VoL 6, 603-610, August 1997 Cancer Epidemiology, Biomarkers & Prevention 603
Predictive Value of Molecular Dosimetry: Individual versus Group
Effects of Oltipraz on Aflatoxin-Albumin Adducts and
Risk of Liver Cancer1
Thomas W. Kensler,2 Stephen J. Gange,Patricia A. Egner, Patrick M. Dolan, Alvaro Mu#{241}oz,John D. Groopman, Adrianne E. Rogers, andBill D. Roebuck
Departments of Environmental Health Sciences [T. W. K., P. A. E, P. M. D.,
J. D. G.] and Epidemiology [S. J. G., A. Ml, Johns Hopkins School ofHygiene and Public Health, Baltimore, Maryland 21205; Department of
Pathology, Boston University School of Medicine, Boston, Massachusetts
021 18 [A. E. RI; and Department of Pharmacology and Toxicology,
Dartmouth Medical School, Hanover, New Hampshire 03755 [B. D. R.]
Abstract
Studies in animals and humans have established serumaflatoxin-albumin adducts as biomarkers of exposure toaflatoxin B1 (AFB1), a food-borne hepatocarcinogen. Toassess the utility of measurements of aflatoxin-albuminadducts to predict risk of hepatocellular carcinoma(HCC), 123 male F344 rats were dosed with 20 �g ofAFB1 daily for S weeks after randomization into threegroups: no intervention; delayed-transient (500 ppm ofoltipraz, weeks 2 and 3 relative to AFB1); or persistent(500 ppm oltipraz, weeks -1 to 5). Serial blood sampleswere collected from each animal at weeldy intervalsthroughout aflatoxin B1 exposure and assayed for levelsof aflatoxin-albumin by radioimmune assay. Area underthe curve (AUC) values for aflatoxin-albumin adductsdecreased 20 and 39% in the delayed-transient andpersistent oltipraz intervention groups, respectively, ascompared to no intervention. Similarly, the totalincidence of HCC dropped from 83 to 60% (P = 0.03)and 48% (P < 0.01) in these groups. Tumor multiplicitywas also reduced in the two oltipraz intervention groups,whereas time to HCC was increased. Mononuclear cellleukemia, a common neoplasm in F344 rats, was seen in39% of the control animals, whereas the two oltiprazinterventions reduced incidence to 18% (P 0.05) and13% (P = 0.01), respectively. Overall, a significantassociation was seen between biomarker AUC and risk ofHCC (P = 0.01). However, when the predictive value ofaflatoxin-albumin adducts was assessed within treatment
Received 1/29/97; revised 5/9/97; accepted 5/19/97.
The costs of publication of this article were defrayed in part by the payment ofpage charges. This article must therefore be hereby marked advertisement in
accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
I This work was supported in part by the National Institute of Environmental
Health Sciences Grant ES06052 and Center Grant ES03819 and National Cancer
Institute Grant CA39416.
2 To whom requests for reprints should be addressed, at Department of Environ-
mental Health Sciences, Johns Hopkins School ofHygiene and Public Health, 615North Wolfe Street, Baltimore, MD 21205. Phone: (410) 955-4712; Fax:
(410) 955-01 16; E-mail: [email protected].
groups, there was no association between AUC and riskof HCC (P = 0.56). Thus, aflatoxin-albumin adducts canbe useful for monitoring population-based changesinduced by interventions, such as in chemopreventiontrials, but have limited utility in identifying individualsdestined to develop HCC. As a consequence, the use ofthis biomarker in quantitative risk assessment should bepursued cautiously.
Introduction
Chemical-specific markers have been developed for a numberof environmental carcinogens for use as molecular dosimetersof individual exposures (1). These markers provide the prospectof contributing substantially to the specificity and sensitivity of
epidemiological studies aimed at determining the role of envi-ronmental agents in the etiology of human cancers (2, 3). Some
of these biomarkers may also assist the monitoring of popula-tions for changes in cancer risk, as may be achieved through
exposure avoidance and chemoprevention (4). Biomarkers ofthe biologically effective dose may be particularly useful in thiscontext in that they, in theory, provide a mechanistic linkage
between exposure and disease outcome. The biologically ef-fective dose reflects the amount of carcinogen that has inter-
acted with its critical macromolecular targets and has beenmeasured as either carcinogen-DNA or carcinogen-protein ad-
ducts. However, despite their increasing prominence, there are
few instances where the promise of these biomarkers has beenfulfilled. This reflects both the actual infancy of their develop-ment as well as intrinsic limitations to their application inpopulations (5, 6). To date, no carcinogen-specific biomarkerhas undergone full validation to quantitatively define the extent
of the relationships between external exposure, biomarker 1ev-els, and cancer outcome.
