cancer research

Environmental Mutagenesis

ELSEVIER

MutationResearch313 (1994) 117-129

Biomarkers and molecular epidemiology in mutation/cancer research F.P. P e r e r a *, R . M . W h y a t t Columbia University School of Public Health, 60 Haven Avenue, New York, NY 10032, USA

Received2 September 1993;accepted 16 February1994 Keywords: Biomarker; Molecular epidemiology

1. Introduction

Molecular epidemiology bridges from basic research in molecular biology to studies of human cancer causation by combining laboratory measurements of internal dose, biologically effective dose, biologic effect and susceptibility with epidemiologic methodologies. Incorporation of biomarkers into epidemiologic studies holds considerable promise for cancer prevention. First, biomarkers offer powerful tools for elucidating etiologies and biologic mechanisms operating along the continuum from external exposure to disease manifestation. Biomarkers can also facilitate quantitative risk assessment, in particular by increasing understanding of factors contributing to interindividual variability in response to carcinogens. Finally, biomarkers provide useful intermediate endpoints for assessing efficacy of interventions. Despite this potential, most biomarkers are in the validation stage, so that the field is at an intermediate stage of development. As seen from Tables 1-3, there exist numerous biomonitoring

* Corresponding author.

studies where persons with known (usually high) exposures to genotoxic compounds have been sampled. However, there are fewer molecular epidemiologic studies in which a hypothesis regarding cancer causation has been tested. In general, biomarkers have not been applied to quantitative risk assessment or in intervention studies. Further, there is a dearth of methods to detect carcinogens that do not interact efficiently with genetic material. In addition, the use of markers to detect individual differences in susceptibility has the potential to stigmatize the individual and can raise serious ethical considerations (for review see Ashford, 1986; Schulte, 1989; Schulte and Perera, 1993).

2. Overview of markers

Internal dose refers to the measurement of the amount of a carcinogen or its metabolite present in ceils, tissues, or body fluids. Examples of internal dosimeters include DDT and PCBs in serum and adipose tissue from environmental contamination, plasma or salivary cotinine from cigarette smoking, and urinary aflatoxin indicative of dietary exposure. These markers take into account

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F.P. Perera, R.M. Whyatt / Mutation Research 313 (1994) 117-129

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individual differences in absorption or bioaccumulation of the compound in question and have the advantage of being comparatively easy to monitor. However, they do not provide data about interactions of the compound with critical cellular targets. Table 1 gives examples of populations monitored for markers of internal dose. Biologically effective dose reflects the amount of carcinogen that has interacted with cellular macromolecules at a target site or with an established surrogate. Examples include carcinogenDNA and carcinogen-protein adducts. This class of markers is more mechanistically relevant to carcinogenesis than internal dose, since it takes into account differences in metabolism (activation versus detoxification) of the chemical in question, as well as the extent of repair of carcinogen-DNA adducts. The biological basis for measuring DNA adducts derives from extensive experimental data supporting their role in the initiation and possibly in the progression of cancer (Miller and Miller, 1981; Harris et al., 1987; Yuspa and Poirier, 1988; Weinstein et al., 1984). Despite their relevance as dosimeters of biologic effect, limitations in current methods should

be noted. Most available assays provide information on total or multiple adducts and are rarely capable of pinpointing the 'critical' adducts on DNA. Further, DNA from target tissue is not readily available, and many studies use surrogate tissues (e.g. peripheral blood cells and placentas). However, the relationship between adducts in target to surrogate tissues has not been well characterized in humans, although for certain carcinogens this relationship has been characterized in experimental animals (Stowers and Anderson, 1985). In addition, while experimental studies indicate that proteins such as hemoglobin and albumin can serve as a valid surrogate for DNA (Neumann, 1984), this correlation varies for different compounds and in different tissues and proteins. Another limitation is that levels of carcinogen-DNA adducts in blood cells predominantly reflect recent exposure rather than distant or past exposure. Indeed studies of carcinogenDNA adducts in peripheral white blood cells suggest that the majority of adducts are rapidly removed after the exposure ceases, within a period of several months. However, a persistent fraction of adducts may remain for years after

