Gene expression profiling of chemically induced rat mammary gland cancer

Liang Shan, Minshu Yu and Elizabeth G. Snyderwine1

Chemical Carcinogenesis Section, Laboratory of Experimental Carcinogenesis, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892-4262, USA

1 To whom correspondence should be addressed. Tel: +1 301 496-5688; Fax: +1 301 496-0734; Email: elizabeth_snyderwine{at}nih.gov


    Abstract
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Exposure to carcinogens through diet, the atmosphere and other means is generally regarded as influencing human cancer risk, but the impact of specific environmental carcinogens on human breast cancer incidence is still unknown. We examined whether distinct chemical carcinogens induce a unique transcriptional profile in mammary gland cancer that is characteristic of the etiologic agent. Rat mammary gland cancers (n = 34) were generated by various carcinogens, including the food-derived heterocyclic amines 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine and 2-amino-3,8-dimethylimidazo[4,5-f]quinoxaline, 7,12-dimethylbenz[a]anthracene, N-nitrosomethylurea and 4-aminobiphenyl. The histopathology of the carcinomas was graded using a modified Scarff-Bloom–Richardson scheme and the gene expression profiles in the carcinomas were evaluated on a 10K cDNA microarray. Unsupervised hierarchical clustering analysis revealed two major clusters of carcinomas irrespective of the carcinogenic agent that distinguished two groups with different histopathological parameters (degree of differentiation, nuclear grade, mitotic activity, epithelial cell growth pattern and necrosis). Using class comparison analysis and hierarchical clustering of all carcinomas irrespective of histopathology, gene expression profiles were further shown to be statistically differentially expressed according to the carcinogenic agent. These findings indicate that the transcriptional program in carcinomas is unique to the etiologic agent and can be observed among a diverse set of carcinogens despite variations in carcinoma histopathology. The ability to use microarray analysis to discern an etiology-specific profile among a pathologically heterogeneous group of breast carcinomas may ultimately be valuable in determining the role of environmental chemical carcinogens in human breast cancer risk.

Abbreviations: 4ABP, 4-aminobiphenyl; DMBA, 7,12-dimethylbenz[a]anthracene; MDS, multidimensional scaling; MelQx, 2-amino-3,8-dimethylimidazo[4,5-f]quinoxaline; NMU, N-nitrosomethylurea; PhIP, 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine


    Introduction
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Human breast cancer is a heterogeneous disease with respect to pathology, biochemistry and etiology. While environmental carcinogen exposure is often regarded as contributing to human cancer risk, identifying specific etiological factors in breast cancer remains a challenge in cancer research (14). The rat has served as a valuable model for understanding the development of human breast cancer because of similarities in pathology, cell of origin and hormone dependency (5,6). Furthermore, the rat model provides a means to study specific etiological factors in breast carcinogenesis in a way not feasible in humans.

Recent cDNA microarray analysis of rat mammary gland cancer has suggested that the profile of gene expression may be characteristic of the etiological agent (7,8). Comparison of the gene expression profiles in histologically similar rat mammary gland cancers revealed carcinogen-specific gene expression profiles in carcinomas induced by 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP), a heterocyclic amine in the human diet, and 7,12-dimethylbenz[a]anthracene (DMBA), an experimental carcinogen belonging to the class of polycyclic aromatic hydrocarbons. PhIP- and DMBA-induced carcinomas could be distinguished by array tools, including hierarchical clustering and multidimensional scaling analysis. Although pioneering, these prior studies used just two carcinogens and only examined carcinomas of similar histopathology. These studies therefore begged the question of whether microarray analysis could distinguish breast cancers according to etiology when carcinomas were induced by multiple chemical carcinogens and were histologically varied. In the light of the diversity in human breast cancers and wide variations in environmental exposures among individuals, the feasibility of using microarray analysis to define the etiology of human breast cancers would likely depend on the ability to categorize specific carcinomas from a heterogeneous group of breast cancers.

Herein we have analyzed the gene expression profiles of 34 rat mammary gland carcinomas induced by carcinogens including PhIP, DMBA, N-nitrosomethylurea (NMU), 2-amino-3,8-dimethylimidazo[4,5-f]quinoxaline (MelQx) and 4-aminobiphenyl (4ABP). These carcinogens represent major classes of environmental carcinogens that have been linked to various human cancers and include the cooked meat derived heterocyclic amines, aromatic amines, polycyclic aromatic hydrocarbons and nitrosoureas (1,9,10). The current study extends earlier work to determine whether carcinogens induce characteristic and etiology-specific gene expression profiles in mammary gland cancers that are resolvable by microarray analysis.


