Environmental Medicine Unit and
1 Molecular Epidemiology Unit, Department of Biosciences, Karolinska Institute, CNT/NOVUM, 141 57 Huddinge and
2 Division of Environmental Epidemiology, Institute of Environmental Medicine, Karolinska Institute, Box 210, 171 77 Stockholm, Sweden
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Abstract |
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Abbreviations: AL, adduct level; ETS, environmental tobacco smoke; hprt, hypoxanthine-guanine phosphoribosyl transferase; MF, mutant frequency; PAH, polycyclic aromatic hydrocarbon; TSL, time since last regular smoking.
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Introduction |
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Some hundreds of DNA adduct biomonitoring studies have been published using either the 32P-post-labelling technique or immunoassay. A large majority of the studies have analysed aromatic DNA adducts, related to polycyclic aromatic hydrocarbons (PAHs) (reviewed in refs 35). The most common study design has been the comparison of adduct levels in target organs or surrogate tissues of exposed and non-exposed populations, such as smokers and non-smokers. Few studies have analysed DNA adducts and point mutations, in the p53 gene (6) or in the hprt marker gene (7,8), in the same tissue and individuals.
Cigarette smoking has been established as the major cause of lung cancer (9,10) and the mutagenic PAHs are well recognized as respiratory carcinogens (11). Peripheral lymphocytes are capable of metabolizing PAHs to DNA-reactive species (12) and are frequently used as a surrogate tissue for the target organ, the lung. Similar turnover of PAHDNA adducts in lung and lymphocytes have been observed in experimental animals (13), suggesting that the lymphocyte adduct level (AL) can be useful for estimating the target PAH dose. Significantly elevated levels of aromatic DNA adducts have been measured in healthy smokers using the longer-lived lymphocyte fraction rather than total leukocytes (14, 15). More recently, repeated sampling of smokers before and after quitting has demonstrated that aromatic DNA adducts in leukocytes are associated significantly with cigarette smoking and decline after quitting (16).
In a recent casecontrol study (17), the PAHDNA AL in leukocytes of lung cancer cases was found to correlate better with adducts in the lung tumor tissue than with those in non-tumor lung tissue. Adducts in leukocytes were increased significantly in smokers and ex-smokers compared with nonsmokers and in cases compared with controls. A dose-related increase in the AL was found in currently smoking cases only, suggesting a constitutional difference in the biological response to smoking between cases and controls. With regard to the hprt MF, however, no comprehensive lung cancer casecontrol study has been reported. The only known investigation, including 66 lung cancer cases and 40 controls, among whom few were never-smokers, reported no significant effect of cancer status on the MF (18).
Using the T cell cloning and 32P-post-labelling assays, we have studied the hprt MFs and aromatic DNA ALs in peripheral lymphocytes of non-smoking and smoking lung cancer patients and matched population controls, including a comparatively large number of never-smokers and females. Special emphasis has been given to the validation of smoking and case status, the effect of dose and timing of active and passive smoking, as well as the relationship between the two biomarkers.
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Material and methods |
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Population controls representing the study base were extracted from the Stockholm residence files every 6 months and frequency matched to cases based on the case distribution at the time. Matching was by hospital catchment area, gender and age strata as well as broad smoking categories of never, former and `current' (non-former) smokers (19). The `current' smokers were supplementary frequency matched with regard to dose (19, 1019, 20 cigarettes/day), based on the latest period of regular smoking of at least 1 cigarette/day.
Never smokers were defined as those who never smoked regularly (<1 cigarette/day during a year) and former smokers as those who quit smoking >2 years ago. The remaining group of `current' smokers was further subdivided into those who quit smoking within 2 years before interview (recent smokers) and those who were still regular smokers at the time of interview (true current smokers). Former and recent smokers were grouped together as ex-smokers for some analyses.
Detailed exposure data were collected by personal interview, usually at the time of blood collection (19). The questionnaire covered active smoking, passive exposure to environmental tobacco smoke (ETS; from spouse, work or other places and in vehicles), dietary habits, residential and working histories. Peripheral blood was drawn into heparinized tubes and transported to the laboratory for direct isolation of lymphocytes (Polymorphprep; Pharmacia, Uppsala, Sweden) and subsequent freezing (135°C). All analyses were performed on coded samples.
