the analyses of intensity or
duration of cigarette use. A lifetime history of alcohol consumption
(beer, wine, and liquor) was obtained, and variables corresponding to
average weekly use, years of use, and drink-years were derived.
Variable cutpoints were selected using the distribution among controls
and the published literature, and they were confirmed with
nonparametric regression (3)
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Blood samples or buccal swabs were not obtained from a total of 13 subjects (6, 3.3% of cases and 7, 3.5% of controls). Blood was collected in one yellow-top 8.5-mL vacutainer tube. Plasma, buffy coat, and red cells were separated and stored at -70°C within 24 h of collection. The buffy coat was thawed, and DNA was extracted using the ABI Nucleic Acid Purification System (Applied Biosystems, Foster City, CA). DNA samples were evaluated for quantity by spectrophotometry and quality by a 1% agarose gel run. Samples were stored at 4° until genotyping. Genotyping of samples was carried out using 50 ng of genomic DNA per assay and published PCR-based methods. Genotyping was performed primarily using DNA from blood samples, and when these were unavailable, buccal cell samples were used. DNA was extracted from buccal swab samples using the Qiagen method (Qiagen, Inc., Chatsworth, CA). When there was difficulty in determining the genotype for a particular sample, both blood and buccal cell samples were used. GSTM1 and GSTT1 genotypes were determined using the multiplex PCR method of Chen et al. (4) . This technique does not distinguish between heterozygote and homozygote GSTM1- or GSTT1-positive genotypes, but it conclusively identifies null genotypes. The GSTP1 (Ile105Val) genotype was determined using the PCR-RFLP method of Watson et al. (5) . NAT1 genotypes (NAT1*4, NAT1*10, NAT1*11) were determined using the PCR-based methods of Bell et al. (6) . CYP1A1 (Ile/Val; CYP1A1*2B) polymorphism was detected using PCR primers (1A1F, GCTTGCCTGTCCTCTATC; 1A1R, AAAGACCTCCCAGCGGGTAA), standard PCR conditions, and annealing at 53°C. The reverse primer contained a mismatched base (italic) that formed a partial MaeIII (Bering Mannheim, Indianapolis IN) restriction site in the presence of the G nucleotide at position 4889 that characterizes the CYP1A1 valine allele. Digestion of the PCR product with MaeIII produced genotype-specific band patterns on agarose gels. GST and NAT genotypes could not be determined for two cases (one for GSTs and one for NAT1) and zero controls. The CYP1A1 genotype could not be determined for 7 cases and 11 controls.
Multivariate logistic regression was used to obtain the OR estimate and 95% CI for the main effects of tobacco and each enzyme genotype (7) . An adjustment was made for the potential confounding effects of age, sex, race (black, white), and alcohol consumption (average number of drinks per week). The joint effects (interaction) of tobacco and each of the genes were evaluated on the additive and multiplicative scales. Dummy variables were created, each representing the combination of a tobacco consumption category and genotype, with nonsmokers having the wild-type genotype as the referent category. Adjusted ORs generated in this manner were evaluated for deviation from the expected null value on the additive or multiplicative scale. To further quantify departures from additivity, the ICR (also previously called the relative excess risk for interaction) and 95% CI were estimated (8 , 9) . In addition, interaction terms including genotype and smoking duration or amount, ungrouped, were evaluated for statistical significance with logistic regression.
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Results |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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. More cases were less than age 45 years at diagnosis (13%
versus 8%), although the distribution of subjects >59
years was similar (46% of cases; 47% of controls). Consumption of
40 drinks/week produced a nearly 6-fold increase in risk (OR, 5.9;
CI, 2.0–17.7). Table 2
presents the main effects of tobacco use, adjusted for age, race, sex,
and alcohol use. A total of 19 cases and 20 controls failed to report
their history of smoking with respect to amount or duration. Also, 25
subjects (13 cases and 15 controls) used tobacco products other than
cigarettes and were counted as missing for the analysis of the amount
and duration of cigarette use. As expected, persons with a history of
tobacco use were at an increased risk of SCCHN (adjusted OR, 4.1; 95%
CI, 2.0–8.7). All of the cigarette use variables showed an indication
of a dose-response gradient, with a 7-fold increased risk for 40
total years of cigarette use.
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shows genotype frequencies (in percent) for each gene and the main
effect (OR) of the genotype stratified by race and adjusted for age and
sex. For each gene, genotype frequencies were found to be in
Hardy-Weinberg equilibrium for both cases and controls. The prevalence
of the at-risk genotypes among the control subjects were generally
similar to those reported in other studies, including samples from
other North Carolina studies (4
, 5
, 10
, 11)
. Differences
in allele frequencies for black and white control groups were similar
to those reported in the literature (10)
. Comparing allele
frequencies between blacks and whites, the only significant difference
observed was for the NAT1*10 allele (allele frequency:
white, 0.24; black, 0.38; P < 0.05).