Table 1 Internal dose Compound analyzed

Exposure source

Biologic sample

Population

Reference

Nitrosamino acids

N-nitroso compounds in diet

Urine

Chinese residing in areas of low and high cancer risk

Lu et al., 1986

N-Nitrosoproline

Cigarette smoke

Urine

Smokers, nonsmokers; unexposed

Garland et al., 1986 Hoffmann et al., 1985

1-Hydroxypyrene

Coal tar products

Urine

Workers, smokers, coal tar treated patients

Bos and Jongeneelen, 1988

Aflatoxin M I Aflatoxin B~

Diet

Urine

Chinese residing in a high exposure area

Zhu et al., 1987 Ross et al., 1992

Mutagenicity of urine

Cigarette smoke, various industrial exposures

Urine

Smokers, workers

Vainio et al., 1984 Everson et al., 1986

MelQx

Diet

Urine

Fried beef consumers

Murray et al., 1989

Phenol

Occupational

Urine

Factory workers

Inoue et al., 1988

Lead

Occupational

Blood

Smelter workers

Valciukas et al., 1980

MelQx, 2-amino-3,8-dimethylimidazo[4,5-f ]quintraline.

F.P. Perera, R.M. Whyatt / Mutation Research 313 (1994) 117-129

exposure ceases (Holz et al., 1990; Perera et al., 1988). Table 2 provides examples of populations monitored for markers of biologically effective dose. Biologic effect markers reflect irreversible damage resulting from a toxic interaction, either at the target or at an analogous site, which is known or believed to be pathogenically linked to cancer. A wide variety of biomarkers fall into this category and include gene mutations at the hprt and glycophorin A (GPA) loci, alterations in oncogenes and tumor suppressor genes, DNA single-strand breaks, unscheduled DNA synthesis, sister-chromatid exchanges (SCEs), chromosomal aberrations (CAs), and micronuclei. None

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of these markers is chemical or exposure specific and other factors (lifestyle and environmental) that affect these endpoints can act as confounding variables in a molecular epidemiologic study. Table 3 provides examples of populations monitored for markers of biologic effect. Susceptibility markers measure individual differences that can modulate response to carcinogens. These include variability in DNA repair capacity, micronutrient levels and inherited mutations. Individual differences in metabolic activation and detoxification mechanisms also appear to affect risks significantly. Since most carcinogens require metabolic activation before binding to DNA, individuals with elevated metabolic ca-

Table 2 Biologically effective dose Endpoint examined

Exposure source

Biologic sample

Population

Reference

N-3-(2-Hydroxy-ethyl) histidine: N-(2hydroxy-ethyl) valine

Ethylene oxide

RBC

Workers, smokers, unexposed

Calleman et al., 1978 van Sittert et al., 1985 Farmer et al., 1986 Tornqvist et al., 1986

4-Aminobiphenyl-Hb

Cigarette smoke

RBC

Smokers, nonsmokers

Bryant et al., 1987 Bryant et al., 1988 MacLure et al., 1990

AFB 1-guanine

Diet

Urine

Chinese and Kenyans residing in a high exposure and high and low risk area respectively

Groopman et al., 1985 Autrup et al., 1983 Ross et al., 1992

AFB1-DNA

Diet

Liver tissue

Taiwanese

Hsieh et al., 1988 Zhang et al., 1992

PAH-DNA

PAH in cigarette smoke, in workplace and air pollution

WBC, lung tissue, placenta

Lung cancer cases smokers, workers residents

Santella, 1988 Santella, 1992 Perera et al., 1992a

PAH-protein

PAH in workplace, in cigarette smoke

Plasma

Workers, smokers

Sherson et al., 1990 Weston et al., 1989 Lee et al., 1991

Spectrum of DNA adducts

Betel and tobacco chewing, smoking, industrial exposures, wood smoke

Placenta, lung tissue, oral mucosa, WBC, bone marrow, colonic mucosa

Smokers, workers, volunteers

Santella, 1988 Santella, 1992

NNK,NNN-Hb

Cigarette smoke

RBC

Smokers

Carmella et al., 1990

AFB1, aflatoxin B1; Hb, hemoglobin, NNK, 4-(methylnitrosamino)-l-(3-pyridyl)-l-hutaxone; NNN, Nl-nitrosonornicotine; PAH, polycyclic aromatic hydrocarbons; RBC, red blood cells; WBC, white blood cells.

