Genomic and Proteomic Analysis of Mammary Tumors Arising in Transgenic Mice Lu Xie,†,# Baogang J. Xu,†,§ Agnieszka E. Gorska,† Yu Shyr,‡ Sarah A. Schwartz,§ Nikki Cheng,† Shawn Levy,| Brian Bierie,† Richard M. Caprioli,§ and Harold L. Moses*,† Department of Cancer Biology, Biostatistics Shared Resource at Vanderbilt-Ingram Cancer Center and Department of Biostatistics, Mass Spectrometry Research Center and Department of Biochemistry, Microarray Shared Resources and Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee 37232, and Shanghai Center for Bioinformation Technology, 100 Qinzhou Rd., Fl. 12, Shanghai, 200235, Peoples Republic of China Received July 14, 2005
Transforming growth factor-β (TGF-β) is the prototype of a large family of signaling molecules. TGF-β signaling profoundly influences tumor development as demonstrated in several engineered mouse models. The present study was designed to identify differences by cDNA microarray and MALDI-TOF MS analyses in mammary carcinomas with and without TGF-β signaling. The results demonstrate a significant potential for combination of profiling technologies to further understand the molecular mechanisms of breast cancer. Keywords: microarray • MALDI-TOF mass spectrometry • mammary tumor • transgenic mouse
1. Introduction Transforming growth factor-β (TGF-β) is the prototype of a large family of signaling molecules with over 30 members which are involved in many cellular processes including cell proliferation, differentiation, apoptosis, cell morphology, and migration. TGF-β1 signals through cell-surface serine-threonine kinase type II (Tgfbr2) and type I (Tgfbr1) receptors to activate numerous signaling networks including the Smad, MAP kinase, PI3k-Akt and RhoA pathways. Smad-dependent signaling has been shown to be required for the antiproliferative activity of TGF-β1.1 Activation of MAP kinase, PI3k-Akt and RhoA pathways have been shown to be associated with changes in cell morphology and migration.2 Several studies in our laboratory have examined the effect of inhibiting TGF-β signaling in vivo through transgenic expression of a truncated, kinase-defective, dominant-negative type II TGF-β receptor (DNIIR) under the control of the mouse mammary tumor virus (MMTV) promoter/enhancer (MMTVDNIIR). These studies have utilized the MMTV-DNIIR, MMTVTGF-R and MMTV-DNIIR/TGF-R (bigenic) mouse moedels to study TGF-β signaling during the initiation and progression of breast cancer. Multiparous MMTV-DNIIR female mice were shown to spontaneously develop carcinoma in situ/high grade mammary intraepithelial neoplasia (CIS/HG-MIN) with a me* To whom correspondence should be addressed. Harold L. Moses, M. D. 691 Preston Research Building 2220 Pierce Avenue Vanderbilt University Medical Center Nashville, TN 37232. Tel: (615) 936-1782. Fax: (615) 9361790. E-mail:
[email protected]. † Department of Cancer Biology. ‡ Biostatistics Shared Resource at Vanderbilt-Ingram Cancer Center and Department of Biostatistics. § Mass Spectrometry Research Center and Department of Biochemistry. | Microarray Shared Resources and Department of Biomedical Informatics. # Shanghai Center for Bioinformation Technology.