    Materials and methods
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Chemical carcinogens and rat mammary gland tumor induction
Female Sprague–Dawley rats (43 days old) were obtained from the NIH animal supply (Animal Production Area, Frederick, MD). All animals were provided NIH Lab Chow and water ad libitum and housed in a NIH animal facility on a 12 h light/12 h dark cycle. Mammary gland carcinomas were induced by PhIP-HCl (Toronto Research Chemicals, North York, Canada), DMBA (Sigma, St Louis, MO), NMU (Sigma), MelQx (Toronto Research Chemicals, Toronto, Canada) and 4ABP (provided by F.F.Kadlubar, NCTR, Jefferson, AR). PhIP-HCl, MelQx and 4ABP were all given at 75 mg/kg p.o. once per day for 10 days over a 12 day period. The incidence of carcinomas for PhIP and MeIQx have been reported (11,12). Five of 11 rats treated with 4ABP developed carcinomas. Other rats were treated with a single dose of DMBA (75 mg/kg p.o.) or NMU (50 mg/kg i.p.). The vehicles used to dissolve the carcinogens were water for PhIP, 0.9% NaCl solution (pH 4.0) for NMU and corn oil for the other carcinogens. All rats were maintained on a defined high fat diet after carcinogen exposure (9). Samples from each tumor were stored at –80°C for RNA extraction or fixed in 10% neutral buffered formalin for pathological examination. The control rats (n = 12) were dosed with vehicle only (n = 4 for water; n = 4 for NaCl solution; n = 4 for corn oil) and maintained on the same diet. Mammary glands were collected from the abdominal and inguinal regions, the lymph nodes removed and samples stored at –80°C prior to RNA isolation.

Histopathological classification of rat mammary gland carcinomas
The classification and grading of carcinomas were based on a scoring system adapted from the Scarff-Bloom–Richardson grading scheme that considers epithelial cell growth (a clear cribriform/papillary growth structure versus a solid pattern), extent of nuclear pleomorphism and mitotic activity (1315). Based on the grading system, carcinomas were classified as well, moderately or poorly differentiated. In well-differentiated carcinomas 75% of the epithelium showed a distinct growth pattern with cells displaying a consistent nuclear size and shape and little or no mitosis. In poorly differentiated carcinomas the majority of the tumor cells (≥90%) showed a solid growth pattern and relatively little cribriform or papillary structure, a markedly variable nuclear size and shape and frequent mitosis (a mitotic figure was detected at least twice within a high power field). Moderately differentiated carcinomas displayed features intermediate between well-differentiated and poorly differentiated carcinomas. The growth pattern of carcinomas was classified as either cribriform or papillary if the majority of the carcinoma displayed the respective growth pattern.

cDNA microarray and hybridization
Total RNA was isolated using TRIzol extraction reagent (Invitrogen, Rockville, MD) and purified using a RNease MinElute Cleanup Kit (Qiagen, Valencia, CA). The mouse cDNA microarray, printed at the National Cancer Institute (NCI, Bethesda, MD) contained 9984 cDNA clones as described on the NCI microarray website (http://nciarray.nci.nih.gov). Detailed target preparation and hybridization protocol can also be found on the above website. Briefly, 30 µg total RNA was used to synthesize cDNA and then labeled with the mono-reactive dyes Cy3 and Cy5 (Amersham Biosciences, Little Chalfont, UK) using a Fairplay Microarray Labeling Kit (Stratagene, La Jolla, CA). The Cy3- and Cy5-labeled cDNA probes were cleaned using a MinElute PCR purification kit (Qiagen, Valencia, CA). The probe (final volume ~18 µl) was mixed with 1 µl of Cot-1 DNA and 1 µl of poly(A). The cDNA probe was denatured for 1 min at 100°C and then mixed with 20 µl of 2x hybridization buffer (50% formamide, 10x SSC, 0.2% SDS). The microarray slides were first prehybridized with 40 µl of prehybridization buffer (5x SSC, 0.1% SDS, 1% bovine serum albumin in TE buffer) for 1 h, rinsed with H2O for 2 min and dehydrated in isopropyl alcohol for 2 min. Hybridization was carried out by adding the probe to the array and incubating overnight at 42°C. After washing, the microarray slides were scanned using an Axon GenePix 4000 scanner and GenePix Pro 3.0 software (Axon, Union City, CA). For data normalization, interpretation and visualization, the image and raw data were deposited in the NCI microarray database system supported by the Center for Information Technology of NIH (http://nciarray.nci.nih.gov). All carcinomas included in the analysis were from different rats. Pooled RNA from the 12 control rats was used as a common reference in the microarray analyses.

