Mutagenesis Advance Access originally published online on November 7, 2007
Mutagenesis 2008 23(1):1-18; doi:10.1093/mutage/gem043
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Correlation between biomarkers of human exposure to genotoxins with focus on carcinogen–DNA adducts
rffyDepartment of Molecular Environmental Epidemiology 1Department of Environmental Epidemiology, National Institute of Environmental Health, Gyáli út 2-6, H-1097 Budapest, Hungary
Correlations among biomarkers, an important issue in biomarker research, provide enhanced insight and understanding of the complexity of molecular mechanisms initiated by environmental genotoxic agents in the human organism. Occupational and environmental exposures mostly represent mixtures of genotoxic agents, whereas the specificity of biomarker measurements varies widely. Here, we give an overview of the correlation studies with particular emphasis on DNA adduct biomarker analysis of exposure to polycyclic aromatic hydrocarbons (PAHs) and/or tobacco smoke. We have collected data on correlations between different DNA adduct detection methods, DNA adduct structures and DNA adduct levels in human tissues. Data are also presented on the correlation between DNA adducts and other biomarkers of exposure and of early biological effects, including protein adducts, urinary metabolites and cytogenetic end points. In numerous studies, 32P-postlabelling and immunoassay measurements of DNA adducts recognized the difference between exposure groups similarly; however, at the individual level, there was, in general, not a statistically significant correlation between the two determinations. Inconsistency was found regarding the correlation between the levels of total bulky adducts and specific single DNA adduct structures. A number of studies found a positive correlation between DNA adduct levels in target and surrogate tissues, although stratification for exposure level may have influenced the results. Characteristically, there was a positive correlation between DNA adduct levels in tumour and normal tissue pairs. In general, there was a lack of correlation between DNA adducts and urinary PAH metabolites, but after stratification for particular genetic polymorphisms correlation may have emerged between the two biomarkers of exposure. The correlations with cytogenetic biomarkers were very complex, with examples of both positive correlation and lack of correlation. Exploration of correlations among biomarkers contributes to the further progress of molecular cancer epidemiology and to the selection of the optimal biomarkers for the investigation of human exposure to carcinogens.
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Introduction |
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Top
Introduction
Sources and chemical types...Correlations among biomarkers, an important issue in biomarker research, provides enhanced insight and understanding of the complexity encountered on a molecular level during exposure to genotoxic agents. Exploration of correlations between biomarkers will contribute to the development of human biomonitoring to genotoxic exposures and will help to select optimal biomarkers for more efficient monitoring of various human exposures.
Biomarkers of human genotoxic exposure include urinary metabolites to measure the internal dose, DNA and protein adducts in various solid tissues and white blood cells to assess the biologically effective dose and cytogenetic markers that are considered both exposure and early effect markers in peripheral blood lymphocytes (1
,2). It is an important aspect in biomonitoring of environmental genotoxic exposure of healthy populations that the target tissues are often not readily available, and exposure assessment can be done in surrogate tissues only. Among the large number of publications in the field of human biomonitoring, there is only a fraction of papers in which the authors analyse correlations among biomarkers. In the present review, we focus primarily on those studies in which DNA adducts were used as a biomarker of genotoxic exposure. We have investigated correlations between (i) different methodologies used for DNA adduct measurement, (ii) levels of DNA adducts with different chemical structures, (iii) levels of DNA adducts measured in various tissues (e.g. target versus surrogate and macroscopically normal versus tumour tissues) and (iv) levels of DNA adducts and other biomarkers of exposure and/or early effects.
Our review covers some eighty research papers that have been published during the last two decades. Figure 1 gives an outline of the scope of the review. It summarizes the main types of exposure that are covered here, namely occupational, environmental (including ambient air, diet and cigarette smoking) and medicinal exposures. It lists the most common genotoxicants in the exposure categories and the biomarkers that were used in these studies. The biomarkers include the following (i) biomarkers of the internal dose (most frequently metabolites in urine); (ii) biomarkers of the biologically effective dose—such as DNA and protein adducts, DNA strand breaks and unscheduled DNA synthesis (UDS); (iii) biomarkers of early biological effects—these are cytogenetic end points which also reflect exposure—such as chromosomal aberration (CA), sister chromatid exchange (SCE), micronucleus (MN), hypoxanthine–guanine phosphoribosyl transferase (HPRT) mutation frequency, glycophorin A (GPA) mutation frequency. These biomarker categories cover different time frames and have different half-lives in a wide range from few hours of some urinary metabolites to several days or weeks of DNA adducts, and several months or years of protein adducts and of the cytogenetic markers (3–7).
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From the papers we have collated information on the source of exposure, the type and size of the study population, the biological sample/tissue/specimen used, the biomarkers studied, the methods of biomarker measurement and the presence or absence of statistically significant correlations with numerical data (number of subjects, correlation coefficients and P values of statistical significance where available). For three major topics, which are (i) the tissue-specific correlation between DNA adducts, (ii) the correlation between DNA and protein adducts and (iii) the correlation between DNA adducts and urinary biomarkers, the most relevant pieces of information have been summarized in Tables I–III.
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Sources and chemical types of exposure and study populations |
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The majority of the human biomonitoring studies in which the key biomarker was DNA adducts were primarily selected to polycyclic aromatic hydrocarbon (PAH) and/or tobacco-smoke exposure that represent complex mixtures of genotoxic agents. Other chemical exposures of interest in this review are also listed in Figure 1.
Occupational populations were the most frequently studied populations. The occupational groups included iron foundry (8–10), coke oven (11–19) and aluminium plant workers (20–24), garage workers or car mechanics (25–27), bus drivers (28–30), asphalt workers (31), policemen (30,32,33), US army soldiers (34), rubber industry workers (26,35,36), shipyard (37), plastic lamination (38,39) and incineration workers (40) and subjects occupationally exposed to epichlorohydrin (41), herbicides (42), ethylene oxide (43,44), benzidine and benzidine-based dyes (45).
Environmental exposures to ambient air pollutants were investigated in Silesian children (46) and adults (47) and in Italian (48) and Danish populations (49,50). Dietary exposure studies to PAHs and to aflatoxin have been also included (51,52).
Several studies analysed the effects of active and involuntary tobacco-smoke exposure in the general population and in healthy volunteers (53–62). Many studies investigated the genotoxic effects of smoking in tissue samples obtained at surgery and/or in blood samples from patients with cancer of the lung (11,24,63–74), the larynx (75,76), the oral cavity(77) and the breast (78).
Typical medicinal exposures included treatment of psoriasis and eczema patients with tar products (15,79,80) and platinum therapy of cancer patients (81).
