Stochastic Simulation of Hepatic Preneoplastic Foci Development for Four Chlorobenzene Congeners in a Medium-Term Bioassay

Ying C. Ou*, Rory B. Conolly{dagger}, Russell S. Thomas{ddagger}, Daniel L. Gustafson§, Michael E. Long§, Ivan D. Dobrev, Laura S. Chubb, Yihua Xu||, Smadar A. Lapidot, Melvin E. Andersen{dagger} and Raymond S. H. Yang,1

* Preclinical Development, Human Genome Sciences, Inc., Rockville, Maryland 20850; {dagger} CIIT Centers for Health Research, Research Triangle Park, North Carolina 27709; {ddagger} Kalypsos, Inc., La Jolla, California 92037; § School of Pharmacy, University of Colorado Health Science Center, Denver, Colorado 80262; Quantitative and Computational Toxicology Group, Center for Environmental Toxicology and Technology, Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, Colorado 80523; and || Department of Oncology, McArdle Laboratory for Cancer Research, Madison, Wisconsin 53706

Received November 20, 2002; accepted March 3, 2003


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
A combination of experimental and simulation approaches was used to analyze clonal growth of glutathione-S-transferase {pi} (GST-P) enzyme-altered foci during liver carcinogenesis in an initiation-promotion regimen for 1,4-dichlorobenzene (DCB), 1,2,4,5-tetrachlorobenzene (TECB), pentachlorobenzene (PECB), and hexachlorobenzene (HCB). Male Fisher 344 rats, eight weeks of age, were initiated with a single dose (200 mg/kg, ip) of diethylnitrosamine (DEN). Two weeks later, daily dosing of 0.1 mol/kg chlorobenzene was maintained for six weeks. Partial hepatectomy was performed three weeks after initiation. Liver weight, normal hepatocyte division rates, and the number and volume of GST-P positive foci were obtained at 23, 26, 28, 47, and 56 days after initiation. A clonal growth stochastic model separating the initiated cell population into two distinct subtypes (referred to as A and B cells) was successfully used to describe the foci development data for the four chlorobenzenes. The B cells are initiated cells that display a selective growth advantage under conditions that inhibit the growth of initiated A cells or normal hepatocytes. The simulation exercise for the four chlorobenzenes indicates a positive correlation between the estimated net growth rate of B cells during the 2-week regeneration period following partial hepatectomy and final foci volume at the end of the bioassay. This observation is consistent with the sensitivity analysis of model parameters. While TECB, PECB, and HCB all significantly increased foci volume, only HCB increased normal hepatocyte proliferation. Together, these results indicate that examining effects of chemicals on regenerative responses following partial hepatectomy may be a means for understanding the carcinogenicity potential of chlorobenzene compounds.

Key Words: preneoplastic foci; simulation; liver carcinogenesis; clonal growth model; chlorobenzene.


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Chlorobenzenes are significant environmental contaminants manufactured worldwide for both industrial and domestic uses (Morita, 1977Go; Tobin, 1986Go). The chlorobenzene family is comprised of 12 congeners differing by the number and position of chlorine substitution on a single benzene ring. Usage of chlorobenzene compounds ranges from room deodorizer and moth repellent to chemical intermediates in commercial production of pesticide (Hill et al., 1995Go; Morita, 1977Go) and significant human exposure to chlorobenzenes is documented (Carey et al., 1986Go; Peattie et al., 1984Go). The potential carcinogenicity of various chlorobenzene congeners in rodents has been studied. Results from a two-year bioassay performed by the National Toxicology Program (NTP) have shown that 1,4-dichlorobenzene (DCB) is a carcinogen (NTP, 1987Go); monochlorobenzene shows equivocal results (NTP, 1985bGo), and 1,2-dichlorobenzene is negative (NTP, 1985aGo). The carcinogenicity of hexachlorobenzene (HCB) has been demonstrated in rats and mice using the two-year chronic animal bioassay (Cabral and Shubik, 1986Go; Cabral et al., 1996Go). Studies using an initiation-promotion hepatocarcinogenesis protocol also suggest that HCB and pentachlorobenzene (PECB) are liver tumor promoters (Cabral et al., 1996Go; Stewart et al., 1989Go; Thomas et al., 1998bGo). However, carcinogenicity of other chlorobenzene congeners has not been systematically evaluated.

We have used the medium-term bioassay of Ito et al. (1989aGo,bGo) to study the carcinogenicity of chlorobenzenes (Gustafson et al., 1998Go, 2000Go). The Ito assay involves the sequential administration of a potent initiator, diethylnitrosamine (DEN), followed by chemical treatment and mitogenic stimulation of hepatocyte growth via partial hepatectomy. This protocol allows the evaluation of carcinogenic potential within eight weeks by identification of glutathione-S-transferase {pi} (GST-P) positive preneoplastic foci as end point marker lesions. A large number of chemicals have been tested using this protocol. When compared with the two-year chronic bioassay, results from the Ito medium-term bioassay have correctly identified 97% of genotoxic hepatocarcinogens and 86% of the known nongenotoxic hepatococarcinogens (Ogiso et al., 1990Go).

Knowledge on the sequential alteration in growth control and cell dynamics of foci may contribute to an understanding of chemical carcinogenesis. Furthermore, to facilitate interpretation of the Ito assay results from our study (Gustafson et al., 1998Go, 2000Go), we measured time-course development of foci, cell proliferation rates, and chemical tissue concentrations of dosed chemicals during the Ito assay. Molecular pathways potentially involved in the development of GST-P foci were also studied. We showed that both 1,2,4,5,-tetrachlorobenzene (TECB) and PECB treatments resulted in an increased ratio of reduced to oxidized glutathione (GSH:GSSG; Thomas et al., 1998aGo), and for PECB a low incidence of GST-P foci in the centrilobular region was accompanied by increased glutathione reductase and {gamma}-glutamylcysteine synthetase (Thomas et al., 1998aGo). PECB and HCB (Thomas et al., 1998bGo), but not TECB and DCB (Carlson, 1977Go; Chu et al., 1983Go), also appear to stimulate production of porphyrin; in addition, TECB, PECB, and HCB increase protein expression of c-fos, c-jun, CYP 2E1, CYP 2B1/2, and CYP 1A1 (Gustafson et al., 2000Go).

The two-stage Moolgavkar, Venzon, and Knudson (MVK) model (Moolgavkar and Luebeck, 1990Go; Moolgavkar and Venzon, 2000Go) has been proposed as an improvement over several existing models for estimating carcinogenic risks to human health, because it incorporates more biological considerations than previous models, notably cell population kinetics. Several quantitative and statistical methods based on the MVK two-stage framework have been used to analyze foci development data (Conolly and Andersen, 1997Go; Luebeck et al., 1991Go; Portier et al., 1996Go). We adopted a discrete-time numerical approach (Ou et al., 2001Go; Thomas et al., 2000Go) of the clonal growth model (Conolly and Kimbell, 1994Go), similar to that implemented by Cohen et al. (Cohen and Ellwein, 1990Go; Ellwein and Cohen, 1992Go), which does not require the analytical solution implemented in the original MVK model. In this current model, time axis is decomposed into a series of time intervals, where parameters are allowed to change between but not within segments. This approach differs from the MVK model, where differential equations are used with variables of the model changed continuously over time. The simulation model described here may be a useful tool for the analysis of the complex data surrounding the development of focal lesions. In particular, this simulation model allows examination of dynamic changes in foci development under different chemical/biological perturbations.

A simulation model incorporating a two-cell hypothesis was successfully used to analyze 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) dose-response foci data (Conolly and Andersen, 1997Go) and also for PECB and HCB time course foci data in the Ito medium-term bioassay (Ou et al., 2001Go; Thomas et al., 2000Go). Inclusion of two foci populations in the framework of the two-stage model (Fig. 1Go) was based on the negative selection hypothesis of tumor promotion (Andersen et al., 1995Go; Jirtle et al., 1994Go). The initiated cells were partitioned in the simulation model into A and B cell populations, where B cells represent the population of cells with a growth advantage in an otherwise mitoinhibitory or cytotoxic environment to initiated A or normal uninitiated cells. Exposure to chemicals like phenobarbital can result in increased production of growth factors, such as transforming growth factor (TGF)-ß1 to constrain proliferation (mitoinhibition; Jirtle et al., 1994Go). This selective environment may lead to outgrowth of clones that are resistant to mitoinhibition (Jirtle et al., 1994Go).



