Affiliations of authors: J. K. Wiencke, A.Varkonyi, Laboratory for Molecular Epidemiology, Department of Epidemiology and Biostatistics, School of Medicine, University of California San Francisco; S. W. Thurston, Department of Biostatistics, Harvard School of Public Health, Boston, MA; K. T. Kelsey, Department of Cancer Cell Biology and Occupational Health Program, Department of Environmental Health, Harvard School of Public Health, Boston; J. C. Wain, Thoracic Surgery Unit, Department of Surgery, Massachusetts General Hospital, Boston; E. J. Mark, Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston; D. C. Christiani, Occupational Health Program, Department of Environmental Health, Harvard School of Public Health, Boston, and Pulmonary and Critical Care Unit, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston.
Correspondence to: John K. Wiencke, Ph.D., Laboratory for Molecular Epidemiology, Department of Epidemiology and Biostatistics, School of Medicine, University of California, San Francisco, 500 Parnassus Ave., San Francisco, CA 94143-0560 (e-mail: wiencke{at}itsa.ucsf.edu).
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ABSTRACT |
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INTRODUCTION |
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As a function of time, the levels of tobacco-related DNA adducts in human tissue reflect a dynamic process that is dependent on a number of factors. These include the intensity and recency of exposure to tobacco smoke and the metabolic balance between activation of detoxification mechanisms and the removal of adducts by DNA repair and/or cell turnover. Thus, in epidemiologic studies, DNA adduct levels may provide a more integrated measurement of carcinogen exposure. However, the interpretation of these data requires an understanding of how various factors interact, including the temporal relationship between exposure to tobacco smoke and the amount of adducts in cells.
Smoking exposure indices, such as cumulative history of tobacco use (in pack-years [number of packs smoked per day x years of smoking]) among current smokers, have been found to be associated with aromatic DNA adduct levels in human lung parenchyma (3,5) and bronchus (4). However, among current smokers with comparable smoking exposure, there exists substantial interindividual variation in adduct levels; evidence suggests that genetic polymorphisms in carcinogen metabolism may be important in determining some of these variations (9,10,19,20). After the cessation of smoking, levels of aromatic hydrophobic DNA adducts decrease in the lung (21), although the kinetics of the disappearance of these lesions has not been well characterized. Animal studies have shown that the kinetics of the removal of polycyclic aromatic hydrocarbon-DNA adducts are affected by the nature of the exposure to polycyclic aromatic hydrocarbons. Repeated exposure and repair cycles may be more likely to cause an increase in the proportion of DNA adducts in repair-resistant areas of the genome (22). Thus, the pattern of exposure to polycyclic aromatic hydrocarbons and the cumulative dose of polycyclic aromatic hydrocarbons may affect the persistence of polycyclic aromatic hydrocarbon-induced DNA damage. These observations have led us to postulate that the physiologic and biochemical parameters governing DNA adduct accumulation in populations currently exposed to tobacco smoke could be distinct from those parameters mediating adduct removal in persons with past exposure. Consequently, one aim of the current study was to explore new analytic approaches to investigate the molecular dosimetry of smoking-induced DNA damage that would be sensitive to potential influences of current exposure versus past exposure. This strategy could be used for studies under way that seek to define the contribution of various factors, including smoking history, genetics, diet, occupation, etc., to individual differences in lung adduct burdens and to assess adduct assays that can be applied to a broad range of human populations to determine risk of lung cancer.
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MATERIALS AND METHODS |
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Chromatographic conditions. Plates were developed overnight in 1 M NaH2PO4 (pH 6.0). The plates were washed for two 7-minute periods in H2O and for a 10-minute period in 0.15 M ammonium formate (pH 3.5). The plates were air-dried and developed in a solution of 3.0 M lithium formate and 7.0 M urea (pH 3.5) from the bottom to the top of the plate. The plates were then washed twice (7 minutes) in H2O, air-dried, and developed at a right angle to the previous direction of development in 0.72 M NaH2PO4, 0.45 M Tris-HCl (pH 8.2), and 7.6 M urea. Next, the plates were washed twice with H2O and then developed in the same direction with 1.7 M NaH2PO4 (pH 6.0).