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. The
frequency of the GSTT1 null genotype was slightly higher in
cases relative to controls (adjusted OR, 1.2; CI, 0.8–1.9), but this
difference was not significant. GSTP1 heterozygote genotypes
were more frequent in case patients, but homozygote valine genotypes
were somewhat less common in cases. The adjusted OR for the combined
GSTP1 genotype (Val/Ile and Val/Val grouped together) was
1.2 (CI, 0.8–1.9). NAT1 genotypes also showed little
consistent evidence for association with risk. NAT1*10
heterozygotes were slightly increased (OR, 1.2) among cases, and
NAT1*10 homozygotes were slightly reduced among cases (OR,
0.6; CI, 0.2–1.5). Risk for genotypes containing any
NAT1*10 allele resulted in an OR of 1.1 (CI, 0.7–1.7).
There were no homozygotes with the CYP1A1 Val/Val genotypes
in our study. Heterozygotes had a moderately increased risk estimate
(OR, 1.5; CI, 0.6–3.6). Analysis of the risk for the presumed at-risk
genotypes did not appear to vary between white and black subjects,
although the race-specific estimates, especially for African-Americans,
were associated with wide CIs. In general, there was little variation
in risk according to the site of the tumor, although the effect of the
GSTM1 null genotype was stronger among cases with laryngeal
cancer (OR, 1.9; CI, 0.9–3.8).
Table 4
presents the results of the analysis of the joint effects or
interaction between tobacco use and GSTT1. The ORs are for
each combination of tobacco use and genotype, they are relative to
those nonusers without the null (deleted) genotype, and they are
adjusted for age, sex, race, and alcohol use. When analyzed
simply as tobacco users compared with nonusers, there was no indication
of any increased risk among users with the null genotype (OR, 5.1
versus 4.9 among users with the GSTT1 gene
present). However, analysis of pack-years of use showed some suggestion
of an interaction. Among those individuals with 40 years of smoking
and the null genotype, the OR was 13.4 (CI, 3.6–50.4)
versus 5.4 (CI, 2.0–14.2) among those with the gene
present. The OR of 13 can be compared with the expected multiplicative
OR of 14 and the additive OR of 7.1. The ICR that measures the extent
of the departure from the additive null is 9.7 (a value of 0 indicates
no excess; CI, -11.4 to 30.9). However, caution should be used in
interpreting these results given the imprecision of the estimates. The
interaction term including GSTT1 and pack-years (not
categorized) was not statistically significant (P =
0.89).
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, although the differences are very small. No interaction can be seen
for GSTP1 at-risk genotypes (Table 6)
. Neither GSTP1 nor GSTM1 showed an interaction
with smoking on the additive scale. The interaction (multiplicative)
between GSTM1 or GSTP1 and either duration or
amount of smoking (not categorized) was not statistically significant.
The combined NAT1*10 heterozygote and homozygote genotypes
(NAT1*10/NAT1*4 and
NAT1*10/NAT1*10) had a suggestive interaction
with some measures of tobacco use (Table 7)
. The NAT1 at-risk genotype was associated with a nearly 5-fold
increased risk among smokers with a history of >40 pack-years (ICR,
3.3; CI, -1.44 to 8.1). The strongest joint effect was with total
years of cigarette use (data not shown). The observed OR of 11.8
(CI = 2.8–49.4) for 40 years of smoking among those with the
NAT1 at-risk genotype (OR, 3.3; CI, 1.0–10.2 for those with
*4 or *11 alleles) is greater than the expected multiplicative OR of
2.6 and expected additive OR of 1.1. The ICR is 9.2 (CI, -5.9 to
24.4). Additionally, a statistically significant interaction was found
between NAT1 and the continuous smoking variables, amount
(P = 0.03), and duration (P = 0.002).
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Discussion |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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There have been 14 published studies that have examined carcinogen metabolizing enzyme genotypes and the risk of SCCHN (13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26) . The results have been inconsistent.
Five of 13 studies have found an increased risk (ORs, 1.5–3.9) for the GSTM1 null genotype (14 , 18 , 19 , 20 , 26) ; five of nine studies reported associations with the GSTT1 null genotype (ORs, 1.2–2.3; Refs. 14 , 17 , 20 , 24 , and 26 ); and two of three studies evaluated the GSTP1 genotype and reported an increased risk (ORs, 1.6–2.0) for the GSTP1 Ile > Val polymorphism (21 , 24) . Two studies investigated a polymorphism of the GSTM3 gene (GSTM3*B); a weak association was reported for oropharyngeal cancer (OR, 1.3) and a 2-fold elevated OR for cancer of the larynx (24, 25) . Two studies examined the CYP1A1*2A Ile > Val polymorphism. Park et al. (13) found an association (OR, 2.6; CI, 1.2–5.7), and the data of Ophuis et al. (22) showed only a weakly increased risk (OR, 1.1; CI, 0.7–1.9). Some studies also evaluated the interaction among multiple genotypes. Hung et al. (17) noted a higher risk of oral cancer among those subjects with the GSTM1 and/or GSTT1 null genotypes. Other studies did not report a significant genotype interaction (15 , 21 , 22)
Evaluation of only the main effect of the enzyme genotype may mask an
underlying interaction with smoking. Given the primary hypothesis of
the polymorphisms modifying the risk of the predominant risk factor,
smoking, an analysis of interactions is necessary to fully evaluate the
role of the genes. Further, examination of the interaction on both the
multiplicative and additive scales is important. Unfortunately, only
five studies have reported any information on the relationship between
genotype, smoking, and the risk of SCCHN. This makes a direct
comparison with our study results difficult. Kihara et al.
