The light at the end of the tunnel for chemical-specific biomarkers: daylight or headlight?

John D. Groopman1 and Thomas W. Kensler

Department of Environmental Health Sciences, School of Hygiene and Public Health, Johns Hopkins University, 615 North Wolfe Street, Baltimore, MD 21205, USA

Abbreviations: AFB1, aflatoxin B1; AFM1, aflatoxin M1; AFB1–N7-gua, aflatoxin B1N7-guanine; AFB1–NAC, aflatoxin–mercapturic acid; HBV, hepatitis B virus; HBsAg, hepatitis B surface antigen; HCC, human hepatocellular carcinoma; RR, relative risk.


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The use of chemical-specific biomarkers for identifying stages in the progression of development of the toxic effects of environmental agents has the potential for providing important information for critical regulatory, clinical and public health problems (1,2). Since the development of a paradigm for molecular biomarkers by a committee of the National Research Council over a decade ago, some progress has been made in applying such chemical biomarkers to specific environmental situations that may be hazardous to humans, as exemplified by the study of aflatoxins discussed in detail below. As in many areas of science, however, the initial excitement at being able to measure a specific entity has been replaced by the challenge of how to interpret the results. This dilemma is exacerbated by the complexities introduced through the interactions between genes and environmental factors that underlie most human disease. For a molecular biomarker paradigm to guide public health issues, molecular epidemiologists must devise and follow careful strategies for validation, application and dissemination of information about these biomarkers to the public.

The major goals of environmental chemical-specific biomarker research are to develop and validate biomarkers that reflect specific exposures and predict disease risk in individuals. Presumably after environmental exposure each person has a unique response to both dose and time to disease onset. These responses will be affected both by intrinsic (genetic) and by extrinsic (such as dietary) modifiers. It is assumed that biomarkers that reflect the mechanism of action of an environmental chemical will be strong predictors of an individual's risk of disease. It is also expected that these biomarkers can more clearly classify the status of exposure of individuals, local communities and larger populations. Misclassification of exposure status is a major contributor to the insensitivity of many epidemiological investigations. Further, biomarkers should provide an objective measure for determining the effectiveness of interventions to lower exposure and risk. These studies should help elucidate the molecular processes of chemically induced human disease and underlying susceptibility factors. Finally, biomarkers should help sort out the interactions of multiple agents and multiple exposures and their relation to disease outcomes. These overall goals are summarized in Table 1.

Chemical-specific biomarkers represent a major addition to our armamentarium for assessing disease risk: persons exposed to chemicals in the environment can routinely be monitored for many of them. Given the potential importance of biomarkers in public health, we must also be concerned about their misuse that can lead to brightlining neighborhoods and/or falsely labeling a person as at high risk for disease. Therefore, in disseminating and interpreting data we must make it clear whether the data are useful for individual or population-based assessment and/or disease risk. This commentary is an attempt to place in perspective the current state-of-the-art of chemical-specific biomarker research and to identify short- and long-term strategies to validate these tools for application to public health.


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A number of issues distinguish the problems encountered in chemical-specific biomarker research from those in traditional epidemiology and experimental bioassays. Since a major objective is to assess an individual's exposure status as judged by chemical biomarker measurements, the analytical approaches to measure these biomarkers must be sufficiently sensitive and specific to quantify levels in a limited sample from a single person. Therefore, interpretations of biomarker levels are made on an individual basis and not by using population means and variance. For these markers to be truly valid and valuable, it is the individual and not the group level that must track with specific disease outcome. Thus, the biomarker must span the range from exposure to disease outcome to meet these criteria. Few such biomarkers have heretofore met such stringent criteria.

The problems faced by the field are exemplified by considering the development of chemical–DNA adducts as biomarkers of specific exposures. Since formation of chemical–DNA adducts are thought to be important in the carcinogenesis pathway, it would be expected that the person with higher levels of DNA adduct biomarkers would be at greater risk of disease. Adduct studies, however, may be hindered because samples from target organs are often not available. Hence, surrogate markers, such as chemical–protein adducts, and surrogate tissues, such as white blood cells, have been used routinely. The rationale for using surrogate targets is well justified on practical grounds. Interpretation of the data obtained with these markers must be very conservative, however, because the more removed a biomarker is from the causal pathway of disease, the less precise it will be to predict disease risk.

Given the difficulty of the studies for comprehensive evaluation of chemical-specific biomarkers, recent efforts have been made to use the levels of a gene or gene product as a surrogate for an exposure when a disease outcome is known. This raises another major issue. It is presumed that if a gene–environment interaction is essential for a disease outcome, if the disease is known and the gene or gene product can be measured, a putative exposure can be inferred with high certainty. These studies are very attractive because of the multitude of genotyping and phenotyping methods now available and the relative ease of designing a case–control study to test the strength of the interaction. In rare instances where the level of exposure is high, the dominance or penetrance of the gene is strong and the disease outcome is closely linked temporally, this strategy will be effective. Given our present lack of information on environmental exposures in people, however, it is doubtful that case–control studies that attempt to backtrack from disease and gene to initial exposure will be very accurate. For the chemical-specific biomarkers to be used effectively, very high standards for validation and utilization must be established, such as those used in evaluating serum cholesterol as a risk factor for heart disease. Years of systematic study and multiple validation methods were required before the public could be provided with risk guidelines for personal use. Consequently, we should strive to meet a similar objective for environmental biomarkers.


    The molecular biomarker paradigm
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In order to enhance the opportunity for biomarkers to guide and assess public health-based strategies for prevention, we have extended the molecular biomarker paradigm by adding molecular interventions along the continuum from exposure to disease (Figure 1Go). Development of putative biomarkers for environmental agents must be based on specific knowledge of metabolism, product formation and general mechanisms of action (3). Validation of any biomarker–effect link requires parallel experimental and human studies. Ideally, an appropriate experimental model is used first to determine the associative or causal role of the marker in the disease or effect pathway and to establish a dose–response effect. The putative marker can then be validated in pilot human studies to establish sensitivity, specificity, accuracy and reliability for individuals (4). Results of these studies can be used to assess intra- or interindividual variability, background levels and relationship of marker to external dose or to disease status, as well as feasibility for use in larger population-based studies. It is important to establish a connection between the biological marker and exposure, effect or susceptibility. Full interpretation of the information obtained may require prospective epidemiological studies to demonstrate the role of the marker in the overall pathogenesis of the disease or effect.



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Fig. 1. Molecular biomarker paradigm.

 
To implement the strategies described above, we have devised a rational systematic approach for the validation and application of chemical-specific biomarkers to human studies. Our model for investigating exposures to and risk from environmental carcinogens by parallel investigations in experimental systems and humans is shown in Figure 2Go. The two endpoints for this process are markers of exposure and markers of risk.



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Fig. 2. Model for validating chemical-specific biomarkers.

 
Exposure markers
Humans are exposed to chemical, physical or biological agents through contaminated air, water, soil or food (3). Thus, a person's exposure is the result of proximity to the agent superimposed over many modifying factors. Biomarkers of exposure may be the parent chemical itself, as exemplified by metals such as lead. Frequently, however, it is the metabolic products of the agent that occur in the body that serve as the markers of exposure and provide the `internal dose measure'. Carcinogen–DNA and carcinogen–protein adducts are also markers of exposure and in the model shown in Figure 1Go are referred to as `biologically effective dose' measures. Finally, altered structure or function, such as those mediated through changes in gene expression, can serve as the exposure marker when the two processes are directly linked.