Studies with the aflatoxins have served as a guidepost inthe development of such molecular approaches in the assess-
ment of exposure status and risk of HCC,3 one of the most
common cancers in Asia and sub-Saharan Africa. Over the past30 years, there have been extensive efforts to investigate theassociation between aflatoxin exposure and HCC (7). These
studies have been generally hindered by the lack of adequatedosimetry data on aflatoxin intake, excretion, and metabolismin humans, as well as by the general poor quality of worldwide
cancer morbidity and mortality statistics. However, recent pro-spective studies using biomarkers of the biologically effectivedose of aflatoxin have provided striking confirmation of thepostulated associations derived from the earlier ecological and
3 The abbreviations used are: HCC, hepatocellular carcinoma; CI, confidence
interval; AFB1, aflatoxin B1 ; AUC, area under the curve; MCL, mononuclear cellleukemia.
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604 Aflatoxin-Albumin Adducts and Liver Cancer
observational studies. Using urinary aflatoxin biomarkers, Qian
et al. (8) demonstrated a dramatic synergistic interaction be-tween aflatoxin and infection with hepatitis B virus in the riskof HCC in residents of Shanghai, People’s Republic of China.
The presence of urinary aflatoxin biomarkers alone resulted ina significant 3.4-fold increase in risk (95% CI, 1.1-10.0). Un-like measurements of urinary excretion of aflatoxins, which
measure very recent exposures to aflatoxins, the determination
of serum levels of aflatoxin-albumin adducts appears to provide
a more integrated assessment of aflatoxin exposures over a
several-month period. A recent longitudinal study in 120 resi-dents of Daxin Township in Qidong, People’s Republic of
China, demonstrated the consistent presence of aflatoxin-albu-mm adducts in most participants (9). A nested case-control
study in a prospective cohort of individuals in Qidong who areseropositive for hepatitis B surface antigen has indicated that
the relative risk for HCC among individuals also positive foraflatoxin-albumin adducts was 2.4 (95% CI, 1.2-4.7; Ref. 10).
Similarly, in Taiwan, Wang et a!. (11) observed an adjustedodds ratio of 2.8 (95% CI, 0.9-9.1) for detectable compared tonondetectable aflatoxin-albumin adducts in hepatitis B surface
antigen-seropositive men.As a component of the ongoing validation of aflatoxin
biomarkers, the present study has sought to expand these im-portant findings by determining the quantitative relationships
between levels of aflatoxin-albumin adducts and individual riskfor the development of HCC. This study has used a rodentmodel in which one major variable, carcinogen exposure, has
been held constant and risk outcome has been attenuatedthrough the use of persistent and delayed-transient interven-tions with the chemopreventive agent oltipraz. Serial measure-
ments of aflatoxin-albumin adducts throughout the period of
carcinogen exposure have allowed for an analysis of the lon-gitudinal association between biomarker levels and individual
development of liver cancer. Such studies, in turn, facilitate theobjective evaluation of the use and limitations of molecular
biomarkers to predict disease outcomes in individuals at high
risk from exposure to environmental toxicants.
Materials and Methods
Chemicals. Oltipraz was obtained from the ChemopreventionBranch, National Cancer Institute, and was determined to be
>99% pure by high-performance liquid chromatography (12).I was obtained from Sigma Chemical Co. (St. Louis, MO)
as was purified rat albumin and kits for albumin determination.
All other chemicals and reagents were of the highest commer-
cially obtainable quality.
Animals, Diets, and Treatments. Male F344 rats (100 g;
Harlan, Indianapolis, IN) were housed under controlled condi-tions of temperature, humidity, and lighting. Food and water
were available ad libitum. Purified diet of the AIN-76A for-mulation (Teklad, Madison, WI) without the recommended
addition of 0.02% ethoxyquin was used, and fresh diet wasprovided to animals at least every other day. Oltipraz at a final
concentration of 0.05% was mixed into the AIN-76A diet witha V-blender, and diet was stored at 4#{176}C.Rats were acclimated
to the AIN-76A diet for 1 week before beginning the experi-
ment. At this time, 123 rats were randomized to three treatmentgroups: no intervention; delayed-transient intervention with
oltipraz during weeks 9 and 10 of age; and persistent interven-tion with oltipraz during weeks 7-13 of age. All rats received20 �g of AFB1 in 100 �tl of DMSO by gavage each morning
during weeks 8-13. All animals were weighed weekly.