View larger version (20K):
[in this window]
[in a new window]
 
FIG. 1. A simple two-stage model of carcinogenesis. Within the framework of the two-stage model, the possibility of two initiated populations for adequately describing the kinetics of foci growth was evaluated. The cell division and death rates of normal hepatocytes are denoted by {alpha} and ß (1/day), respectively. The probability of mutation per cell division to A or B initiated cells per day are denoted by µa and µb. The division and death rates of A, B cells are denoted by {alpha}a, ßa, {alpha}b, ßb, respectively.

 
The goal of the current study is threefold. First, we determined whether the previous clonal growth simulation model (Ou et al., 2001Go) for PECB or HCB could also be employed to analyze foci growth kinetics following exposure to two other chlorobenzene congeners, TECB and DCB. Second, we performed a sensitivity analysis on the clonal growth model to gain a better understanding of model behavior. Finally, simulation modeling for four chlorobenzene congeners provided an opportunity for comparing model parameter values and effects of four chlorobenzene congeners on molecular endpoints involved in the development of GST-P foci. To our knowledge, this is the first demonstration of combined experimental and simulation analysis on the time-course foci development for four structurally related compounds.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The method used in the present study consists of two main parts: experimentation and stochastic simulation modeling. Experimental data collection included time course measurements of liver growth, hepatocyte proliferation, and foci development. The following sections describe how we collected the experimental data for TECB and DCB. Similar data collections for PECB and HCB have been reported earlier (Ou et al., 2001Go). Together, these data were used for comparative simulation modeling of all four congeners.

Experimental Data Collection
Chemicals.
Chlorobenzenes were purchased from Aldrich Chemical (Milwaukee, WI). DEN was purchased from Sigma Chemical (St. Louis, MO).

Animals and treatment.
Male F344 rats, 30 days of age, from Harlan Sprague-Dawley (Indianapolis, IN) were acclimated for four weeks before the start of the experimentation. The rats were randomized by weight and divided into three treatment groups (Fig. 2Go). At week 0, animals received a single ip injection of DEN (200 mg/kg) dissolved in 0.9% saline. After two weeks, the rats received daily gavage administration of corn oil or 0.1 mmol/kg TECB or DCB in a corn oil vehicle through the remainder of the eight-week study. At week 3 (Day 21), a partial hepatectomy was performed on all animals. Animals were given food (Harlan Teklad NIH-07 Diet, Madison, WI) and water ad libitum and lighting was set on a 12-h light/dark cycle. On days 23, 26, 28, 47, and 56, at least five animals from each treatment group were sacrificed by aortic exsanguination (Fig. 2Go). Whole livers were removed, tissues were fixed in either 10% neutral-buffered formalin or ice-cold acetone, and embedded in paraffin. Several sections from each liver lobe were taken and then serial sectioning was done at a thickness of 5 µm each. One serial sectioning was used for the BrdU analysis, the other for GST-P foci identification. The studies were conducted in accordance with the National Institutes of Health (NIH) guidelines for the care and use of laboratory animals. Animals were housed in a fully accredited American Association for Accreditation of Laboratory Animal Care (AAALAC) facility.



View larger version (24K):
[in this window]
[in a new window]
 
FIG. 2. Experimental design for the initiation/promotion study. The initiation agent, DEN, was administered ip (200 mg/kg) on Day 0. TECB or DCB was delivered by gavage starting on day 14 at a dose of 0.1 mmol/kg per day, seven days/week. A two-thirds partial hepatectomy was performed on all animals on day 21. Liver tissues were collected 23, 26, 28, 47, 56 days (days 23, 26, 28, 47, 56) following initial DEN dosing.

 
Quantification of GST-P positive foci.
Acetone fixed tissues were used for the immunohistochemical identification of GST-P foci (Thomas et al., 1998aGo). Liver sections were deparaffinized in xylene and rehydrated by passage through an alcohol series. Endogenous peroxidase was quenched in 3% hydrogen peroxide for 10 min. The slides were rinsed with deionized water and placed in phosphate-buffered saline (PBS; pH 7.4, 2.7 mM KCl, 0.14 M NaCl, 1.5 mM KH2PO4, 8.1 mM Na2PO4). A standard avidin/biotin (ABC) protocol (Vector Labs, Burlingame, CA) was followed, and foci were detected with GST-P primary antibody (Binding Site, San Diego, CA). GST-P foci were measured using a Leitz light microscope coupled with the BioQuant image analysis system (Version 4; R & M Biometrics, Nashville, TN). Foci containing more than two cells were recorded. One serial sectioning from each of the four lobes was analyzed. The number and area of foci recorded before conversion to three-dimensional representations is the sum total from all four lobes.

Stereology methods.
Quantitative stereology data for each rat were obtained by using STEREO, a Windows 98/NT program developed at the McArdle Laboratory for Cancer Research, University of Wisconsin (Xu et al., 1998Go). STEREO uses a data file containing tissue information (tissue areas and individual focal transections) as the input to provide quantitative stereology results on a three-dimensional basis. Numbers of foci per cubic cm of liver were calculated according to the method of Saltykov (2000). Calculations were performed at the truncation value of 50.12 microns. Preneoplastic foci were classified according to Saltykov size classes 1 to 11 with a maximal diameter of 63, 79, 100, 125, 154, 199, 251, 316, 398, 501, and 630 microns in each class, respectively. Data files from the same group were combined together first on a two-dimensional level and then three-dimensional data (number of the foci in each size class) were calculated according to the method of Saltykov. The volume fraction of the liver occupied by GST-P foci was computed by the method of Delesse (1848)Go.

Determination of cell division rate.
Osmotic minipumps (Alzet model 2ML1, 10 ml/h; Alza Corporation, Palo Alto, CA), filled with 5-bromo-2'-deoxyuridine (BrdU; 20 mg/ml), were implanted subcutaneously over the dorsal midscapular region. Animals were anesthetized with isofluorane (Anaquest, Madison, WI) and the incision closed using stainless steel wound clips. To avoid saturation of labeling, pumps were implanted one day prior to tissue collection on time points soon after partial hepatectomy (days 23, 26, 28). For other time point collection, pumps were implanted three days prior to the sacrifice. Control animals not hepatectomized were given BrdU for 3 days. Detection of BrdU-labeled cells was performed on formalin-fixed liver sections using standard avidin/biotin (ABC) immunoperoxidase kits (Vector Labs, Burlingame, CA) with primary BrdU antibody (Biogenex Labs, San Ramon, CA) and 3-amino-9-ethylcarbozole (AEC, Biomeda, Foster City, CA). At least 1000 cells per animal and four animals per group were counted. The labeling index (LI) was calculated as the number of cells labeled divided by the total number of cells counted. The cell division rate ({alpha}, /day) was calculated from the LI using Equation 1 below (Moolgavkar and Luebeck, 1992Go) where t is the number of days of exposure to BrdU.


(1)

Stochastic Clonal Growth Modeling
Description of the clonal growth model.
The simulation model used for the current analysis was based on the clonal growth model as previously described (Conolly and Andersen, 1997Go; Conolly and Kimbell, 1994Go; Ou et al., 2001Go). A summary of the basic modeling framework is provided here.

  1. Proliferation of normal hepatocytes. Proliferation of normal hepatocytes is described as a function of cell division ({alpha}; 1/day) and death rate (ß; 1/day) deterministically by,


(2)

where N is the number of normal hepatocytes per cm3, ß represents all modes of cell death including apoptosis and necrosis, and t is simulation time (days) beginning with DEN treatment on day 0.

  1. Mutation to initiated cells. The expected number of initiated cells generated is modeled stochastically as a Poisson process and is linked to the number of hepatocyte cell divisions during each time step {Delta}t by Equation 3. The model assumes that cells act independently and have an equal chance of mutation in every time interval. Since mutations are rare events drawing from roughly an infinite pool, the number of mutations will be approximately Poisson distributed.