Ideally, a biomarker of exposure would indicate the presence and magnitude of previous exposure to an environmental agent. In the absence of biomarkers, assessment of exposure typically requires measurement of toxicant levels in the environment and characterization of the individual's presence in, and interaction with, that environment. Toxicants can be measured in air, water, soil or food by a wide array of analytical methods. In addition to measurement of chemical or physical agents in the ambient environment, exposure can be assessed by use of personal external monitors and questionnaires. Although questionnaires have been extensively used to determine broad dietary exposures to compounds, smoking histories and genetic backgrounds, except for certain circumstances such as assessing smoking status, this approach is imprecise unless specific chemical agents are already known.

Use of ambient measurements to determine exposure status of individuals is complicated because most environmental contaminations are heterogeneous. It is rare for an agent to be evenly distributed in the environment, so that it is very difficult to extrapolate data from these measurements to an individual's exposure. Therefore, the requirements for the practical development of specific biomarkers to assess exposure must include an ability to integrate multiple routes and fluctuating exposures over time, relate time of exposure to dose and examine mechanisms in important biological targets. This is important, because safety regulations designed to limit human risk are often set on the basis of ambient exposure determinations. For example, these markers are critically important for verifying that interventions such as dumpsite clean-ups have effectively lowered exposure in individuals. Accurate biomarkers of exposure will limit misclassification, which is often the greatest source of error in environmental epidemiology.

Risk markers
Almost any measurement of an environmental chemical-specific marker in a human sample is going to reflect some index of exposure. The relation between exposure and dose or effect can be represented by a simple linear or a more complex non-linear response curve. It is apparent from basic toxicological principles that not every measure of exposure will accurately reflect risk of disease outcome. Most biomarkers will only be risk markers if they are involved in the mechanism of disease. Thus, a chemical–DNA adduct biomarker, if linked through experimental studies to specific mutations that are critical for the induction of cancer, might become a validated risk marker in appropriate human investigations. Given the multistage process and long latency of cancer and other chronic human diseases, it is likely that relatively few chemical-specific biomarkers will prove to be validated risk markers. Rather, certain chemical-specific biomarkers, such as a DNA adduct, in combination with other markers, including metabolic phenotype and/or DNA repair capacity, will be needed to determine individual risk. Thus, most validated risk markers may turn out to be composites of a group of biomarkers, each of which contributes in some quantifiable way to overall risk.


    The aflatoxin story: a paradigm for environmental biomarkers
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The molecular epidemiological studies of aflatoxin may well represent the most complete example of an investigation that encompasses each of the components for the molecular biomarker paradigm shown in Figure 1Go. In the following section we highlight the aflatoxin story as a case study for an approach to the development and validation of exposure and risk markers. Many of the studies to be described are from our own laboratories or collaborations; thus we can be critical of our own research without offending the many other contributors to this field. We will use the aflatoxin data to illustrate the validation model in Figure 2Go.

Chemical carcinogen in animals
Aflatoxin B1 (AFB1) has been suspected to contribute to human hepatocellular carcinoma (HCC) since the 1960s, when its potent activity as a carcinogen in many species of animals, including rodents, non-human primates and fish, was first reported (5). Generally the liver was the primary target organ, however, under certain circumstances, depending on animal species and strain, dose, route of administration and dietary factors, significant numbers of tumors have been induced at other sites, such as kidney and colon. Indeed, very few animal species have been found to be resistant to aflatoxin carcinogenesis. Wide cross-species potency, including sensitivity of primates, provided the justification for suspecting that this agent could contribute to human cancer.

Suspect human carcinogen/disease linkage
On the basis of animal studies, extensive efforts have been made to investigate the association between aflatoxin exposure and risk of HCC in humans. These studies have been hindered by the lack of adequate data on aflatoxin intake, excretion and metabolism in people and underlying susceptibility factors such as diet and viral exposure, as well as by the incomplete cancer morbidity and mortality statistics world wide. These deficiencies provided the impetus to develop biomarker technologies to assess exposure status. Subsequently these biomarkers were applied to traditional and molecular epidemiological investigations to determine their relationships to risk.

The first element in the molecular biomarker paradigm is exposure. The potential for widespread exposure to aflatoxin from the food supply has been well documented and food safety regulations for aflatoxins have been implemented around the world. Commodities most often found to be contaminated are peanuts, various other nuts, cottonseed, corn and rice (6). Humans can also be exposed by consumption of products derived from these sources, such as eggs and milk [e.g. aflatoxin M1 (AFM1) from animals that consume contaminated feeds]. Requirements for aflatoxin production are relatively non-specific since molds can produce them on almost any foodstuff. Whereas contamination by molds may be widespread in a given geographical area, the final concentrations in the grain product can vary from <1 µg/kg (1 p.p.b.) to >12 000 µg/kg (12 p.p.m.) (7). Consequently, measurement of human exposure to aflatoxin by sampling foodstuffs or by dietary questionnaires is extremely imprecise and identification of aflatoxin biomarkers represented a significant advance for accurate assessment of exposure.

During the late 1960s and early 1970s several epidemiological studies conducted in Asia and Africa related estimated dietary intake of aflatoxin with the incidence of HCC. They showed that increased ingestion of aflatoxin corresponded to increased incidence of HCC (reviewed in ref. 8). Biomarkers of aflatoxin exposure were not available for these early studies and the dietary surveys were population-based rather than measures of direct exposure of the persons who developed disease. Biomarkers of other important etiological agents in HCC, such as hepatitis B virus (HBV), were also not available. Thus, although these data provided a strong circumstantial association between aflatoxin ingestion and HCC incidence, these findings were not considered sufficient for determining causality.

Identify and develop methodologies for measuring chemical-specific biomarkers
Development of biomarker methods to monitor human exposure to aflatoxins required analytical techniques that were sensitive, specific and, perhaps more important, could be applied to large numbers of samples. Measurement of aflatoxin–DNA and aflatoxin–protein adducts were of major interest because they are direct products of (or surrogate markers for) damage to a critical cellular macromolecular target. The chemical structures of the major aflatoxin macromolecular DNA and protein adducts were known (9,10). The finding that the major aflatoxin–nucleic acid adduct aflatoxin B1N7-guanine (AFB1–N7-gua) was excreted exclusively in urine of exposed rats (11) spurred interest in using this metabolite. Serum aflatoxin–albumin adduct was also examined as a biomarker of exposure. Because of the longer half-life in vivo of the albumin compared with the DNA adduct excreted in urine, the serum albumin adduct apparently reflects exposures over longer time periods. Despite these kinetic differences, it was later found in experimental models that the formation of aflatoxin–DNA adducts in liver, excretion of the urinary aflatoxin–nucleic acid adduct and formation of the serum albumin adduct are highly correlated (12). Thus results from the two biomarkers should be comparable.

A variety of different analytical methods are available for quantitation of these adducts in biological samples, including chromatography (thin layer and high performance liquid chromatography), immunological assays with specific antibodies or antisera (enzyme-linked immunosorbent assays, radioimmunoassays and immunohistochemical visualization in tissues) and instrumentation-based methods (synchronous fluorescence and mass spectroscopy) (reviewed in ref. 6). Each methodology has unique specificity and sensitivity and, depending on the application, the user can choose which is most appropriate. For example, to measure a single aflatoxin metabolite, a chromatographic method can resolve mixtures of aflatoxins into individual compounds, providing that the extraction procedure does not introduce large amounts of interfering chemicals. Antibody-based methods are often more sensitive than chromatography, but immunoassays are less selective because the antibody may cross-react with multiple metabolites.