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Most frequently used biomarkers |
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The most frequently measured end points and analysed for correlations in various combinations were:
- (i) DNA adducts, namely large aromatic or bulky (13,24,34,55,56,61,63,65,69,75,82), PAH (22,24,30,32,34,62,63,65,68,79), benzo[a]pyrene-tetrol (BP-tetrol) (21,64), O6-styrene guanine (39), 7-methylguanine (7-meGua) (54,67), N7-alkylguanine (76), O4-ethylthymidine (70), mixtures of aromatic amines (26,78), 4-aminobiphenyl (4-ABP) (78), cis-diamminedichloro platinum (II) (cisplatin) (81), malondialdehyde (MDA) (30), benzidine (45), polyphenol-associated (73) 8-oxo-2'-deoxyguanosine (8-oxo-dG) (16,18,29,30,50,83), 1,N6-etheno(2'-deoxy)guanosine (edA) (83), 3,N4-etheno(2'-deoxy)cytidine (edC) (83) and pyrimido[1,2-alpha]purin-10(3H)-one (m1G) (83).
- (ii) Protein adducts including (a) albumin adducts: PAH (29
,32,79), benzo[a]pyrene-7,8-diol-9,10-epoxide (BPDE) (27,32,59) and aflatoxin (52) and (b) haemoglobin adducts: BPDE (27,59), 4-ABP (53,62), epichlorohydrin (41), 3-chloro-4-fluoroaniline (42), N-(2,3,4-trihydroxy-butyl)valine (36), ethylene oxide (43,47), hydroxyethylvaline (25,44) and N-terminal valine adduct of styrene (39).
- (iii) DNA strand breaks (16
,17,31,39,47,49,77) and UDS (28,43,58,84).
- (iv) Metabolites in urine, namely 1-hydroxypyrene (1-OHPY) (9
,14,15,19,22–26,32,46,80), 1-hydroxypyrene glucuronide (1-OHPG) (34,37,51,85), cotinine (13), 8-oxo-dG (28,29,58), benzidine metabolites (45) and urinary mutagenicity (29,46,53).
- (v) Cytogenetic end points, such as CA (38
,43,47,52,84), SCE (17,41,47,52,62,74,77), MN (17,35,41,43,47,52,84), HPRT mutation frequency (8,39,41) and GPA mutation frequency (8,37).
- (ii) Protein adducts including (a) albumin adducts: PAH (29
DNA adducts were determined in a number of solid tissues and in white blood cell fractions, protein adducts were measured in blood serum and erythrocytes. DNA strand breaks and most cytogenetic end points were detected in peripheral blood lymphocytes, except GPA which was assessed in erythrocytes. Sensitivity and specificity of the techniques for the identification of biomarkers, as well as the assessment of the current status of validation mainly with regards to the use of biomarkers in molecular epidemiology have been reviewed in details in two recent publications by Environmental Cancer Risk, Nutrition and Individual Susceptibility (2,3).
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Correlation between DNA adduct determinations in human samples from a methodological viewpoint |
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DNA adduct levels measured by different methods
A battery of laboratory techniques have been used for bulky/PAH–DNA adduct detection (5
,86). Each method has particular advantages and disadvantages regarding its specificity, sensitivity, limit of detection and amount of sample required for analysis (2,87). 32P-postlabelling and immunoassays are the most widely used methods. Synchronous fluorescence spectroscopy (SFS) detection coupled to immunoaffinity chromatography (IAC) and high-performance liquid chromatography (HPLC) have been also used for bulky DNA adduct determination. Structure-specific methods such as mass spectrometry (MS) have been used less often.
DNA adduct levels determined by the two adduct-enrichment versions of 32P-postlabelling, nuclease P1 digestion and butanol extraction (88) were compared for bronchoalveolar lavage (BAL) cells and mononucleated white blood cells obtained from healthy volunteers (61). DNA adduct levels in BAL cells after butanol enrichment were 
2 times as high as the levels after nuclease P1 adduct enrichment and the results were highly correlated (r = 0.86, P < 0.001, n = 12). In mononucleated white blood cells from the same subjects, however, no such relationship was observed. Higher adduct levels were also obtained by the butanol extraction procedure than by the nuclease P1 method with lymphocyte DNA from garage workers exposed to diesel exhaust (25), and again a strong positive correlation was observed between the two adduct-enrichment methods (r = 0.81, P = 0.001). However, in a comparison of thin-layer chromatography (TLC) and HPLC adduct separation following 32P-postlabelling of lymphocyte DNA samples from coke oven workers, no correlation was found between the results obtained by the two chromatographic techniques (17).
Several studies compared the levels of bulky DNA adducts with PAH–DNA adducts in human samples by using 32P-postlabelling and BPdG–DNA immunoassays, respectively. Some early studies were reviewed by Schoket (89), and additional studies have been reported since that time. The applied immunoassays were enzyme-linked immunosorbent assay (ELISA) (10,11,26,47), ultrasensitive radioimmunoassay (USERIA) (12), dissociation-enhanced lanthanide fluoroimmunoassay (DELFIA) (34) and chemiluminescence immunoassay (CIA) (63,71).
A positive correlation was found between the ELISA and 32P-postlabelling in white blood cells from foundry workers (10). By using the rank correlation test, one study observed a weak negative correlation between ELISA and 32P-postlabelling data in peripheral blood lymphocytes from aluminium plant workers (r = –0.219, P < 0.05), although at the group level both methods indicated the same relative differences in exposure among the groups (20). In the comparison of BPdG–DNA CIA data with corresponding 32P-postlabelling values, lung DNA adduct levels measured by both methods were in the same range, between 0.3 and 27.8 adducts per 108 nucleotides among smokers and between 0.3 and 14.4 adducts per 108 nucleotides in non-smokers (63). Here, a borderline-significant positive correlation was found between the two detection methods for lung tumour DNA samples (r = 0.27, P = 0.054, n = 50), but there was a lack of correlation for normal lung DNA. In a different study performed on normal lung DNA samples from lung cancer patients, there was a positive correlation between the PAH–DNA adduct levels determined by ELISA and the radioactivity measured by 32P-postlabelling on the TLC plates that co-migrated with the BPDE–DNA standard (Kendall W = 0.97, P < 0.01) (11). However, PAH–DNA adducts were detectable in only six DNA samples out of 21. Other investigations using 32P-postlabelling and PAH–DNA immunoassays did not show a significant correlation between the individual DNA adduct data pairs (26,90).
Bulky DNA adducts in white blood cells from coke oven workers and from controls were determined by USERIA and 32P-postlabelling (12). Approximately one-third of the samples had detectable adduct levels by USERIA. Mean levels of adducts determined by USERIA were approximately 10-fold higher than the mean levels obtained by 32P-postlabelling in the whole-study population, without a significant correlation.