Mutant frequency (MF)
Four matched samples, one from each subgroup (never-smoking cases/controls, ever-smoking cases/controls), were analyzed concurrently in order to minimize the influence of possible methodological variation over time. The culture media used and the T cell cloning procedure have been described in detail previously (20). Briefly, all the lymphocytes were quickly thawed, washed and stimulated for 44 h with 0.3% phytohaemagglutinin (PHA; Difco, Detroit, MI) in RPMI1640-based medium containing 5% fetal calf serum and 5% human serum. Two 96-well non-selection plates were then prepared by inoculating 1 or 2 target cells and 2x104 lethally X-irradiated lymphoblastoid (RJK853) feeder cells in each well with a growth medium enriched with T cell growth factor (20% conditioned medium). Selection plates received 2x104 target cells and 1x104 feeder cells per well, with 2 µg/ml 6-thioguanine (Sigma) added in the growth medium. After 2 weeks, cell growth in wells was scored visually using an inverted microscope.
The cloning efficiency was calculated from the proportion of negative wells assuming a Poisson distribution (1). The MF was obtained by dividing the cloning efficiency in the presence of 6-thioguanine with that in the absence of 6-thioguanine. The 95% confidence interval (CI) for MF was calculated from the variance of lnMF (approximated from the number of negative and plated wells in selection and non-selection plates) according to Furth et al. (21).
Adduct level (AL)
For DNA isolation, frozen lymphocytes (510x106 cells) were thawed and washed in 10 mM Tris buffer (pH 7.4) containing 0.15 M NaCl. Crude nuclei were isolated by treatment of the pellet with 1% Triton X-100 followed by centrifugation. After degrading RNA by 50 µg RNase A and 40 U RNase T1, DNA was isolated from the crude nuclei by proteinase K (500 µg) treatment and organic solvents extraction.
The 32P-thin-layer chromatography (TLC) assay of PAHDNA adducts was carried out as described previously (22). Briefly, DNA (5 µg) was digested by micrococcal nuclease (0.4 U) and spleen phosphodiesterase (8 mU) to 3' nucleotides and adducts were then enriched by treatment with 5 µg nuclease P1. The post-labelling reaction was carried out in 2 µl solution containing 40 mM bicine (pH 9.6), 20 mM dithiothreitol, 20 mM MgCl2, 2 mM spermidine, 2.4 U T4 polynucleotide kinase and 7 µCi [-32P]ATP (3000 Ci/mM). The whole solution was applied on a TLC plate and adducts were separated by chromatography in three dimensions. The adduct spots were excised from the TLC plate for counting of radioactivity after autoradiography. Two to five assays were carried out for each sample. The AL was defined as the mean of at least two valid measurements.
Statistical methods
Group differences were tested by the non-parametric MannWhitney U-test. To investigate the influence of various factors on the AL and MF, we used univariate and multivariate linear regression. The outcome variables were ln-transformed to achieve approximately normal distribution. For a ß value (slope) obtained with a ln-transformed outcome variable, the proportional change in the outcome variable for each unit increase in the explanatory variable is represented by eß 1 which is approximately equal to ß for small values of ß. Using the example of MF and age, the change in MF from age n to n + 1 is MFn + 1 MFn = e + ß(n + 1) e
+ ßn = (eß 1)(e
+ ßn)
ßxe
+ ßn, or ßxMFn, which we translated to the % change in MF per year of age. We used correlation analysis to investigate the relationship between variables where neither was an obvious outcome variable. P-values (95% two-sided test) or 95% CIs are given as appropriate.
Factors tested include age, gender, case/control status, smoking status (ever/never), smoking category (current/recent/former), ETS exposure (ever/never), average (time-weighted) or final smoking dose (g/day = cigarettes/day), total duration (years) of regular smoking or cumulative packyears of exposure (1 pack of cigarettes/day for 1 year), houryears (1 h of exposure/day for 1 year) of ETS exposure and years since last regular smoking or ETS exposure.