In work in our laboratories we took advantage of the strengths of antibody and chromatographic separations to develop an immunoaffinity chromatography/high performance liquid chromatography procedure to isolate and measure aflatoxin metabolites in biological samples (13,14). With this approach, we performed initial validation studies for the dose-dependent excretion of urinary aflatoxin biomarkers in rats after a single exposure to AFB1 (15). A linear relationship was found between AFB1 dose and excretion of the AFB1–N7-gua adduct in urine over the initial 24 h period of exposure. In contrast, excretion of other oxidative metabolites, such as aflatoxin P1, showed no linear association with dose. Subsequent studies in rodents that assessed the formation of aflatoxin macromolecular adducts after chronic administration also support the use of DNA and protein adducts as molecular measures of exposure (1517).

Experimental models play a very important role in the development of analytical methods for measuring biomarkers. In studies of xenobiotics such as aflatoxin either a single administration of a known dose is used or multiple exposures of a constant amount of toxin. This approach greatly diminishes the wide variations in exposure usually encountered in humans and ensures that, unless the method is extremely insensitive, all samples will be detected. Unfortunately, extrapolation of the data in these experimental models to humans often neglected to take into account the enormous day-to-day variations that occur in exposure of people to toxins. Further, statistical assumptions of normality used in animal models, where there are few non-detectable values, do not apply to human studies, where >50% of the values may be non-detectable. Thus, early studies in rodents with aflatoxin biomarkers did not indicate the complexity of future investigations.

Determine relation of biomarker to exposure and disease in experimental animals
In the early 1980s our laboratories started a collaboration to identify effective chemoprevention strategies for aflatoxin carcinogenesis. We hypothesized that reduction of aflatoxin–DNA adduct levels by chemopreventive agents would be mechanistically related to and therefore predictive of cancer preventive efficacy. Preliminary studies with a variety of established chemopreventive agents demonstrated that after a single dose of aflatoxin, levels of DNA adducts were reduced (18). Therefore, we next carried out a more comprehensive study using multiple doses of aflatoxin and the chemopreventive agent ethoxyquin to examine effects on DNA adduct formation and removal and hepatic tumorigenesis in rats (16). Treatment with ethoxyquin reduced both area and volume of liver occupied by presumptive preneoplastic foci by >95%. This same protocol also dramatically reduced binding of AFB1 to hepatic DNA, from 90% initially to 70% at the end of a 2 week dosing period. No differences in residual DNA adduct burdens, however, were discernible several months after dosing. Thus, the efficacy of the intervention apparently depended on the time of analysis.

The experiment was then repeated with several different chemopreventive agents and in all cases aflatoxin-derived DNA and protein adducts were reduced, however, even under optimal conditions, the reduction in the macromolecular adducts always under-represented the magnitude of tumor burden (19,20). These macromolecular adducts can track with disease outcome on a population basis, but in the multistage process of cancer the absolute level of adduct provides a necessary but insufficient measure of tumor formation. Indeed, it is reasonable to envision a situation where a chemopreventive agent could suppress adduct formation, but through other actions promote tumors, leading to a dichotomous outcome of fewer adducts and more tumors. Finally, because the design of these DNA adduct studies requires serial sacrifice of the animals, it is not possible to track the fate of an individual's adduct burden with tumor outcome. Hence, these investigations could only be used to predict the protective effects of an intervention at the level of the group, but not individual risk of disease.

Modulation of biomarker and disease in animal chemoprevention studies
Using the chemopreventive agent oltipraz, Roebuck et al. (19) established correlations between reductions in levels of AFB1–N7-gua excreted in urine and incidence of HCC in aflatoxin-exposed rats. Overall, reduction in biomarker levels reflected protection against carcinogenesis, but these studies did not address the relationship between biomarker and individual risk. Thus, in a follow-up study, rats dosed with AFB1 daily for 5 weeks were randomized into three groups: no intervention; delayed-transient intervention with oltipraz during weeks 2 and 3 of exposure; persistent intervention with oltipraz for all 5 weeks of dosing (21). Serial blood samples were collected from each animal at weekly intervals throughout aflatoxin exposure for measurement of aflatoxin–albumin adducts. The integrated level of aflatoxin–albumin adducts decreased 20–39% in the delayed-transient and persistent oltipraz intervention groups, respectively, as compared with no intervention. Similarly, the total incidence of HCC dropped significantly from 83 to 60 and 48% in these groups. Overall, there was a significant association between integrated biomarker level and risk of HCC (P = 0.01). When the predictive value of aflatoxin–serum albumin adducts was assessed within treatment groups, however, there was no association between integrated biomarker levels and risk of HCC (P = 0.56). These data clearly demonstrated that levels of the aflatoxin–albumin adducts could predict population-based changes in disease risk, but had no power to identify individuals destined to develop HCC. Because of the multistage process of carcinogenesis, in order to determine individual risk of disease, a panel of biomarkers reflecting different stages will be required.

Cross-sectional studies of biomarker levels in exposed humans
Environmental chemical exposures in people are generally first explored by cross-sectional surveys in which samples are obtained from potentially exposed populations. Although these surveys are very valuable for testing the sensitivity and specificity of analytical methods for studying biomarkers, they rarely include a comprehensive examination of exposure, making it difficult to determine dose–response characteristics of individuals. Since health outcomes are not assessed, interpretation of the findings must also be conservative. Nonetheless, these surveys are critical first steps in translating information from experimental studies to an assessment of exposure and risk in humans.

Early studies in the Philippines (22) demonstrated that an oxidative metabolite of aflatoxin could be measured in urine and thus had potential to serve as an internal dose marker. In later studies, Autrup et al. (23,24) used synchronous fluorescence spectroscopy to detect AFB1–DNA adducts in human urine samples in Kenya. Together, these findings showed that humans had the metabolic capacity to produce aflatoxin metabolites previously only detected in experimental animals.

Subsequent work conducted in the People's Republic of China and The Gambia, West Africa, areas with high incidence of HCC, determined both the dietary intake of aflatoxin and the levels of urinary aflatoxin biomarkers (14,25,26). These were the first dose–response investigations in populations. Urinary AFB1–N7-gua and AFM1 showed a dose-dependent relationship between aflatoxin intake and excretion. Gan et al. (27) and Wild et al. (28) also monitored levels of aflatoxin–serum albumin adducts and observed a highly significant association between intake of aflatoxin and level of adduct. Interestingly, these studies also indicated that the kinetics of formation and excretion of AFB1–N7-gua in urine were similar in rats and humans, thereby adding to our understanding of the mechanisms of aflatoxin carcinogenesis.

The relationship between aflatoxin exposure and development of HCC was further highlighted by the more recent molecular biological studies on the p53 tumor suppressor gene, the most common mutated gene detected in many human cancers (29,30). Many studies of p53 mutations in HCC occurring in populations exposed to high levels of dietary aflatoxin have found high frequencies of guanine to thymine transversions, with clustering at codon 249 (3134). In contrast, no mutations at codon 249 were found in p53 in HCC from Japan and other areas where there was little exposure to aflatoxin (35,36).

Results from previous studies on mechanisms showed that AFB1 exposure caused almost exclusively guanine to thymine transversions in bacteria (37) and that aflatoxin-8,9-epoxide could bind to codon 249 of p53 in a plasmid in vitro (38). Further, Aguilar et al. examined mutagenesis of the p53 gene in human HepG2 cells and hepatocytes exposed to AFB1 and found preferential induction of the transversion of guanine to thymine in the third position of codon 249 (39). These experimental results strongly supported the cross-sectional epidemiological data indicating AFB1 as an etiological factor in HCC.

Although useful, cross-sectional epidemiological studies have the least power to relate an exposure to disease outcome since these studies only provide a view during a short time frame. Data from the cross-sectional aflatoxin biomarker studies demonstrated short-term dose–response effects for a number of the aflatoxin metabolites, including the major nucleic acid adduct, serum albumin adduct and AFM1. This information could then be used in follow-up studies to test a number of hypotheses about risk to individuals having high exposures, the efficacy of exposure remediation and interventions and mechanisms underlying susceptibility.