SFS and 32P-postlabelling were used, the latter for the measurement of radioactivity in the TLC diagonal radioactive zone (DRZ), and of the spot designated to the BPDE–DNA adduct, of lung DNA samples from 39 lung cancer patients (72). Adduct levels were detectable only in 11 DNA samples by IAC/SFS and in 6 by HPLC/SFS and varied within ranges of 2–298 adducts per 108 nucleotides and 2–176 adducts per 108 nucleotides, respectively. The 32P-postlabelling data corresponding to the DRZ ranged between 6.81 and 108.5 adducts per 108 nucleotides, whereas the area attributed to BPDE–DNA was estimated to be in the range of 0.3–9.83 adducts per 108 nucleotides. There was a strong correlation between IAC/SFS and HPLC/SFS (r = 0.94, P = 0.005, n = 6), however, neither one correlated with the 32P-postlabelling (72). In a similar study, a borderline correlation was found between IAC/32P-postlabelling and IAC/HPLC/SFS for BPDE–DNA adducts in human lung DNA samples (r = 0.67, P = 0.047, n = 6) (66). However, there was no correlation between HPLC/SFS and USERIA for lung autopsy samples (91).
BP-tetrols derived from BPDE–DNA adducts were quantified by an improved HPLC/fluorometric assay for normal lung parenchyma from lung cancer patients and DNA adducts were detected in 9 of 11 samples from smokers and in 2 of 2 ex-smokers (64). The range of BPDE–DNA was between 0.6–9.9 adducts per 108 nucleotides, whereas bulky DNA adducts were between 1.3–13.4 adducts per 108 nucleotides as determined by 32P-postlabelling. A strong correlation was observed between the BPDE–DNA adducts and bulky DNA adducts (r = 0.78, P < 0.02, n = 13) (64). In a similar study, there was also a high correlation between BP-tetrol fluorometry and 32P-postlabelling (r = 0.95, P < 0.001, n = 8), but lack of correlation between the fluorometric assay and ELISA (21).
BPDE–DNA adduct levels were determined by a recently developed HPLC–electrospray–mass spectrometry (ES-MS/MS) method from normal lung tissues and were compared with adduct levels determined by 32P-postlabelling and CIA, respectively (71). In any of the samples in which DNA adducts were detectable by both 32P-postlabelling and CIA, the chemical structure-specific method did not show detectable levels of BPDE–DNA adducts.
It can be pointed out that predominantly there was a lack of correlation between the DNA adduct levels determined by different DNA adduct methods, and most of the 32P-postlabelling and immunoassay studies failed to show a significant positive correlation between the individual pairs of DNA adducts.
Structurally different DNA adducts
Smoking-derived polyphenol-associated DNA adducts were determined in comparison with bulky DNA adducts in normal lung and blood mononuclear cells from 38 lung cancer patients by using the 32P-postlabelling assay (73). Adduct levels attributed to polyphenolic adducts were five times higher than aromatic adducts in both lung and mononuclear cells. The levels of polyphenol adducts correlated significantly with aromatic adducts in the mononuclear cells (r = 0.84, P < 0.001, n = 12) and in the lung (r = 0.46, P < 0.01, n = 38), respectively.
In a comparative study of smoking-related N7-alkyl- and bulky adducts in the same DNA samples, a lack of correlation was observed in both tumour and normal laryngeal tissues (76). There was a positive correlation between smoking-related O4-ethylthymidine levels determined by IAC/32P-postlabelling and bulky DNA adduct levels in normal lung tissues from lung cancer patients (r = 0.66, P < 0.01, n = 24) (70). This relationship was of borderline significance after exclusion of non-smokers from the analysis (r = 0.54, P = 0.05, n = 13).
Aromatic amine-derived DNA adducts detected by G-C8-4-ABP-DELFIA and bulky DNA adducts determined by 32P-postlabelling in peripheral blood lymphocytes from rubber industry workers did not correlate significantly (26).
A weak but significant positive correlation was observed between 8-oxo-dG and bulky adducts in leukocyte DNA of coke oven workers exposed to PAHs (r = 0.19, P = 0.03) (18). In normal pancreatic tissues, oxidative stress-related adducts were investigated, such as edA, edC, 8-oxo-dG and m1G. It was only the pair of 8-oxo-dG and m1G for which a positive correlation was detected (r = 0.76, P < 0.01, n = 11) (83).
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Correlation between levels of the same type of DNA adducts in various tissues |
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DNA adducts in normal target and surrogate tissues
The target organs/tissues are those biological tissues that are most adversely affected by exposure to a chemical substance. Correlation data between biomarkers of exposure in the target tissue and in another tissue that is considered a surrogate for the usually inaccessible target tissue provide valuable information for human biomonitoring. Such data help to assess genotoxic doses affecting the target tissue when only surrogate tissue is available in common environmental and occupational exposure situations of healthy populations. Table I summarizes literature data on tissue-specific correlations between DNA adducts. Classical target–surrogate comparisons were done mostly for smoking-related bulky and PAH–DNA adduct levels in macroscopically normal lung, bronchus, larynx and BAL cells and in various fractions of peripheral white blood cells, the most readily obtainable surrogates in many human biomonitoring studies. A few studies investigated nasal mucosa, induced sputum, mouth-floor cells, urothelial cells or made the comparison among different white blood cell fractions. The typical adduct levels were usually different in the target and the corresponding surrogate tissues; therefore in addition to the correlation itself, we report some descriptive data on the differences in the adduct levels.
In a study population of Hungarian lung cancer patients, smokers had 1.7- to 2.4-fold higher bulky DNA adduct levels in the normal lung and bronchial tissues than non-smokers (P < 0.02) (63). In the peripheral blood lymphocytes, the adduct levels were 50% lower with no difference between the smokers and the non-smokers. There was a significant positive correlation between bulky DNA adducts in lymphocytes and in normal lung or bronchial tissue among non-smokers, but a lack of correlation among smokers (63). In a different lung cancer population, mean adduct levels were three times higher in the lung (range 0–7.81 adducts per 108 nucleotides) than in the blood mononuclear cells (range 0–1.93 adducts per 108 nucleotides) and a significant correlation was observed (69). In a Dutch study, PAH–DNA adduct levels detected by ELISA were in the same range in the lung and white blood cells, whereas 32P-postlabelling resulted in much higher bulky DNA adduct levels in the white blood cells (range 0.2–407 adducts per 108 nucleotides) than in the lung (range 1.9–34 adducts per 108 nucleotides) (65). No significant correlation was found between DNA adducts in the lung and white blood cells either in current smokers or in ex-smokers by both 32P-postlabelling and ELISA. Elsewhere, mean PAH–DNA adduct levels determined by ELISA were similar in leukocytes and normal lung from non-smokers, but current smokers' and ex-smokers' leukocytes had 2- to 2.5-fold elevated mean adduct levels than their lungs (68). No correlation was found even after stratification for smoking status. 7-meGua–DNA adduct levels were compared in normal bronchial tissue and peripheral blood lymphocytes from five smoking lung cancer patients (67). The adduct levels were 1.5- to 2-fold higher in the bronchial tissue than in the lymphocytes and a significant positive correlation was found.