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120 Table 3 Early biologic effect or response Endpoint examined

Exposure source

Biologic sample

Population

Reference

Sister-chromatid exchange

Various industrial exposures, radiation

WBC

Workers, residents

Carrano and Moore, 1982 Wilcosky and Rynard, 1990 (for review) Perera et al., 1992a

Micronuclei

Organic solvents, heavy metals, cigarette smoke, betel quid

WBC, oral mucosa

Workers

Hogstedt et al., 1983 Stich and Dunn, 1988

Chromosomal aberrations

Various industrial exposures, radiation air pollution

WBC

Workers, residents

Evans, 1982 (for review) Perera et al., 1992a

hprt mutation

Chemotherapeutic agents, radiation

WBC

Patients, workers

O'Neill et al., 1987 Messing et al., 1986 McGinniss et al., 1990 Ostrosky-Wegman et al., 1990

GPA mutation

Chemotherapeutic agents, radiation

RBC

Patients, Japanese atom bomb survivors

Langlois et al., 1987 Jensen et al., 1986 Bigbee et al., 1990 Kyoizumi et al., 1989

Mutation in tumor suppressor genes

AFB 1

Tumor tissue

Patients

Hsu et al., 1991 Bressac et al., 1991

Oncogene activation

PAH, cigarette smoke

Serum

Workers, cancer patients

Brandt-Rauf, 1988 Perera et al., 1988

AFB1, aflatoxin Bfi GPA, glycophorin A; HPRT, hypoxanthine guanine phosphoribosyl transferase; PAIl, polycyclic aromatic hydrocarbons; RBC, red blood cells; WBC, white blood cells.

pacity may be at heightened risk of cancer. Examples include the relationship of cytochrome P-450 enzyme activities to lung cancer risk. The normal role of these 'phase I' enzyme systems is to convert lipid soluble xenobiotics to more water soluble substances that can be excreted. However, some of the intermediates in this oxidative process are highly reactive electrophiles capable of binding to DNA. CYP1A1, a P-450 enzyme with aryl hydrocarbon hydroxylase (AHH) activity, catalyzes the oxidation of polycyclic aromatic hydrocarbons such as benzo[a]pyrene. This enzyme system is highly inducible and inducibility has been associated with higher risk of lung cancer in smokers (Vahakangas and Pelkonen, 1989). A MspI RFLP in the 3' coding region of the CYP1A1 (associated with a mutation in exon 7 of the gene) has been associated with lung cancer

risk in Japan (Kawajiri et al., 1990; Hayashi et al., 1992). 'Phase lI' enzymes conjugate the phase I metabolites with glucuronide, glutathione or sulfate resulting in less reactive, hydrophilic products for excretion. Glutathione S-transferases (GST) are a family of multifunctional proteins that play an important role in the detoxification of PAHs and other xenobiotics through conjugation with glutathione (Ketterer, 1988; Liu et al., 1991). A polymorphism has been detected in the GSTM1 gene which has been shown to be a deletion of the entire gene locus. Published reports indicate that 30-60% of the population may be homozygous deleted for this gene (Helm et al., 1990; Bell et al., 1991). Smokers with low lymphocyte GSTM1 activity are reported to be at an estimated three-fold higher risk for adenocarci-

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noma of the lung (Seidegard et al., 1990). More recently, in a cancer case-control study in Japan, a relative risk of 1.87 was seen for the null genotype and squamous cell carcinoma of the lung (Hayashi et al., 1992). A remarkably high relative risk of 9.1 for squamous cell carcinoma was seen with the combined CYP1A1 exon 7 mutation and GSTM1 null genotypes. In addition, a three-fold higher risk of stomach and colon adenocarcinoma has been associated with the null phenotype for this gene (Strange et al., 1991). The GSTM1 null genotype has been associated with increased SCEs in smokers (van Poppel et al., 1992) and P A H - D N A adduct levels in lung tissue from autopsy specimens (Shields et al., 1993). Another metabolic phenotype associated with increased cancer risk is related to the ability to N-acetylate aromatic amines. Aromatic amines, such as 4-aminobiphenyl, a carcinogenic constituent in cigarette smoke, are activated by N-hydroxylation, primarily in the liver. N-Acetylation is a competing detoxification reaction catalyzed by N-acetyltransferase. This enzyme is non-inducible and under autosomal dominant genetic control. 'Slow' acetylators appear to be at high risk of bladder cancer, especially those occupa-

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tionally exposed (Cartwright et al., 1982; Evans et al., 1983; Karkaya et al., 1986; Mommsen and Aagaard, 1986). In contrast to the protective effect of the fast acetylator phenotype in bladder cancer, increased risk has been observed in two studies of colon cancer patients (Ilett et al., 1987; Wohlleb et al., 1990), although lack of an association has also been reported (Ladero et al., 1991). While it is assumed that N-acetylation is a detoxification step, a potential activation role exists involving the formation of N-acetoxy arylamines either by O-acetylation of N-OH arylamines or by N,O-acetyl transfer of arylhydroxamic acid (Kirlin et al., 1991). Table 4 provides examples of populations monitored for markers of susceptibility.