Longitudinal study of biomarkers in humans
In order to carry out longitudinal studies, we needed to know whether aflatoxin biomarkers were stable over the long term. Therefore, the stability of aflatoxin biomarkers was monitored in the Shanghai cohort study (described below) by supplementing urine samples with aflatoxins at the time the samples were collected. Analysis of the samples over the course of 8 years showed that the aflatoxins were stable. Similarly, aflatoxin–albumin adducts in human sera from Guangxi, PRC, were found to be stable for at least 10 years when stored at –20°C. Therefore, at least for some of the aflatoxin biomarkers, degradation over time was not a major problem, however, similar studies are required for all chemical-specific biomarkers.

An objective in development of aflatoxin biomarkers is to use them as predictors of past and future exposure status in people. This concept is embodied in the principle of tracking, which is an index of how well an individual's biomarker remains positioned in a rank order relative to other individuals in a group over time. Tracking within a group of individuals is expressed by the intraclass correlation coefficient. When the intraclass correlation coefficient is 1.0, a person's relative position, independent of exposure, within the group does not change over time. If the intraclass correlation coefficient is 0.0, there is random positioning of the individual's biomarker level relative to the others in the group throughout the time period. The tracking concept is central to interpreting data related to exposure and biomarker levels and requires acquisition of repeated samples from subjects. Unfortunately, data on the temporal patterns of formation and persistence of aflatoxin macromolecular adducts in human samples are very limited. Obviously, chemical-specific biomarkers measured in cross-sectional studies cannot provide information on the predictive value or tracking of an individual's marker level over time.

Tracking is important in assessing exposure and this information is essential in the design of intervention studies. In all these situations it is critical to know how many biomarker samples are required and when they should be obtained. For example, if exposure remains constant and the tracking value for a marker changes over time, it might be assumed that the change in tracking is due to a biological process, such as an alteration in the balance of metabolic pathways responsible for adduct formation. On the other hand, lack of tracking can also be attributable to great variance in exposure. Therefore, to determine unequivocally the contributions of intra- and interindividual variations to biomarker levels, experiments must assess tracking over time. Perhaps measurement of blood pressure is the biomarker most familiar to the public. Blood pressure tracks well over the course of months; therefore, when blood pressure changes significantly, a person knows that this is a true indicator of a change in health status.

Very few multiple sampling or tracking studies has been conducted in humans for biomarkers of aflatoxin or indeed any other carcinogen exposure. One of the most extensive investigations was conducted in Qidong, PRC, where the temporal modulation of aflatoxin–albumin adduct formation over multiple lifetimes of serum albumin in both HBV-positive and -negative subjects was examined (40). During a 12 week monitoring period and a subsequent follow-up 6 months later for an additional 12 weeks, the levels of aflatoxin–albumin adducts were found not to track from one time point to the next (intraclass correlation coefficient = 0.0). In contrast, in our rat model the intraclass correlation coefficient was 0.29 (40). In comparison, repeated monthly measures of blood pressure in groups of people resulted in intraclass correlation coefficients of 0.6–0.7. There were two possible explanations for the disparity between the human and rat studies: variance in exposure; difference in the experimental method of analysis. In the rat model, exposure to aflatoxin was constant throughout the study. In people, the short-term variation in exposure could be so large that it could mask tracking from one point to the next, even in a long-lived biomarker. Thus, inherent differences in exposure could explain the interclass correlation coefficients. If this were found to be true, the utility of using aflatoxin–albumin adducts as biomarkers of exposure in individuals would be greatly diminished.

Another explanation for these results is that there were differences in the study design for analyzing the aflatoxin–albumin adducts depending on whether cross-sectional or longitudinal analysis was used. In the human study, 120 people each contributed up to 15 blood samples over a 1 year period. Samples could be analyzed cross-sectionally, i.e. all 120 people analyzed at one time point, or each individual could be analyzed longitudinally, i.e. all 15 samples for a single person measured on the same day in the laboratory. Since our procedure could only measure up to 50 samples a day, ~3 days would be required to measure one cross-section or the 15 samples from three people could be measured in 1 day. Thus, the choice of cross-sectional or longitudinal analysis would distribute the assay-to-assay variation in the method of analysis differently. To avoid delay while obtaining all the blood samples from the participants, we chose to analyze these samples cross-sectionally as they were collected. In contrast, levels of aflatoxin–albumin adducts in serum in the rat study were determined longitudinally. Therefore, when the disparity in tracking values was found we re-analyzed a subset of human samples longitudinally. We found that longitudinal analysis of the human samples showed tracking similar to that in the rat study. Therefore, the inherent day-to-day contributions of the variation in our assay method were the major contributor to the lack of tracking in the human study. Although on a population basis the means and standard errors remain unchanged, an individual's ranking in a group can be dramatically affected by how the cohort is analyzed. This lesson in study design proved to be extremely important in the analysis of biomarkers from the now ongoing clinical intervention studies.

Case–control studies, cohort studies and clinical trials
Case–control studies.
Many published case–control studies have examined the relation of aflatoxin exposure and HCC. Compared with cohort studies, case–control studies are both cost- and time-effective. Unfortunately, however, case–control studies are initiated long after exposure has occurred and with chemical-specific biomarkers it cannot be assumed that the exposure has not appreciably changed over time. Also, such studies involve assumptions in the selection of controls, including that the disease state does not alter metabolism of aflatoxin. Thus, matching of cases and controls in a chemical-specific biomarker study is much more difficult than in a case–control study involving genetic markers. Presumably these inherent problems would bias the results to the no effect conclusion and a positive finding probably represents an underestimation of a true effect.

In one of the first case–control studies, Bulatao-Jayme et al. (42) compared the dietary intake of aflatoxin in cases of HCC in the Phillipines with intake in age- and sex-matched controls. They found that the mean aflatoxin exposure per day in cases of HCC was 4.5 times higher than in the controls, however, alcohol consumption was a confounder in this study that may have enhanced this effect. Van Rensburg and his collaborators (43) and Peers (44) used a similar design in Mozambique and Swaziland, respectively. Again the mean dietary intake values were significantly correlated with HCC rates and suggested evidence for a dose-dependent increase in aflatoxin intake corresponding to increased liver disease.

In the Guangxi Autonomous Region of China, Yeh and Shen (45,46) examined the interaction between HBV infection and dietary aflatoxin exposure dichotomized for heavy and light contamination. Those individuals who were positive for hepatitis B surface antigen (HBsAg) and had heavy aflatoxin exposure had an incidence of HCC 10-fold higher than did people living in areas with light aflatoxin contamination. People who were HBsAg-negative and who ate diets heavily contaminated with aflatoxin had a rate of HCC comparable with the HBsAg-positive people with light aflatoxin contamination (46). In a recent case–control study in Taiwan, two biomarkers, aflatoxin–albumin adducts and aflatoxin–DNA adducts in liver tissue samples, were measured (47). The proportion of subjects with a detectable level of aflatoxin–albumin adducts was higher for cases of HCC than for matched controls (odds ratio 1.5). There was also a statistically significant association between detectable level of aflatoxin–albumin adduct and risk of HCC among men younger than 52 years old (multivariant adjusted odds ratio 5.3).