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dian latency of two years, while transgenic mice overexpressing the TGF-R transgene (MMTV-TGF-R) developed invasive mammary adenocarcinomas at approximately 11 months. Interestingly, coexpression of the MMTV-DNIIR and MMTV-TGF-R transgenes resulted in mammary tumor formation with a similar latency to that observed in MMTV-TGF-R mice. In contrast to the individual transgenic tumor models, the bigenic tumors were characterized as DCIS cribriform and cystic, exhibiting decreased reactive stroma and invasion. These studies suggested that signaling from endogenous TGF-β could suppress early stages of tumor formation, but contribute to tumor invasion once carcinomas developed.3 Due to phenotypic differences in the mouse models it was hypothesized that there were distinct molecular mechanisms inherent to each model that contributed to the regulation of tumorigenesis. Therefore, extensive global transcript and proteomic analyses were likely to broaden our insight into these mouse models of human breast cancer. The use of cDNA microarray analysis has been applied to the exploration of specific signaling pathways in mammary cell lines treated with TGF-β in order to elucidate novel target genes and signaling cascades regulated by TGF-β.4-6 These studies have demonstrated the utility of cDNA microarray in exploring the multifunctional profiles of TGF-β in relation to tumor suppression or tumor progression associated behaviors in vitro. The results obtained in vitro suggested that utilizing cDNA microarray analysis on transgenic mouse mammary tumor models could provide additional insight in vivo related to the role for TGF-β signaling in breast cancer. Despite the power of global transcript and genomic analyses, one significant shortcoming of these approaches stems from the observation that areas of gene amplification or changes in mRNA levels do not always correlate with similar changes in 10.1021/pr050214l CCC: $30.25
2005 American Chemical Society
Mammary Tumors Arising in Transgenic Mice
protein expression.7 Thus, complementary proteomic analysis may provide a more complete assessment of the distinct molecular profile within each experimental system. Direct tissue analysis using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) analysis enables rapid analysis of proteins. This method yields the best peak resolution for proteins with molecular weights under 30 kDa. This mass range is well represented by cytokines, signaling peptides, and proteolytic fragments.7 The technique of analyzing protein patterns directly from thin frozen tissue sections using MALDI-TOF MS is relatively new, fast, sensitive and accurate.8-12 In addition, this technique has been successfully applied within several published studies including those conducted in the mouse epididymis,13 human brain tumors,14 and human lung carcinoma.15 Our present study is the first to use direct tissue section protein profiling by MALDI-TOF MS on mouse mammary tumors. Overall, the present study was designed to analyze the mammary tumors arising from MMTV-DNIIR, MMTV-TGF-R, and bigenic mouse models using both cDNA microarray and direct tissue protein profiling by MALDI-TOF MS. This study not only identified novel genes regulated by TGF-β signaling involved in mammary tumor development, but also correlated results from cDNA microarray and proteomic technologies to identify unique tumor differences at both the RNA and protein levels. The combination of these tumor profiling approaches may eventually become a valuable clinical diagnostic strategy for human tumors used to identify biomarkers and potential targets for therapeutic intervention in cancer.
2. Materials and Methods Mammary Tumor Formation in Transgenic Mice. Transgenic mice of MMTV-DNIIR, MMTV-TGF-R, and bigenic genotypes were generated as described previously.3 Briefly, MMTVDNIIR mice were generated by microinjection of the DNIIR from pRHC102 into (C57BL/6xDBA)F2 fertilized eggs, then bred to (C57BL/6). MMTV-TGF-R mice were obtained from Robert J. Coffey.16 Male MMTV-TGF-R mice were mated to female MMTV-DNIIR mice to obtain bigenic transgenic models. CIS/ HG-MIN mammary tumors arising from DNIIR transgenic mice, mammary invasive adenocarcinoma arising from TGF-R transgenic mice and cribriform DCIS mammary tumors arising from bigenic mice with both transgenes were used for