Bronchial mucosa, nasal mucosa and peripheral blood lymphocytes from patients undergoing bronchoscopy were compared (82). Bulky DNA adduct levels were different in the three tissues, with nasal mucosa showing 34% higher level than bronchial mucosa, and two times higher than peripheral blood lymphocytes. The authors found a positive correlation between the levels of DNA adducts in bronchial mucosa and lymphocyte samples and in bronchial mucosa and nasal mucosa; however, no correlation was found between nasal mucosa and peripheral blood lymphocytes. In a similar study on healthy volunteers, bulky DNA adduct levels were five times higher in the nasal mucosa cells than in white blood cells in smokers and three times higher in non-smokers (55). No correlation was found between the adduct levels in these tissues. Comparison of BAL cells and mononucleated white blood cells from healthy volunteers showed no correlation for bulky adduct levels as determined by 32P-postlabelling (61). Adduct levels in induced sputum were two times higher than in lymphocytes from smokers, whereas lower levels and similar ranges were found in both tissues from non-smokers (56). There was a borderline correlation between the levels of bulky DNA adducts in the two specimens from smokers, but no correlation in non-smokers or in the whole-study population.
Comparison of bulky DNA adduct levels in normal larynx tissue and leukocytes showed that the mean levels of DNA adducts were two times higher in the larynx than in leukocytes and positively correlated (75). PAH–DNA adducts were in a similar range in mouth-floor cells and buccal mucosa cells by immunochemical staining (57) and a positive correlation was found between the adduct levels in the two tissue types.
Skin and white blood cell fractions were analysed for bulky DNA adducts in coal tar-treated eczema patients (80). The median DNA adduct levels in skin increased 20-fold from 2.89 adducts per 108 nucleotides to 63.3 adducts per 108 nucleotides due to the treatment for 1 week. The DNA adduct levels also increased from the baseline of 0.3 adducts per 108 nucleotides 2- to 3-fold in the monocytes, lymphocytes and granulocytes. In a week after cessation of coal tar treatment, there was a significant decrease in the DNA adduct level in monocytes and granulocytes, although it was still elevated in lymphocytes. DNA adduct levels in skin correlated strongly with those in lymphocytes and monocytes, but not with those in granulocytes.
Total white blood cell samples and peripheral blood lymphocyte fractions obtained from coke oven workers and controls exhibited similar ranges of bulky DNA adducts by 32P-postlabelling and the levels were positively correlated (13). O6-styrene guanine–DNA adduct levels were compared between lymphocytes and granulocytes in styrene-exposed workers and a significant correlation was found (39). A cross-sectional study was conducted in Indian workers exposed to benzidine (45). The correlation between the specific benzidine–DNA adduct levels determined by 32P-postlabelling in white blood cells and in exfoliated urothelial cells was strong and highly significant. For comparison of different white blood cell fractions, 7-meGua–DNA adduct was determined in total white blood cells, granulocytes and lymphocytes from healthy volunteers (54). Mean adduct levels were similar in total white blood cells and granulocytes, and as compared to these cell fractions, 4-fold higher in the lymphocytes, with a lack of correlation.
To sum up, the most investigated surrogate–target tissue pairs were white blood cell fractions in comparison with the lung/bronchus/BAL cells/larynx. Approximately 50% of the studies found statistically significant correlations in tissue pairs between bulky/PAH–DNA adduct levels. In a few studies, stratification for low/high-exposure dose was applied. There was a correlation within one of the exposure groups whereas a lack of correlation in the other, dose dependently.
DNA adduct levels in normal and tumour tissues
The comparison between DNA adduct levels in tumour and non-tumour tissues may add knowledge on xenobiotic transport and metabolism and DNA repair in the tumour tissue. DNA adduct levels were compared in tumour and in normal peripheral lung tissue by using 32P-postlabelling and BPdG–DNA CIA methods (63). Both bulky and PAH–DNA adducts were approximately two times higher in the normal tissues than in the tumours. There was a significant correlation between bulky DNA adduct levels in the tumours and normal lung tissues in both the smokers (r = 0.58, P = 0.001, n = 29) and the non-smokers (r = 0.70, P = 0.0001, n = 25). Immunoassay data also correlated between tumour and normal lung in both the smokers (r = 0.49, P = 0.003, n = 34) and the non-smokers (r = 0.37, P = 0.048, n = 29) (63). In a larynx cancer study, bulky DNA adducts varied in similar ranges in the tumour and surrounding normal tissues, and a correlation coefficient of 0.17 was indicated for the 30 smokers and 0.98 for the 4 non-smokers (P values were not given) (75).
By immunohistochemistry, a correlation was found between PAH–DNA adduct levels in liver tumour and normal liver tissue from hepatocellular carcinoma patients (r = 0.3, P < 0.01, n = 105) (92). There was also a highly significant positive correlation between 4-ABP–DNA adducts determined in breast tumours and the adjacent normal breast tissue (r = 0.72, P < 0.0001, n = 55) (78).
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Correlation between different biomarkers of human genotoxic exposure |
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DNA adducts and protein adducts
Two major types of protein adducts, albumin- and haemoglobin-bound adducts, have been compared with DNA adducts. Table II summarizes these studies.
No correlation was found between PAH–albumin adducts and white blood cell PAH–DNA adducts in psoriasis patients treated with tar product (79). A positive correlation was obtained between serum BP–albumin adducts and bulky DNA adducts in peripheral mononuclear white blood cells from police officers at low level of exposure to traffic pollution, but no correlation existed at high level of exposure (32). In a study population of Danish bus drivers and postal workers, a significant negative correlation was observed between PAH–albumin adducts and bulky DNA adducts in peripheral mononuclear white blood cells (29). Stratification according to the dose of exposure to air pollution resulted in lack of correlation among bus drivers working in the rural/suburban area, but in a significant negative correlation among those who worked in the city centre.