Analysis of Aflatoxin-Albumin Adducts in Serum. Approx-imately 100-150 1.d of blood from each animal were collectedfrom the tail vein in hematocrit tubes at weekly intervals beginning1 week after the first dose of carcinogen and extending to 1 week
after the last dose. Samples were collected 2 h after the daily
administrations of AFB1. The hematocrit tubes were subsequently
centrifuged at 10,000 X g, and the resulting serum was decantedinto Eppendorf tubes and stored at -70#{176}Cuntil assay. Serumsamples were first concentrated by high speed centrifugation using
Amicon Microcon-50 microconcentrators. Briefly, 50 pi of rat
serum were loaded onto a prewashed filter containing 300 �il ofPBS and centrifuged at 8500 rpm for 15 mm. Each serum samplewas then washed with an additional 200 �.d of PBS and recentri-
fuged. The concentrated sample was pipetted from the filter, and
the volume was accurately measured. The amount of serum albu-
mm in each sample was determined by a bromcresol purple dyebinding method using rat albumin as standard (Sigma). Total
protein content was determined by the method of Bradford (Bio-Rad Laboratories, Hercules, CA). Serum proteins were digested
overnight at 37#{176}Cwith nuclease-free Pronase (Calbiochem-Nova-
biochem Co., La Jolla, CA) in a ratio of 1:4 (w/w, enzyme:totalprotein) as described by Shebar et a!. (13). Two volumes ofice-cold acetone were added to the tubes followed by incubation at
4#{176}Cfor 1 h to precipitate the proteins. Following centrifugation at12,000 rpm, the supernatant was decanted into an Eppendorf tube
and concentrated under N2 to remove the acetone. Volumes wereaccurately adjusted with PBS to reflect a final protein concentra-tion of 10 mg/mi. Samples were stored at -70#{176}Cprior to assay.
To minimize potential biases afforded by assay variability
over the course of the analyses, longitudinal sets of serial samplesfrom three animals in each of the treatment groups were assayed
in each assay set. A radlioimmune assay was used to quantitate
aflatoxin-albumin adducts in duplicate rat serum digests based
upon the method described by Wang et a!. (9). Prior to assay, allsamples were adjusted to contain a final concentration of 1 mg ofprotein/mi using digested protein prepared from a normal ratserum pool. Nonspecific inhibition in the assay was determined
from this normal rat serum pool, and the average value wassubtracted from all study samples. Standard curves for the radio-
immune assays were determined using a nonlinear regressionmethod described by Gange et a!. (14).
Analysis of Cancers. All moribund rats were autopsied whenclinical observations indicated that the rat would not survive.The criterion for autopsy was the inability of the rat to rightitself or walk. This ataxia was usually accompanied by a sig-
nificant (>15%) and rapid loss in body weight. All remaining
rats were euthanized and autopsied 20 months after the first
dose of FB1 . All grossly abnormal tissues were taken forhistopathological analysis. This included all spleens weighing
>2 g (i.e., >0.5% body weight). Standardized sections of liverwere cut from the two largest lobes from all livers that appeared
normal; most normal-appearing spleens were also sampled. Alltissues were fixed in formalin, processed by routine methods,and embedded into paraffin, sectioned, and stained with H&E.
Hepatic foci, adenomas, and carcinomas were identified by two
observers blinded to the identity of the treatments.
Statistical Analyses. The relationship of treatment group andoutcome was quantified using both incidence and time-to-event
(“survival analysis”) measures. Separate analyses were con-ducted for the primary outcome ofHCC by considering whetherthe animals had either any or multiple carcinomas separately.Incidence of all hepatic lesions (foci, adenomas, and carcino-
mas) as well as MCL in the three protocols were compared
using Fisher’ s exact test for independence (15). The relation-
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0)
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500
400
300
200
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Cancer Epideminlo�, Bionsarkers & Prevention 605
ships between aflatoxin-albumin adduct levels and incidence
was evaluated using standard logistic regression models. Ad-duct levels from each weekly serum collection and each sum-mary measure (initial rate, peak, and AUC) were evaluated in
separate regression models to determine their univariate asso-ciation with cancer risk.
To assess the degree to which treatment modified the risk
of HCC through the biomarker, the bivariate relationship of thetreatments and biomarker levels in predicting disease risk wasinvestigated. Specifically, the strength of association between
treatment and outcome in logistic regression models with treat-ment as the only covariate and with both treatment and biomar-ker as covariates was examined. Notationally, the models were
of the following forms:
logit (Pr(HCC))
logit (Pr(HCC))
= a + f3�I (Delayed-Transient) + f3�I (Persistent)
= a* + f3�’I (Delayed-Transient) + f3�J (Persistent) + yX
where I( ) is an indicator function for intervention group; � and
P2 represent the univariate log-odds of HCC for the delayed-transient and persistent intervention groups relative to the con-trol group; and (3�* and (32* represent the log-odds after ad-justing for the biomarker X. Thus, the reduction of the
magnitude of coefficients � and � �0 (3�� and (32*, respec-tively, reflects the degree to which the biomarker X is a surro-gate for the relationship of cancer risk and intervention. The
two extremes that could occur would be: (a) given the level ofbiomarker, one does not need to know the intervention that was
received (i.e., P1 and (32 significantly different from zero, butPI* and 1�2 � not); and (b) once intervention is known, thelevel of biomarker does not provide any additional informationon risk of disease (i.e., y not significantly different from zero).