(3)

Nm represents the number of normal hepatocytes mutating during the time interval {Delta}t, where t is simulation time (days) beginning with DEN treatment on day 0, µ is the probability of mutation per cell division. A random deviate about Nm denoting the number of mutations during {Delta}t is drawn from a Poisson distribution using the function PODEV (Bratley et al., 2000). Inputs to PODEV are the mean of the Poisson distribution and a pseudorandom number between 0 and 1 generated with the algorithm UNFL (Bratley et al., 2000). The current work describes the first stage of the two-stage model (from normal to initiated cells) for the GST foci data (Fig. 1Go), using the two-cell model hypothesis as described previously (Ou et al., 2001Go; Thomas et al., 2000Go). In this case, the probability of mutation per cell division to A or B initiated cells are denoted by µa, µb. The division and death rates of A and B cells are denoted by {alpha}a, ßa, {alpha}b, and ßb, respectively (Fig. 1Go).

  1. Proliferation of initiated cells. Division of single mutated cells derived directly from normal cells may give rise to clones of initiated cells (preneoplastic foci). The growth of initiated cells is described by a multinomial distribution, a generalization of the binomial distribution for two category (binary) outcomes. We assumed that the three possible events of initiated cells during each time step-cell division, cell death, or no change, are independent of each other. Thus, the probability distribution of the sums in each event is given by the multinomial distribution. The program keeps track of each clone over time allowing description of average clone size (cells/clone) and number (clones/cm3). The simulation results can then be compared with three-dimensional foci volume (the percent volume of liver occupied by the foci) and foci number data converted from two-dimensional focal transection data using the STEREO program as described above (Xu et al., 1998Go). For comparing the simulation output with the foci data collected, only clones larger than two cells in size were considered detectable.

Throughout the simulation, the model keeps track of the total hepatocyte number in the liver. Simulated liver weight is calculated on the basis of total hepatocyte number divided by the corresponding hepatocyte density (number of hepatocytes/unit volume). Partial hepatectomy is described as an instantaneous decrease in liver weight and cell number.

Modeling strategies.
Model parameters were obtained in a stepwise manner as described in our previous report (Ou et al., 2001Go). Briefly, the same piecewise constant implementation was used in six time intervals (Days 1–7, 7–14, 14–21, 21–28, 28–35, 35–120). Division and death rates of normal hepatocytes at the various time points were obtained based on the study of Kato et al.(1993)Go and the present study. The probability of mutation of normal hepatocytes and the growth characteristics (division and death rates) of initiated cells are not known, and have to be estimated based on fitting of the model to experimental data. Parameterization of mutation rates for the different time intervals was based on the time course of DEN-induced DNA adduct levels (Dragan et al., 1994Go) and consistency of modeling outputs to the observed total foci number. Thus, the highest number of detectable foci in any of the treatment groups (DEN, DEN + TECB, and DEN + DCB groups) in any given time point was used as an approximation of the number of mutated cells available for subsequent expansion of initiated cells. We have previously shown that by visual inspection, model parameters assuming one population of initiated cells with homogeneous growth characteristics could not be used to fit the time-course foci development of DEN controls with partial hepatectomy (Ou et al., 2001Go). Time-course foci data could only be adequately described by partitioning GST-P foci into two populations of focal lesions (the two-cell hypothesis). This observation was further supported by the published report showing that DEN treatment alone creates heterogeneity of initiated cells, and resistant cells (referred to as B cells here) can account for 5–23% of total GST-P foci (Yusuf et al., 1999Go). The two-cell hypothesis (i.e., A and B cells) assuming two populations of focal lesions was therefore used as a basis for the present modeling exercise. The percentage of resistance clones (B cells) following DEN initiation is in a range of 5–23% (Yusuf et al., 1999Go), thus the mutation rate to initiated A cells was confined to at least three times higher than that to initiated B cells. Division and death rates of initiated cells were estimated based on consistency with several published data sets on the time-course appearance of GST-P foci following DEN treatment (Jang et al., 1993Go; Kato et al., 1993Go; Satoh et al., 1989Go; Tiwawech et al., 1991Go) and time course changes of foci number and volume obtained in the present study. In addition, the cell division rate of initiated hepatocytes are parameterized to conform to the experimental observation showing that after a sufficiently large dose of DEN, the cell division rate of initiated hepatocytes declines over time (Travis et al., 1991Go), whereas the death rate of initiated cells increases with time (Rotstein et al., 1986Go). The foci growth parameters were obtained by iterative optimization to the time course changes of foci number and volume simultaneously using the two-cell hypothesis. Both cell division and death rates of A and B initiated cells were parameterized to ensure that these values were within biologically plausible intervals for putative preneoplastic cells (Rotstein et al., 1984Go; Travis et al., 1991Go).

Parameter estimation.
Parameter estimation was conducted by iterative fitting of time-course foci number and volume data simultaneously. The parameter values were identified in a two-step procedure. First, an "initial value" was obtained by visual observation of the model fit to the data. During this iterative process, an understanding of the impact of parameter changes on the model outputs was gained. Next, multiple neighboring values around the "initial values" were picked and were subjected to comparison of their weighted sum of square values calculated from the mean of 100 runs for both the foci volume and foci number data. The optimized parameters were identified with the sum of least squares.

Modeling analysis of DEN + TECB and DEN + DCB data.
The growth parameters defined in the DEN group were used to evaluate experiments involving the administration of TECB and DCB. Thus, we were interested in identifying the necessary parameter changes from those of DEN controls in order to describe time-course foci development of the DEN + TECB and DEN + DCB group. Modeling exercises were performed assuming that TECB and DCB act as mutagens, as agents affecting cell proliferation, or as both. Because we did not measure cell division and death rates in the GST-P foci, effects of TECB or DCB on foci growth could be assumed as either increasing cell division rates, decreasing cell death rates, or a combination of both. Parameter estimation based on fitting the model to the data could not distinguish whether TECB or DCB affect the cell division or death pathway of initiated cells. Thus, the current model only permitted accurate estimation on how TECB or DCB treatments affect the net growth rate (cell division rate minus death rate) of A and B cells (i.e., an identifiability dilemma). For convenience, we present the data with TECB or DCB affecting only the cell death rate of initiated cells. Parameter estimation was conducted by iterative fitting of time-course foci number and volume data in the DEN + DCB or DEN + TECB groups. The parameter values were again identified in a two-step procedure as described above.

Sensitivity analysis.
Sensitivity analysis determines the changes in a measurement, m, given small changes in a given input parameter, p, i.e., the partial derivative of the measurement with respect to the parameter, {partial}m/{partial}p. In the current analysis, we used the slope around the parameter for an approximation of the sensitivity coefficient, {partial}m/{partial}p {approx} {Delta}m/{Delta}p (Beck and Arnold, 1977Go). Furthermore, we calculated the normalized sensitivity coefficient (NSC) with the following formula.


(4)

The larger the sensitivity coefficient, the larger the impact on the measurement given a change in the parameter. The current analysis was to identify the sensitive parameters for the final total foci volume at the end of the eight-week medium-term bioassay. A 2.5, 5, 10, 20, and 30% change in each model parameters was tested for 160, 80, 20, 10, or 5 runs, respectively. A smaller number of runs (e.g., 5 and 10 runs) were also tested in this study, although in general smaller changes in model parameters (e.g., 2.5 or 5% changes) required more runs to observe consistent results due to the stochastic nature of simulation.

Software and hardware.
The simulation model was written in Advanced Continuous Simulation Language (ACSL; The Aegis Technology Group, Inc., Huntsville, AL), and run on a 300 mHz Intel Pentium II (Gateway 2000, Sioux City, SD). The least square optimization method and sensitivity analysis were implemented in the ACSL Math (ACSL; The AEgis Technology Group, Inc., Huntsville, AL) environment. A listing of the simulation program and sensitivity analysis/optimization routine is available from one of the authors (rconolly{at}ciit.org). STEREO, a software application for converting two-dimensional tissue transection data to quantitative stereology results on a three-dimensional basis is available from Dr. Xu (xu{at}oncology.wisc.edu).

Statistical analysis.
Time-dependent and treatment-dependent changes in liver weight, cell proliferation, foci volume, foci number, and hepatocyte density were analyzed using a mixed-effects ANOVA model, followed by post-hoc Tukey-Kramer multiple comparison tests at p = 0.05. The analysis was programmed using the SAS system (SAS Institute Inc., Cary, NC).