For time since last smoking (TSL), we used rounded years before interview based on age of quitting recorded in the questionnaire. For quitting times <2 years before interview, we approximated TSL (months) by using the midpoint of the reported age of quitting. If the person reached his age of quitting <1 year before interview, we used the midpoint between this birthday date and the interview. For 57% of the 35 subjects (26 cases and 9 controls) with TSL <2 years we did, however, have the date of quitting available and these were then used to calculate a more exact TSL.
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Results |
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Background information is presented in Table I for a total of 340 subjects from whom at least one of the two measures (MF, AL) could be included in the statistical analysis. The age distribution was similar in the groups of patients (mean 66.7 years) and population controls (mean 65.0 years) due to matching. The majority of study subjects were women (73%) due to the use of never-smoking cases as index cases. The case group was dominated by adenocarcinoma (51%) and squamous cell carcinoma (22%), with few cases of small cell carcinoma. Adenocarcinoma was mainly represented by never-smoking cases (54% of women and 86% of men) and squamous cell carcinoma mainly by ever-smoking cases (26% of women, 67% of men).
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The AL was higher among men than women (median 4.0 versus 3.4x108, P = 0.01), mainly due to the contribution from never- or ex-smokers (4.0 versus 3.2x108, P = 0.01). There was no overall difference in MF between men and women, but women had a higher MF than men among controls (17.9 versus 12.6x106, P = 0.03), especially among never- or ex-smokers (17.9 versus 10.0x106, P = 0.002).
There was no significant difference in the AL or MF between cases and controls within each of the smoking categories (current or others, current/recent or former/never for AL, ever or never for MF; Table II). Similar results were obtained when calculating the odds ratio (cases versus controls) for higher tertile (versus lower tertile) of AL or MF, indicating that the lung cancer status had no apparent effect on these two endpoints.
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Combining cases and controls, current smokers had the highest AL among ever-smokers and there seemed to be a decreasing trend in AL with increasing TSL (Figure 2A). Never and former smokers had significantly lower ALs than current smokers (P = 0.0003 when combined). With regard to MF, there was no clear trend with increasing TSL. However, both recent and current smokers showed a significantly higher MF than never-smokers (Figure 2B
).
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Although ever-smoking cases had more packyears of smoking and shorter time since quitting than the corresponding controls (Table I), none of these smoking variables seemed to contribute to the higher age-dependent increase of MF observed in ever-smoking cases. After adjustment for packyears and TSL, the increase of MF with age remained significant in ever-smokers (1.6%/year, 95% CI 0.62.5), with a higher rate in the patients (2.1%/year, 95% CI 0.53.7) than in the controls (1.2%/year, 95% CI 0.12.5). The ETS exposure did not affect the MF or AL in any of the subgroups, including never-smokers currently exposed to ETS.
Among the current smokers, even after adjustment for final smoking dose (1.7% per cigarette smoked daily, 95% CI 2.35.7), the age effect was seen in cases only (2.6%/year, 95% CI 0.45.6). Age remained the strongest predictor for MF in ex-smokers (2.0%/year, 95% CI 0.73.3) after adjustment for final dose and TSL, especially in recent smokers (2.5%/year, 95% CI 0.74.4). While the age effect in recent smokers was mainly due to the contribution from cases (2.9%/year, 95% CI = 0.45.5), similar to what was seen for the current smokers, only controls showed an age-related increase in MF (2.3%/year, 95% CI = 0.14.5) among former smokers.
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Discussion |
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The ALMF relationship
Consistent with the more pronounced increase of both AL and MF with age and smoking dose in currently smoking cases, we found a significant positive correlation between the two biomarkers among the high-dose smoking cases. Previously, significant correlations between hprt MF and AL have been found in PAH exposed foundry workers (7) and garage workers (8), suggesting a mutagenic effect of the aromatic DNA adducts measured. The limitation of the correlation to the high-dose smoking cases in the present study is reasonable considering the age and disease-associated complexities, in addition to the differences in the specificity and turnover of the two biological measures. Such differences were also illustrated in the study of Perera et al. (7) where a significant correlation between PAHDNA AL and hprt MF was observed in foundry workers in the first high-exposure year only. While the AL was markedly reduced by the decline in exposure in the second year of study, the MF was less affected and remained elevated in several long-term workers.