3. Biomarkers in etiologic studies Cross-sectional and longitudinal studies

Cross-sectional and longitudinal studies have enabled assessment of the relationship between exposures and biomarker levels, although study design precludes establishment of causal relationships between exposures or biomarkers and disease. Research has shown marked elevations in biomarkers associated with a wide spectrum of

Table 4 Examples of genetic factors influencing susceptibility Gene Population CYPIA1 Lung cancer cases and controls

Reference Kawajiri et al., 1990

CYP2D6

Lung cancer cases and controls

Ayesh et al., 1984 Caporaso et al., 1989

CYP2E1

Lung cancer cases and controls

Uematsu et al., 1991

GSTM1

Lung, stomach, colon cancer

Seidegard et al., 1986 Seidegard et al., 1990 Strange et al., 1991

NAT

Smokers, bladder cancer

Cartwright et al., 1982 Vineis et al., 1990

Tumor suppressor genes, Rb, p53

Retinoblastoma, Li-Fraumeni syndrome

Li, 1990

CYP, cytochromeP450; GST, glutathione S-transferase; NAT, N-acetyltransferase;Rb, retinoblastoma.

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exposures and has demonstrated the ability of multiple biomarkers to detect genetic and molecular damage in humans. Cohorts studied include individuals with cigarette smoke exposures, workers occupationally exposed and populations with ambient exposures. Study results are not uniform and appear highly dependent on sample size, tissue analyzed and laboratory methodology used, inconsistencies in accounting for confounding variables, and adequacy of data on exposure. Consistent findings include the importance of background exposures manifested in so-called unexposed control populations and the significant interindividual variation in biomarker levels in persons with comparable exposures. Several studies of cigarette smokers have assessed adducts formed between DNA and polycyclic aromatic hydrocarbons (PAH-DNA). PAHs are ubiquitous xenobiotics to which there is widespread human exposure including from cigarette smoke. Studies that have assessed PAHDNA adducts in peripheral leukocytes by enzyme-linked immunosorbent assays (ELISA) have generally found nonsignificant or borderline differences between smokers and nonsmokers (Perera et al., 1987). However, two have seen a significant smoking-related effect (van Schooten et al., 1990; Tang et al., 1993). Similarly, studies using the 32p-postlabeling method, which detects a broad spectrum of adducts on DNA, found no difference in adduct levels related to cigarette smoke exposure when adducts were measured in peripheral leukocytes (Phillips et al., 1986, 1990). However, when adducts were measured in peripheral lymphocytes, the longer-lived cell populations, increases have been seen in PAH-DNA adducts by ELISA and adducts by postlabeling in smokers compared to nonsmokers (Santella et al., 1992; Savela and Hemminki, 1991). Using the postlabeling method, a linear relationship between adduct levels and daily or lifetime cigarette consumption was seen in lung tissue (Phillips et al., 1988a) and smoking-related adducts have also been detected in placental tissue (Everson et al., 1986). Research also suggests that aromatic amines contribute significantly to the integrated genotoxic effect of cigarette smoke. Significantly elevated levels of adducts formed between 4-