analyses in the present study. These mouse tumor models were selected to complement our previously published pathological studies involving these transgenic mouse lines.3 Mammary tumors were removed three to four weeks after first detection by palpitation when the tumors had reached 0.5-2.0 cm in diameter. Portions of tumor tissue were snap frozen in liquid nitrogen and stored at -80 °C until protein and RNA processing. Remaining mammary tumor tissues were fixed in 4% formaldehyde and embedded in paraffin for histology and immunostaining analyses as described below. cDNA Microarray Analysis. Total RNA from MMTV-DNIIR, MMTV-TGF-R and bigenic tumors was extracted using TRIzol (Invitrogen) and hybridizations were performed as previously described.4 Five samples from each tumor type were labeled with Cy3 while a universal mouse reference control consisting of RNA from normal mammary cells (Stratagene, La Jolla, CA) was labeled with Cy5. Samples were hybridized in triplicate to cDNA microarrays that contained 22 400 elements derived from the National Institutes of Aging (NIA) 15K cDNA microarray and the Research Genetics 5k Mouse cDNA library. Ap-
research articles proximately 1000 additional genes and controls were added to the array. Data acquisition was performed using GenePix (Molecular Devices). Data normalization (Lowess) and filtering was performed using GeneSpring 7.2 (Agilent Technologies). After data normalization, the expression of 21 895 remaining genes was analyzed by principal component analysis (PCA). Data were filtered using the following criteria: (1) microarray elements with hybridization signals of 300 units and higher (on a 16-bit scale) compared to background hybridization signals that were less than 300 units and (2) only genes identified as present across all three replicate chips of the three tumor groups were considered for further analysis. MALDI-TOF Mass Spectrometry. Six tumors from the MMTVDNIIR group, five tumors from the MMTV-TGF-R and five tumors from the bigenic mouse models were analyzed by the direct tissue protein profiling technique as previously described.17,18 Briefly, to identify regions of homogeneous morphology for protein analysis, tissue sections of 5 µm thickness were cut in a cryostat, collected on glass slides and stained by hematoxylin and eosin (H&E). Adjacent sections (12 µm) were then cut, mounted on gold coated MALDI plates and vacuum desiccated for an hour. Using a syringe pump attached to a fused-silica capillary, 100 nL of matrix containing sinapinic acid (Sigma, St. Louis, MO) solution (20 mg/mL, 50/50/0.3, v/v/v, acetonitrile (ACN)/water/trifluoroacetic acid) was deposited on the specific regions of interest, as guided by the H&E stain of the adjacent tissue serial section. Upon solvent dehydration, the matrix and proteins were cocrystallized then irradiated by a series of nitrogen laser pulses (λ ) 337 nm). The MALDI MS analyses were performed in the linear mode under the optimized delayed extraction condition using Applied Biosystems DE-STR Voyager mass spectrometer (Framingham, MA). Each mass spectrum was obtained by summing 750 laser shots from one matrix droplet. After internal calibration the spectra were normalized using the DataExplorer software (Applied Biosystems) and algorithms developed at the Biostatistics Shared Resource in the Vanderbilt-Ingram Cancer Center (Vanderbilt University, Nashville, TN). Statistical Analysis. The statistical analyses were focused on identifying the genes that were differently expressed when compared to the references within each study group, and examining the prediction power of the statistically significant genes and proteins. Within group comparisons were performed, and each gene was considered statistically significant if it was upregulated or downregulated 2-fold with a p-value of the t-test less than 0.05. Statistical prediction analyses were performed on the transcriptomic and proteomic data using the following methods. (1) The significant differentially expressed genes or proteins from MMTV-DNIIR, MMTV-TGF-R and bigenic groups were selected. The gene or protein was chosen as a significant marker if it met at least three (gene data) or four (protein data) of the six selection methods, which include Kruskal-Wallis test, Fisher’s exact test (protein data only), permutation t-test, Significance Analysis of Microarrays (SAM), Weighted Gene Analysis (WGA) and the modified info score method. The cutoff points for each method were p < 0.005, p < 0.005, p < 0.005, 2.5, 2, and 0 respectively, which were determined based on the significance and the prediction power of each method. (2) The class prediction model was employed to assess whether the gene or protein expression patterns could be used to classify tissue samples into three classes (DNIIR vs TGF-R, DNIIR vs bigenic, or TGF-R vs bigenic) according to the selected markers based upon the Weighted Flexible Compound Covariate Method Journal of Proteome Research • Vol. 4, No. 6, 2005 2089