No correlation was found between hydroxyethylvaline in haemoglobin and bulky DNA adducts in lymphocytes among bus garage workers (25). In a study population of Hungarian garage workers, BPDE–globin and BPDE–serum albumin adduct levels were determined (27). The levels of BPDE–serum albumin adduct were one order of magnitude lower than the levels of globin adduct, and the two protein adduct biomarkers were positively correlated. Exfoliated urothelial cells and red blood cells were obtained from smokers and non-smokers and in smokers total bulky DNA adduct levels correlated significantly with the 4-ABP–haemoglobin adduct levels (53). Inconsistent results were obtained for the correlation between white blood cell PAH–DNA adduct levels and 4-ABP–haemoglobin adduct levels in a small group of smokers and non-smokers at two samplings (62). Among plastic lamination workers exposed to styrene, there was a positive correlation between the O6-styrene guanine–DNA adduct in lymphocytes and the N-terminal valine adduct of styrene in haemoglobin (39). A positive correlation was found between cisplatin–DNA adduct levels and total protein-bound platinum in blood from testicular cancer patients (81).
DNA adducts and urinary biomarkers
The urinary PAH metabolite 1-OHPY or its glucuronide conjugate was measured in numerous studies together with DNA adducts determined by 32P-postlabelling, immunoassays or HPLC/fluorescence, also in association with genetic susceptibility factors such as metabolism polymorphism. Table III summarizes studies on correlations between DNA adducts and urinary biomarkers.
A positive correlation was found between the 1-OHPY concentration in urine and white blood cell DNA adducts among smoking aluminium workers (22). A borderline-significant correlation was found between 1-OHPY and peripheral blood lymphocyte PAH–DNA adducts from coke oven workers (19). By using a BPDE–DNA-specific adduct determination from white blood cells from coal tar-treated psoriatic patients, coke oven workers, chimney sweeps and aluminium plant workers, no correlation was detected with the urinary 1-OHPY level (15). There was a lack of correlation between white blood cell/peripheral blood lymphocyte DNA adduct levels and 1-OHPY or 1-OHPG in aluminium plant (23), coke oven (93), shipyard (37) and Finnish iron foundry workers (9), in bus garage workers (25), garage mechanics and vulcanizing plant workers (26), traffic officers (32) and US Army soldiers (34). However, in some studies in which there was no correlation for the overall study population, a statistically significant correlation emerged when the population was stratified for genetic polymorphisms. 1-OHPY and white blood cell DNA adducts significantly correlated in coke oven workers after stratification for the CYP1A1 Ile462Val genetic polymorphism (14), in aluminium plant workers with the GSTM1 homozygous null genotype (24) and in waste incineration workers, specifically with the GSTM1 null genotype (40). No correlation was found between bulky DNA adducts in white blood cells and 1-OHPY from bus drivers and postal workers (29), although in the GSTM1 null genotype subgroup of the study population, a weakly significant negative correlation was shown between bulky DNA adducts and urinary mutagenic activity.
Regarding environmental exposure to PAHs, a Polish study conducted among 5- to 14-year old children obtained a significant correlation between urinary 1-OHPY and peripheral white blood cell DNA adducts (46). In dietary PAH exposure originating from charbroiled beef consumption, mean levels of PAH–DNA adducts and 1-OHPG significantly correlated 8 days after the feeding started and also at the end of the study (51).
A lack of correlation was found between total bulky DNA adduct level in skin biopsy samples from eczema patients treated with coal tar and urinary 1-OHPY and 3-hydroxy-BP (80). However, the level of 3-hydroxy-BP excretion at the time of biopsy correlated significantly with the level of adducts that co-migrated on TLC with the BPDE-DNA adduct spot.
There was a positive correlation between bulky DNA adducts in exfoliated urothelial cells and the mutagenicity of urine in healthy volunteers (53). There was no correlation between urinary 1-OHPY and the white blood cell 8-oxo-dG level in coke oven workers and graphite electrode-producing plant workers (16,18).
For benzidine exposure, a highly significant correlation was found between benzidine–DNA adduct levels, determined by MS, in white blood cells and urinary metabolites of benzidine in Indian workers (45).
It can be pointed out that the majority of the occupational biomonitoring studies reported a lack of statistically significant correlation between bulky DNA adducts and urinary PAH metabolites. However, after stratification for particular metabolic genotypes, mostly GSTM1 and CYP1A1 polymorphisms, significant association may have emerged between the two exposure markers.
Protein adducts and urinary biomarkers
BPDE–haemoglobin adducts and urinary 1-OHPY positively correlated in one study among garage mechanics (r = 0.58, P = 0.02, n = 15) (27). Conversely, there was a lack of correlation between the same biomarkers among office employees not occupationally exposed to PAHs (48). The correlation between hydroxyethylvaline in haemoglobin and 1-OHPY in bus garage workers was positive (25). There was no correlation between PAH–albumin levels and urinary 1-OHPY excretion in other studies on traffic exposure (29,32).
There was a lack of correlation between BPDE–haemoglobin adduct and cotinine levels in both smokers and non-smokers (59). However, a positive correlation was found between the BPDE–albumin adduct and cotinine levels in smokers (r = 0.44, P < 0.05, n = 27). A correlation between 3-chloro-4-fluoroaniline–haemoglobin adduct level and urinary 2-amino-4-chloro-5-fluorophenol sulphate was described related to occupational exposure to 3-chloro-4-fluoraniline in a herbicide plant (r = 0.987, n = 21) (42).
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Correlation between cytogenetic end points and other biomarkers in different chemical exposure categories |
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A multiple biomarker approach is desirable to integrate for metabolism, temporal response and exposure dose–response kinetics and to learn about the biological relevance of biomarkers and their value for predicting health risks. Correlation between different categories of biomarkers may be substantially influenced by the different lifespan of the biomarkers. Several human biomonitoring studies have been carried out in which multiple biomarkers were used to assess the genotoxicity of exposure. Because of their great complexity, the studies have been arranged here according to the exposure types: occupational, ambient air, smoking-related exposures to PAHs and exposures to styrene, to ethylene oxide and to other genotoxic agents.
Biomarkers of PAH exposure
Occupational exposure.
