REPORTS

Early Age at Smoking Initiation and Tobacco Carcinogen DNA Damage in the Lung

John K. Wiencke, Sally W. Thurston, Karl T. Kelsey, Andrea Varkonyi, John C. Wain, Eugene J. Mark, David C. Christiani

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).


    ABSTRACT
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 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 
BACKGROUND: DNA adducts formed as a consequence of exposure to tobacco smoke may be involved in carcinogenesis, and their presence may indicate a high risk of lung cancer. To determine whether DNA adducts can be used as a "dosimeter" for cancer risk, we measured the adduct levels in nontumorous lung tissue and blood mononuclear cells from patients with lung cancer, and we collected data from the patients on their history of smoking. METHODS: We used the 32P-postlabeling assay to measure aromatic hydrophobic DNA adducts in nontumorous lung tissue from 143 patients and in blood mononuclear cells from 54 of these patients. From the smoking histories, we identified exposure variables associated with increased DNA adduct levels by use of multivariate analyses with negative binomial regression models. RESULTS/CONCLUSIONS: We found statistically significant interactions for variables of current and former smoking and for other smoking variables (e.g., pack-years [number of packs smoked per day x years of smoking] or years smoked), indicating that the impact of smoking variables on DNA adduct levels may be different in current and former smokers. Consequently, our analyses indicate that models for current and former smokers should be considered separately. In current smokers, recent smoking intensity (cigarettes smoked per day) was the most important variable. In former smokers, age at smoking initiation was inversely associated with DNA adduct levels. A highly statistically significant correlation (r = .77 [Spearman's correlation]; two sided P<.001) was observed between DNA adduct levels in blood mononuclear cells and lung tissue. IMPLICATIONS: Our results in former smokers suggest that smoking during adolescence may produce physiologic changes that lead to increased DNA adduct persistence or that young smokers may be markedly susceptible to DNA adduct formation and have higher adduct burdens after they quit smoking than those who started smoking later in life.



    INTRODUCTION
 Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Biomarkers of the biologically effective dose of tobacco carcinogens are used to estimate the formation of stable complexes of tobacco carcinogens and DNA that are thought to be a crucial early step in cancer induction (1,2). Tobacco smoke contains a highly complex mixture of carcinogens that have the potential to damage DNA; polycyclic aromatic hydrocarbons, aromatic amines, and tobacco-specific nitrosamines have been implicated as the major mutagenic carcinogens responsible for DNA adduct formation. The most widely studied class of smoking-induced DNA adducts is derived from polycyclic aromatic hydrocarbons (3-13). Increased amounts of aromatic hydrophobic DNA adducts are associated with lung cancer (14), and polycyclic aromatic hydrocarbon adducts form preferentially at hot spots in the p53 gene (also known as TP53), sites that are frequently mutated in smoking-induced lung cancer cells (15,16). Thus, these observations suggest that levels of DNA adducts are important in the cause of lung cancer. In addition, in blood mononuclear cells, the levels of DNA adducts induced by in vitro treatments with benzo[a]pyrene diol-epoxide were significantly greater in patients with lung cancer than in control subjects (17). An earlier study (18) also found higher levels of benzo[a]pyrene-induced DNA adducts in blood monocytes from patients with lung cancer who had a presumed predisposition to lung cancer (i.e., early age at cancer onset) than in blood monocytes from control subjects or older patients.

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.


    MATERIALS AND METHODS
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 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Specimen/data collection, DNA isolation, and 32P-postlabeling assay. Lung and blood mononuclear cells were obtained from 143 consecutive patients with lung cancer who were undergoing surgical resection of their tumors at the Massachusetts General Hospital. Surgically resected noninvolved lung tissue was obtained from patients undergoing surgery for histopathologically confirmed lung cancer. The protocol was approved by the Committees on the Use of Human Subjects in Research at the Massachusetts General Hospital and the Harvard School of Public Health. Tissue specimens were immediately frozen on dry ice and maintained frozen at -70 °C until DNA adduct studies were undertaken. Blood samples (heparin-treated whole blood) were obtained from each subject and applied to Ficoll-Hypaque density gradients to separate mononuclear cells from erythrocytes and granulocytes. Of the 143 cases, blood volumes considered sufficient for DNA adduct studies (20 mL) were available for 54 patients. A questionnaire was administered to each subject to obtain information on smoking history, occupation, and other demographic factors. Genotyping data for the GSTM1 and CYP1A1 (MSPI) loci were also available for the patients. Frozen tissue or mononuclear cells were homogenized in 0.1 M Tris-HCl (pH 8.0), 0.1 M NaCl, 50 mM EDTA, and 1% sodium dodecyl sulfate on ice and then extracted twice with equal volumes of chloroform/isoamyl alcohol, 24 : 1 (vol/vol). The aqueous supernatant was incubated with ribonuclease (RNase) A and RNase T1 (250 µg/mL; Sigma Chemical Co., St. Louis, MO) at 37 °C for 60 minutes followed by digestion with proteinase K (Life Technololgies, Inc. [GIBCO BRL], Gaithersburg, MD;10 µg/mL; 37 °C for 60 minutes). The digest was extracted twice with chloroform/isoamyl alcohol, after which sodium acetate (0.4 M, final concentration) was added to the aqueous supernatant. DNA was precipitated with ethanol at -20 °C and dissolved in 0.1x standard saline citrate. The quantity of DNA was determined by a fluorometric method (Hoechst 33258; Hoeffer Scientific, San Francisco, CA). Four micrograms of purified DNA was enzymatically digested to deoxynucleotide 3'-monophosphates with micrococcal nuclease (Worthington Biochemical Corp., Freehold, NJ) and spleen phosphodiesterase. Samples were then treated with P1 nuclease. The modified nucleotides were converted into 32P-labeled deoxynucleotide 3',5'-diphosphates by incubation with 150 µCi of [32P]adenosine triphosphate (ATP) (6000 Ci/mmol; Du Pont NEN, Boston, MA) and 2.5 µL of T4 polynucleotide kinase. The total volume of 32P-labeled deoxynucleotide 3',5'-diphosphates was applied to each polyethyleneimine-cellulose plate (10 x 10 cm).