research articles (WFCCM)15. (3) The agglomerative hierarchical clustering algorithm was applied to investigate the selected marker expression patterns as well as the classification accuracy for different tumor samples using M. Eisen’s software. Protein Identification. Proteins were extracted from the MMTV-DNIIR, MMTV-TGF-R and bigenic tumor samples for protein marker identification. Briefly, using an ice-chilled Duall glass homogenizer, tissues were homogenized in 500µL of protein extraction buffer containing 0.25M sucrose (J. T. Baker, Phillipsburg, NJ), 0.01 M Tris-HCl (J. T. Baker) and 0.1mM PMSF (Sigma, St. Louis, MO). The homogenate was centrifuged according to the following sequence: 10 min at 680 × g, 10 min at 10 000 × g, and 1 h at 100 000 × g. A total of 200 µg of protein from the final supernatant was separated on a Vydac 208TP54 polymeric reversed-phase column (Hesperia, CA) at 40 °C using a Waters Alliance HPLC system (Milford, MA). The fractions were collected every minute and dried using a Thermo Quest Savant Speedvac (Holbrook, NY). Dried HPLC fractions were reconstituted in 10 µL of sinapinic acid (20 mg/mL, 50/ 50/0.3, v/v/v, ACN/water/TFA) and analyzed by MALDI-TOF MS. The fractions containing target protein markers, as identified by statistical analysis were completely lyophilized again and reconstituted with10 µL of 0.4 M ammonium hydrogen carbonate (Sigma, St. Louis, MO). These fractions were reduced with 5 µL of 45 mM dithiothreitol (Sigma, St. Louis, MO) and incubated at 60 °C for 15 min, followed by alkylation with 5 µL of 100 mM iodoacetamide (Sigma, St. Louis, MO) in the dark for 15 min. One µL of 0.5 µg/µL sequencing-grade trypsin (Promega, Madison, WI) was added, and the sample was digested for 4-6 h at 37 °C. The digested fractions were subjected to liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis using a ThermoFinnigan LTQ mass spectrometer (San Jose, CA). Two µL of sample was loaded into a 100 µm i.d. self-packed microcapillary reversed-phase column packed with Monitor C18-Spherical Silica from Column Engineering Inc. (Ontario, CA). The fragment ion mass spectra were searched against the National Center for Biotechnology Information (NCBI) mouse protein database using the SEQUEST algorithm.19 Immunohistochemistry. Tumor tissues were fixed in 4% paraformaldehyde overnight at 4 °C, dehydrated in a series of xylenes and ethanols, then embedded in paraffin. Sections (5 µm) were immunostained for detection of profilin 1 and thioredoxin expression using Elite PK-6101 (Rabbit IgG) Vectastain ABC kit (Vector Laboratories, Burlingame, CA), according to the manufacturer’s instructions. Briefly, sections were dewaxed in xylene, rehydrated in an ethanol series and PBS, then subjected to antigen retrieval using 10 mM sodium citrate pH 6.0 (BioGenex, San Ramor, CA). Following antigen retrieval, sections were treated with methanol and hydrogen peroxide to quench endogenous peroxidase activity then blocked with 3% goat serum in PBS for 1 hour. Sections were then incubated at room temperature for 1 h with a rabbit polyclonal IgG antibody to profilin 1 (1 µg/mL; ALX-210-740, Alexis Biochemicals, San Diego, CA) or a rabbit polyclonal IgG antibody to thioredoxin (2 µg/mL; SC-20146, Santa Cruz Biotechnology, Santa Cruz, CA). Specific immunoreaction was detected using a goat anti-rabbit IgG biotinylated secondary antibody which was subsequently conjugated to streptavidin peroxidase, and visualized by DAB substrate reaction (Vector Labs). Sections were counterstained with hematoxylin, rinsed and mounted with Aqua poly/Mount (Polysciences, Inc., War2090
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rington, PA). Stained sections were visualized and photographed using an Olympus BX41 Microscope (Melville, NY).