Separate survival analyses were conducted to investigate thetime to the occurrence of either any or multiple carcinomas. Thetune from initial aflatoxin exposure to the presence of either of
these events was considered censored for rats who were sacrificedeither prior to the administrative end ofthe study (20 months fromfirst exposure) that did not show the HCC outcome (i.e., any or
multiple tumors) or who did not display ataxia or pronouncedweight loss prior to the end of the study (regardless of subsequent
carcinoma diagnosis). For analyses of multiple HCC lesions, an-imals showing single tumors were treated as tight-censored (mul-tiple tumors could occur after the single tumor event). Each animalwith no carcinoma present at sacrifice was assumed to be at risk for
the occurrence of carcinoma at any time past the censoring time.Because hepatic tumorigenesis is a relatively slow event, theassumption that HCC would occur immediately after the censoring
time may not be appropriate. Thus, analyses using transformedcensoring times were conducted; these times were computed byadding a conservative constant to the censoring time of 180 days
for observations with no lesion or hepatic foci and 30 days forobservations with hepatocellular adenoma. Using these outcomeand censoring features, standard Kaplan-Meier methods (for anyor multiple HCC events; Ref. 16) were used to graphically depict
the time to HCC among the three groups. To evaluate the rela-tionship between intervention groups and biomarker levels both
separately and simultaneously on the time to HCC events, pars-metric log-normal regression models were fit for the time to HCCoutcomes (16). These models have an advantage over standard
Cox proportional hazards models in that they naturally parame-terize the percentiles of the survival distribution, where the pth
0 10 20 30 40 50 60 70 80 90 100
WEEKS OF AGE
Fig. 1. Body weights ofrats treated with 20 �&g ofAFB,/day during weeks 8-13
of age (solid bar) and receiving either no (0), delayed-transient (Li), or persistent
(0) dietary intervention with oltipraz.
percentile is the time at which p percentage develop HCC (hence
50th percentile = median). Additionally, it is easy to compute therelative percentile as a alternative measure to the relative hazardestimated in the Cox model for the difference in time to HCC
between two groups; relative percentiles describe the relative in-crease or decrease in time to HCC between groups (17).
The relationships between intervention groups and bi-
omarker levels were investigated using both simple one-wayANOVA models and regression methods that account for the
correlation among repeated measurements (1 8). The lattermethods were used to estimate and compare the mean of
logarithmically transformed adduct levels among the interven-
tion protocols at each of the six weekly time points. In addition,
summary measures (peak and AUC) of adduct response overthe entire observation period were computed for each animal.
The average of these summary measures was calculated foreach protocol and compared using standard ANOVA.
The repeated-measures regression model described aboveincorporates two parameters that describe the longitudinal
tracking of aflatoxin-albumin adduct levels over time. Specif-ically, the correlation between two adduct measurements on
any single rat taken s weeks apart is assumed to be equal to y’.The parameter y represents the correlation between consecutivemeasurements, and the parameter 0 represents how much the
correlation increases or decreases as the time between meas-urements (lag) increases. Using this model, one can statisticallytest among several forms of the correlation structure, includingwhether the correlation can be summarized using a compoundsymmetry structure (which implies that the correlation is due to
inherent animal effects) or an autoregressive structure (whichimplies that the correlation is due to direct dependence on prior
outcomes). Parameter estimates were obtained using all data
and separately for each intervention protocol.All computations were done using either SAS version 6. 1 1
(SAS, Inc., Cary, NC) or Splus (MathSoft, Seattle, WA).
Results
Growth Rates of Rats. The growth curves for the rats areshown in Fig. 1. All rats were treated with FB1 during weeks
8-13 of age. The overall growth rates of the rats fed oltipraz-supplemented diets in either the delayed-transient or persistentprotocols were similar to that of the rats maintained on thecontrol diet. Moreover, there were no significant differences ingrowth rates in the three experimental groups during the period
of aflatoxin exposure and oltipraz interventions.
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00
0
Ca
0
0
0
0
“t.
0
0
0
Fig. 2. Kaplan-Meier estimates forthe proportion free of any (A) ormultiple (B) HCCs in rats treatedwith 20 pg of AFB1/day duringweeks 8-13 of age and receiving
either no (solid line), delayed-iran-sient (dashed line), or persistent
(dotted line) dietary interventionswith oltipraz. Each animal without
the event present at sacrifice orthose not sacrificed by the end of thestudy (20 months) were treated ascensored, where censoring timeswere modified as detailed in “Mate-
rials and Methods.”