The AL measures biologically effective dose of adduct-forming PAHs in total genomic DNA, whereas the MF is related to the fixation of unrepaired or persistent DNA damage (not all of which is related to PAHs) following replication of a specific gene sequence (3). Other differences between the two endpoints include the apparently rapid turnover of DNA adducts in contrast to the requirement for expression of mutations at the cellular level and the seemingly slower turnover of somatic mutants. In a study of cynomolgus monkeys treated with ethylnitrosourea, the hprt MF was found to peak at 70100 days after exposure (23). Repeated treatment 2 years later resulted in a peak level of MF at 70 days, followed by a decline to a plateau that was higher than the pretreatment MF, suggesting an accumulation of hprt mutants in vivo. In contrast, a rapid turnover of DNA adducts in lymphocytes was demonstrated in rats injected with single doses of benzo[a]pyrene. Maximal ALs occurred at 34 days after administration, followed by a gradual decline over a 2 month examination period (13). The half-life of induced adducts calculated from a linear regression of mean ALs measured between 3 and 56 days was 17 days.
Data on the half-life of DNA adducts in human lymphocytes or other cell types in vivo are scanty. Although our study is not well suited for calculation of a half-life, smokers with a TSL of 04 months (patients and controls combined) showed a rapid decline of 4.2% per month (95% CI 13.04.1, adjusted for age and final dose). Based on a smoking-related elevation of 1.6x108 in AL (calculated from the mean levels in current smokers, 4.8x108 and former smokers, 3.2x108), it would take 4.3 months (ln0.83/ln(10.042)) after smoking cessation to reduce the smoking-induced adducts by 50% (which corresponds to a 17% decrease from the start level). This is comparable with the half-life of 23 weeks (95% CI 1036) calculated by Mooney et al. (16) using linear regression of log-transformed data over an 8 month observation period after smoking cessation. In this study (16), a shorter half-life of 9 weeks was also estimated (with the background level reached after a minimum of 16 weeks) by extrapolation from the mean ALs 10 weeks after smoking cessation, which might partly be explained by a non-linearity in the rate of decline, as suggested by our data. A shorter half-life of 12 months was also indicated by a drastic decrease of lymphocyte AL (by up to a factor of 12) between winter and summer samples from Polish subjects with heavy environmental PAH exposure (24). This decrease occurred from a mean winter AL which was almost twice as high as the AL among our current smokers.
The in vivo expression and turnover time for the hprt mutation in human T lymphocytes is not known. The slightly higher MF in recent smokers compared with current smokers may be related to the delay in the expression of hprt mutations as this group contained a relatively large proportion of very recent quitters, especially among cases. The median TSL among recent smokers was 5 months, 4 months among cases and 8 months among controls. Smokers (cases and controls combined) with a TSL of 04 months showed a pronounced increase of MF with time since quitting (10.7%/month, 95% CI 3.126.5, adjusted for age and final dose). These data suggest an expression time for smoking-induced hprt mutations of several months. This is in reasonable agreement with the expression time of 3 months calculated in cancer patients after cessation of chemotherapy by Tates et al. (25) who also estimated a persistence time of 16 months for induced hprt mutations. In two other prospective studies of cancer patients receiving radio- and/or chemotherapy, the hprt MF was elevated after 6 months and remained at a high level for at least 2 years (26,27). Our finding of a higher MF in former smokers (median TSL 12.5 years) than in never-smokers, although lower than in recent or current smokers, suggests a much longer persistence time for smoking-induced hprt mutations. Interestingly, elevated MF at both the hprt locus (28) and the glycophorin A locus (29) has been detected in atomic bomb survivors 40 years after the presumed mutational event, indicating that a fraction of the induced mutants may persist for decades.