(18)
noted an increased risk of the GSTM1 null
genotype among smokers compared with nonsmokers. Park et al.
(13)
did not find a relationship between pack-years of
smoking and risk of SCCHN among cases with the CYP1A1
polymorphism or the GSTM1 null genotype. Jourenkova et
al. (20)
reported finding an increased risk of
laryngeal cancer with the GSTM1 null genotype and with a
lower amount of smoking and a higher risk with the GSTT1
null genotype and a longer duration of smoking. The same research group
reported significant interactions (multiplicative) between smoking
(31 years of smoking) and the GSTT1 and GSTP1
polymorphisms (24)
and no interaction between smoking
(amount per week) and GSTM3, GSTP1, and risk of
laryngeal cancer (25)
.
There are several possible explanations for the variation among study results and our failure to find a strong interaction between the enzyme genotypes and smoking. First, as previously noted, despite being among the largest, our study did not have a sufficient number of subjects to allow for the precise estimation of the genotype-smoking interaction. This is especially important because it is likely that these genes do not act in isolation, and evaluation of multiple genes interacting with exposure may be required to understand the phenomenon. Second, few studies directly examined the interaction with tobacco; none evaluated departures on the additive scale. Third, the composition of the control groups used in previous studies were variable; some included out-patients from the source hospital for the cases and others used friends or spouses. Only one study appeared to have used population-based controls. Our study identified controls from the same clinic as the cases and included patients with conditions requiring surgery. In the present study, it is possible that some of the diagnoses prevalent among the control population (such as chronic sinusitis) are related to smoking and that these conditions are mediated by the metabolizing enzymes resulting in an underestimation of risk. The main effect of tobacco use in our study, although imprecise, is greater than that found in a large population-based case-control study of oral and pharyngeal cancer conducted in four areas of the United States (27) . Additionally, the frequencies of the various enzyme genotypes among control subjects are very similar to those reported for other North Carolina studies (4 , 5 , 10 , 11) , and this suggests that it is unlikely that genotype influences the conditions found among clinic controls.
It is possible that interindividual variability in the expression level
of metabolizing enzymes in head and neck tissues (that is independent
of genotype) could confound this type of case-control analyses.
Activity levels of GST
and GST
(not analyzed) vary dramatically
between individuals in both normal and cancerous tissues and among
tissues in the head and neck (28)
. Both CYP1A1
and NAT1 activity have been reported to vary in human larynx
tissues, and the expression of both enzymes vary widely among people in
other tissues (29, 30, 31, 32, 33)
. Variation in CYP1A1
activity has been associated with cigarette smoking exposure
(29, 30)
. It has been hypothesized that CYP1A1
and NAT1 polymorphisms detected in this study may be related
to differences in expression, but at this time, CYP1A1 and
NAT1 genotype/phenotypes relationships are still poorly
understood. Thus, risks associated with specific genotypes analyzed in
this study many be obscured by other factors influencing the expression
of carcinogen metabolizing enzymes.
The distribution and disposition of cigarette smoke carcinogens in the oral cavity have not been well studied, and the specific pathways for the activation and detoxication of carcinogens in head and neck tissues have not been characterized.
Genotype analysis may provide an insight into the role of specific carcinogen pathways. The findings of this study related to smoking and GSTT1 and NAT1 and the interaction of CYP1A1 and GSTM1 null genotypes provide some suggestive leads. However, owing to the imprecision of the results from this and previous studies, future efforts should be sufficiently large to allow a more definitive assessment of gene-environment interactions.
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Acknowledgments |
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Footnotes |
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1 Supported by NIH Grant CA61188 and by a grant
from the Institute of Nutrition, University of North Carolina. 
2 To whom requests for reprints should be
addressed, at Department of Epidemiology, CB #7400, School of Public
Health, Chapel Hill, NC 27599.
3 The abbreviations used are: SCCHN, squamous cell
carcinoma of the head and neck; GST, glutathione
S-transferase; OR, odds ratio; CI, confidence interval;
ICR, interaction contrast ratio.
Received 2/ 9/99; revised 8/10/99; accepted 12/ 2/99.
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References |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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1 gene defect. Lancet, 347: 295-297, 1996.[Medline]
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