70 75 80 85 90 95
0
0
Weeks of age
70 75 80 85 90 95
Weeks of age
606 Aflatoxln-Albwnin Adducts and Liver Cancer
Table I Effect of oltipraz interventio ns on incidence of hepa tic foci, adeno max, and HCCs”
TreatmentIncidence” (%)
No lesions Foci Adenomas HCC
No intervention
Delayed-transient intervention
1/41 (2.4)
1/40 (2.5)
4141 (9.8)
9/40 (22.5)
2/41 (4.9)
6/40 (10)
34/41 (82.9)
24/40’� (60)
Persistent intervention 4/40 (10) 13/40 (32.5) 4/4.0 (10) 19/4()C.d(475)
a Male F344 rats were treated daily with 20 jzg of AFB1. p.o., during weeks 8-12 of age and received either no intervention or delayed-transient or persistent interventions
with 500 ppm oltipraz in the diet as detailed in “Materials and Methods.”
b Only the most advanced hepatic lesion is reported for each rat.
C Significantly different from no intervention (P < 0.05, Fisher’s exact test).d Significantly different from delayed-transient intervention (P < 0.05, Fisher’s exact test).
00I0
C0.ta2
0�
00Ia,0.
0
a,a,
C0
.ea0
0�
0
CsJ
0
Effect of Treatment on Neoplasms. Apart from two earlydeaths due to anesthetic overdose with ether during bloodcollection, the first death in this study occurred during the 26thweek, and the first death with a gross liver tumor was in the
67th week after initiation of aflatoxin dosing. Table 1 presentsthe effects of the oltipraz interventions on the incidence ofAFB1-induced hepatic preneoplastic foci, adenomas, and HCC;however, only the most advanced hepatic lesion is reported foreach rat. Foci were of course present in the livers of rats withadenomas or carcinomas. After 20 months, the 5-week expo-sure to AFB, produced a high incidence of HCC. There was a
significant trend toward more HCCs in untreated animals (83%)relative to the delayed-transient intervention group (60%; Fish-er’s exact test, P 0.028) and persistent intervention group
(48%; Fisher’s exact test, P = 0.001). Almost all of the tumor-bearing animals (32 of 34) in the no intervention group hadmultiple hepatic carcinomas, whereas in the delayed-transient
and persistent intervention groups, significantly lower propor-tions of animals with multiple tumors were observed: 15 of 24
and 10 of 19, respectively. No differences in the histopatho-logical characteristics of the HCCs were observed between the
three groups. However, among the animals with no HCCs, therewere no trends between treatment group and the occurrence ofeither foci or adenomas.
In addition to occurrence, the timing of HCC was signifi-
cantly associated with treatment group. The estimated proportions
free of HCC and free of multiple HCC events over the time fromexposure using the censoring mechanisms described in the “Ma-terials and Methods” section are shown in Fig. 2, A and B,respectively. Comparison of the Kaplan-Meier curves in Fig. 24indicated that the animals in the persistent intervention group had
marginally different HCC-free time than the delayed-transientgroup (P = 0.075)but significantly longer HCC-free time than the
no intervention group (P = 0.002). Log-normal regression modelsestimated that the time to any HCC for the delayed-transient and
persistent groups was 9 and 22% longer than the no interventiongroup, respectively. For the time to multiple HCCs shown in Fig.2B, Kaplan-Meier analyses indicated similar results when com-paring delayed-transient and persistent groups (P = 0.055), butboth the delayed transient and the persistent groups had longertimes free of multiple HCCs than the no intervention group (P <0.001 and P = 0.019, respectively). Log-normal regression models
estimated that the time to multiple HCC lesions for the delayed-transient and persistent groups was 15 and 32% longer than the no
intervention group, respectively.A variety of extrahepatic neoplasms were observed in these
aged rats. The most common extrahepatic neoplasm was MCL.The incidence of MCL was 39% in the no intervention group, and
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Cancer Epldemiologjr, Blomarkers & Prevention 607
500
� 450 . 20 �zg afiatoxin B1/day. p.o.
� 400
� .� 350 .!I�� (1�TT’TN�� 100 . 500ppmolipraz \� 50
< 0 � � I I I I I7 8 9 10 11 12 13 14 15
WEEKS OFAGE
Fig. 3. Mean serum levels of serial aflatoxin-albumin adducts in the no (0),
delayed-transient (V), or persistent (D) dietary intervention groups; error bars,SD. One hundred twenty-three male F344 rats were randomized into threetreatment groups as detailed in “Materials and Methods.” Serum samples werecollected at weekly intervals from all rats during the first 6 weeks of theexperiment and analyzed for content of aflatoxin-albumin adducts by radioim-
mune assay. The solid black bar displays the period of aflatoxin exposure,
whereas the striped bars display the periods of oltipraz administration for thedelayed-transient (upper bar) and persistent (lower bar) intervention groups.
this incidence is typical for the F344 rat (19). The incidence ofMCL decreased to 17.5% (P 0.048) with the delayed-transientintervention with oltipraz and dropped even lower to 12.5% (P =0.010) with the persistent intervention. The incidence of otherextrahepatic neoplasms was low in all groups. No metastases wereobserved to the liver from other organs.