Autoradiography and adduct quantitation. DNA adducts were located by autoradiography using Kodak XAR-5 film and a Dupont Chronex-Lightning Plus intensifying screen. The films were exposed at -70 °C for 3-4 days. Areas of the radioactive spots on the polyethyleneimine-cellulose sheets were measured. Radioactive spots were then scraped off the sheets into liquid scintillation vials containing 5 mL of scintillation fluid (ScintiVerse II; Fisher Chemical Co., Fairlawn, NJ); radioactivity was measured by liquid scintillation counting. Regions adjacent to the radioactive spots of equal area were scraped and placed into scintillation vials, and radioactivity was measured to determine background levels. The measurements of radioactivity in the adducts were corrected for background counts after adjusting for the area of the thin-layer chromatography sample. The level of modification was calculated as described (23). For example, if 4 µg of DNA is 1.21 x 104 pmol of deoxynucleotide 3'-monophosphate and the specific activity of the [32P]ATP is 9.36 x 106 cpm/pmol, then adduct levels were calculated as follows: relative adduct level = cpm in adducts/11.32 x 1010 cpm. For each experiment, we prepared a positive control sample of DNA containing benzo[a]pyrene diol-epoxide-labeled deoxyguanosine. Each sample was run at least two times on different days and the relative adduct levels for all experiments were combined to obtain an average adduct level. The coefficient of variation for repeated analyses of the positive control was 14%.
Statistical methods. DNA adduct levels per 1010 nucleotides or the
transformed adduct level (log[adduct] + 1) were examined with S-plus
statistical software (24). Although regression models for adduct count
data have assumed a Poisson model, more recently negative binomial models have also been
used (25). The negative binomial regression model is one generalization
of the Poisson model that allows the variance to be larger than the mean. We compared the
Poisson and negative binomial models for the number of DNA adducts as a function of smoking
variables. Highly correlated variables were not included together in models to avoid possible
effects of colinearity. Parameters in the negative binomial regression were estimated by
maximum likelihood (26,27). In the Poisson model, the dispersion
parameter, estimated by 2/df (where
2 is the
sum of the squared Pearson residuals and df is the residual degrees of freedom), should
have a value of about one (28). To evaluate the Poisson model, we
examined this quantity in separate models of the number of DNA adducts in normal lung tissue
from patients with lung cancer who were current smokers or ex-smokers. To test assumptions of
the models that we tested, we examined the Studentized standardized deviance residuals. In this
case, the deviance residuals (i.e., differences between observed and expected values under the
model) are standardized by dividing them by their standard deviations. Such deviance residuals
should be approximately normally distributed with a mean of 0 and a variance of 1 (28). We plotted these residuals versus the fitted values for each of four models
(Poisson, negative binomial, normal, and log normal) as a check of model assumptions. All
reported P values are from two-sided statistical tests.
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RESULTS |
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The study population consisted of 143 lung cancer patients; 136
patients were current or ex-smokers and seven were never smokers.
Demographic and clinical data on the patient group are summarized in
Table 1. We first investigated different statistical
approaches for analyzing the DNA adduct data. For modeling analyses,
never smokers were excluded as was one outlying case subject who
reported starting smoking at 60 years of age and one former smoker
whose years of smoking data were missing. DNA adduct values from lung
tissue for never smokers were very low (n = 7; mean, 32; range, three
to 89 adducts per 1010 nucleotides) compared with values for
current or former smokers. Current smokers (n = 57) had higher DNA
adduct levels in lung tissue than did ex-smokers (n = 78), 255 versus
113 adducts per 1010 nucleotides, respectively
(P<.001; Welch modified t test). Initially, we
investigated the Poisson model including three smoking covariates
(i.e., years smoked, cigarettes smoked per day, and age at smoking
initiation). We observed that the dispersion parameter for the entire
group was 152. For current smokers, the dispersion parameter was
estimated to be 123, and for ex-smokers (with the same three covariates
plus years since the subject quit smoking), the estimated dispersion
parameter was 112. The expected dispersion parameters would be equal to
one if the Poisson model were correct. These results indicate strongly
that the Poisson model does not hold and that there is excess
dispersion in the data. In addition, when we plotted the Studentized
standardized deviance residuals against the fitted values in separate
models for current smokers and ex-smokers (data not shown), it was
clear that the Poisson model was not appropriate for either current
smokers or ex-smokers because the variance of the residuals is much
larger than one and increased with the magnitude of the fitted values.
The value of these residuals also increased when a normal model was
used for ex-smokers. However, these plots indicated no clear violation
of model assumptions for either negative binomial or log normal distributions.
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DISCUSSION |
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To our knowledge, no other study has considered age at smoking initiation as a potential predictor of tobacco smoke-related DNA damage in former smokers. For the group of ex-smokers studied, the average time since the patient quit smoking was 11.9 years. Thus, we assume that substantial reductions in adduct levels in lung tissue have occurred in these subjects compared with the levels when they were actively smoking. This idea is supported by the fact that adduct levels are 2.3-fold lower in former smokers than in current smokers. Our observation is especially intriguing given the epidemiologic evidence that age at smoking initiation is an independent risk factor for lung cancer (30,31). The relative importance of an early age at initiation of smoking as a risk factor for lung cancer, however, is controversial (32). Individuals who begin smoking very early in life tend to be heavier smokers. Thus, it can be difficult to identify the independent risks associated with age at initiation. We modeled DNA adduct levels in the lung tissue of former smokers by using several smoking variables and genetic markers (i.e., GSTM1 and CYP1A1 genes) in addition to the age at smoking initiation; none of these variables achieved statistical significance. Age at smoking initiation, on the other hand, was consistently associated with variations in adduct levels.