Age and smoking
The mean MF (17.4x106) in our never-smoking controls was twice as high as the overall non-exposed adult mean (8.4x 106, mean age 40 years) from many different laboratories (2). This is mainly explained by the age distribution (mean 65.5 years) in our study population. The overall mean MF of 20.6x106 in our study agrees well with that (20.6x106) obtained from 106 subjects of similar age (mean 64 years) in the study by Cheng et al. (18). The smoking-related increase of MF in our controls (5.4x106) is similar in magnitude to the age-dependent increase of MF over a 50-year period from age 30 to age 80 in a never-smoker. The significant age dependence of hprt MF seen in our study agrees well with the overall range of 0.8%3% per year reported in most previous studies (reviewed in ref. 2). The stronger age effect in ever-smokers compared with never-smokers is consistent with the results of Cole et al. (30) who reported a 0.8% increase per year of age in non-smokers and 2.9% per year in smokers.
The smoking-related increase in the age dependency of MF might partly be explained by an age-related decline in the capacity of repairing exogenously induced DNA damages in lymphocytes (31) and partly by the accumulation of persistent mutants as a result of chronic smoking exposure. In a study of 58 young smokers (mean 32 years), Jones et al. (32) found years or packyears of smoking to be a much better predictor of MF than age, suggesting a predominance of a duration effect in younger smokers. A clear relationship between duration of smoking and MF (even when adjusted for age) was also reported by Branda et al. (33) in 19 current smokers with benign or malignant breast masses. The age dependency of MF observed in our currently smoking cases was to a large extent associated with a significant increase of MF with increasing age at smoking start (4.9%/year, 95% CI 0.39.5) and to a smaller degree with increasing years of smoking (2.4%/year, 95% CI 0.65.4) in addition to the effect of final smoking dose (2.5%/cigarette/day, 95% CI 1.66.6). The age effect observed in recent smoking cases and former smoking controls, however, could almost entirely be explained by a significant duration effect, with 3.9% (95% CI 1.56.3, adjusted for age at smoking start, final dose and TSL) and 2.6% (95% CI 0.34.9) per year of smoking, respectively. Thus, the age effect seemed to be influenced by smoking in a complex way, different in cases and controls. Further studies are needed to elucidate the persistence of the smoking effect and the higher susceptibility of cancer-prone individuals towards tobacco exposure.
The AL was found to significantly decrease with increasing duration of smoking (1.1%/year, P = 0.001) when analyzed along with TSL (1.7%/year, P = 0.0002) in ex-smokers. This might explain the significant decrease of AL with age in the univariate regression of AL in these smokers since both duration and TSL contribute to age and are correlated with it. An older quitter generally stopped smoking longer ago (and also perhaps smoked longer) and a more complete clearance of smoking-induced DNA adducts might well be expected. One may speculate that the lower AL in former smokers compared with never-smokers is due to a persistence of smoking-induced elevation in DNA repair activity. Interestingly, the decrease of AL with increasing duration of smoking among ex-smokers was stronger in cases (1.3% per year of smoking, P = 0.008) than in controls (0.9%/year, P = 0.08). A significant inverse relationship between years of smoking (without specifying current smoking status) and DNA adduct levels in lung cancer patients was recently reported by Ryberg et al. (34) and the high-AL individuals were suggested to be more cancer susceptible as reflected in their shorter duration of smoking before the clinical onset of lung cancer.
The steep increase of AL and MF with age in the currently smoking patients may reflect a higher susceptibility towards current mutagen exposure in cancer-prone individuals during aging and suggests a constitutional susceptibility to smoke-related DNA damage and lung cancer. Reduced O6-methylguanine repair has been observed in fibroblast cultures from patients with lung cancer (35). Furthermore, inherited polymorphisms of biotransformation (and possibly also repair) enzymes, which have been suggested to contribute to individual cancer susceptibility, may be directly responsible for variations in the levels of DNA damage and mutations induced by carcinogenic or mutagenic exposures (reviewed in ref. 36). The question whether lung cancer risk is modulated by inherited differences in the ability to activate or inactivate smoke-related carcinogens is currently under extensive investigation (reviewed in ref. 37). Our recent results from the same study subjects (19) suggest that the rapid acetylator genotype may interact with packyears of smoking to produce a steeper risk gradient for lung cancer among smokers. The possible interaction between smoking and other predisposing factors in the causation of the disease is intriguing. The influence of host polymorphism on the levels of DNA adducts and hprt mutants and on the p53 and hprt mutational spectra, will be further studied in this unique collection of subjects.
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Acknowledgments |
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Notes |
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References |
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