Effect of Oltipraz Interventions on Biomarker Levels.Shown in Fig. 3 is the time course for the formation of afla-
toxin-albumin adducts in the serum of rats. Steady-state levels
of approximately 350 pmol of aflatoxin adducts/mg albuminwere reached within 2-3 weeks of daily dosing with 20 p.g ofAFB1. These levels represent 1-2% of the administered dose of
AFB1 . Levels of aflatoxin-albumin adducts declined slightlyduring the remaining period of AFB� exposure; however, theydropped rapidly once AFB, exposure was discontinued, reflect-ing the short half-life of circulating albumin in the rat. Therewere clear differences among treatment groups in the levels ofbiomarkers, as had been seen in earlier intervention studies with
oltipraz (20). At each collection point during the aflatoxinexposure period, the group receiving the persistent intervention
with oltipraz had significantly (P < 0.0001) lower biomarkerlevels than the control, no intervention group, such that steady-
state levels were reduced by 50% to approximately 175 pmol ofaflatoxin adducts/mg albumin. The group receiving the de-layed-transient intervention with oltipraz had biomarker levelsthat began to decline after the addition of oltipraz into the diet,
such that they were intermediate between the control and per-sistent intervention groups after several weeks and converged
with the levels of the persistent intervention group by the endof AFB1 dosing. AUCs for the aflatoxin-albumin adducts overthe entire aflatoxin exposure period decreased by 20 and 39%for the delayed-transient and persistent oltipraz interventions,
respectively, compared to the no intervention group. Therewere linear trends (P < 0.001) among no, delayed-transient,and persistent intervention groups with respect to peak adductlevels (means: 434, 346, and 248 pmol/mg albumin, respec-
tively) and AUC (means: 1321, 1012, and 740, respectively).
Tracking of Biomarkers. The serum samples that were col-lected weekly from the same animals during the period of expo-
sure provided an opportunity to evaluate the degree to whichbiomarker levels tracked over time. An important observation wasthat there was little, if any, decrease in the correlation as the
distance (lag) between the observations increased. This was con-
firmed in a formal analysis with the damped exponential correla-
ton model that indicated that the 0 parameter was not statisticallysignificant from 0.0 when using either data from all three groups
or when using data from each intervention group separately. Thus,
the correlation between observations can be summarized by using
a single intraclass correlation coefficient y, which was estimated tobe 0.296, and the 95% CI (0.205-0.368) indicated this was sig-nificantly greater than zero. There were no statistical differences in
the estimate of y among the three intervention groups. The mag-niwde of this correlation is difficult to interpret absolutely; com-
paratively, higher correlation implies stronger predictability suchthat animals with high (low) adduct levels will tend to remain high
(low).
Relationship of Biomarker Levels to Group and IndividualCancer Outcomes. Aflatoxin-albumin adduct measurementsmade at single time points and summary measures ofadducts overtime were used as covariates in a logistic regression model, pre-
dicting the incidence of HCCs. The variables showed a range inthe strength of the relationship with carcinoma outcome, with
AUC (P = 0.010) and peak adduct level (P = 0.018) showing thestrongest associations. The parametric survival models fit with
biomarker levels predicting the time to any or multiple HCCsshowed similar trends in the direction and magnitude as with thelogistic regression analysis (e.g., any HCC: AUC, P = 0.009,
peak, P = 0.010). Hence, biomarker levels were significantly
associated with both the occurrence and the timing of HCC.Although statistically significant predictors of cancer mci-
dence, neither AUC nor treatment group explained much of thevariability seen in cancer outcomes. Using an R� measure of
explained variability appropriate for binary outcomes (21), it is
estimated that 5.7% of the variability in cancer outcomes wasexplained by AUC, and 6.5 and 13.9% of the variability in cancer
outcomes was explained by the delayed-transient and persistent
groups relative to the no intervention group, respectively.The relationship of biomarker (AUC) and outcome (HCC)
including observations from all three intervention groups is
graphically depicted in Fig. 4A. It shows that the AUC was
higher in animals developing HCCs than those that did not (P =0.01). Fig. 4B shows the data in Fig. 4A stratified by treatment
group. Previously described features of the oltipraz interven-
tions can be seen in this figure; there is a downward trend ofAUC with each of the interventions, and there is a downward
trend of the proportion of HCCs (depicted by the relative
distribution of white and non-white points) among interventiongroups. These relationships fulfill the requirements for the
intervention group to be a confounder for the AUC-HCC rela-
tionship. The distribution of AUC values for rats with eithermultiple or single HCC events substantially overlaps with those
for HCC-free rats once the data are split by intervention group.Thus, there are no differences in the average AUC for those
animals with no HCC versus any HCC when looking at theobservations in each intervention group separately.
