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In Vivo Gene Expression Profile Analysis of Human Breast Cancer Progression¹

Department of Pathology, Harvard Medical School, and the Molecular Pathology Unit, Massachusetts General Hospital, Boston, Massachusetts 02129 [D. C. S., R. L.]; Ophthalmology Research, Children’s Hospital, Boston, Massachusetts 02115 [G. R.]; and Microarray Department, Research Genetics, Inc., Huntsville, Alabama 35801 [S. T., J. R. H., A. G. E.]

	ABSTRACT

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The development and use of molecular-based therapy for breast cancer and other human malignancies will require a detailed molecular genetic analysis of patient tissues. The recent development of laser capture microdissection and high density cDNA arrays now provides a unique opportunity to generate gene expression profiles of cells from various stages of tumor progression as it occurs in the actual neoplastic tissue milieu. We report the combined use of laser capture microdissection and high-throughput cDNA microarrays to monitor in vivo gene expression levels in purified normal, invasive, and metastatic breast cell populations from a single patient. These in vivo gene expression profiles were verified by real-time quantitative PCR and immunohistochemistry. The combined use of laser capture microdissection and cDNA microarray analysis provides a powerful new approach to elucidate the in vivo molecular events surrounding the development and progression of breast cancer and is generally applicable to the study of malignancy.

	Introduction

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The elucidation of the genetic events underlying the initiation and progression of human breast cancer has been hampered by limitations inherent to both in vitro and in vivo methods of study. The most significant limitation of in vitro-based systems is that genetic information derived from cell lines may not accurately reflect the molecular events taking place in the actual tissue milieu from which they were derived. On the other hand, in vivo genetic analysis of breast cancer has been limited by our inability to directly and specifically procure pure populations of cells from complex heterogeneous tissue (1) . The recent development of LCM,³ a technique that allows for the rapid, reliable, and accurate procurement of cells from specific microscopic regions of tissue sections under direct visualization, now affords the opportunity to perform molecular genetic analysis of pure populations of malignant breast cells in their native tissue environment (2) . This technical advance helps overcome the limitations associated with traditional in vivo and in vitro approaches.

The advent of high-density cDNA microarray technology (3) , with its capacity for simultaneous monitoring of thousands of genes, provides a unique opportunity for high-throughput genetic analysis of cancer. Although most current microarray studies have been performed with in vitro-derived genetic material from both mammalian and nonmammalian systems (4, 5, 6) , a major leap in functional genomic investigations would be the ability to perform array-based expression analysis with in vivo-derived genetic material originating from morphologically distinct cellular subpopulations within neoplastic tissue. Here we report the first application of combining LCM and cDNA microarray technologies to analyze gene expression in a clinical cancer specimen. Furthermore, we demonstrate that expression profiles of greater than 8000 genes can be successfully generated using nonamplified RNA derived from distinct cell populations within several different morphological stages of human breast cancer progression. Expression profile data were verified by real-time quantitative PCR and immunohistochemistry. Our results indicate that high-throughput in vivo gene expression analysis can be achieved and should be of value in elucidating the genetic events associated with breast cancer progression.

	Materials and Methods

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LCM.
All tissue used for this study was obtained from a modified radical mastectomy specimen from a single patient. Tissue in excess of what was necessary for diagnostic purposes was obtained <15 min after removal from the patient, embedded in TissueTek OCT medium (VWR Scientific Products Corporation, San Diego, CA), and frozen in liquid nitrogen. The tissues were sectioned at 8 µm in a cryostat, mounted on uncoated glass slides, and immediately stored at -80°C. Slides containing frozen sections were immediately fixed in 70% ethanol for 30 s, stained with H&E, followed by 5-s dehydration steps in 70, 95, and 100% and a final 5-min dehydration step in xylene. Once air-dried, the sections were laser microdissected with a PixCell I and II LCM system from Arcturus Engineering (Mountain View, CA). Following the standard protocol of Emmert-Buck et al. (2) , 0.5 x 10⁵ morphologically normal breast epithelial cells, malignant invasive breast carcinoma cells, and malignant metastatic (to an axillary lymph node) breast carcinoma cells were "laser captured." Each population was estimated to be >98% "homogeneous" as determined by microscopic visualization of the captured cells.