3. Results cDNA Microarray Analysis. To identify potential novel biomarkers regulated by TGF-β signaling, tumors from MMTVDNIIR, MMTV-TGF-R, and bigenic mice were subjected to cDNA microarray analysis. Samples were hybridized to NIA cDNA microarray containing 22.4 K genes and compared against labeled RNA from normal mammary epithelial cells. Principal component analysis (PCA) indicated three trends of gene expression: (1) 82.7% of the genes analyzed did not show significant changes in their expression levels in the mammary tumor samples from all the three tumor types. (2) 10.9% of genes from the array showed expression level changes in both MMTV-DNIIR and bigenic mammary tumors. (3) 6.3% of genes exhibited changes in expression level in both MMTV-TGF-R and bigenic tumors (Figure 1A). Filtering of the raw gene expression data resulted in 6036 genes identified as appropriate for further statistical analysis. Following further statistical analysis, 18 upregulated genes and 8 downregulated genes were identified that were differentially expressed in either MMTVDNIIR tumors alone or in both the MMTV-DNIIR and bigenic tumors, but were not differentially expressed in MMTV-TGF-R tumors (Figure 1B). These observations suggest that resulting changes in gene expression were likely caused by expression of the DNIIR transgene, and were therefore indicated as such in Table 1. Upregulated genes associated with DNIIR expression included actin binding genes such as destrin (probe Id H3122E03) and thymosin β4 (H3143A02) as well as calcium ion binding genes such as calsyntenin 1 (H3029E06), EF hand domain containing 2 (H3001B10) and calmodulin binding gene Mlp (H3027D09). Downregulated genes associated with DNIIR included the cell cycle gene cycline-dependent kinase 8 (H3082H09), SH3-domain GRB2-like 2 (H3142C09) and growth factor receptor bound protein 10 (H3028E09). Hierarchical clustering analysis was performed by WFCCM software to determine possible class-distinguishing gene expression profiles among the MMTV-DNIIR, MMTV-TGF-R, and bigenic tumors. The resulting analysis indicated that each tumor group could be 100% correctly distinguished from the others by the expression profile of approximately 20 genes unique to each model. Specifically, 22 genes could distinguish MMTV-DNIIR from MMTV-TGF-R tumors, 17 genes could distinguish MMTVDNIIR from bigenic tumors and 21 genes could distinguish MMTV-TGF-R from bigenic tumors (Figure 2). MALDI-TOF Mass Spectrometry Analysis. The different protein expression profiles among the MMTV-DNIIR, MMTVTGF-R and bigenic tumors were analyzed using MALDI-TOF MS analyses. Frozen tumor tissues were sectioned and stained with H&E. To obtain reproducible results, eight regions of homogeneous tumor growth for each section were chosen for spectrum analysis. A total of 48 mass spectra were obtained from the MMTV-DNIIR group containing six tumors, 40 mass spectra from the MMTV-TGF-R group containing five tumors, and 40 mass spectra from the bigenic group containing five tumors (Figure 3). After data processing, approximately two hundred protein signals were detected per spectrum in the m/z range of 2000-70 000, with the signals under m/z 30 000 yielding the best resolution. The results were combined using a peak binning algorithm, and a total of 413 protein signals detected across all tumor protein spectra were used for subsequent statistical analysis.
Mammary Tumors Arising in Transgenic Mice
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Figure 1. NIA 22K cDNA microarray principal component analysis (PCA) and DNIIR related genes. A. The majority genes analyzed (82.8%) do not exhibit significant differences in expression among the MMTV-DNIIR (simplified as DNIIR), MMTV-TGF-R (simplified as TGF-R) and bigenic mammary tumor samples in comparison to normal mammary reference samples. In the bigenic mammary tumors, 10.9% of the genes exhibited significant changes in expression with similar patterns to DNIIR mammary tumors while 6.3% genes were observed to exhibit significant changes in expression with similar patterns in TGF-R mammary tumors. B. After statistical analysis in GeneSpring, the DNIIR related upregulated (upper panel) or downregulated (lower panel) in DNIIR only (left panels), or those only observed in both DNIIR and bigenic mammary tumors (right panels) are shown. These genes were not differentially expressed in TGF-R mammary tumors, thus were defined as DNIIR related genes.