The quantitation of the ability of AUC to predict HCC mci-dence and time-to-HCC after adjusting for the relationship of HCC
and intervention group is described in Table 2. The entries in theleft columns of the table relate to predicting incidence of HCC
using a logistic regression model, and the parameter that measures
the strength of the association of HCC incidence and covariates is
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000Cl) B
S
S Multiple HCCSingle HCC
0 N0HCC
A
S
S
‘p.
1i-�S
1%
9 S�_��!1- 0 #{149}� BB
� “II-
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S
SB
0
No HCC
HCC
No Delayed- PersistentIntervention Transient
608 Aflatoxin-Alhumin Adducts and Liver Cancer
8..-..L0oc%J
#{149}0#{149}0
Cancer Epidemielogy, Biomarkers & Prevention 609
efficacy of a delayed, transient intervention with oltipraz waspredicted based upon the substantive reduction in the hepatic
burden of GST-P positive foci, a presumptive preneoplasticlesion, reported earlier (23). Interestingly, both interventionprotocols engendered significant delays in the development of
single HCCs or the amplification of single to multiple HCCswhen neoplasms occurred.
A significant decrease in hematopoietic neoplasia was also
observed with oltipraz interventions. MCL is a common neo-plasm in the F344 rat that is not associated with exposure toAFBJ (19). Roebuck et al. (22) initially reported that feeding750 ppm ofoltipraz significantly reduced the incidence of MCL
from 53 to 30%. In the current study, the incidence of MCL wasreduced from 39 to 18 and 13%, respectively, by the delayed-
transient and persistent interventions with 500 ppm of oltipraz.Thus, oltipraz appears to block one or more early processes ofMCL development. Intriguingly, Rao et al. (24) have recentlyreported that oltipraz inhibits the formation of another hema-topoietic neoplasm, thymic lymphosarcoma, in rats treatedwith the heterocycic amine 2-amino-l-methyl-6-phenylimi-
dazo[4,5-bjpyridine (PhIP). With multiple reports of oltiprazpreventing hematopoietic tumors and the paucity of approachesfor the prevention of such tumors, oltipraz may present a unique
opportunity for chemoprevention in this setting.A heretofore neglected opportunity in the development and
use of experimental models of cancer chemoprevention is theability to examine the predictive significance of intermediate bi-
omarkers to the cancer process. Such models afford the opportu-nity to modulate risk of cancer while maintaining carcinogen
exposure constant. A particularly promising class of markers arethose that measure biologically effective dose (4). These biomar-
kers reflect the internal dose at the molecular target (or a reason-able surrogate thereof) and are usually measured as DNA-adducts
or protein-adducts. Numerous epidemiological and experimentalstudies have established dose-response associations between cx-
ternal exposures and adduct levels. Correlations have also beenobserved with chemopreventive regimens in parallel cohorts of
animals between the degree of inhibition of tumorigenesis and theextent of reductions in DNA-adduct and/or protein-adduct levels.
Collectively, these correlative linkages between biomarkers andreduced risk of cancer outcomes suggest that adduct biomarkers
might serve: (a) as short-term end points for assessing the efficacyof chemopreventive agents; and (b) as tools for personalized risk
assessments.Albumin is quantitatively the most abundant target for afla-
toxin, and aflatoxin-lysine has been identified as the major adductin rat and human albumin (25). This adduct is formed by the samemetabolic pathway that leads to aflatoxin-DNA adduct formation.Indeed, a constant relationship between levels of aflatoxin binding
to serum albumin and hepatic DNA have been observed in several
studies in experimental animals (26, 27). Levels of aflatoxin-albumin adducts also qualitatively reflect the relative susceptibility
of different species to aflatoxin-induced hepatocarcinogenesis(28). Moreover, several studies have demonstrated that levels ofaflatoxin-albumin adducts can be modulated by a variety of che-mopreventive interventions with oltipraz and its unsubstituted con-gener, l,2-dithiole-3-thione (20, 29). The repetitive measurements
of aflatoxin-albumin adducts performed during this bioassay re-fleet, quantitatively and qualitatively, the adduct patterns deter-mined earlier from serum samples obtained from animals treatedwith identical aflatoxin/oltipraz regimens, but using daily serialsacrifices of animals for measurements of multiple intermediate
end points (20). Independent analytical methodologies were usedfor the aflatoxin-albumin adduct measurements in these two stud-ies. AUC measurements in both studies revealed approximately 20
and 40% reductions in aflatoxin-albumin adduct burdens through-out the AFB1 exposure period with the delayed-transient and
persistent oltipraz interventions, respectively. Strikingly, compa-rable reductions were seen in the final incidence of HCC with thetwo oltipraz interventions in the present study. As a consequence,there was a strong association between biomarker burden (AUC)and carcinoma outcome (P = 0.01). Another notable feature was
the significant intraclass correlation (tracking) between sequentialbiomarker measurements. These studies, therefore, directly con-firm the usefulness of monitoring levels of the aflatoxin-albuminadduct in population-based approaches to evaluate pharmacody-namic action in interventions with oltipraz and similarly acting