In iron foundry workers, HPRT mutations correlated with PAH–DNA adducts (r = 0.67, P = 0.004); however, no correlation was seen between GPA and HPRT mutation frequencies or between GPA mutation frequency and PAH–DNA adducts (8). In a complex biomonitoring study of coke oven workers, several biomarkers were determined such as urinary 1-OHPY, bulky DNA adducts, DNA strand breaks and SCE in lymphocytes and MN in exfoliated urothelial cells (17). No occupational increase of the biomarker levels was detected and no significant correlation was observed between any pair of the biomarkers. In a similar study on coke oven and graphite electrode-producing plant workers, there was a strong correlation between white blood cell 8-oxo-dG and comet tail moment (r = 0.64, P < 0.01), but no correlation was found for the other pairs of biomarkers including urinary 1-OHPY and the sum of five urinary hydroxyphenanthrenes (16). Urinary 1-OHPY, the sum of five hydroxyphenanthrenes, white blood cell 8-oxo-dG DNA adducts, DNA strand breaks and alkali-labile sites were measured in asphalt workers (31). DNA strand breaks correlated with 1-OHPY and hydroxyphenanthrenes in the post-shift urines (r = 0.32, P = 0.001 and r = 0.27, P = 0.004, respectively). A statistically significant correlation was found between changes during shift of DNA strand break levels and 1-OHPY (r = 0.24, P = 0.01), but no correlation with hydroxyphenanthrenes. In shipyard workers, there was no correlation between urinary 1-OHPY/1-OHPG, peripheral white blood cell bulky DNA adduct levels and GPA variant frequency in red blood cells (37). In a study among 29 rubber industry workers exposed to a complex mixture of PAHs, alkenes and 1,3-butadiene, the investigated biomarkers were DNA strand breaks, chromatid/chromosome breaks, MN in peripheral blood lymphocytes, urinary mutagenicity and immunotoxicological end points (35). The strongest correlations were found between strand breaks and MN (r = 0.51, P < 0.001) and chromatid/chromosome breaks and MN (r = 0.60, P < 0.001). No correlation was found between SCE and CA and between CA and UDS among stainless steel welders (84).
Ambient air exposure.
Exposure of Danish bus drivers and postal workers to ambient air pollutants was monitored by bulky carcinogen–DNA adducts in peripheral mononuclear white blood cells, biomarkers of oxidative damage to proteins 2-amino-apidic semialdehyde (AAS) in plasma proteins and 
-glutamyl semialdehyde (GGS) in haemoglobin, MDA in plasma, PAH–albumin adducts, 8-oxo-dG and 1-OHPY in urine and by urinary mutagenic activity (29). A significant negative correlation was observed between bulky carcinogen–DNA adduct and PAH–albumin adduct levels (r = –0.20, P = 0.005, n = 192). Highly significant, but moderate correlations were found between PAH–albumin adducts and plasma AAS (r = –0.22, P = 0.001, n = 206) and haemoglobin GGS (r = 0.26, P = 0.001, n = 147), respectively. Significant correlations were also observed between 8-oxo-dG and AAS (r = –0.29, P = 0.001, n = 114) and PAH–albumin adducts (r = 0.28, P = 0.002, n = 114), respectively (29). Oxidative DNA damage was investigated in blood mononuclear cells and urine from non-smoking bus drivers (n = 57) from the greater Copenhagen area (28). There was a positive correlation between 8-oxo-dG excretion and CYP1A2 activity on workdays (r = 0.53, P = 0.0001), but there was no correlation with UDS measurements. A large-scale epidemiological study was conducted among Czech, Slovakian and Bulgarian bus drivers and policemen and controls (30). Numerous biomarkers of exposure and effect were determined, such as bulky DNA adducts in lymphocytes, urinary cotinine and 1-OHPY levels, plasma vitamin A, C, E and folate levels, CA in peripheral lymphocytes, 8-oxo-dG and MDA-dG adducts in lymphocytes and repair efficiency in peripheral lymphocytes. The statistically significant negative correlation between 8-oxo-dG and bulky DNA adducts (r = –0.15, P = 0.04, n = 203) or the radioactive chromatographic spot designated to the BPDE–DNA adduct (r = –0.30, P = 0.002, n = 102) is of interest. When the population was stratified for smoking and PAH exposure, the correlation between 8-oxo-dG adducts and bulky DNA adducts was much stronger in non-smokers (r = –0.71, P = 0.05). The correlation between 8-oxo-dG and BPDE–DNA adduct was strongly influenced by the GSTM1 and GSTT1 genetic polymorphisms. Individuals with the GSTM1 and GSTT1 wild-type genotype showed a significant inverse correlation between the two adducts (r varying between –0.68 and –0.29, P varying between 0.002 and 0.04), while this association was not found in individuals with the GSTM1 and GSTT1 null genotype (30). In a group of 50 policemen working in downtown Prague and in 50 controls from a suburban area, a significant positive association was found between urinary cotinine and total bulky DNA adducts (r = 0.37, P < 0.001) or the radioactive chromatographic spot designated to the BPDE–DNA adducts (r = 0.44, P < 0.001), respectively (33). A negative association was found between DNA adducts and vitamin C (r = –0.29, P < 0.01). A significant positive correlation was reported between BPDE–DNA-like adducts and the frequency of CAs determined by fluorescence in situ hybridization (P < 0.01) (94).
In a Polish study of an environmentally exposed healthy adult population from the highly industrialized region of Silesia, SCE and bulky DNA adducts determined by 32P-postlabelling positively correlated, whereas PAH–DNA adducts determined by ELISA did not correlate either with SCE or with bulky adducts (47). In a study of 5- to 14-year old children exposed to environmental PAHs in Upper Silesia, a weak, but statistically significant, correlation was found between the levels of SCE and white blood cell PAH–DNA adducts (r = 0.25, P < 0.05) (46). In a large epidemiological study on healthy volunteers from Pisa and from two small cities, SCE and MN frequency were determined in lymphocytes and no correlation was found between the two cytogenetic end points (60).
Cigarette smoke.
In a study of healthy smokers (n = 21) and non-smokers (n = 24), the levels of 8-oxo-dG and oxidized pyrimidine bases in lymphocytes, urinary 8-oxo-dG excretion, overall DNA repair capacity in blood leukocytes expressed by UDS and plasma antioxidative capacity were determined (58). In general, there was no correlation among the biomarkers, except between plasma antioxidative capacity and lymphocyte 8-oxo-dG after adjustment for gender and age (r = 0.4, P = 0.01). In a group of healthy smokers and non-smokers, there was a significant positive correlation between 4-ABP–haemoglobin adducts and SCE (r = 0.66, P < 0.01, n = 19) (62).
There was no association between white blood cell PAH–DNA adducts and SCE in both lung cancer patients (n = 81) and controls (n = 67) (74). In a German study SCE, DNA single-strand breaks (SSB) and DNA adducts were determined in lymphocytes from oral cancer patients before therapy (77). The SCE values correlated weakly with the DNA adduct levels (r = 0.39, P = 0.068, n = 22) and with the DNA SSB frequencies (r = 0.56, P = 0.054, n = 12). The correlation between SCE and DNA SSB was significant in the smoking cancer patients only (r = 0.67, P = 0.036, n = 10).