Gene–environment interactions with aflatoxins have also been reported in case–control studies. In one investigation, genetic variation in epoxide hydrolase and glutathione S-transferase M1 was compared with aflatoxin–albumin adduct biomarkers, the presence of HCC and with codon 249 mutations in p53 (48). Mutant alleles at both loci were significantly over-represented in individuals with aflatoxin–albumin adduct and mutant alleles of epoxide hydrolase were significantly over-represented in persons with HCC. The relationship of epoxide hydrolase varied by HBsAg status and indicated that synergism may exist between the two. Codon 249 mutations were observed only among HCC patients with one or both high risk genotypes. These results indicated that individuals with mutant genotypes at epoxide hydrolase and glutathione S-transferase M1 may be at greater risk of developing aflatoxin adducts, p53 mutations and HCC when exposed to AFB1. These findings support the existence of genetic susceptibility to AFB1 in humans and indicate that in HCC such susceptibility may interact with HBV infection to enhance disease.

Although a number of negative case–control studies of aflatoxin and HCC have been reported (reviewed in ref. 6), the overwhelming evidence from many investigations points to an etiological role for aflatoxin in human HCC. In general, the greatest difficulty in case–control studies lies in the selection of the controls. Continued development of aflatoxin biomarkers should reduce misclassification of cases and controls.

Cohort studies.
Data obtained from cohort studies have the greatest power to determine a true relationship between an exposure and disease outcome because one starts with a healthy cohort, obtains biomarker samples and then follows the cohort until significant numbers of cases are obtained. A nested study within the cohort can then be designed to match cases and controls. An advantage of this method is that the controls are truly matched to the cases since both were recruited at the same time and with the same health status. A major disdvantage, however, is the time needed in follow-up (often years) to accrue the cases. This disadvantage can be overcome in part by enrolling large numbers of people (often tens of thousands) to ensure case accrual at a reasonable rate.

To date two major cohort studies with aflatoxin biomarkers have demonstrated the role of this carcinogen in the etiology of HCC. The first study, comprising >18 000 people in Shanghai, examined the interaction of HBV and aflatoxin biomarkers as independent and interactive risk factors for HCC. The nested case–control data revealed a statistically significant increase in the relative risk (RR) of 3.4 for those HCC cases where urinary aflatoxin biomarkers were detected. For HBsAg-positive people only RR = 7, but for individuals with both urinary aflatoxins and positive HBsAg status RR = 59 (49,50). These results strongly support a causal relationship between the presence of the chemical and viral-specific biomarkers and the risk of HCC.

Subsequent cohort studies in Taiwan have substantially confirmed the results from the Shanghai investigation. Wang et al. (51) examined HCC cases and controls nested within a cohort and found that in HBV-infected people there was an adjusted odds ratio of 2.8 for detectable compared with non-detectable aflatoxin–albumin adducts and 5.5 for high compared with low levels of aflatoxin metabolites in urine. In a follow-up study, there was a dose–response relationship between urinary AFM1 levels and risk of HCC in chronic HBV carriers. Similar to the Shanghai data, the HCC risk associated with AFB1 exposure was more striking among the HBV carriers with detectable AFB1–N7-gua in urine.

Therefore, these cohort data from two different populations demonstrate the power of validated aflatoxin biomarkers to define a heretofore unrecognized chemical–viral interaction in the induction of human HCC (52). These findings have significant public health implications. First, vaccination to prevent HBV infection would substantially reduce a major factor in HCC. Unfortunately, in most parts of the world HBV infection is acquired before age 3 years, therefore, complete world wide elimination of HBV infection by vaccination will require much of the next century to accomplish. Second, elimination or reduction of aflatoxin exposure should also reduce the risk of HCC. This goal can be attained by using available technologies and the dose–response data from epidemiological studies indicate that, similar to smoking cessation, the reduction in aflatoxin exposure during an individual's lifetime should reduce risk of HCC. Taken together, these cohort studies provide the final data sets in the validation scheme to establish the use of some of the aflatoxin biomarkers as validated risk markers.

Clinical trials.
Clinical trials and interventions are designed to translate findings from human and experimental investigations to public health prevention. Both primary (to reduce exposure) and secondary (to alter metabolism and deposition) interventions can use chemical-specific biomarkers as endpoints of efficacy. Such biomarkers can be applied to the pre-selection of exposed individuals for study cohorts, thereby reducing study size requirements. They can also serve as short-term modifiable endpoints (53). In a primary prevention trial the goal is to reduce exposure to aflatoxins in the diet. Interventions can range from attempting to lower mold growth in harvested crops to using trapping agents that block the uptake of ingested aflatoxins. In secondary prevention trials one goal is to modulate the metabolism of ingested aflatoxin to enhance detoxification processes.

Aflatoxin biomarkers were first used as intermediate biomarkers in a clinical Phase IIa chemoprevention trial of oltipraz in Qidong, PRC (5456). This was a placebo-controlled, double-masked study in which participants were randomized to receive placebo or 125 mg oltipraz daily or 500 mg oltipraz weekly. All subjects received two identical appearing capsules every day. Blood and urine specimens were collected biweekly over the 8 week intervention period and subsequent 8 week follow-up period to monitor toxicities and evaluate biomarkers. Levels of aflatoxin–albumin adducts in serum and AFM1 and aflatoxin–mercapturic acid (AFB1–NAC) excreted in urine were examined as primary biomarker endpoints in this study. There were no consistent changes in levels of aflatoxin–albumin adducts in the placebo arm or in the arm receiving 125 mg oltipraz daily. However, individuals receiving 500 mg oltipraz once a week for 8 weeks had a significant longitudinal decline in aflatoxin–albumin biomarker levels beginning 1 month into the intervention and continuing for 1 month after treatment was stopped. Because of the apparent tracking of this biomarker, each individual was able to serve as his/her own control.

In contrast, because of the short biological half-life of urinary aflatoxin metabolites, such as AFM1 and AFB1–NAC, a cross-sectional analysis was used for these biomarkers. AFM1, the primary oxidative metabolite of AFB1, was detected in >80% of the urine samples from the study participants. Immunoaffinity and HPLC analyses of samples from the week 4 sampling point indicated that urinary AFM1 levels were reduced by 51% compared with the placebo group in persons receiving the 500 mg weekly dose. No significant differences were seen in urinary AFM1 levels in the 125 mg group compared with placebo. An association was observed within participants in the 500 mg group between rate of decline of aflatoxin–albumin adducts and excretion levels of AFM1, suggesting that there was a common mechanism between these two endpoints. Indeed, AFM1 and the reactive intermediate leading to protein and DNA adducts are produced by a common cytochrome P450, CYP1A2. In contrast, median levels of AFB1–NAC were elevated 2.6-fold in the 125 mg group, but were unchanged in the 500 mg group. Increased AFB1–NAC reflects induction of aflatoxin conjugation through the actions of glutathione S-transferases. The apparent lack of induction in the 500 mg group probably reflects masking due to diminished substrate formation through the inhibition of CYP1A2 seen in this group. Overall, these results highlight the use of carcinogen biomarkers for the efficient optimization of dose and schedule of chemopreventive agents, as well as assessment of their efficacy. These biomarkers are currently being used in a chemopreventive intervention with chlorophyllin (57) and in a follow-up 12 month Phase IIb intervention with oltipraz.


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Implications of biomarker data for public health
Historically, public health professionals have depended on ambient environmental monitoring from air, food, soil or water to calculate or extrapolate exposures across populations. There are numerous examples of the success of this strategy in the prevention of acute and chronic infectious diseases. For example, monitoring of drinking water for fecal coliforms at a chlorination plant can assure a degree of microbiological safety in the water supply. Biomarker strategies measuring exposure in individuals could completely change how environmental regulations are established to protect the largest fraction of the population from the deleterious effects of exposure to an environmental pollutant. The development of biomarkers that can be used to address questions such as what is the exposure to toxic agents of individuals living along a fence-line of a hazardous waste area and are there subsets of the population at enhanced risk for disease? Since one of the prime reasons for developing chemical-specific biomarkers is to identify high risk individuals, then some of the current uncertainties in the risk assessment process will be addressed directly. The long-term implications for the use of biomarkers in the environmental regulatory setting may fundamentally change all approaches to ensuring public health safety.