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Experimental Data Collection
Body weight and general histopathology.
The effects of TECB or DCB on final body and liver weight are summarized in Table 1Go. Following DEN initiation, exposure to TECB, at 0.1 mmol/kg per day for six weeks led to a significant increase in liver weight (expressed as % total body weight) at the end of the study compared to the DEN controls (p < 0.05). Exposure to DCB at the same dose and duration did not cause a significant increase in liver weight. Treatment with DCB or TECB alone without DEN initiation resulted in similar liver weight changes as observed for DEN + DCB and DEN + TECB groups (data not shown). Neither TECB nor DCB (with DEN initiation) caused a significant deviation in total body weight by the end of the eight-week study (Table 1Go). The degree of liver weight change in the DEN + TECB group was much less compared to that observed previously in the DEN + HCB group, but more than those in the DEN + PECB group (Ou et al., 2001Go). Histopathological examination of hematoxylin & eosin (H&E)-stained liver sections of TECB-treated animals showed hepatocellular hypertrophy indicated by enlargement in cytoplasm, and nucleus in the centrilobular region. Previously, hypertrophy was also observed in the DEN + PECB group and even more pronounced in the DEN + HCB group (Ou et al., 2001Go), while no such abnormality was observed in tissues of DCB-treated animals.


View this table:
[in this window]
[in a new window]
 
TABLE 1 Comparison of 1,2,4,5-Tetrachlorobenzene or 1,4-Dichlorobenzene on Body and Liver Weight of F344 Rats Subjected to an Initiation/Promotion Protocol
 
Time-course changes in cell division rates.
Time-dependent changes in cell division rates were determined by the BrdU-labeling index among DEN controls, DEN + TECB, and DEN + DCB groups (Table 2Go). As shown in Table 2Go, there were approximately 60-fold increases in the division rate of normal hepatocytes following partial hepatectomy in all treatment groups. Neither DCB nor TECB treatments caused any significant change in cell division rates compared to DEN controls at any of the time points examined. The slowest division rate of normal hepatocytes determined in this study was 0.0016 ± 0.0004/day (Table 2Go). The basal division rate of hepatocytes was thus approximately 0.0015/day as reported previously (Ou et al., 2001Go), and was used in our simulation model (Table 3Go).


View this table:
[in this window]
[in a new window]
 
TABLE 2 Determination of Cell Proliferation Rate of Normal Hepatocytes in Liver Tissues of F344 Rats Subjected to an Initiation/Promotion Protocol
 

View this table:
[in this window]
[in a new window]
 
TABLE 3 Model Parameters Used in the Two-Stage Clonal Growth Model
 
Time-course development of GST-P foci.
Preneoplastic focal lesions in liver were identified by positive staining for GST-P. A regional (centrilobular), nonfocal GST-P staining pattern outside of the putative preneoplastic live foci was also observed in PECB- or HCB-treated animals (Thomas et al., 1998aGo, bGo), but not in TECB or DCB-treated animals. Tissue areas and individual focal transections from each tissue section were recorded and two-dimensional focal transection data were then converted to three-dimensional foci number and volume as described above. As shown in Figure 3Go, a time-dependent reduction in foci number was accompanied by a concurrent increase in foci volume from day 23 to day 56 in the DEN + TECB, DEN + DCB, and DEN control groups. At the final time point (day 56), DEN + TECB treatment group demonstrated a marked increase in the percentage of total foci volume in the liver (0.369 ± 0.038%) compared to DEN controls 0.261 ± 0.024 %). At the same time, the percentage of total foci volume in the DEN + DCB group (0.241 ± 0.06%) did not differ significantly from the DEN control group. The total foci number in the DEN + TECB (2123 ± 241) and DEN + DCB group (2394 ± 190) at the final time point (day 56) is significantly increased compared to DEN controls (1599 ± 126; p < 0.05).



View larger version (21K):
[in this window]
[in a new window]
 
FIG. 3. Time-dependent changes in GST-P foci number and volume in animals subjected to an initiation/promotion protocol using DCB or TECB as the promoting agents. Animals were administered a dose of DEN on Day 0, TECB or DCB was delivered by gavage starting on Day 14 at dose of 0.1 mmol/kg per day, seven days/week. A two-thirds partial hepatectomy was performed on all animals on day 21. Liver tissues were collected 23, 26, 28, 47, 56 days (days 23, 26, 28, 47, 56) following initial DEN dosing. Data are expressed as mean ± SD of at least four animals at each time point *Significantly different from DEN control group (p < 0.05).

 
Clonal Growth Simulation Modeling
Model parameters for proliferation of normal hepatocytes.
Model parameters for normal hepatocytes prior to DCB or TECB treatment on day 14 (Fig. 4aGo) were obtained using published values (Kato et al., 1993Go). A single high dose of DEN resulted in the loss of cells via cytotoxicity and a corresponding decrease in liver weight (Fig. 4bGo), which was followed by compensatory proliferation responses (Fig. 4aGo). Because there were no observed effects of DCB or TECB on the cell division rate of normal hepatocytes (Table 2Go), DEN + DCB and DEN + TECB groups were modeled as that of DEN controls from day 14 to the end of the modeling period (Fig. 4aGo). By quantitative morphometric analysis, we observed a reduced hepatocyte density (number of hepatocytes per unit volume) attributable to increases in both cytoplasmic and nucleus volumes in PECB, HCB, and TECB treated animals (Ou et al., 2001Go). Changes in cell division rates and hepatocyte density were incorporated into the simulation model. The model describes the increased liver weight in the DEN + TECB group (Fig. 4bGo) and in the DEN + PECB and DEN + PCB groups as shown previously (Ou et al., 2001Go).



View larger version (25K):
[in this window]
[in a new window]
 
FIG. 4. Comparison of the clonal model outputs with experimental measurements of cell division rates of normal hepatocytes and liver weights. Time-dependent changes in cell division rates of normal hepatocytes and liver weights were measured in animals subjected to an initiation/promotion protocol using DEN as an initiator and TECB or DCB as promoting agents. Solid lines represent simulation results from the clonal growth model. (a) Solid circles shown are hepatocyte division rates determined using the BrdU-labeling index as described in the Methods. Data points prior to Day 20 in all three groups are published data from Kato et al., 1993Go. (b) Solid circles shown are liver weight data expressed as mean ± SD of at least four animals at each time point.

 
Model parameters for the DEN controls.
The model was first parameterized to describe foci development in animals receiving DEN initiation but without partial hepatectomy (Fig. 5aGo, DEN panel, solid gray line). The total number of foci rapidly increased to its peak level around day 14, and with a rapid decline afterwards (Fig. 5aGo). This profile is not surprising since only foci clones larger than two cells in size were recorded in the simulation model. In comparison to foci data of DEN controls with partial hepatectomy, both foci volume and total foci number were significantly elevated soon after partial hepatectomy (Fig. 5aGo, DEN panel). Stochastic modeling generates a series of similar outcomes given the same model parameters, as illustrated by the time course changes in foci volume for five runs (Fig. 5aGo). Model parameters for describing the concurrent DEN control data for PECB and HCB studies (Ou et al., 2001Go) were used to see whether they could also describe concurrent DEN control data in the TECB and DCB studies. To describe the foci data of the concurrent DEN controls for the DCB and TECB study (Fig. 5bGo), no modifications to the division or death rates of A and B cells ({alpha}a, ßa, {alpha}b, ßb) were needed and only modifications of the mutation rate to A or B cells were necessary (Table 3Go). The mutation rate to A and B cells immediately following DEN treatment (days 0–7) is 9.9 x 10-5 and 1.5 x 10-5, respectively, compared to 12.1 x 10-5 and 3.3 x 10-5 as estimated for the previous study (Ou et al., 2001Go). The mutation rate to initiated A cells is approximately sixfold higher than that to initiated B cells, which is consistent with the reported percentage (5–23%) of resistance clones (B cells) following DEN initiation (% resistant phenotype is dependent on the promoter tested; Yusuf et al., 1999Go). Except for the period of partial hepatectomy, the cell division rate of initiated cells is highest immediately following DEN treatment (0.42/day), and gradually declines to 0.002–0.006/day (Table 3Go). The growth characteristics of A and B cells do not differ significantly from each other prior to the administration of chlorobenzenes on day 14. For both A and B cells, it appears that cell death mechanisms are activated in response to the excess growth of initiated cells (i.e., death rates of A and B cells are high from days 28–35 and days 35–120, Table 3Go). The death rate of initiated A cells tends to be higher than that for initiated B cells. The death rate of initiated A cells is 0.08/day on days 14–21 compared to 0.002/day for B cells for the same time interval. The net growth rate of both A and B cells is significantly elevated immediately following partial hepatectomy (days 21–28, Table 3Go).