We propose the following two general explanations for our results in former smokers: 1) decreased adduct removal through DNA repair and cell turnover or 2) increased adduct accumulation. Under the first scenario, early-age smoking, during a time of rapid lung growth and development, may induce long-lasting physiologic changes that impair the removal of damaged bases in the DNA. The kinetics of the disappearance of carcinogen adducts in human lung and the influence of individual variability in adduct removal are not known precisely. Within 1-2 years, levels of DNA adducts in ex-smokers appear to be greatly reduced (21) when compared with the levels in current smokers. In mice, both monophasic and biphasic kinetics have been observed for the removal of aromatic hydrophobic DNA adducts in lung tissue of animals treated with benzo[a]pyrene (33). In addition, different rates of decay of benzo[a]pyrene-DNA adducts have also been detected among different strains of mice and were found to be inversely related to the age of the animals (34). It is of interest that, in current smokers, age at smoking initiation was not significant. This is consistent with an effect on the removal of adducts rather than an effect on the accumulation of adducts; in current smokers, the higher adduct levels and influence of recent smoking may mask the effects of early-age smoking and impaired adduct removal.
Alternatively, very young smokers may be markedly susceptible to adduct formation but have normal rates of adduct removal. In this scenario, young smokers accumulate more damaged DNA that, even with normal repair, is demonstrable many years after smoking cessation, in contrast to subjects who begin to smoke later. Neither of these mechanisms has been examined in humans, although adduct measurements on DNA from white blood cells are being used to document the effects of polycyclic aromatic hydrocarbon exposure in early life and in adolescence (35,36). Finally, it should also be noted that, if the chemical nature of DNA adducts was different in former versus current smokers and these had different 32P-postlabeling efficiencies, then apparent differences in adduct levels could occur between the two groups. Further chemical characterization of tobacco-carcinogen-induced DNA damage in former and current smokers is needed to address this possibility.
In blood mononuclear cells, levels of individual polycyclic aromatic hydrocarbon-DNA adducts have been reported to follow a log normal distribution (14). Our data agree with this observation but indicate that the negative binomial and even the normal distribution may also be appropriate. These results indicate that the overdispersion of adduct levels seen in the target tissue (lung) is not as pronounced in the blood surrogate (mononuclear cells). One study of smoking cessation estimated the half-life of blood leukocyte DNA adduct levels to be 9-13 weeks (37). We did not have enough DNA adduct measurements from blood mononuclear cells to examine the question of half-life or the effect of early smoking initiation on the levels of these adducts.
Another important finding of the present study is that adduct levels in blood mononuclear cells are highly correlated with adduct levels in the lung, regardless of smoking status. We estimate that 60% of the variations in adduct measurements in lung tissue is explained by adduct measurements in the blood mononuclear cells. Given the overdispersed nature of adduct levels in the lung and the observed differences in the effect of cigarette smoke exposure on DNA adducts in the lungs of current versus former smokers, this high degree of concordance is especially striking. We previously proposed that longer-lived mononuclear cells from peripheral blood could be a better source for DNA adduct measurement than short-lived blood granulocytes (23).
The close correlation of adduct levels in the blood mononuclear cells and lung also indirectly supports the notion that the overdispersed nature of adduct levels is not due to variations in sampling or laboratory errors that could conceivably vary across individuals. For example, if lung tissue samples were obtained from different locations in the lungs, then this could give rise to overdispersed lung adduct data if there were significant anatomic variations in adduct levels within the lung. Alternatively, if different subsets of lymphocytes (short-lived versus long-lived) were sampled in different individuals, this could give rise to overdispersed adduct levels in blood cells. These types of sampling effects, however, would be expected to be tissue specific and, therefore, would lead to a weak correlation of adduct levels between tissues, whereas we found a very strong relationship. Consequently, we believe that there is a statistically significant, and as yet unexplained, individual variation in the levels of DNA adducts in lung tissue and blood mononuclear cells and we hypothesize that the factors responsible for this variability affect adduct levels similarly in both tissues. Other researchers using a longitudinal study design of polycyclic aromatic hydrocarbon-DNA adducts in blood lymphocytes estimated that 70% of the total variance in adduct levels was contributed by interindividual variability and probably represented true biologic variability in the response to carcinogenic exposure (38).
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NOTES |
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We thank H. Kazemi, Fred Grillo, Louise Ryan, William J. Bodell, Linda Lineback, Marcia Chertok, Marlys Rogers, Lucy-Ann Principe-Hasan, Rosito Lamela, and David Miller.
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Manuscript received July 24, 1998; revised December 30, 1998; accepted January 27, 1999.
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