The public has a great personal stake in the development of chemical-specific biomarkers and in the end the public will decide how the data will be used. Fortunately, people have been relating personal risk of disease to biomarker levels for decades. The correlation of serum cholesterol levels with risk of cardiovascular disease is widely appreciated and this is a result of the many prospective epidemiological studies. This information permits the individual to change his/her lifestyle, use a medication to lower serum cholesterol or do nothing. In turn, medical and public health professionals have validated information that can be presented to an individual. Interestingly, even now survival benefits to individuals trying to lower cholesterol have not been firmly established. Even this `gold standard' biomarker has unfulfilled promise.

Many people are apparently extremely eager to learn about their genetic background and degree of chemical exposure. Indeed, recent advances in the identification of genetic risk markers for cancer, such as the I1307K mutation in the APC gene and the risk of colon cancer among Ashkenazi Jews (58), have demonstrated that laboratory-based findings are often turned into commercially based screening procedures within weeks or months. A major question for the application of these new laboratory-based methods for measuring chemical biomarkers will be the certification and quality control of these measurements to ensure that the public receives accurate information.

There are few examples of environmental chemical-specific markers that can be used in the manner described for serum cholesterol. The best example is probably blood lead, where elevated levels can be equated with a number of health deficits. Unfortunately, the overwhelming majority of the chemical-specific biomarkers have yet to be validated for a person to be able to to use this information in a health analysis. Further, many experimental studies show that individual chemical-specific biomarkers under-predict toxic endpoints. Hence, we will probably have to use multiple chemical-specific biomarkers to estimate risk. Assembling and validating this collection of biomarkers will be a major challenge over the next decade.

The promise of biomarkers in public health prevention: genes, environment and gene–environment interactions
An axiom underlying environmental chemical-specific biomarker research is that after exposure to environmental compounds, the biological outcome is determined by the interaction of the exposure with expressed genes within an individual to define the etiological course of a specific disease outcome. This concept underlies the supposition that gene–environment interactions are at the core of most chronic human diseases. Among the most studied genetic modifiers for environmental exposures are the genes responsible for activation and detoxification of xenobiotics and those involved in the repair of macromolecular adducts. In the near future many other genes will be investigated with respect to modifying the course of environmentally induced diseases, however, we must remain focused on the environmental chemical exposure creating the disease risk that is then modified through expression of different genes. Genetic screening to detect a number of germline abnormalities, including Tay–Sachs disease, phenylketonuria and Down syndrome, have become routine medical practice for the management of pregnancy and newborns. The integration of environmental chemical-specific biomarkers into this equation raises many more complicated issues. In the case of a germline mutation or alteration, the timing of acquisition of a sample for analysis is important for early diagnosis, but the occurrence of this change throughout a person's life ensures that a diagnosis can be made regardless of when a sample is taken. The timing of the measurement of an environmental chemical-specific biomarker is much more critical and problematical. With temporal changes in exposure and the dynamics of biomarker formation and removal, both exposure and marker levels can vary by many orders of magnitude. Since many of the important effects modifying genes in a chemical exposure situation are the result of the action of metabolic enzymes that can also be induced or inhibited, the timing of sample acquisition for measurement of a gene–environment interaction is absolutely critical.

The light at the end of the tunnel for chemical-specific biomarkers: daylight or headlight?
The enthusiasm of the molecular epidemiology community for chemical-specific biomarkers has been sustained for the past 15 years because many studies have shown that specific biomarkers can be measured in populations. The analytical methods have continually improved such that ambient exposures to common environmental pollutants can be assessed in relatively small samples of biofluids and tissues. The results from molecular epidemiological studies are not only used by the scientific community, however, but these data are often rapidly transferred into the regulatory/political arenas. A recent example is the set of studies of pesticide exposure and the risk of breast cancer in a number of communities. We all need to recognize that the public's right of access to the data obtained from publicly funded studies are critical to the progress of public health research.

Our obligation as scientists is to specify boundaries for interpreting chemical biomarker studies and to facilitate the support of systematic research to identify the major environmental contaminants that affect human health. This is an ever more difficult task, given the increasing specialization of doctoral and post-doctoral training in our universities. To reach the full potential of the applications of chemical-specific biomarkers to public health, we must: (i) provide a foundation for interdisciplinary research training of graduate and post-graduate students who are committed to molecular epidemiology through opportunities to learn the tools of epidemiology, biostatistics, toxicology and molecular/cellular biology; (ii) facilitate the translation of basic laboratory and clinical research findings to the issues of exposure and disease risk in individuals, communities and populations; and (iii) build the infrastructure between stakeholders, scientists and policy makers to foster the partnerships required to interpret and apply biomarker data to existing and emerging public health problems.

If the light at the end of the tunnel is actually daylight rather than an on-rushing freight train, then we must make a long-term commitment to the rigorous validation of chemical-specific biomarkers so that their promise can be realized, or their limitations clearly defined.


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Table I. Goals for chemical-specific biomarker development and application
 

    Notes
 
1 To whom correspondence should be addressed Email: jgroopma{at}jhsph.edu Back


    Acknowledgments
 
We acknowledge the significant contributions of many of our colleagues to the development of aflatoxin biomarkers and our experimental and clinical studies on chemoprevention of aflatoxin hepatocarcinogensis. We also wish to thank Dr Pamela Talalay for her editorial suggestions and comments. Financial support for this work has been provided by grants R01 CA39416, P01 ES06052, NIEHS Center P30 ES03819 and contract N01-CN-25437.