agents. Indeed, this biomarker is currently being used to assess theefficacy of oltipraz in humans at high risk for aflatoxin exposureand development of HCC in rural China (30). Measurements ofalbumin adducts appear to be particularly useful because they use
simple, facile immunoassays that can be applied to large numbers
of samples in field studies.However, the apparent strength of the observed association
with biomarker burden and cancer outcome reflects the propor-
tional distribution of cases and non-cases of cancer within the nointervention and persistent intervention arms. Thus, very strong
associations will be seen when protective efficacy is maximizedthrough judicious selection of dose and duration of carcinogen andanticarcinogen (29, 31). As one example, Hecht et a!. (32) haveshown complete segregation of the distribution of 4-(methylnitro-
samino)-l-(3-pyridyl)-l-butanone-derived hemoglobin adducts
under dosing conditions in which 4-(methylnitrosamino)-l-(3-pyr-idyl)-l-butanone alone induced lung tumors in 70% of the rats,whereas an intervention with phenethylisothiocyanate reduced the
tumor incidence to background. In the present study, the twointervention protocols with oltipraz produced roughly equivalent
distributions of HCC cases and controls within each interventionarm and afforded the opportunity to examine the extent to whichthere exists a prospective link between a molecular biomarker ofthe biologically effective dose of aflatoxin (i.e., aflatoxin albuminadduct) and HCC outcome in individual animals. However, in thisstringent setting, the biomarker provided no assistance in discrim-mating between individual rats destined to develop HCC and those
that did not. Given the complex interactive nature of the carcino-genic process, it is perhaps unreasonable to expect a single, earlymarker to predict cancer outcomes. Production of genetic damage
by hepatocarcinogens is not a sine qua non for cancer. For exam-plc, the genotoxic component of 2-acetylaminofluorene has been
recently suggested to play a minor role in its hepatocarcinogenicity(33). Many other factors, including recurrent cytotoxicity, cellproliferation, and nutritional status, can exert strong postinitiation
effects to either enhance or retard tumorigenesis. Sensitive, non-invasive biomarkers or combinations of markers for these pro-
cesses need to be developed and applied. An additional confound-ing factor relates to the strength of the surrogacy of the aflatoxin-
albumin adduct for genotoxicity. Although there appears to begood correspondence between total aflatoxin DNA and protein-
adduct burden following aflatoxin exposure, reflecting the com-mon metabolic origin of these adducts, the relationship betweenDNA adduct burden and the effective mutational target within thegenome may be less precise. The effective burden of genomic
damage is likely to directly influence tumor incidence and multi-plicity. Such correlative degeneracy may reflect the important rolethat DNA sequence context plays in aflatoxin mutagenesis as well
as the impact of differential rates of repair for damage to CrIticaland noncritical target genes (34). Lastly, it should be noted thatAFB1 exposure levels (and hence, biomarker levels) used in this
rodent experiment were considerably higher than found in humanpopulations. Whether stronger relationships between biomarker
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610 Aflatoxin-Albumin Adducts and Liver Cancer
and outcomes might be observed at lower exposure levels remainto be established.
The overall strategy to use chemical-specific biomarkers
as intermediate end points has a wide range of potential, largelyunexplored applications for the refinement of risk quantitation.In the carcinogen testing arena, determinations of these types ofearly markers could accelerate predictions of chronic toxicity
and cancer outcomes well before the traditional 2-3-year timepoints. Furthermore, these mechanistically based biomarkers
might become efficient tools with which to rationally compare
new transgenic animal models with standard animal bioassayspecies. Such applications are needed as the number and diver-sity of chemicals to be tested increase. Similarly, these biomar-kers can be used to assess alterations in risk following chemo-
preventive interventions. In our study, strong concordance isobserved between the reduction in risk of HCC and diminutionof biomarker levels following treatment with oltipraz. Group-wide, population-based changes in biomarker levels are readily
detected as a function of intervention status. However, ourresults also point to strong limitations in the interpretation ofcarcinogen-adduct biomarker measurements. These adducts areunlikely to serve as unilateral measures of personal risk. Al-though changes in relative risk can be readily discerned, these
markers do not appear suitable for absolute prediction of dis-ease outcome. As a consequence, the use of adduct biomarkersin quantitative risk assessment should be pursued cautiously.
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1997;6:603-610. Cancer Epidemiol Biomarkers Prev T W Kensler, S J Gange, P A Egner, et al. of liver cancer.group effects of oltipraz on aflatoxin-albumin adducts and risk Predictive value of molecular dosimetry: individual versus
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