View larger version (20K):
[in this window]
[in a new window]
 
FIG. 5. Comparison of the clonal model outputs with experimental measurements of foci growth for four chlorobenzene congeners. Time-dependent changes in foci growth were measured in animals subjected to an initiation/promotion protocol using DEN as an initiator and DCB, TECB, PECB, or HCB as a promoting agent. To illustrate the stochastic nature of foci growth, solid black lines show results from five runs of simulation. Solid circles shown are experimental data expressed as mean ± SD of at least four animals at each time point. (a) Results from PECB or HCB and the concurrent DEN control. For comparison, simulation results for the DEN controls without partial hepatectomy (lower solid gray lines, including experimental data of Jang et al. (ref. 43; triangle symbols) are shown along with those of the DEN controls. (b) Results from TECB or DCB and the concurrent DEN controls.

 
Modeling analysis of DEN + TECB and DEN + DCB data.
Since the time course of DCB-induced foci number and volume did not differ significantly from those for DEN controls, the parameters for the DEN controls were used to describe the data for the DEN + DCB group (Table 3Go, Fig. 5bGo). As for the DEN + TECB group, we first assumed that TECB was a mutagen. With this assumption, the simulation model did not yield satisfactory fit to the time-course dynamic changes of foci number and foci volume simultaneously. However, when incorporating the negative selection hypothesis of tumor promotion (two-cell hypothesis), the model effectively described experimental data in the DEN + TECB group (Fig. 5bGo). The model describes the first increase in foci volume as a result of TECB treatment during the two weeks following partial hepatectomy. Peak formation of foci was observed around day 35, regardless of continuous treatment with TECB (Fig. 5bGo). The increase in foci number during partial hepatectomy regeneration is followed by a rapid decline (Fig. 5bGo). These time-course changes were similar to those of the DEN + PECB and DEN + HCB group, except that relative increases in the total foci volume (%) above the concurrent DEN control following partial hepatectomy were much larger in the DEN + PECB and DEN + HCB group than those of the DEN + TECB group (Fig. 5Go). The parameters for modeling foci growth of the DEN + TECB group are summarized in Table 3Go. The assumption of two initiated cell populations (A and B cells) effectively describes the rapid increase in foci volume, with a concomitant decline of foci number associated with DEN controls, DEN + DCB, and DEN + TECB treatments. The effect of TECB on foci volume expansion is consistent with the increase in net growth rate of B cells following partial hepatectomy (day 28–35 and day 35–120, Table 4Go). Despite continuous treatments of TECB from day 14 to day 56, the net growth rate of B cells is gradually reduced between days 21–28 and 35–120.


View this table:
[in this window]
[in a new window]
 
TABLE 4 Summary of Molecular Indices and Simulation Model Parameters for Four Chlorobenzene Congeners
 
Sensitivity analysis of model.
A summary of the sensitivity analysis is presented in Figure 6Go with the normalized sensitivity coefficient for each model parameter calculated. As shown in Figure 6Go, as few as five runs were able to identify sensitive parameters similar to those derived from larger numbers of runs (e.g., 160 runs). The most sensitive parameter for determining the final total foci volume at the end of the eight-week medium-term bioassay is {alpha}b(21–28), the division rate of B cells, immediately following partial hepatectomy (Fig. 6Go). This model parameter, {alpha}b(21–28) has a mean normalized sensitivity coefficient of about 2.5, implying that a 5% change in parameter values could result in a 12.5% increase in total foci volume. The mutation rate to B cells b) is also among the most sensitive parameters of the model (Fig. 7Go). Other sensitive determinants of total foci volume are {alpha}b(28–35) and ßb(28–35), the division and death rates of B cells one week following partial hepatectomy (days 28–35). A negative sensitivity coefficient, in the case of ßb(28–35), implies that a decrease in the parameter value would result in an increase in the final measurement. Examining time course progression in foci growth as stipulated in the simulation model provided another means for identifying critical determinants of final total foci volume. For example, the simulation model described distinct time courses for numbers of A and B foci in the DEN + TECB group (Fig. 7Go). For the initial stage up to day 40, there is a significantly higher number of foci, which are comprised mostly of A cell foci. As time progresses, total foci number is diminished. B cells with a significant growth advantage following partial hepatectomy continue to thrive and eventually become the major foci type around day 60 (Fig. 7Go). Since these putative B cells are ultimately the dominant determinant for final foci volume, it is not surprising to find that the mutation rate to B cells b) is one of the most sensitive parameters of the model (Fig. 6Go).



View larger version (18K):
[in this window]
[in a new window]
 
FIG. 6. Sensitivity analysis of model parameters for total foci volume. Normalized sensitivity coefficients for total foci volume at the end of 8-week medium-term bioassay were calculated based on 5, 20, or 30% changes in the parameter. A 20% change in parameter calculated based on the result of five runs gave similar changes with 5% changes in parameters for 160 runs. The sensitive parameters are ßb(21–28), ßb(28–35), {alpha}b (28–35), and µb(1–7).

 


View larger version (15K):
[in this window]
[in a new window]
 
FIG. 7. Progression of A and B initiated clones in the model simulation of foci development using TECB as a promoting agent. Model predictions of A, B and total foci number during the course of DEN initiation/TECB promotion treatment. Partial hepatectomy was performed on day 21.

 
Comparison of four chlorobenzene congeners.
The implementation of clonal growth modeling for four chlorobenzene congeners allowed us to compare their estimated parameter values. We have previously shown marked differential foci size distribution between the DEN + PECB and DEN + HCB groups (Ou et al., 2001Go). On the final day of the Ito assay (day 56), the DEN + HCB group consists of mainly large foci, while the DEN + PECB group contains foci in all size classes (Ou et al., 2001Go). In order to describe the differential progression profiles of total foci volume and foci number for the four chlorobenzenes, different sets of parameters were necessary (Table 4Go). The striking similarity observed for all congeners is that despite continuous treatment from day 14 to 56, the net growth rate of B cells gradually diminishes between days 21 to 120 (Table 4Go). This analysis is consistent with observation in focal hepatocytes during administration of other chemicals such as TCDD (Moolgavkar et al., 1996Go). Molecular endpoints contributing to foci development were examined on the last time point (day 56) of the Ito assay for the four chlorobenzenes (Table 4Go) and revealed that the estimated net growth rate of B cells during days 28–35 ({alpha}b – ßb (28–35)) was the only parameter that showed correlation with cell and molecular indices. Induction of CYP2B1/2, CYP1A2, c-fos, enlarged liver, and histological changes are correlated with an increasing {alpha}b – ßb(28–35). Furthermore, larger {alpha}b ßb(28–35) values are also associated with higher total GST-P foci volume. Interestingly, the sensitivity analysis indicated that the model parameter, {alpha}b – ßb(28–35) is among the most sensitive parameters for determining final total foci volume and the normalized sensitivity coeffecients of ßb(28–35) and {alpha}b(28–35) are approximately -0.5 and 1, respectively (Fig. 7Go). Thus, although the {alpha}b ßb (28–35) parameter value for PECB is only 30% higher than that for TECB, this increase could contribute to a significantly (30 to 45%) elevated final foci volume. The effects of different chlorobenzenes on normal hepatocyte proliferation do not correlate with final foci growth and {alpha}b – ßb(28–35). In addition, altered GSH:GSSG status, or induction of CYP2E1 and porphyria do not appear to be correlated with the {alpha}b ßb(28–35) parameter.


    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
We previously implemented a stochastic simulation model for describing foci development following exposure to PECB or HCB in a medium-term bioassay (Ou et al., 2001Go). In the present work, we describe an independent set of TECB and DCB foci data with the same simulation model. The model construction, incorporating two initiated cell populations (referred to as A and B cells), effectively simulated results from all four chlorobenzene congeners. Comparison of simulation parameters and molecular endpoints of the four congeners showed correlation between the identified model parameter governing the net growth rate of B cells during the two-week regenerative period following partial hepatectomy with induction of c-fos, CYP2B1/2, and CYP1A2 (Gustafson et al., 2000Go), enlarged liver, and total foci volume. These findings are consistent with the sensitivity analysis indicating that the net growth rate of B cells during the two-week regenerative period is a sensitive model parameter for determining final total foci volume.