Clone selection and statistical analysis
Data were analyzed by median normalization [total intensity normalization (50th percentile)] using BRB-ArrayTools software version 3.2. This is an integrated software package for the visualization and statistical analysis of gene expression data developed by the Biometric Research Branch of the National Cancer Institute (http://linus.nci.nih.gov/~brb/). The spots of the array were excluded from analysis if any of the following were observed: the spot diameter was <10 µm; both red (Cy5) and green (Cy3) intensities of the spot were <100; the truncate intensity ratio was >64; <20% of the data showed a >1.5-fold change in either direction from spot intensity median value across all arrays; the percentage of data missing or filtered out for a specific spot was >30% for all arrays. The criteria for spot inclusion guaranteed a high quality of spots for analysis and decreased the potential influence of clones with little or no expression.

Statistical analysis of the microarray data was also performed using BRB-ArrayTools software. BRB-ArrayTools contains utilities for processing expression data from multiple experiments, visualization of data, multidimensional scaling (MDS), clustering of genes and samples and classification and prediction of samples. Unsupervised hierarchical clustering and MDS were performed to analyze the global gene expression profiles of all 34 carcinomas irrespective of the carcinogenic agent. Supervised hierarchical clustering was used to comparatively analyze the predefined groups. Both agglomerative hierarchical clustering and MDS were carried out by centered correlation and average linkage. Univariate permutation analysis (F-test) was used to find genes that were differentially expressed between two or more predefined classes. The criterion for a statistically significant difference in expression was a P value less than a specified threshold value and specified limits on the false discoveries as controlled by the multivariate permutation test (95% confidence level of false discovery rate assessment; maximum allowable number of false positive genes 10; maximum allowable proportion of false positive genes 0.1). The number of permutations for both the univariate and multivariate tests was 2000. The association of carcinoma clustering and pathological features was evaluated using Fisher's exact test (SigmaStat 2.0 statistical software; Jandel Scientific Software, San Rafael, CA).


    Results
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 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Gene expression profiles specific for carcinoma histopathology
Table I reports the number of carcinomas of each histologic grade (and per carcinogen) that were used for microarray analysis. A total of 34 rat mammary gland carcinomas induced by five different chemical carcinogens were analyzed for gene expression and histopathology. Forty-four percent of the carcinomas were well-differentiated, 32% were moderately differentiated, and 24% were poorly differentiated. There was a tendency for well-differentiated carcinomas to be papillary and poorly differentiated carcinomas to be cribriform (Fisher exact test, P < 0.05). Most grades of carcinoma were represented for each carcinogen.


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Table I. Chemical carcinogens and rat mammary gland carcinomas used in this study

 
The global gene expression profiles of all 34 carcinomas were first examined by unsupervised hierarchical clustering irrespective of the carcinogenic agent (Figure 1a). Two major clusters (designated clusters A and B in Figure 1a) were discerned among the carcinomas based on 3803 clones (representing 38% of the total 9984 clones). Other clones were excluded as described in Materials and methods. Clusters A and B were composed of 13 and 21 carcinomas, respectively, and represented two groups of carcinomas that were largely different by histopathology. In cluster A, six of the 13 carcinomas were poorly differentiated whereas only two were well-differentiated. In contrast, only two carcinomas were poorly differentiated (9%) and the majority of carcinomas (14/21, 67%) were well-differentiated in cluster B. The differentiation grade was statistically significantly different between carcinomas in clusters A and B (P < 0.05, Table II). Other histopathological features, including nuclear grade, mitotic activity, epithelial cell growth pattern (papillary or cribriform) and necrosis were also statistically different between the two major clusters (all P < 0.05, Table II). Additional features, including the presence of inflammatory cell infiltrate and stromal response, tended to be slightly lower in cluster B carcinomas, but the difference was not statistically significant (all P > 0.05). Thus, in comparison to cluster B carcinomas, cluster A carcinomas were less differentiated with a higher nuclear grade, more mitosis, more necrosis and a cribriform architecture.