aminobiphenyl and hemoglobin (4-ABP-Hb) have been detected by G C / M S in smokers compared to nonsmokers (Bryant et al., 1987; Perera et al., 1987). Dramatic effects of exposure on biomarker levels have been seen in workers exposed to high levels of PAHs. Elevated levels of white blood cell adducts were found in foundry workers, roofers, coke oven workers and aluminum plant workers using both ELISA and postlabeling methods (Harris et al., 1985; Haugen et al., 1986; Perera et al., 1988; Shamsuddin et al., 1985; van Schooten et al., 1990; Hemminki et al., 1990a,b; Herbert et al., 1990; Phillips et al., 1988b; Schoket et al., 1991). Exposure to carcinogens in the foundry air has also been associated with increases in hprt mutations in lymphocytes and urinary 1-hydroxypyrene levels (Perera et al., 1993a; Santella et al., 1993). A significant correlation was seen between levels of PAH-DNA adducts and hprt gene mutations, consistent with experimental data for PAHs (Perera et al., 1993a). Studies of environmental exposures have found ambient pollution to be associated with increases in biomarker levels (Hemminki et al., 1990a; Perera et al., 1992a). Ambient air pollution in Poland was significantly related to PAH-DNA adducts by ELISA, adducts by postlabeling, SCE and chromosomal aberrations (CA), including gaps. Further, PAH-DNA adducts were significantly correlated with chromosomal mutation, linking molecular dose with a genetic effect of air pollution (Perera et al., 1992a). In a series of studies in China and West Africa, Groopman and colleagues showed a high correlation between dietary exposure to aflatoxin and excretion of aflatoxin-DNA adducts (aflatoxinN T-guanine) in the urine (Groopman et al., 1992a,b). In an ecological study of the association between dietary aflatoxin exposure and liver cancer, AFB 1 metabolites were monitored by competitive ELISA in urine of 250 individuals living in eight townships in Taiwan with similar hepatitis B virus carrier status but with different hepatocellular carcinoma (HCC) incidence rates. A significant association was observed between levels of AFB 1 metabolites and background rate of HCC (Hatch et al., 1993).

F.P. Perera, R.M. Whyatt /Mutation Research 313 (1994) 117-129

Case-control studies Case-control studies provide the first step in exploring the role of various biomarkers as risk factors for cancer as well as in evaluating their value in assessing mechanisms of carcinogenesis. However, as with cross-sectional studies, casecontrol designs cannot establish the causal sequence between biomarker formation and cancer, especially since the latency from exposure to cancer induction is usually years to decades. Unless exposure has been continuous and unchanged and metabolic processes have not altered over time, biologic measurements made now are not relevant to the present risk of cancer. However, findings from our lung cancer casecontrol study suggest that adducts are not only an environmentally relevant dosimeter, but may also indicate heightened risk of cancer. A significant association was seen between lung cancer risk and PAH-DNA adduct formation among current smokers after controlling for the number of cigarettes smoked per day (Tang et al., 1993). Other recent case-control studies suggest a link between a specific carcinogen exposure and target organ specificity (Hsu et al., 1991; Bressac et al., 1991; Harris, 1993). A total of 26 liver tumors were obtained from patients living in regions of China and Southern Africa characterized by high exposure to AFB 1 and by high prevalence of liver cancer. Eleven (43%) of the tumors exhibited the same G to T mutation at codon 249 of the tumor suppressor gene, p53. This signal mutation is produced by AFB 1when administered to experimental animals. Taken together, the human and experimental data strongly implicate AFB 1 as the causal agent in liver cancers and indicate the mechanism by which the carcinogen may be exerting its effect. An optimal study design to establish the chain of causation is the nested case-control study. Questionnaire data and biologic samples are collected and stored prior to disease manifestation. Once a diagnosis of cancer is made, cases are matched to appropriate controls and their stored samples analyzed. The predictive value of the specific markers can thus be determined in biologic samples collected prior to clinical disease and therefore not subject to the concern that the

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marker rather than the disease itself reflects events on the causal pathway. A recent example of this approach has revealed relative risks of 6.2 for AFB 1 metabolites and 4.9 for AFBl-guanine adducts in urine and liver cancer, as well as an interaction between hepatitis B infection and aflatoxin-related biomarkers (Ross et al., 1992). A nested case-control study of lung cancer and markers including DNA adducts, oncogenes, tumor suppressor genes and certain genotypes is currently in progress (Perera et al., 1993b).