Using the WFCCM statistical analysis described above, MMTV-DNIIR and MMTV-TGF-R proteomic spectra were classified with 92% accuracy using the top 80 differentially expressed protein MS signals. Similarly, 98% classification accuracy was obtained for the comparison of MMTV-DNIIR vs bigenic protein spectra using the top 75 differentially expressed MS signals. We obtained 93% classification accuracy for MMTVTGF-R vs bigenic protein spectra using the top 48 differentially expressed MS signals (Table 2). Following statistical analysis an agglomerative hierarchical clustering algorithm was applied to investigate the differentially expressed protein patterns among all the tumor protein spectra using Eisen’s software. As shown in Figure 4, hierarchical clustering analyses indicated differences in protein spectra expression profiles among different types of tumor samples. Protein Identification. Direct tissue analysis using MALDI MS was only able to provide the molecular weight for detected proteins. Identification of each individual protein was therefore necessary for the detected peaks obtained by MALDI MS. Protein markers that yielded the most statistically significant classification power among the tumor types were selected for identification. The identification of one such protein is illustrated in Figure 5. After tissue homogenization and HPLC separation, a peak with the [M+H]+ value 14 870 was detected in a single HPLC fraction by MALDI MS (Figure 5A). LC-MS/
MS analysis of the resulting tryptic peptides corresponding to the peak identified profilin 1 as the target protein. The identity of profilin 1 was confirmed by its tryptic peptide MS/MS spectra (Figure 5B). The sequences of three peptides from profilin 1 were confirmed by the MS/MS spectra, and covered 33.6% of the total amino acid sequence (Figure 5C). With removal of the initial methionine and N-terminal acetylation, the target protein molecular weight was found to be in agreement with the theoretical average molecular weight of profilin 1 which is 14 869 Daltons. The signal from profilin 1 was present in 11 spectra of MMTV-DNIIR tumors, but not found in any of the MMTV-TGF-R and bigenic spectra, indicating an association with expression of the DNIIR transgene. In addition, proteins not previously shown to be associated with mammary tumor progression or TGF-β signaling were identified in our samples. The proteins included epidermal fatty acid binding protein 5 (Fabp5), nucleoside-diphosphate kinase 2 (Nme2), and thioredoxin (TRX). Ten proteins that were identified previously13 were also present as potential biomarkers in the MMTV-TGF-R, MMTV-DNIIR and bigenic tumors after statistical analysis (Table 3). These proteins included calmodulin 1, thymosin β4 and macrophage migration inhibitory factor (MIF). These data suggested a potential connection between the identified biomarkers and TGF-β signaling in mammary tumor development. Journal of Proteome Research • Vol. 4, No. 6, 2005 2091
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Table 1. Up- and Downregulated Genes Revealed by Microarray Analysis in Mouse Mammary Tumors Affected by DNIIR Expression gene ontology
symbol
unigene name
clone ID
DNIIR related up regulated genes molecular function calcium ion binding calcium ion binding calmodulin binding actin binding actin binding DNA binding diacylglycerol binding acyl-CoA dehydrogenase activity aspartic-type endopeptidase activity biological process bone resorption cell differentiation carbohydrate metabolism cellular component integral to membrane integral to membrane intracellular extracellular space cytoplasm cytosol molecular function DNA binding DNA binding DNA binding disulfide oxidoreductase activity acyltransferase activity SH3/SH2 adaptor activity biological process cell cycle G-protein coupled signaling pathway
Clstn1 Efhd2 Mlp Dstn Tmsb4x Ets2 Cdc42bpb Acadl Ctsd
calsyntenin 1 EF hand domain containing 2 MARCKS-like protein destrin thymosin, beta 4, X chromosome E26 avian leukemia oncogene 2, 3′ domain cdc42 binding protein kinase beta acetyl-Coenzyme A dehydrogenase, long-chain cathepsin D
H3029E06 H3001B10 H3027D09 H3122E03 H3143A02 H3028G09 H3119C11 H3045C12 H3138H10
Il7 Zfp313 Pgm2l1
interleukin 7 zinc finger protein 313 phosphoglucomutase 2-like 1
H3079H02 H3120D12 H3099B11
Sidt2 SID1 transmembrane family, member 2 Np15 nuclear protein 15.6 Rpl13a ribosomal protein L13a Agt angiotensinogen Rps3 ribosomal protein S3 Psma7 proteasome subunit, alpha type 7 DNIIR related down regulated genes
H3125C07 H3111F05 H3136G06 H3149E05 616356 H3075G03
Ankrd25 Cebpd Mll5 Sqrdl Sh3gl2 Grb10
ankyrin repeat domain 25 CCAAT/enhancer binding protein (C/EBP), delta myeloid/lymphoid or mixed-lineage leukemia 5 sulfide quinone reductase-like (yeast) SH3-domain GRB2-like 2 growth factor receptor bound protein 10
H3057H01 H3067D12 H3060D11 H3122H06 H3142C09 H3028E09
Cdk8 Ramp2
cyclin-dependent kinase 8 RIKEN cDNA 9430072K23 gene
H3082H09 H3109A04
Figure 2. Hierarchical clustering of microarray analysis of mammary tumors arising in MMTV-DNIIR (DT), MMTV-TGF-R (TT), and bigene (DTT) transgenic mice. Each row indicates a tumor number. Each column represents a gene, green color indicates decreased gene expression in a tumor compared to normal reference. Red indicates increased expression, the deeper the color the more significant the change. The selection of the genes distinguishing DT from TT (A), DT from DTT (B), TT from DTT (C) was based on biostatistic method WFCCM which was described in Material and Methods.