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Fig. 1. (a) Global gene expression analysis of the 34 carcinogen-induced rat mammary carcinomas using unsupervised hierarchical clustering with centered correlation and average linkage analysis. Carcinomas were classified into two major clusters (A and B). Carcinomas written in green, black and red were well, moderately and poorly differentiated, respectively. (b) Supervised hierarchical clustering of well and poorly differentiated carcinomas using 38 differentially expressed genes (P < 0.001). Microarray profile showing two clusters of genes with differential expression between well and poorly differentiated carcinomas. Green squares, clones with low expression; black squares, clones with similar expression (ratio ~ 1); red squares, clones with high expression; gray squares, insufficient data.

 

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Table II. Correlation between carcinoma gene expression profile and histopathologic features

 
To examine the specific genes potentially associated with histologically different carcinomas, a class comparison analysis of the microarray data was carried out between well-differentiated (n = 15) and poorly differentiated (n = 8) carcinomas (Figure 1b). Moderately differentiated carcinomas were omitted to minimize the potential overlap of features between the subgroups. Using the univariate permutation test with 95% probability, 38 clones were identified as differentially expressed between well-differentiated and poorly differentiated carcinomas (P < 0.001). Eleven clones showed relatively higher and 27 clones showed relatively lower expression in the poorly differentiated in comparison to the well-differentiated carcinomas.

Gene expression profiles specific for carcinomas induced by different chemicals
Class comparison analysis was next used to examine whether gene expression profiles were different among the five groups of carcinomas induced by distinct carcinogens irrespective of histopathology. Using univariate and multivariate permutation analyses, 245 clones were statistically different at P < 0.0001 among the carcinomas (Supplementary material, Table SI). Supervised hierarchical clustering analysis using the 245 clones revealed clustering according to the carcinogenic agent (Figure 2a). Carcinomas induced by the same carcinogen were also shown to cluster by MDS analysis (Figure 2b). Univariate analysis was then used to compare the carcinomas induced by one carcinogen with the other carcinomas (two-sample t-test, number of permutations 2000). Unique expression profiles were observed among carcinomas induced by each carcinogen. Statistically significant expression changes were identified that were specific to 4ABP (204 clones, P < 0.0001), MelQx (123 clones, P < 0.0001) and NMU (35 clones, P < 0.001), PhIP (32 clones, P < 0.01) and DMBA (26 clones, P < 0.01) (Supplementary material, Tables SII–SVI). Based on the expression of clones specific for each carcinogen, clustering of specific carcinogen-induced cancers was observed (Figure 3a–e). Clustering according to carcinogen was most rigorous when a higher number of statistically significant clones were associated with the specific carcinogen-induced tumors, as was the case for 4ABP and MeIQx.



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Fig. 2. (a) Supervised hierarchical clustering of carcinomas based on class comparison analysis among carcinomas induced by five different carcinogens. Carcinomas were classified from 245 clones differentially expressed among different carcinogen-induced carcinomas (P < 0.0001). (b) Multidimensional scaling analysis of the same data set shows that distinct chemical carcinogen-induced cancers can be resolved and clustered in 3-dimensional space. Ca, carcinomas.

 


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Fig. 3. (a) 4ABP-induced carcinomas were classified against the other carcinomas from 204 differentially expressed clones (P < 0.0001). (b) MelQx-induced carcinomas were classified against the other carcinomas from 123 differentially expressed clones (P < 0.0001). (c) NMU-induced carcinomas were classified against the other carcinomas based on 35 differentially expressed clones (P < 0.001). (d) PhIP-induced carcinomas were classified against the other carcinomas based on 32 genes differentially expressed clones (P < 0.01). (e) DMBA-induced carcinomas were classified against the other carcinomas based on 26 differentially expressed clones (P < 0.01). The underlining indicates clusters of carcinomas induced by the same carcinogen.