Liver Regeneration and Carcinogenicity
Our quantitative analysis underscores the importance of assessing effects of chemicals on liver regeneration for understanding the carcinogenic potential of these compounds. We showed that larger increases in total foci volume caused by PECB or HCB were consistently accompanied by larger net growth rate of B cells from days 28 to 35, {alpha}b – ßb(28–35) when compared to TECB or DCB. A positive correlation between increased foci volume and {alpha}b – ßb(28–35) may suggest a mechanism for chemicals to interfere with cellular mechanisms governing hepatocytes returning to baseline growth rates after stimulation of cell proliferation (e.g., 28–35 days). This quantitative analysis is consistent with several reports showing that focal hepatocytes, unlike hepatocytes in the surrounding liver, often show a failure to return to baseline growth rates after stimulation of cell proliferation by partial hepatectomy (Rotstein et al., 1986Go; Tiwawech et al., 1991Go). Furthermore, molecules involved in maintaining optimal liver mass after partial hepatectomy are shown to overlap extensively with those whose perturbations are linked to carcinogenic events. These include a large number of early response genes such as c-myc, c-fos, and c-jun, those involved in the priming of quiescent hepatocytes, such as injury-related cytokines, TNF and IL-6, and the subsequent activation of NF-kappa B, AP-1, and STAT3 transcription factors (Fausto and Webber, 1994Go). Once primed, hepatocytes respond to growth factors such as hepatocyte growth factor (HGF) and TGF-{alpha} to increase DNA synthesis (Fausto and Webber, 1994Go). An important mechanistic determinant of liver regeneration after partial hepatectomy is the release of growth suppressing molecules such as TGF-ß to slow down or stop the G1/S transition (Fausto and Webber, 1994Go). Dysregulation of TGF-ß, and other growth regulatory molecules such as TGF-{alpha} and c-myc, c-fos are often found in chemically induced preneoplastic foci and tumors (Pitot, 1996Go). Thus, TGF-ß could potentially represent a candidate molecular target underlying the failure of return to basal growth rates after partial hepatectomy in the presence of promoter treatment. The marked similarity between molecules associated with hepatic regeneration and carcinogenesis suggests that examining interactive effects of chemicals on the window of regeneration following partial hepatectomy may be important to understanding mechanisms of carcinogenesis.

Correlation between Clonal Growth Model Parameters and Other Molecular Endpoints
Our combined experimental and modeling work with four chlorobenzene congeners under equimolar dose levels indicate that the ability of a chemical to induce CYP2B1/2, CYP1A2, is in agreement with concurrent effects on B cell net growth rate from day 28 to 35 ({alpha}b – ßb(28–35)) and increases in foci volume. Theses results indicate that the effect of chlorobenzene administration on the net growth rate of initiated B cell population during the regeneration period may be related to the activation of one or more transcription pathways, possibly driven by two ligand activated transcription factors, the aryl-hydrocarbon (Ah) receptor and the putative phenobarbital-type response receptor. For example, induction of CYP1A1, an Ah receptor-mediated response is used as an indicator of TCDD-induced changes in growth kinetics (Conolly and Andersen, 1997Go). Induction of CYP2B1/2 and hypertrophy are observed following exposure to phenobarbital (Staubli et al., 1969Go) and the comparative potency of barbiturate-type tumor promoters is correlated with their potency as inducers of CYP2B1/2 (Nims et al., 1987Go). Enlarged livers observed following the chlorobenzene treatments in this study are a result of hypertrophy, and are partly due to increased cytochrome P450 content and enlarged endoplasmic reticulum of hepatocytes (Staubli et al., 1969Go). Our comparative assessment of four chlorobenzene congeners provides an approach to the identification of molecular candidates that may serves as effective indicators for estimation of foci growth. However, agents with different mechanisms of action are likely to promote distinct and overlapping subsets of initiated hepatocytes (Dragan and Pitot, 1992Go), probably in a region-specific pattern within the liver (Chen et al., 1995Go; Thomas et al., 1998aGo). Therefore, identification of a common subset of molecular markers for predicting final foci growth will depend on collection of time and region dependent mechanistic information for many other compounds.

Comparison of the Current Model with Previously Reported Models
Key differences between our simulation model and previously reported models should be noted here. The current stochastic model simulates three-dimensional foci data, whereas two-dimensional data is implemented in the work of Moolgavkar and others (Luebeck et al., 1991Go; Portier et al., 1996Go). Proliferation of normal hepatocytes is described deterministically in the current model, whereas a stochastic mode is used in previous implementations (Luebeck et al., 1991Go; Portier et al., 1996Go). The simplification of our model speeds computation, and provides results equivalent to a fully stochastic calculation. The current approach also assumes that with a much larger sampling of normal hepatocytes compared to the number of mutational events, the variability in the proliferation of normal hepatocytes is not a significant factor in the prediction of final foci volume. This assumption was confirmed by our sensitivity analysis, showing that the primary determinants of final foci volume reside in the mutation rate to initiated cells and growth parameters of initiated cells. The current simulation model also uses subjective means (such as visual inspection) rather than objective means of parameter estimation methods (in most cases maximal likelihood estimation) (Luebeck et al., 1991Go; Portier et al., 1996Go). While subjective means to approximate the mean behavior might be sufficient for delivering an understanding of underlying biological processes, development of rigorous parameter estimation methods would extend the acceptance of the current simulation model for carcinogenesis studies (Luebeck et al., 1991Go; Portier et al., 1996Go; Sherman and Portier, 1998Go). One of our attempts in the current work was to begin to address these issues. By performing sensitivity analysis, we are able to identify key model parameters that should make future implementation of formal optimization methods computationally feasible.

Experimental Challenge to Verify the Model Hypothesis
The success of the two-cell hypothesis for describing the current data sets further strengthened previous model predictions (Ou et al., 2001Go) on the heterogeneity of cell kinetics among initiated cells. In particular, the need to implement the two-cell hypothesis for describing the time-course data of DEN controls in the absence of further treatments is consistent with the experimental observations that initiated cells with resistant phenotypes (such as B cells) can arise early during carcinogenesis (Yusuf et al., 1999Go). The need for two types of foci is not surprising given the known phenotypic diversity of altered hepatic foci (Dragan and Pitot, 1992Go). The challenge remains as to verify the model hypothesis experimentally. Experimental work to measure cell division and death rate of initiated foci could be a starting point, however, regional differences in foci formation, cell division rate, and enzyme induction are known to occur following chemical exposure in the liver (Chen et al., 1995Go). Effects of chemicals on initiated cell population may be inferred from their effects on normal hepatocytes. While TECB, PECB, and HCB all induced significant increase in foci volume, only HCB had effects on the cell division rate of normal hepatocytes. Increased cell proliferation in normal tissues may not necessarily represent a stimulus for the growth of preneoplastic foci, and agents affecting apoptosis could also contribute to the growth of preneoplastic foci. Repeated administration of a mitogen 3,3',5-triiodo-L-thyronine results in an enhanced proliferation of normal rat liver, but leads to a reduction in the number of GST-P lesions with no increase in the size of the remaining ones (Ledda-Columbano et al., 1999Go). Thus, effects of chemicals on normal hepatocytes are not necessarily correlated with their effects on the initiated cell population. Measuring cell division and death rates of enzyme-altered foci along with concurrent analysis of global protein and gene expression in the foci may help identify candidate marker genes for the resistant initiated clones (B cell phenotype). Possible experimental means to distinguish A and B cells may include immunocytochemistry analysis for both GST-P and other markers such as TGF-ß and mannose 6-phosphate/insulin-like growth factor II receptor (Mills et al., 1998Go). The resistant initiated cells (B cells) are likely to stain positive for GST-P, but with reduced TGF-ß receptor levels (Mills et al., 1998Go).