Immunohistochemistry. To verify the proteomic identification and determine a relative distribution for the potential biomarkers in the tumor microenvironment, immunohistochemistry of profilin 1 and thioredoxin was performed on 2092
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MMTV-DNIIR, MMTV-TGF-R, and bigenic tumor sections. By immunohistochemical staining, profilin 1 demonstrated a high level of expression in MMTV-DNIIR CIS/HG-MIN tumors and weak expression in invasive MMTV-TGF-R tumors. Individual cell populations demonstrating either high or low expression of profilin 1 were observed in bigenic cribriform tumors while the strongest expression of profilin 1 was detected in nonneoplastic mammary glands (Figure 5D). These results suggest a correlation between profilin 1 expression and suppression of tumor malignancy. Similar to profilin 1, thioredoxin was detected at high levels in DNIIR mammary tumors and weakly detected in invasive tumors from TGF-R transgenic mice (Figure 6). The immunohistochemical expression profile for thioredoxin also indicated a correlation with tumor suppression. The expression detected by immunohistochemistry for profilin 1 and thioredoxin demonstrated the same trend detected in MALDI-TOF analysis of the original tumors (Table 3). Comparison of Genomic and Proteomic Analysis. To determine if a correlation could be identified between proteomic and cDNA microarray analyses, data obtained from the two approaches were compared with each other. The 13 proteins identified as class prediction markers in the proteomic analyses were compared to the profiles contained within the microarray experimental results (Figure 7). Thymosin β4 and heat shock protein 1 demonstrated consistent protein and transcript levels. MALDI-TOF and cDNA microarray indicated that thymosinβ4 was more prevalent in both MMTV-DNIIR and bigenic tumors. In contrast, the heat shock protein 1 was most prevalent in MMTV-DNIIR tumors. Of the remaining proteins, profilin 1 and thioredoxin exhibited changes in protein expres-
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Figure 3. Protein profiling by MALDI-TOF MS analysis of frozen sections. A. H&E stain of one freshly frozen MMTV-TGF-R tumor. B. The consecutive tumor section was mounted on a gold-coated MALDI plate and regions of interest were coated with matrix. Based on examination of the adjacent H&E stained serial section, eight areas with homogeneous tumor cellularity were spotted with matrix and subject to MALDI-TOF MS analysis. Individual matrix droplets were numbered to help with sample orientation and data storage. C. Example spectra obtained from spots 1 and 2 of the matrix-covered tumor. Each individual peak indicates one detected protein. X-axis represents m/z (mass/charge) value. Y-axis indicates the intensity of each protein. Table 2. Group Comparison and Class Prediction among Protein Spectra of Mammary Tumors Arising from Transgenic Mice
group comparison samples
DNIIR (48) vs TGF-R (40) DNIIR (48) vs Bigenic (40) TGF-R (40) vs Bigenic (40)
probability of no. of random differentially no. of permutation expressed misclassified with peaks samples misclassification
80 75 48
1 0 3