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  1. Anonymous (1987) Biological markers in environmental health research. Environ. Health Perspect., 74, 3–9.[ISI][Medline]
  2. Wogan,G.N. (1992) Molecular epidemiology in cancer risk assessment and prevention: recent progress and avenues for future research. Environ. Health Perspect., 98, 167–178.[ISI][Medline]
  3. Links,J.M., Kensler,T.W. and Groopman,J.D. (1995) Biomarkers and mechanistic approaches in environmental epidemiology. Annu. Rev. Public Health, 16, 83–103.[ISI][Medline]
  4. Hulka,B.S. (1991) Epidemiological studies using biological markers: issues for epidemiologists. Cancer Epidemiol. Biomarkers Prev., 1, 13–20.[ISI][Medline]
  5. Busby,W.F. and Wogan,G.N. (1984) Aflatoxins. In Searle,C.E. (ed.) Chemical Carcinogens. American Chemical Society, Washington, DC, pp. 945–1136.
  6. Eaton,D.L. and Groopman,J.D. (eds) (1994) The Toxicology of Aflatoxins: Human Health, Veterinary and Agricultural Significance. Academic Press, San Diego, CA.
  7. Ellis,W.O., Smith,J.P., Simpson,B.K. and Oldham,J.H. (1991) Aflatoxin in food: occurrence, biosynthesis, effects on organisms, detection and methods of control. Crit. Rev. Food Sci. Nutr., 30, 403–439.[ISI][Medline]
  8. Groopman,J.D., Scholl,P. and Wang,J.S. (1996) Epidemiology of human aflatoxin exposures and their relationship to liver cancer. Prog. Clin. Biol. Res., 395, 211–222.[ISI][Medline]
  9. Essigmann,J.M., Croy,R.G., Nadzan,A.M., Busby,W.F.Jr, Reinhold,V.N., Büchi,G. and Wogan,G.N. (1977) Structural identification of the major DNA adduct formed by aflatoxin B1 in vitro. Proc. Natl Acad. Sci. USA, 74, 1870–1874.[Abstract]
  10. Sabbioni,G., Skipper,P.L., Büchi,G. and Tannenbaum,S.R. (1987) Isolation and characterization of the major serum albumin adduct formed by aflatoxin B1 in vivo in rats. Carcinogenesis, 8, 819–824.[Abstract]
  11. Bennett,R.A., Essigmann,J.M. and Wogan,G.N. (1981) Excretion of an aflatoxin–guanine adduct in the urine of aflatoxin B1-treated rats. Cancer Res., 41, 650–654.[Abstract]
  12. Groopman,J.D., DeMatos,P., Egner,P.A., Love-Hunt,A. and Kensler,T.W. (1992) Molecular dosimetry of urinary aflatoxin–N7-guanine and serum aflatoxin–albumin adducts predicts chemoprotection by 1,2-dithiole-3-thione in rats. Carcinogenesis, 13, 101–106.[Abstract]
  13. Groopman,J.D., Trudel,L.J., Donahue,P.R., Marshak-Rothstein,A. and Wogan,G.N. (1984) High-affinity monoclonal antibodies for aflatoxins and their application to solid-phase immunoassays. Proc. Natl Acad. Sci. USA, 81, 7728–7731.[Abstract]
  14. Groopman,J.D., Donahue,P.R., Zhu,J., Chen,J. and Wogan,G.N. (1985) Aflatoxin metabolism in humans: detection of metabolites and nucleic acid adducts in urine by affinity chromatography. Proc. Natl Acad. Sci. USA, 82, 6492–6496.[Abstract]
  15. Groopman,J.D., Hasler,J.A., Trudel,L.J., Pikul,A., Donahue,P.R. and Wogan,G.N. (1992) Molecular dosimetry in rat urine of aflatoxin–N7-guanine and other aflatoxin metabolites by multiple monoclonal antibody affinity chromatography and immunoaffinity/high performance liquid chromatography. Cancer Res., 52, 267–274.[Abstract]
  16. Kensler,T.W., Egner,P.A., Davidson,N.E., Roebuck,B.D., Pikul,A. and Groopman,J.D. (1986) Modulation of aflatoxin metabolism, aflatoxin–N7-guanine formation and hepatic tumorigenesis in rats fed ethoxyquin: role of induction of glutathione S-transferases. Cancer Res., 46, 3924–3931.[Abstract]
  17. Egner,P.A., Gange,S.J., Dolan,P.M., Groopman,J.D., Muñoz,A. and Kensler,T.W. (1995) Levels of aflatoxin–albumin biomarkers in rat plasma are modulated by both long-term and transient interventions with oltipraz. Carcinogenesis, 16, 1769–1773.[Abstract]
  18. Kensler,T.W., Egner,P.A., Trush,M.A., Bueding,E. and Groopman,J.D. (1985) Modification of aflatoxin B1 binding to DNA in vivo in rats fed phenolic antioxidants, ethoxyquin and a dithiothione. Carcinogenesis, 6, 759–763.[Abstract]
  19. Roebuck,B.D., Liu,Y.-L., Rogers,A.E., Groopman,J.D. and Kensler,T.W. (1991) Protection against aflatoxin B1-induced hepatocarcinogenesis in F344 rats by 5-(2-pyrazinyl)-4-methyl-1,2-dithiole-3-thione (oltipraz): predictive role for short-term molecular dosimetry. Cancer Res., 51, 5501–5506.[Abstract]
  20. Bolton,M.G., Muñoz,A., Jacobson,L.P., Groopman,J.D., Maxuitenko,Y.Y., Roebuck,B.D. and Kensler,T.W. (1993) Transient intervention with olitpraz protects against aflatoxin-induced hepatic tumorigenesis. Cancer Res., 53, 3499–3504.[Abstract]
  21. Kensler,T.W., Gange,S.J., Egner,P.A., Dolan,P.M., Munoz,A., Groopman,J.D., Rogers,A.E. and Roebuck,B.D. (1997) Predictive value of molecular dosimetry: individual versus group effects of oltipraz on aflatoxin–albumin adducts and risk of liver cancer. Cancer Epidemiol. Biomarkers Prev., 6, 603–610.[Abstract]
  22. Campbell,T.C., Caedo,J.P.Jr, Bulatao-Jayme,J., Salamat,L. and Engel,R.W. (1970) Aflatoxin M1 in human urine. Nature, 227, 403–404.[ISI][Medline]
  23. Autrup,H., Bradley,K.A., Shamsuddin,A.K.M., Wakhisi,J. and Wasunna,A. (1983) Detection of putative adduct with fluorescence characteristics identical to 2,3-dihydro-2-(7'-guanyl)-3-hydroxyaflatoxin B1 in human urine collected in Murang'a district, Kenya. Carcinogenesis, 4, 1193–1195.[ISI][Medline]
  24. Autrup,H., Seremet,T., Wakhisi,J. and Wasunna,A. (1987) Aflatoxin exposure measured by urinary excretion of aflatoxin B1–guanine adduct and hepatitis B virus infection in areas with different liver cancer incidence in Kenya. Cancer Res., 47, 3430–3433.[Abstract]
  25. Groopman,J.D., Jiaqi,Z., Donahue,P.R., Pikul,A., Lisheng,Z., Jun-shi,C. and Wogan,G.N. (1992) Molecular dosimetry of urinary aflatoxin–DNA adducts in people living in Guangxi Autonomous Region, People's Republic of China. Cancer Res., 52, 45–52.[Abstract]
  26. Groopman,J.D., Hall,A.J., Whittle,H., Hudson,G.J., Wogan,G.N., Montesano,R. and Wild,C.P. (1992) Molecular dosimetry of aflatoxin–N7-guanine in human urine obtained in The Gambia, West Africa. Cancer Epidemiol. Biomarkers Prev., 1, 221–227.[Abstract]
  27. Gan,L.-S., Skipper,P.L., Peng,X., Groopman,J.D., Chen,J., Wogan,G.N. and Tannenbaum,S.R. (1988) Serum albumin adducts in the molecular epidemiology of aflatoxin carcinogenesis: correlation with aflatoxin B1 intake and urinary excretion of aflatoxin M1. Carcinogenesis, 9, 1323–1325.[Abstract]
  28. Wild,C.P., Hudson,G.J., Sabbioni,G., Chapot,B., Hall,A.J., Wogan,G.N., Whittle,H., Montesano,R. and Groopman,J.D. (1992) Dietary intake of aflatoxins and the level of albumin-bound aflatoxin in peripheral blood in The Gambia, West Africa. Cancer Epidemiol. Biomarkers Prev., 1, 229–234.[Abstract]