The successful application of the existing model parameters (Ou et al., 2001Go) for analyzing new chemical data sets in an independent experiment indicates the versatility of the current model, and that the current model might also be used to analyze data for other chemical compounds in medium-term bioassay. The present model may also comprise a flexible platform for incorporation of kinetic data from characterized biochemical pathways and interactions as well as genome and proteome expression profiling associated with development of carcinogenesis.


    ACKNOWLEDGMENTS
 
The authors want to thank Dr. Peter Brockwell at Dept. of Statistics, Colorado State University, for statistical consultations on the optimization implementation of the current model. Many colleagues at the Center for Environmental Toxicology and Technology participated in these experimental studies, in particular, Drs. Wendy Pott, Charlie Dean, and Mr. Lixin Feng. Their help is gratefully acknowledged. This work was supported in part by a research contract from the Air Force Office of Scientific Research (F49620-94-1-0304), an NIEHS Superfund Basic Research Program Project Grant (P42 ES05949), and a cooperative agreement (U61/ATU881475) from the Agency for Toxic Substance and Disease Registry.


    NOTES
 
1 To whom all correspondence should be addressed at the Department of Environmental and Radiological Health Sciences, Colorado State University, CETT, Foothills Campus, Fort Collins, CO 80523. Fax: (970) 491-8304. E-mail:raymond.yang{at}colostate.edu. Back


    REFERENCES
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Andersen, M. E., Mills, J. J., Jirtle, R. L., and Greenlee, W. F. (1995). Negative selection in hepatic tumor promotion in relation to cancer risk assessment. Toxicology 102, 223–237.[CrossRef][ISI][Medline]

Beck, J. V., and Arnold, K. J. (1977). Parameter estimation in engineering and science. Wiley and Sons, New York.

Bratley, P., Fox, B. L., and Schroeder, P. (1996). A Guide to Simulation. 2nd ed. Springer-Verlag, New York.

Cabral, J. R., and Shubik, P. (1986). Carcinogenic activity of hexachlorobenzene in mice and hamsters. IARC Sci. Publ. 77, 411–416.[Medline]

Cabral, R., Hoshiya, T., Hakoi, K., Hasegawa, R., and Ito, N. (1996). Medium-term bioassay for the hepatocarcinogenicity of hexachlorobenzene. Cancer Lett. 100, 223–226.[CrossRef][ISI][Medline]

Carey, A. E., Dixon, T. E., and Yang, H. S. (1986). Environmental exposure to hexachlorobenzene in the USA. IARC Sci. Publ. 77, 115–120.[Medline]

Carlson, G. P. (1977). Chlorinated benzene induction of hepatic porphyria. Experientia 33, 1627–1629.[ISI][Medline]

Chen, Z. Y., White, C. C., He, C. Y., Liu, Y. F., and Eaton, D. L. (1995). Zonal differences in DNA synthesis activity and cytochrome P450 gene expression in livers of male F344 rats treated with five nongenotoxic carcinogens. J. Environ. Pathol. Toxicol. Oncol. 14, 83–99.[Medline]

Chu, I., Villeneuve, D., Secours, V., and Valli, V. E. (1983). Comparative toxicity of 1,2,3,4 -, 1,2,4,5-, and 1,2,3,5-tetrachlorobenze in the rat: Results of acute and subacute studies. J. Toxicol. Environ. Health 11, 663–677.[ISI][Medline]

Cohen, S. M., and Ellwein, L. B. (1990). Cell proliferation in carcinogenesis [see comments]. Science 249, 1007–1011.[ISI][Medline]

Conolly, R. B., and Andersen, M. E. (1997). Hepatic foci in rats after diethylnitrosamine initiation and 2,3,7,8-tetrachlorodibenzo-p-dioxin promotion: Evaluation of a quantitative two-cell model and of CYP 1A1/1A2 as a dosimeter. Toxicol. Appl. Pharmacol. 146, 281–293.[CrossRef][ISI][Medline]

Conolly, R. B., and Kimbell, J. S. (1994). Computer simulation of cell growth governed by stochastic processes: Application to clonal growth cancer models. Toxicol. Appl. Pharmacol. 124, 284–295.[CrossRef][ISI][Medline]

Delesse, M. (1848). Procede mecanique pour determiner la composition des roches. Ann. Mine 13, 379–388.

Dragan, Y. P., Hully, J. R., Nakamura, J., Mass, M. J., Swenberg, J. A., and Pitot, H. C. (1994). Biochemical events during initiation of rat hepatocarcinogenesis. Carcinogenesis 15, 1451–1458.[Abstract]

Dragan, Y. P., and Pitot, H. C. (1992). The role of the stages of initiation and promotion in phenotypic diversity during hepatocarcinogenesis in the rat. Carcinogenesis 13, 739–750.[Abstract]

Ellwein, L. B., and Cohen, S. M. (1992). Simulation modeling of carcinogenesis. Toxicol. Appl. Pharmacol. 113, 98–108.[CrossRef][ISI][Medline]

Fausto, N., and Webber, E. M. (1994). Liver regeneration. In The Liver: Biology and Pathology (I. M. Arias, J. L. Boyer, and N. Fausto, Eds.), pp. 1059–1084. Raven Press, New York.

Gustafson, D. L., Coulson, A. L., Feng, L., Pott, W. A., Thomas, R. S., Chubb, L. S., Saghir, S. A., Benjamin, S. A., and Yang, R. S. (1998). Use of a medium-term liver focus bioassay to assess the hepatocarcinogenicity of 1,2,4,5-tetrachlorobenzene and 1,4-dichlorobenzene. Cancer Lett. 129, 39–44.[CrossRef][ISI][Medline]

Gustafson, D. L., Long, M. E., Thomas, R. S., Benjamin, S. A., and Yang, R. S. H. (2000). Comparative hepatocarcinogenicity of hexachlorobenzene, pentachlorobenzene, 1,2,4,5-tetrachlorobenzene, and 1,4-dichlorobenzene: Application of a medium-term liver focus bioassay and molecular and cellular indices. Toxicol. Sci. 53, 245–252.[Abstract/Free Full Text]

Hill, R. H., Jr., Ashley, D. L., Head, S. L., Needham, L. L., and Pirkle, J. L. (1995). p-Dichlorobenzene exposure among 1,000 adults in the United States. Arch. Environ. Health 50, 277–280.[ISI][Medline]

Ito, N., Imaida, K., Hasegawa, R., and Tsuda, H. (1989a). Rapid bioassay methods for carcinogens and modifiers of hepatocarcinogenesis. Crit. Rev. Toxicol. 19, 385–415.[Medline]

Ito, N., Tatematsu, M., Hasegawa, R., and Tsuda, H. (1989b). Medium-term bioassay system for detection of carcinogens and modifiers of hepatocarcinogenesis utilizing the GST-P positive liver cell focus as an endpoint marker. Toxicol. Pathol. 17, 630–641.[Medline]

Jang, J. J., Henneman, J. R., Kurata, Y., Uno, H., and Ward, J. M. (1993). Alterations in populations of GST-p-immunoreactive single hepatocytes and hepatocellular foci after a single injection of N-nitrosodiethylamine with or without phenobarbital promotion in male F344/NCr rats. Cancer Lett. 71, 89–95.[CrossRef][ISI][Medline]

Jirtle, R. L., Hankins, G. R., Reisenbichler, H., and Boyer, I. J. (1994). Regulation of mannose 6-phosphate/insulin-like growth factor-II receptors and transforming growth factor beta during liver tumor promotion with phenobarbital. Carcinogenesis 15, 1473–1478.[Abstract]

Kato, M., Popp, J. A., Conolly, R. B., and Cattley, R. C. (1993). Relationship between hepatocyte necrosis, proliferation, and initiation induced by diethylnitrosamine in the male F344 rat. Fundam. Appl. Toxicol. 20, 155–162.[CrossRef][ISI][Medline]

Ledda-Columbano, G. M., Perra, A., Piga, R., Pibiri, M., Loi, R., Shinozuka, H., and Columbano, A. (1999). Cell proliferation induced by 3,3',5-triiodo-L-thyronine is associated with a reduction in the number of preneoplastic hepatic lesions. Carcinogenesis 20, 2299–2304.[Abstract/Free Full Text]

Luebeck, E. G., Moolgavkar, S. H., Buchmann, A., and Schwarz, M. (1991). Effects of polychlorinated biphenyls in rat liver: Quantitative analysis of enzyme-altered foci. Toxicol. Appl. Pharmacol. 111, 469–484.[CrossRef][ISI][Medline]