4. Biomarkers in risk assessment

The traditional approach to risk estimation for environmental carcinogens has been based on dose-response data derived from controlled experiments in genetically homogeneous laboratory animals, although data from epidemiological studies are occasionally available. Response at the relevant low level of exposure in human populations is extrapolated from results observed at doses several orders of magnitude higher. Even the most conservative models make the assumption that the human population is homogeneous in its biologic effect. Biomarkers have the potential to improve risk assessment in the following specific areas: (1) Calibration of the biologically effective dose at the target (or established surrogate) in humans to that in laboratory animals from whom tumor incidence is known, should greatly enhance extrapolation of risks between species. Using this approach, our group is validating a molecular risk assessment model for PAHs based on PAH-DNA adduct data and tumor incidence derived from experimental bioassays and PAH-DNA adduct data and lung cancer incidence from human studies of foundry workers. We anticipate greater variability in adduct formation among human populations than seen in in-bred animals that could lead to an underestimate of risks to humans based on estimates generated from in-bred animals using traditional models. (2) Biomarkers can also improve risk assessment by providing an earlier occurring, more sensitive outcome than tumor incidence. Thus

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markers can allow epidemiology to escape the tyranny of cancer latency to become a more timely contributor to risk assessment and cancer prevention. Serum protein products of activated oncog e n e s / t u m o r suppressor genes may provide such predictive markers. In a recent study of lung cancer patients, the extracellular domain (ECD) of the erbB-2 oncogene-encoded p185 protein was found by ELISA to be elevated in 64% of lung cancer cases compared to 0% of age-sexrace-smoking matched controls (Paul BrandtRauf, personal communication). For positive cases with prior available samples, samples were found to be positive on the average 35 months before clinical diagnosis. Similarly, in a study of breast cancer, protein products of myc and erbB-2 oncogenes were detected with a significantly higher frequency in cases compared to controls and, most interestingly, were found in the sera of two-thirds of the cases with in situ cancer without evidence of infiltration (Breuer et al., 1993; Paul Brandt-Rauf, personal communication). (3) Biomarkers can also fill a major void in risk assessment, by providing information on factors contributing to interindividual variation in response to carcinogenic exposures. Examples include a study of blond and black tobacco smokers, in which 'slow' acetylators had higher levels of 4-ABP-Hb adducts for the same type and quantity of cigarettes smoked than 'fast' acetylators (Bartsch et al., 1990; Vineis et al., 1990). Those with a 'fast' N-oxidation and 'slow' Nacetylation phenotype had the highest hemoglobin adduct levels (Bartsch et al., 1990), indicating that determination of both phenotypes may provide a better prediction of risk. Similarly, our research has indicated a protective effect of the GSTM1 gene on D N A adduct formation in preliminary analyses of blood samples drawn from heavily exposed smokers (based on cotinine levels). Several studies have also shown an association between high A H H activity and D N A adduct formation. Among 16 placenta samples, mean A H H activity was significantly greater in placentas in which B P D E - D N A adducts were detected than in placentas in which adducts were not detected (Manchester et al., 1992). Another study found a significant linear correlation

between adducts and A H H activity in normal lung tissue from 19 smokers (Geneste et al., 1991).

5. Biomarkers in intervention studies

Biomarkers offer promise in intervention studies as tools for behavior modification and to provide feedback on efficacy of chemoprevention. Recent results from our studies of smokers enrolled in a smoking cessation program suggest that biomarkers may prove valuable in efforts to motivate ex-smokers in continued abstinence from smoking cigarettes. In a prospective study of heavy smokers enrolled in a smoking cessation program, biomarkers were measured in peripheral blood samples drawn at baseline and in serial samples at 6 and 12 months after subjects had quit smoking. Results showed that serum cotinine and 4ABP-Hb declined significantly after 10 weeks of smoking cessation while PAH adducts were reduced 6-12 months after subjects quit smoking (Mooney et al., unpublished). Biomarkers also appear to provide useful intermediate endpoints for assessing mechanisms and efficacy of chemopreventive agents. There is convincing epidemiological evidence that the antioxidant and free radical-scavenging vitamins C, E and ¢J-carotene (/3-C) protect against cancer of the lung and other epithelial tissues, with somewhat weaker evidence for retinol (NRC, 1989; Block, 1992). The possible modifying effect of several serum vitamins on biomarkers has been investigated. In the lung cancer case-control study described above, serum levels of /3-C were inversely correlated with P A H - D N A adducts in peripheral leukocytes of cases and controls, respectively, and also of cases and controls combined (Perera et al., 1993b). In a cross-sectional study of 63 heavy smokers, the serum concentrations of o~-tocopherol (cholesterol-adjusted), vitamin C, /3-C and retinol were evaluated in relationship to the levels of P A H - D N A adducts in lymphocytes measured in the same individuals (GrinbergFunes et al., 1992). A significant inverse correlation was found between serum a-tocopherol and P A H - D N A adducts. Although the relationship was not statistically significant, serum vitamin C,

F.P. Perera, R.M. Whyatt~Mutation Research 313 (1994) 117-129

fl-C and retinol were inversely correlated with adducts (Grinberg-Funes et al., 1992).