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 {chi}2/df (where {chi}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.


    RESULTS
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 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Poisson and Negative Binomial Modeling of DNA Adducts

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.Go 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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Table 1. Characteristics of patients with lung cancer (total n = 143) and DNA adduct levels

 
Because of our observations, we used negative binomial regression models to assess the importance of smoking exposure variables on DNA adduct levels in lung tissue and blood mononuclear cells. Models were constructed with both current smokers and ex-smokers, with covariates including interactions of smoking status (current versus former smoker) with other variables (e.g., cigarettes smoked per day and age at smoking initiation). A global test indicated statistical significance for all interaction terms (P = .04). These models (Table 2, A)Go indicate that the slopes for smoking variables differ statistically significantly for current smokers versus ex-smokers. The slopes for current and ex-smokers were determined by fitting a model for adduct level which included an indicator variable that takes on only two values (i.e., 1 for ex-smokers and 0 for current smokers). Consequently, we used separate models for analyzing DNA adducts in current and former smokers. In current smokers, daily cigarette consumption (cigarettes smoked per day) is the most important variable (P = .03), but it is not statistically significant for ex-smokers (P = .3) (Table 2Go, B). In ex-smokers, the age at smoking initiation was most significant (P<.001). In current smokers, an earlier starting age was not associated with higher adduct levels (P = .14). To illustrate the association of age at smoking initiation with DNA adduct levels, we compared the mean DNA adduct levels of former smokers by the quartile of their age at smoking initiation (Fig. 1).Go We also examined age of smoking initiation in negative binomial models that included the GSTM1 and CYP1A1 gene polymorphisms; the inverse correlation between age at smoking initiation and adduct levels was not affected by these genetic variants. Additional models were studied that included sex and tumor histology. Neither of these variables was statistically significant. To test whether DNA adducts in blood mononuclear cells are a valid surrogate for DNA adduct levels in the lung, we determined whether the two variables were correlated. As shown in Fig. 2,Go we observed a highly statistically significant correlation (r = .77 [Spearman's correlation]; P<.001) between polycyclic aromatic hydrocarbon-DNA adducts in blood mononuclear cells and polycyclic aromatic hydrocarbon-DNA adduct levels in the lung.


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Table 2. Negative binomial regression model

 


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Fig. 1. Inverse relationship between lung DNA adduct levels in patients with lung cancer who were former smokers (n = 77) and age at smoking initiation. Boundaries for quartiles of the age at initiation of smoking are as follows: first quartile = 7.0-15.0 years; second quartile = 15.1-17.2 years; third quartile = 17.3-20.1 years; and fourth quartile = 20.2-33.0 years. Mean adduct levels (per 1010 nucleotides) and 95% confidence intervals for the 1st quartile were 164 (75-251), 2nd quartile, 115 (49-182), 3rd quartile, 94 (45-142), and 4th quartile, 81 (49-111).

 


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Fig. 2. Correlation between levels of aromatic hydrophobic DNA adducts in blood mononuclear cells and levels observed in nontumorous lung tissue from patients with lung cancer. DNA adduct data are shown for 54 consecutive patients for whom both peripheral blood specimens and surgically excised lung tissue were available for 32P-postlabeling analysis. A best-fit linear regression analysis is shown. Individual adduct levels on the abscissa and the ordinate are presented as adducts per 1010 nucleotides.

 

    DISCUSSION
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 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 
To explore the role of cigarette smoke exposure on DNA adduct levels in lung tissue, we constructed regression models that simultaneously tested the effects of several smoking variables on adduct levels. Our initial analysis indicated overdispersion in the DNA adduct data, and hence we used negative binomial regression, an approach that has previously been proposed for biomarkers of chromosomal damage in blood lymphocytes (29). The regression models included interaction terms to assess the effects of smoking status (current versus former smoking) in combination with conventional measures of smoking intensity (cigarettes smoked per day), duration (years of smoking), cumulative lifetime exposure (pack-years), or age of smoking initiation on DNA adduct levels in lung tissue. The purpose of including the interaction terms was to examine whether the slopes for smoking variables differed significantly in current smokers versus ex-smokers. We found that several variables differed significantly in current and former smokers with respect to their effects on DNA adduct levels in the lung. For example, in current smokers the intensity of smoking measured as cigarettes smoked per day was positively associated with adduct levels in the lung but was not statistically significantly associated with adduct levels in the lungs of ex-smokers. In former smokers, a statistically significant negative correlation between adduct levels and the age at smoking initiation was observed, but this variable was not important for current smokers. Consequently, these data indicate that investigations of smoking-related DNA adducts should consider current and former smokers separately.

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).


    NOTES
 
Supported by Public Health Service grants P42ES04705, ES08357, and ES00002 (National Institute of Environmental Health Sciences) and CA06409 and R01CA74386 (National Cancer Institute), National Institutes of Health (NIH), Department of Health and Human Services (DHHS). The project described was supported by the National Institute of Environmental Health Sciences, NIH, DHHS, with funding provided by the Environmental Protection Agency.

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.


    REFERENCES
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 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

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Manuscript received July 24, 1998; revised December 30, 1998; accepted January 27, 1999.


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