Mills, J. J., Falls, J. G., De Souza, A. T., and Jirtle, R. L. (1998). Imprinted M6p/Igf2 receptor is mutated in rat liver tumors. Oncogene 16, 2797–2802.[CrossRef][ISI][Medline]

Moolgavkar, S. H., and Luebeck, E. G. (1992). Interpretation of labeling indices in the presence of cell death [see comments]. Carcinogenesis 13, 1007–1010.[Abstract]

Moolgavkar, S. H., and Luebeck, G. (1990). Two-event model for carcinogenesis: Biological, mathematical, and statistical considerations. Risk Anal. 10, 323–341.[ISI][Medline]

Moolgavkar, S. H., Luebeck, E. G., Buchmann, A., and Bock, K. W. (1996). Quantitative analysis of enzyme-altered liver foci in rats initiated with diethylnitrosamine and promoted with 2,3,7,8-tetrachlorodibenzo-p-dioxin or 1,2,3,4,6,7,8-heptachlorodibenzo-p-dioxin. Toxicol. Appl Pharmacol. 138, 31–42.[CrossRef][ISI][Medline]

Moolgavkar, S. H., and Venzon, D. J. (2000). Two-event model for carcinogenesis. Math. Biosci. 47, 55–77.[CrossRef]

Morita, M. (1977). Chlorinated benzenes in the environment. Ecotoxicol. Environ. Saf. 1, 1–6.[ISI][Medline]

Nims, R. W., Devor, D. E., Henneman, J. R., and Lubet, R. A. (1987). Induction of alkoxyresorufin O-dealkylases, epoxide hydrolase, and liver weight gain: Correlation with liver tumor-promoting potential in a series of barbiturates. Carcinogenesis 8, 67–71.[Abstract]

NTP (U.S. National Toxicology Program). (1985a). Toxicology and carcinogenesis studies of 1,2 di-chlorobenzene (O-dichlorobenzene) in F344/N rats and B6C3F1 mice. TR255/ NTP, Research Triangle Park, NC.

NTP (U.S. National Toxicology Program) (1985b). Toxicology and carcinogenesis studies of chlorobenzene in F344/N rats and B6C3F1 mice. TR261/ NTP, Research Triangle Park, NC.

NTP (U.S. National Toxicology Program) (1987). Toxicology and carcinogenesis studies of 1,4 di-chlorobenzene (O-dichlorobenzene) in F344/N rats and B6C3F1 mice. TR319/ NTP, Research Triangle Park, NC.

Ogiso, T., Tatematsu, M., Tamano, S., Hasegawa, R., and Ito, N. (1990). Correlation between medium-term liver bioassay system data and results of long-term testing in rats. Carcinogenesis 11, 561–566.[Abstract]

Ou, Y. C., Conolly, R. B., Thomas, R. S., Xu, Y., Andersen, M. E., Chubb, L. S., Pitot, H. C., and Yang, R. S. H. (2001). A clonal growth model: Time-course simulations of liver foci growth following penta- or hexa-chlorobenzene treatment in a medium-term bioassay. Cancer Res. 61, 1879–1889.[Abstract/Free Full Text]

Peattie, M. E., Lindsay, D. G., and Hoodless, R. A. (1984). Dietary exposure of man to chlorinated benzenes in the United Kingdom. Sci. Total. Environ. 34, 73–86.[CrossRef][ISI][Medline]

Pitot, H. C. (1996). Stage-specific gene expression during hepatocarcinogenesis in the rat. J. Cancer Res. Clin. Oncol. 122, 257–265.[ISI][Medline]

Portier, C. J., Sherman, C. D., Kohn, M., Edler, L., Kopp-Schneider, A., Maronpot, R. M., and Lucier, G. (1996). Modeling the number and size of hepatic focal lesions following exposure to 2,3,7,8-TCDD. Toxicol. Appl. Pharmacol. 138, 20–30.[CrossRef][ISI][Medline]

Rotstein, J., Macdonald, P. D., Rabes, H. M., and Farber, E. (1984). Cell cycle kinetics of rat hepatocytes in early putative preneoplastic lesions in hepatocarcinogenesis. Cancer Res. 44, 2913–2917.[Abstract]

Rotstein, J., Sarma, D. S., and Farber, E. (1986). Sequential alterations in growth control and cell dynamics of rat hepatocytes in early precancerous steps in hepatocarcinogenesis. Cancer Res. 46, 2377–2385.[Abstract]

Saltykov, S. A. (1967). The determination of the size distribution of particles in an opaque material from a measurement of the size distribution of their sections. In Proceedings of the Second International Congress for Stereology, Chicago, IL, April 8–13 (H. Elias, Ed.), pp. 163–173. Springer-Verlag, Berlin.

Satoh, K., Hatayama, I., Tateoka, N., Tamai, K., Shimizu, T., Tatematsu, M., Ito, N., and Sato, K. (1989). Transient induction of single GST-P positive hepatocytes by DEN. Carcinogenesis 10, 2107–2111.[Abstract]

Sherman, C. D., and Portier, C. J. (1998). Eyes closed: Simple, intuitive, statistically sound, and efficient methods for estimating parameters of clonal growth cancer models [letter; comment]. Risk Anal. 18, 529–534.[CrossRef][ISI][Medline]

Staubli, W., Hess, R., and Weibel, E. R. (1969). Correlated morphometric and biochemical studies on the liver cell. II. Effects of phenobarbital on rat hepatocytes. J. Cell Biol. 42, 92–112.[Abstract/Free Full Text]

Stewart, F. P., Manson, M. M., Cabral, J. R., and Smith, A. G. (1989). Hexachlorobenzene as a promoter of diethylnitrosamine-initiated hepatocarcinogenesis in rats and comparison with induction of porphyria. Carcinogenesis 10, 1225–1230.[Abstract]

Thomas, R. S., Conolly, R. B., Long, M. E., Benjamin, S. A., and Yang, R. S. H. (2000). A physiologically-based pharmacodynamic analysis of hepatic-foci within a medium-term liver bioassay using pentachlorobenzene as a promoter and diethylnitrosamine as an initiator. Toxicol. Appl. Pharmacol. 166, 128–137.[CrossRef][ISI][Medline]

Thomas, R. S., Gustafson, D. L., Pott, W. A., Long, M. E., Benjamin, S. A., and Yang, R. S. H. (1998a). Evidence for hepatocarcinogenic activity of pentachlorobenzene with intralobular variation in foci incidence. Carcinogenesis 19, 1855–1862.[Abstract]

Thomas, R. S., Gustafson, D. L., Ramsdell, H. S., el-Masri, H. A., Benjamin, S. A., and Yang, R. S. (1998b). Enhanced regional expression of glutathione S-transferase P1–1 with colocalized AP-1 and CYP 1A2 induction in chlorobenzene-induced porphyria. Toxicol. Appl. Pharmacol. 150, 22–31.[CrossRef][ISI][Medline]

Tiwawech, D., Hasegawa, R., Kurata, Y., Tatematsu, M., Shibata, M. A., Thamavit, W., and Ito, N. (1991). Dose-dependent effects of 2-acetylaminofluorene on hepatic foci development and cell proliferation in rats. Carcinogenesis 12, 985–990.[Abstract]

Tobin, P. (1986). Known and potential sources of hexachlorobenzene. IARC Sci. Publ. 77, 3–11.[Medline]

Travis, C. C., McClain, T. W., and Birkner, P. D. (1991). Diethylnitrosamine-induced hepatocarcinogenesis in rats: A theoretical study. Toxicol. Appl. Pharmacol. 109, 289–304.[CrossRef][ISI][Medline]

Xu, Y. H., Dragan, Y. P., Campbell, H. A., and Pitot, H. C. (1998). STEREO: A program on a PC-Windows 95 platform for recording and evaluating quantitative stereologic investigations of multistage hepatocarcinogenesis in rodents. Comput. Methods Programs Biomed. 56, 49–63.[CrossRef][ISI][Medline]

Yusuf, A., Rao, P. M., Rajalakshmi, S., and Sarma, D. S. (1999). Development of resistance during the early stages of experimental liver carcinogenesis. Carcinogenesis 20, 1641–1644.[Abstract/Free Full Text]