6. Research needs

Biomarkers which have demonstrated the greatest feasibility and relevance to carcinogenesis need to be validated (Perera and Santella, 1993). This process is two-staged. The first step is analytical or laboratory validation in which the fundamentals of the assays are characterized. Low dose sensitivity and reproducibility are key concerns. Within-laboratory variability must also be determined so that differences in marker concentration are not erroneously attributed to intra- or interindividual variation. There should be confirmation of assay results by other methods. An important tool in understanding the characteristics of an individual assay is the use of corroborative methods on the same sample. The next stage is epidemiologic validation in order to understand the factors which control the test results in human populations. The extent to which the marker is chemical or exposure specific (i.e., selective for a particular chemical, exposure source, or period of exposure) should be known. Biologic markers can integrate exposure via multiple routes (inhalation, oral, dermal), multiple sources (ambient and indoor air, workplace air, cigarette smoke, diet, drinking water), and across all patterns of exposure (past, current, intermittent, continuous). The extent to which the marker will document specific time periods of exposure will depend upon the pharmacokinetics of the chemical and the persistence of the marker in the biologic sample assayed (itself a function of the turnover rate of the sample and repair processes). The same criteria of adequate sensitivity, specificity, and predictive value that apply to the validation of screening methods should be met by biomarkers. How feasible is the marker? That is, how acceptable is it to the public, how cost-effective, and how stable in stored samples? Finally, the dose-response relationship and the extent of interindividual and intraindividual variability in humans must be characterized. It is often said that to be valid a marker of exposure

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or biologically effective dose must be highly correlated with an individual's estimated exposure. This assumption ignores the fact that the search for biologic markers was triggered in large part by the awareness of large differences in individual processing of xenobiotics. Indeed, all evidence to date suggests that there is a high degree of variability in biomarker levels among persons with similar exposures. This is probably because of variability in metabolic processing of xenobiotics a n d / o r in repair as well as errors in estimating individual exposure. Thus the development and application of biomarkers has tremendous potential to increase the power of epidemiologic studies of cancer. However, use of susceptibility markers can engender some unique ethical concerns as well.

References Ashford, N.A. (1986) Policy considerations for human monitoring in the workplace, J. Occup. Med., 28, 563-568. Autrup, H., K.A. Bradley, A.K.M. Shamsuddin, J. Wakhisi and A. Wasunna (1983) Detection of putative adduct with fluorescence characteristics identical to 2,3-dihydro-2-(7guanyl)-3-hydroxyaflatoxin B1 in human urine collected in Murang'a District, Kenya, Carcinogenesis, 4, 1193-1195. Ayesh, R., J. Idle, J. Richie, M. Crothers and M. Hetzel (1984) Metabolic oxidation phenotypes as markers for susceptibility to lung cancer, Nature, 312, 169-170. Bartsch, H., N. Caporaso, M. Coda, F. Kadlubar, C. Malaveille, P. Skipper, G. Talasaka and S.R. Tannenbaum (1990) Carcinogen hemoglobin adducts, urinary mutagenicity, and metabolic phenotype in active and passive cigarette smokers, J. Natl. Cancer Inst., 82, 1826-1831. Bell, D.A., C.L. Thompson, C.R. Miller, Z. McCoy and G. Lucier (1991) Genotyping human polymorphic cancer susceptibility genes by the polymerase chain reaction, Environ. Mol. Mutagen., Abstr. Suppl. Bigbee, W.L., A.W. Wyrobeck, R.G. Langlois, R.H. Jensen and R.B. Everson (1990) The effect of chemotherapy on the in vivo frequency of glycophorin A 'null' variant erythrocytes, Mutation Res., 240, 165-175. Block, G. (1992) The data support a role for antioxidants in reducing cancer risk, Nutr. Rev., 50, 207-13. Bos, R.P. and F.J. Jongeneelen (1988) Nonselective and selective methods for biological monitoring of exposure to coal tar products, IARC, 389-395. Brandt-Rauf, P.W. (1988) New markers for monitoring occupational cancer: The example of oncogene proteins, J. Occup. Med., 30, 399-404. Bressac, B., M. Kew, J. Wands and M. Ozturk (1991) Selective

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