Biomarkers of styrene exposure
A large set of biomarkers were compared within a comprehensive study on styrene-exposed lamination workers and controls, such as urinary mandelic acid, O6-guanine DNA adducts of styrene in lymphocytes and granulocytes, N-terminal valine adducts of styrene in haemoglobin, DNA SSB, HPRT mutation frequency in T lymphocytes and styrene level in blood (39). Subjects were repeatedly sampled during a 3-year period and the authors discussed separately the results of the last sampling and of the overall follow-up period. Regarding the last sampling, the HPRT mutation frequency correlated with the haemoglobin adducts (r = 0.68, P = 0.003). SSB parameters showed a significant and strong correlation with O6-guanine DNA adducts in lymphocytes (r = 0.63–0.70, P = 0.002). Urinary mandelic acid excretion correlated with all the studied biomarkers (r = 0.43–0.73, P = 0.001–0.04) except with O6-guanine DNA adducts in lymphocytes. Concerning the overall 3-year sampling period, HPRT frequency showed no correlation with any of the other biomarkers. SSB parameters correlated with O6-guanine DNA adducts in both lymphocytes (r = 0.44–0.72, P = 0.001–0.05) and granulocytes (r = 0.47–0.61, P = 0.006–0.04). Urinary mandelic acid also correlated with styrene levels in blood (r = 0.87, P < 0.001), with O6-guanine DNA adduct levels in lymphocytes (r = 0.67, P = 0.001) and granulocytes (r = 0.48, P = 0.03) (39). In a separate study on styrene-exposed workers, a significant correlation was seen between styrene–haemoglobin adduct levels and MN and between MN and SSB (47). In a third study, the biomarkers determined were styrene in exhaled air and in blood, DNA strand breaks, oxidized bases in mononuclear leukocytes, CA in lymphocytes, immunological and haematological parameters (38). A statistically significant correlation was found between DNA strand breaks and frequency of CA (r = 0.5, P = 0.002) and also between the quantities of a series of CD antigens and DNA strand breaks (r = –0.43 to +0.60, P < 0.05) and CA (r = –0.51 to +0.75, P < 0.05), respectively.
Biomarkers of ethylene oxide exposure
In hospital workers exposed to ethylene oxide, ethylene oxide–haemoglobin adduct levels, SCE, MN, CA, SSB and an index of DNA repair were analysed. Ethylene oxide–haemoglobin adduct levels correlated with SCE (P < 0.02) (47). A panel of biomarkers was determined in peripheral blood cells of 34 workers at a sterilization unit of a university hospital and 23 controls, including ethylene oxide–haemoglobin adducts, SCE, MN, CA, SSB and the number of high-frequency cells (HFCs, the proportion of cells with high SCE frequency) (43). There was a highly significant correlation between ethylene oxide–haemoglobin adduct and SCE (P < 0.02) or HFCs (P < 0.01), but no correlation with MN, CA and SSB. Blood samples were collected from 9 hospital workers and 15 factory workers exposed to ethylene oxide and from matched controls, and the samples were analysed for hydroxyethylvaline adducts in haemoglobin, HPRT mutation frequency, CA, MN and SCE (44). There was a positive correlation between hydroxyethylvaline adduct and SCE (r = 0.88, one-tailed P < 0.001) or CA (r = 0.64, one-tailed P < 0.001) or MN (r = 0.39, one-tailed P < 0.01).
Biomarkers of other genotoxic exposures
Benzene exposure was biomonitored among urban inhabitants in Copenhagen by comparing 8-oxo-dG in lymphocytes and urine, DNA strand breaks in lymphocytes by the comet assay, endonuclease III- and fapyguanine glycosylase-sensitive sites, urinary trans,trans-muconic acid (ttMA) and S-phenylmercapturic acid (S-PMA) levels (50). Significant correlations were found between the ttMA and S-PMA excretion (r = 0.41, P < 0.05), as well as between the S-PMA excretion and 8-oxo-dG level in lymphocytes (r = 0.39, P = 0.04, n = 28). Various GST polymorphisms had no effect, whereas for subjects with the NQO1 heterozygous genotype the correlation between S-PMA and 8-oxo-dG was of borderline significance (r = 0.58, P = 0.08, n = 10).
A multi-end point investigation was performed among German workers exposed to epichlorohydrin (n = 14) and controls (n = 10) (41). There were no statistically significant correlations between haemoglobin adduct, frequencies of HPRT mutants, MN, SCE and HFC.
A spectrum of biomarkers was determined among workers exposed to butadiene-polymer (n = 41) and controls (n = 38) in China (36). Lymphocyte counts as a percent of white blood cells correlated moderately with haemoglobin N-(2,3,4-trihydroxy-butyl)valine adduct levels (r = 0.32, P = 0.07).
Personal exposure to ambient PM2.5 was biomonitored among 50 healthy non-smoker volunteers by detecting 8-oxo-dG in lymphocytes and urine, urinary 1-OHPY, bulky DNA adducts in lymphocytes, DNA strand breaks by the comet assay and endonuclease III- and fapyguanine glycosylase-sensitive sites (49). There was no correlation between any pair of the studied biomarkers.
Dietary exposure to aflatoxin was investigated in 35 individuals from The Gambia, West Africa and 22 controls from Italy using biomarkers such as aflatoxin–albumin adducts, CA, MN and SCE (52). The aflatoxin–albumin adduct was detectable in 92% of the Gambian individuals. The levels of structural CA, SCE and MN were significantly higher in the exposed population than in the Italian controls, but there was no significant correlation among the biomarkers.
In summary, the results of the studies including cytogenetic biomarkers are very complex. There have been examples for both positive correlation and lack of correlation with numerous widely applied biomarkers of exposure.
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Conclusions |
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It is clear from the large number of research papers in the field of human biomonitoring of genotoxic exposure that a variety of exposure-related and early effect biomarkers is suitable for recognizing exposure/dose-dependent differences among the exposure groups. In general, the various biomarker assays very probably provide the same qualitative answer in the comparisons of different exposure groups. There may, however, be some differences in the sensitivity and resolution of the different biomarkers/methods. One method may recognize a statistically significant difference between the exposure groups in terms of genotoxic burden, whereas another method may recognize a trend only. The correlation between the biomarker levels is a more complicated issue. From a genotoxicological point of view, a positive correlation between two biomarkers may reflect molecular events running in parallel or in a chain of consecutive events. From a methodological point of view, it may be the case that a major genotoxic contributor to the biomarker level (i.e. a specific chemical component) is equally recognized by each method such as by 32P-postlabelling, immunoassay and HPLC-ES-MS/MS.
