* Preclinical Development, Human Genome Sciences, Inc., Rockville, Maryland 20850;
CIIT Centers for Health Research, Research Triangle Park, North Carolina 27709;
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
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ABSTRACT |
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Key Words: preneoplastic foci; simulation; liver carcinogenesis; clonal growth model; chlorobenzene.
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INTRODUCTION |
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We have used the medium-term bioassay of Ito et al. (1989a,b
) to study the carcinogenicity of chlorobenzenes (Gustafson et al., 1998
, 2000
). 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
(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., 1990
).
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., 1998, 2000
), 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., 1998a
), and for PECB a low incidence of GST-P foci in the centrilobular region was accompanied by increased glutathione reductase and
-glutamylcysteine synthetase (Thomas et al., 1998a
). PECB and HCB (Thomas et al., 1998b
), but not TECB and DCB (Carlson, 1977
; Chu et al., 1983
), 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., 2000
).
The two-stage Moolgavkar, Venzon, and Knudson (MVK) model (Moolgavkar and Luebeck, 1990; Moolgavkar and Venzon, 2000
) 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, 1997
; Luebeck et al., 1991
; Portier et al., 1996
). We adopted a discrete-time numerical approach (Ou et al., 2001
; Thomas et al., 2000
) of the clonal growth model (Conolly and Kimbell, 1994
), similar to that implemented by Cohen et al. (Cohen and Ellwein, 1990
; Ellwein and Cohen, 1992
), 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, 1997) and also for PECB and HCB time course foci data in the Ito medium-term bioassay (Ou et al., 2001
; Thomas et al., 2000
). Inclusion of two foci populations in the framework of the two-stage model (Fig. 1
) was based on the negative selection hypothesis of tumor promotion (Andersen et al., 1995
; Jirtle et al., 1994
). 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., 1994
). This selective environment may lead to outgrowth of clones that are resistant to mitoinhibition (Jirtle et al., 1994
).
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MATERIALS AND METHODS |
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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. 2). 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. 2
). 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.
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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., 1998). 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)
.
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 (, /day) was calculated from the LI using Equation 1 below (Moolgavkar and Luebeck, 1992
) where t is the number of days of exposure to BrdU.
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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, 1997; Conolly and Kimbell, 1994
; Ou et al., 2001
). A summary of the basic modeling framework is provided here.
![]() | (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.
![]() | (3) |
Nm represents the number of normal hepatocytes mutating during the time interval 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
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. 1
), using the two-cell model hypothesis as described previously (Ou et al., 2001
; Thomas et al., 2000
). 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
a, ßa,
b, and ßb, respectively (Fig. 1
).
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., 2001). Briefly, the same piecewise constant implementation was used in six time intervals (Days 17, 714, 1421, 2128, 2835, 35120). Division and death rates of normal hepatocytes at the various time points were obtained based on the study of Kato et al.(1993)
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., 1994
) 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., 2001
). 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 523% of total GST-P foci (Yusuf et al., 1999
). 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 523% (Yusuf et al., 1999
), 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., 1993
; Kato et al., 1993
; Satoh et al., 1989
; Tiwawech et al., 1991
) 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., 1991
), whereas the death rate of initiated cells increases with time (Rotstein et al., 1986
). 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., 1984
; Travis et al., 1991
).
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, m/
p. In the current analysis, we used the slope around the parameter for an approximation of the sensitivity coefficient,
m/
p
m/
p (Beck and Arnold, 1977
). Furthermore, we calculated the normalized sensitivity coefficient (NSC) with the following formula.
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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).
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RESULTS |
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DISCUSSION |
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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, b ßb(2835) when compared to TECB or DCB. A positive correlation between increased foci volume and
b ßb(2835) 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., 2835 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., 1986
; Tiwawech et al., 1991
). 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, 1994
). Once primed, hepatocytes respond to growth factors such as hepatocyte growth factor (HGF) and TGF-
to increase DNA synthesis (Fausto and Webber, 1994
). 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, 1994
). Dysregulation of TGF-ß, and other growth regulatory molecules such as TGF-
and c-myc, c-fos are often found in chemically induced preneoplastic foci and tumors (Pitot, 1996
). 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 (b ßb(2835)) 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, 1997
). Induction of CYP2B1/2 and hypertrophy are observed following exposure to phenobarbital (Staubli et al., 1969
) and the comparative potency of barbiturate-type tumor promoters is correlated with their potency as inducers of CYP2B1/2 (Nims et al., 1987
). 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., 1969
). 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, 1992
), probably in a region-specific pattern within the liver (Chen et al., 1995
; Thomas et al., 1998a
). 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., 1991; Portier et al., 1996
). Proliferation of normal hepatocytes is described deterministically in the current model, whereas a stochastic mode is used in previous implementations (Luebeck et al., 1991
; Portier et al., 1996
). 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., 1991
; Portier et al., 1996
). 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., 1991
; Portier et al., 1996
; Sherman and Portier, 1998
). 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., 2001) 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., 1999
). The need for two types of foci is not surprising given the known phenotypic diversity of altered hepatic foci (Dragan and Pitot, 1992
). 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., 1995
). 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., 1999
). 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., 1998
). The resistant initiated cells (B cells) are likely to stain positive for GST-P, but with reduced TGF-ß receptor levels (Mills et al., 1998
).
The successful application of the existing model parameters (Ou et al., 2001) 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.
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ACKNOWLEDGMENTS |
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NOTES |
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