 

    Discussion
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
The rat mammary gland cancer model involving treatment of adolescent female rats with specific chemical carcinogens is a long-standing model for studying breast carcinogenesis (16). In the current study rat mammary gland cancers induced by five distinct chemical carcinogens were classified histopathologically and gene expression profiles examined by microarray analysis. Although relatively homogeneous and well-differentiated in comparison to human breast cancer, chemically induced rat mammary gland carcinomas nevertheless show variations in degree of differentiation and diversity of other histopathological features (17). All grades of carcinomas were observed in the majority of chemically induced cancers studied herein. Carcinogens such as DMBA and NMU that induce a high incidence of carcinomas as well as carcinogens with a lower overall incidence, such as MeIQx, appear to be capable of inducing both well-differentiated and poorly differentiated carcinomas.

Irrespective of the carcinogen, rat mammary gland carcinomas with comparable histopathological features showed statistical similarities in gene expression profiles as ascertained by microarray analysis. There was a statistically significant clustering of gene expression patterns in carcinomas with similar degrees of differentiation, epithelial growth pattern, nuclear grade and mitotic activity. These findings indicate that the gene expression profiles in carcinomas reflect the histopathological phenotype of rat mammary gland carcinomas that is common to carcinomas despite the etiological differences. This is the first study to show that histopathologically distinct rat mammary gland carcinomas can be distinguished through gene expression patterns. The findings in rats are consistent with microarray studies of human breast cancers that have identified subgroups with distinctive gene expression profiles (1821). We further identified 38 clones that showed a statistically significantly different expression between well-differentiated and poorly differentiated carcinomas. Several clones correspond to genes implicated in various cancers, including proliferin, reelin, periplakin and galectin 7, or linked to gene families associated with carcinogenesis, such as TRIM13, T-box 6 and ADAM2 (2226). Interestingly, galectin 7, previously shown to be elevated in DMBA-induced rat mammary gland cancers (27), showed relatively higher overexpression in well-differentiated than in poorly differentiated carcinomas. Further studies are expected to provide insight into the role of these specific genes in carcinoma pathology and may ultimately provide new diagnostic markers.

In addition to discerning different gene expression profiles among carcinomas having different histopathological features, etiology-specific profiles in gene expression were observed by microarray analysis. Previous microarray analyses indicated distinct expression patterns between PhIP- and DMBA-induced rat mammary gland carcinomas (7,8). These studies were limited in the use of only two chemical carcinogens and a homogeneous small set of carcinomas. Herein we observed that the profile of gene expression in carcinomas was specific to chemical carcinogen initiating agents. These carcinogen-specific gene expression profiles were observed irrespective of the variation in carcinoma histopathology. Thus, despite the variation in gene expression with histopathology, statistically significant differences in gene expression could be found that were specifically attributed to carcinoma etiology.

The carcinogen-specific molecular profile may represent the particular selective pressures on the transcriptional program associated with tumor development. It is well-recognized that distinct chemical carcinogens induce different DNA adducts and have different mutation frequencies and spectra, as well as target different oncogenes for activating mutations (2830). All five carcinogens examined are known to form adducts in DNA. The current findings are consistent with the notion that distinct chemical carcinogens differentially alter cell signaling pathways during carcinogenesis in part through the induction of different activating mutations. In agreement with this possibility, a study using transgenic mice overexpressing c-myc, c-neu, c-Ha-ras, PyMT or SV40 T-antigen showed that the initiating oncogenic event effected gene expression patterns in mammary gland carcinomas (31). Furthermore, it was reported that cell lines exposed to specific carcinogens can select for tumor cells with distinct forms of genetic instability (32). Further studies are required to clarify the divergent molecular pathways involved in various carcinogen-induced mammary gland cancers and to ascertain biomarkers suitable for assessing the contribution of a specific chemical carcinogen to human breast cancer.

Human breast cancer is a heterogeneous disease with a potentially diverse contribution of environmental exposures and etiological factors. Knowledge of the unique expression profiles in carcinomas induced by distinct environmental chemical carcinogens may ultimately be valuable for risk assessment and a better understanding of cancer etiology in humans. The findings in the rat model support the feasibility of using a heterogeneous set of carcinomas to dissect the contribution of an environmental carcinogen in human breast cancer using a microarray analysis approach.


    Acknowledgments
 
The authors thank Dr Fred F.Kadlubar for providing purified 4-ABP.


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 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 

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Received September 1, 2004; revised October 25, 2004; accepted October 26, 2004.