The main conclusions that can be drawn from the studies on correlations among biomarkers, primarily those involving analysis of exposure to complex mixtures of PAHs and to tobacco smoke are:
- (i) Despite the very large literature of biomarkers of exposure, only a relatively small number of studies have investigated the issue of correlation between methods, tissues and biomarkers. The relative scarcity of data may make it difficult to draw firm conclusions on association.
- (ii) Predominantly there was a lack of correlation between the DNA adduct levels determined by different DNA adduct methods. Most of the 32P-postlabelling and immunoassay studies failed to show a significant positive correlation between the individual pairs of DNA adducts. At a certain level, 32P-postlabelling and immunoassay are not comparable due to only partial overlapping of their substrate specificities: immunoassays measure adducts of a specific chemical class of DNA damage whereas 32P-postlabelling determines adducts of many chemical genotoxicants (5
,95). Furthermore, immunoassays are influenced by the binding efficiency and cross-reactivity of the DNA adducts whereas 32P-postlabelling is not always successful in labelling all the available adducts and there is a tendency for underestimation. Lack of correlation between methods can be also caused by the different sensitivity/limit of detection. Regarding the adduct-enrichment procedures of the 32P-postlabelling protocol, butanol extraction in general resulted in higher adduct levels than the nuclease P1 enrichment and the correlation was strong and highly significant.
- (iii) Scarce and controversial results have been found regarding the correlations between bulky/PAH–DNA adduct structures and a chemically specific single DNA adduct structure. Bulky DNA adducts significantly correlated with polyphenol-associated DNA adducts, O4-ethylthymidine levels and 8-oxo-dG adducts, respectively, but not with the N7-alkylguanine and aromatic amine adducts. We should think of the co-existence of closely linked and independent pathways among the metabolic activation processes of the different xenobiotic structures within a complex mixture and of differences in the kinetics of DNA adduct formation and elimination.
- (iv) The most investigated surrogate–target tissue pairs are white blood cell fractions in comparison with the lung/bronchus/BAL cells/larynx. Approximately 50% of the studies found statistically significant correlations in tissue pairs between bulky/PAH–DNA adduct levels. In a few studies, stratification for low/high-exposure dose was applied. There was a correlation within one of the exposure groups, whereas a lack of correlation in the other, dose dependently. This clearly shows that the existence/lack of correlation can be exposure dose dependent and can be influenced by the metabolic capacity of the tissues. This should be carefully taken into consideration when human biomonitoring studies are set up with the use of white blood cells because such cells may not reflect accurately the level of DNA damage of the target tissues. Only a very few studies have investigated other potential surrogate tissues such as nasal mucosa, induced sputum, mouth-floor cells and buccal mucosa cells. Therefore, those results cannot be generalized so far, and further comparative investigations are needed. Such potential surrogates, however, due to the non-invasive nature of their sample collection, may open new perspectives in human biomonitoring studies.
- (v) There have been only a few studies in which DNA adduct levels in tumour and surrounding normal tissues were compared, but characteristically, a statistically significant positive correlation was found. This suggests common routes in the xenobiotic activation/elimination processes in the tumour and normal tissues, although DNA adduct levels may be influenced by the higher cell proliferation rate in the tumour tissue.
- (vi) Very few studies have investigated the correlation between DNA and protein adducts and it is therefore difficult to draw an appropriate conclusion. Albumin adducts did not correlate with bulky/PAH–DNA adducts, only after stratification for exposure level or for the GSTM1 genetic polymorphism. Two studies showed a significant correlation between haemoglobin adducts and DNA adducts, but a third one did not find such correlation. Protein adducts can be surrogates for DNA adducts although one should be aware of their different life-times (5
,6).
- (vii) The correlation between DNA adducts and urinary PAH metabolites was investigated predominantly in occupational PAH exposure. The majority of investigations reported a lack of statistically significant correlation between the two biomarkers. 1-OHPY is a single surrogate for a mixture of PAH metabolites in urine whereas bulky DNA adducts are derived from multiple genotoxic compounds. This fact plus the short lifetime of the urinary metabolites as compared to that of the DNA adducts may explain the lack of correlation. However, after stratification for particular genotypes, mostly GSTM1 and CYP1A1 polymorphisms, correlation can emerge between the two exposure markers. This phenomenon invites new studies on the effect of metabolism and DNA repair polymorphisms on the biomarker levels.
- (viii) There are insufficient data in the literature to draw a generally applicable conclusion regarding the correlation between protein adducts and urinary metabolites.
- (ix) The results of the studies including cytogenetic biomarkers are very complex. There have been examples for both positive correlation and lack of correlation with the widely applied biomarkers of exposure. One reason for the lack of correlation could be the different time-frame covered by these end points and the complexity of cellular mechanisms leading to chromosomal changes. The persistence of a biomarker depends on several factors including its inherent chemical stability, whether any active repair processes are present, the turnover of the macromolecule to which the genotoxic agent is bound and the rate of turnover of the cells in which the damage occurs (6
). All the same, multi-end point studies represent a great opportunity to better explore the linkage between molecular and cellular events induced by different genotoxic agents.
- (ii) Predominantly there was a lack of correlation between the DNA adduct levels determined by different DNA adduct methods. Most of the 32P-postlabelling and immunoassay studies failed to show a significant positive correlation between the individual pairs of DNA adducts. At a certain level, 32P-postlabelling and immunoassay are not comparable due to only partial overlapping of their substrate specificities: immunoassays measure adducts of a specific chemical class of DNA damage whereas 32P-postlabelling determines adducts of many chemical genotoxicants (5
In our review, we have attempted to provide an overall picture and to find generally valid phenomena on the correlations among the major biomarkers applied in molecular environmental epidemiology studies. This work has revealed significant gaps in knowledge, areas of controversy and inconsistencies in the data in this specific and still largely unexplored research field. Further research is desirable in order to make progress in the understanding of the molecular background of the correlations among biomarkers and to apply this tool successfully for human genotoxic risk prediction.
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Funding |
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Environmental Cancer Risk, Nutrition and Individual Susceptibility, a Network of Excellence operating within the European Union 6th Framework Program, Priority 5: ‘Food Quality and Safety’ (Contract No 513943).
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Acknowledgments |
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The present paper has been elaborated and largely expanded from a short review chapter, entitled "Correlations among biomarkers" written by Gy
rffy, E., Anna, L., Rudnai, P., Kovács, K. and Schoket, B. in Ref. 2, pp. 143–159. Conflict of interest statement: None declared.
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Notes |
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* To whom correspondence should be addressed. Tel: +36 1 476 1293; Fax: +36 1 215 0148; Email: schoket.bernadette{at}oki.antsz.hu
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References |
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Received on July 23, 2007; revised on September 21, 2007; accepted on September 21, 2007.
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