  29. Harris,C.C. (1993) p53: at the crossroads of molecular carcinogenesis and risk assessment. Science, 262, 1980–1981.[ISI][Medline]
  30. Greenblatt,M.S., Bennett,W.P., Hollstein,M. and Harris,C.C. (1994) Perspectives in cancer research. Mutations in the p53 tumor suppressor gene: clues to cancer etiology and molecular pathogenesis. Cancer Res., 54, 4855–4878.[ISI][Medline]
  31. Hsu,I.C., Metcalf,R.A., Sun,T., Welsh,J.A., Wang,N.J. and Harris,C.C. (1991Mutational hotspot in the p53 gene in human hepatocellular carcinomas (letter). Nature, 350, 427–428.
  32. Bressac,B., Kew,M., Wands,J. and Ozturk,M. (1991) Selective G to T mutations of p53 gene in hepatocellular carcinoma from southern Africa. Nature, 350, 429–431.[ISI][Medline]
  33. Wild,C.P., Fortuin,M., Donato,F., Whittle,H.C., Hall,A.J., Wolf,C.R. and Montesano,R. (1993) Aflatoxin, liver enzymes and hepatitis B virus infection in Gambian children. Cancer Epidemiol. Biomarkers Prev., 2, 555–561.[Abstract]
  34. Fujimoto,Y., Hampton,L.L., Wirth,P.J., Wang,N.J., Xie,J.P. and Thorgeirsson,S.S. (1994) Alterations of tumor suppressor genes and allelic losses in human hepatocellular carcinomas in China. Cancer Res., 54, 281–285.[Abstract]
  35. Aguilar,F., Harris,C.C., Sun,T., Hollstein,M. and Cerutti,P. (1994) Geographic variation of p53 mutational profile in nonmalignant human liver. Science, 264, 1317–1319.[ISI][Medline]
  36. Ozturk,M. (1991) p53 mutation in hepatocellular carcinoma after aflatoxin exposure. Lancet, 338, 1356–1359.[ISI][Medline]
  37. Foster,P.L., Eisenstadt,E. and Miller,J.H. (1983) Base substitution mutations induced by metabolically activated aflatoxin B1. Proc. Natl Acad. Sci. USA, 80, 2695–2698.[Abstract]
  38. Puisieux,A., Lim,S., Groopman,J. and Ozturk,M. (1991) Advances in brief. Selective targeting of p53 gene mutational hotspots in human cancers by etiologically defined carcinogens. Cancer Res., 51, 6185–6189.[Abstract]
  39. Aguilar,F., Hussain,S.P. and Cerutti,P. (1993) Aflatoxin B1 induces the transversion of G->T in codon 249 of the p53 tumor suppressor gene in human hepatocytes. Proc. Natl Acad. Sci. USA, 90, 8586–8590.[Abstract/Free Full Text]
  40. Wang,J.-S., Qian,G.-S., Zarba,A. et al. (1996) Temporal patterns of aflatoxin–albumin adducts in hepatitis B surface antigen-positive and antigen-negative residents of Daxin, Qidong County, People's Republic of China. Cancer Epidemiol. Biomarkers Prev., 5, 253–261.[Abstract]
  41. Kensler,T.W., Gange,S.J., Egner,P.A., Dolan,P.M., Munoz,A., Groopman,J.D., Rogers,A.E. and Roebuck,B.D. (1997) Predictive value of molecular dosimetry: individual versus group effects of oltipraz on aflatoxin–albumin adducts and risk of liver cancer. Cancer Epidemiol. Biomarkers Prev., 6, 603–610.[Abstract]
  42. Bulatao-Jayme,J., Almero,E.M., Castro,M.A.C.A., Jardeleza,M.A.T.R. and Salamat,L.A. (1982) A case–control dietary study of primary liver cancer risk from aflatoxin exposure. Int. J. Epidemiol., 11, 112–119.[Abstract]
  43. Van Rensburg,S.J., Cook-Mozaffari,P., Van Schalkwyk,D.J., Van Der Watt,J.J., Vincent,T.J. and Purchase,I.F. (1985) Hepatocellular carcinoma and dietary aflatoxin in Mozambique and Transkei. Br. J. Cancer, 51, 713–726.[ISI][Medline]
  44. Peers,F.G. (1977) Dietary aflatoxins and human primary liver cancer. Ann. Nutr. Aliment., 31, 1005–1018.[ISI][Medline]
  45. Yeh,F.-S. and Shen,K.-N. (1986) Epidemiology and early diagnosis of primary liver cancer in China. Adv. Cancer Res., 47, 297–329.[ISI][Medline]
  46. Yeh,F.-S., Yu,M.C., Mo,C.-C., Luo,S., Tong,M.J. and Henderson,B.E. (1989) Hepatitis B virus, aflatoxins and hepatocellular carcinoma in southern Guangxi, China. Cancer Res., 49, 2506–2509.[Abstract]
  47. Lunn,R.M., Zhang,Y.-J., Wang,L.-Y., Chen,C.-J., Lee,P.-H., Lee,C.-S., Tsai,W.-Y. and Santella,R.M. (1997) p53 mutations, chronic hepatitis B virus infection and aflatoxin exposure in hepatocellular carcinoma in Taiwan. Cancer Res., 57, 3471–3477.[Abstract]
  48. McGlynn,K.A., Rosvold,E.A., Lustbader,E.D. et al. (1995) Susceptibility to hepatocellular carcinoma is associated with genetic variation in the enzymatic detoxification of aflatoxin B1. Proc. Natl Acad. Sci. USA, 92, 2384–2387.[Abstract/Free Full Text]
  49. Ross,R.K., Yuan,J.-M., Yu,M.C., Wogan,G.N., Qian,G.-S., Tu,J.-T., Groopman,J.D., Gao,Y.-T. and Henderson,B.E. (1992) Urinary aflatoxin biomarkers and risk of hepatocellular carcinoma. Lancet, 339, 943–946.[ISI][Medline]
  50. Qian,G.-S., Ross,R.K., Yu,M.C., Yuan,J.-M., Gao,Y.-T., Henderson,B.E., Wogan,G.N. and Groopman,J.D. (1994) A follow-up study of urinary markers of aflatoxin exposure and liver cancer risk in Shanghai, People's Republic of China. Cancer Epidemiol. Biomarkers Prev., 3, 3–10.[Abstract]
  51. Wang,L.-Y., Hatch,M., Chen,C.-J. et al. (1996) Aflatoxin exposure and risk of hepatocellular carcinoma in Taiwan. Int. J. Cancer, 67, 620–625.[ISI][Medline]
  52. Harris,C.C. (1994) Solving the viral–chemical puzzle of human liver carcinogenesis (editorial). Cancer Epidemiol. Biomarkers Prev., 3, 1–2.[ISI][Medline]
  53. Kensler,T.W., Groopman,J.D. and Wogan,G.N. (1996) Use of carcinogen–DNA and carcinogen–protein adduct biomarkers for cohort selection and as modifiable end points in chemoprevention trials. Principles Chemoprev., 139, 237–248.
  54. Jacobson,L.P., Zhang,B.-C., Zhu,Y. et al. (1997) Oltipraz chemoprevention trial in Qidong, People's Republic of China: study design and clinical outcomes. Cancer Epidemiol. Biomarkers Prev., 6, 257–265.[Abstract]
  55. Kensler,T.W., He,X., Otieno,M. et al. (1998) Oltipraz chemoprevention trial in Qidong, P.R.C.: modulation of serum aflatoxin albumin adduct biomarkers. Cancer Epidemiol. Biomarkers Prev., 7, 127–134.
  56. Wang,J.-S., Shen,X., He,X. et al. (1999) Protective alterations in phase 1 & 2 metabolism of aflatoxin B1 by oltipraz in residents of Qidong, P.R.C. J. Natl Cancer Inst., in press.
  57. Kensler,T.W., Groopman,J.D. and Roebuck,B.D. (1998) Use of aflatoxin adducts as intermediate endpoints to assess the efficacy of chemopreventive interventions in animals and man. Mutat. Res., 402, 165–172.[ISI][Medline]
  58. Laken,S.J., Petersen,G.M., Gruber,S.B. et al. (1997) Familial colorectal cancer in Ashkenazim due to a hypermutable tract in APC. Nature Genet., 17, 79–83.[ISI][Medline]
Received May 13, 1998; revised August 26, 1998; accepted September 2, 1998.