RNA Extraction from Microdissected Samples.
The total RNA from each population of laser captured cells was independently extracted by means of a modification of the RNA microisolation protocol as described (2) . Briefly, the transfer film and adherent cells were incubated with guanidinium isothiocyanate buffer at room temperature, extracted with phenol/chloroform/isoamyl alcohol, and precipitated with sodium acetate and glycogen carrier (10 µg/µl) in isopropanol. After initial recovery and resuspension of the RNA pellet, a DNase step was performed for 2 h at 37°C using 10 units of DNase (GenHunter, Nashville, TN) in the presence of 10 units of RNase inhibitor (Life Technologies, Inc., Gaithersburg, MD), followed by reextraction and precipitation. The pellet was resuspended in 27 µl of RNase-free H₂O; one-third (9 µl) of the total RNA from each sample was used for RTQ-PCR analysis, and the remaining two-thirds (18 µl) were used for high-density cDNA array analysis.

RNA Labeling and Hybridization.
For each labeling, total RNA corresponding to 1.7–2.0 x 10⁴ cells was reverse-transcribed in the presence of 50 µCi of [³³P]dCTP, 50 µCi of [³³P]dATP, 500 ng of Oligo-dT, and 200 units of SuperScript II RT (Life Technologies, Inc.). The second strand was synthesized in the presence of 50 µCi of [³³P]dCTP, 50 µCi of [³³P]dATP, 500 ng of random hexamers, and 2500 units of large fragment DNA polymerase I (Life Technologies, Inc.). The labeled, double-stranded cDNA was denatured and hybridized to the cDNA GeneFilter arrays as follows. The GeneFilters were prehybridized at 42°C in a roller oven (Hybaid; Midwest Scientific, St. Louis, MO) with 1.0 µg/ml poly-dA (Research Genetics, Inc, Huntsville, Al) and 1.0 µg/ml Cot1 DNA (Life Technologies, Inc.) in 5 ml of Microhyb solution (Research Genetics, Inc.) for at least 2 h. After an overnight hybridization with the radiolabeled probe, the filters were washed twice at 50°C in 2x SSC (1x SSC, 15 mM trisodium citrate, and 150 mM NaCl), 1% SDS for 20 min and once at room temperature in 0.5x SSC, 1% SDS for 15 min. The filters were then exposed overnight to a Packard screen and scanned at 50-µm resolution in a phosphorimager instrument (Cyclone Instrument from Packard, Inc.). After each hybridization, the filters were stripped by boiling in 0.5% SDS solution and scanned for residual leftover hybridization.

Image Analysis.
The tiff images resulting from the phosphorimager were directly imported into the image analysis software Pathways (Research Genetics, Inc.). The software uses control spots present throughout the filter to align the images and performs autocentering, which aligns and centers well-shaped spots and deforms the calculated grid around spots that have a high confidence factor. When comparing two images, the software normalizes the two different hybridizations on the basis of the average total intensity on each filter. The software locates, calculates, and stores each cDNA spot intensity from each tiff file and simultaneously compares two different normalized tiff images. The differential expression ratios represent the average of two independent experiments.

Microarray cDNA Filters.
The clone selection was based on the criteria that the clones: (a) contain the 3' untranslated region; (b) are of average size (1 kb); and (c) originated from oligo-dT primed libraries. These selected clones have been sequence verified at the sequencing facilities of Research Genetics. All of these clones are from the IMAGE libraries. After PCR amplification, 10 ng of insert cDNA was printed on a charged nylon membrane by a custom-made robot. Genes (n = 5184) were spotted on a 5 x 7-cm nylon membrane. Another 576 spots consisted of total genomic DNA, which served as reference points for the image analysis software, for normalization purposes, and for verifying the homogeneity of the hybridization. The GF211 GeneFilter contained 4000 named genes, and the CBGF contained 2800 ESTs and 2384 named genes. GF211 and CBGF shared 1100 cDNAs in common; thus, the total number of genes scanned was 8084.

RTQ-PCR.
One-third of the same total RNA pool used for the GeneFilter hybridizations was reverse-transcribed using 50 µg/ml oligo(dT), 500 µM deoxynucleotide triphophosphate, and 200 units of Superscript II reverse transcriptase (Life Technologies, Inc.) for 1 h at 37°C, and the resulting first-strand cDNA was diluted and used as template for the following RTQ-PCR analysis. Sequences for genes identified using array technology were determined by direct sequence analysis and confirmed using National Center for Biotechnology Information (NCBI) GenBank and Unigene databases. The specificity of amplicon sequence selection was determined using two methods: (a) primer and probe sequences that specifically detect the experimental gene sequence, as determined by means of the NCBI Blast module, were used; (b) amplicons generated during the PCR reaction were analyzed using the first derivative primer melting curve software supplied by Perkin-Elmer/Applied BioSystems. Analysis of gene expression was generated using an ABI Prizm 7700 Sequence Detection System (TaqMan), which uses the 5' nuclease activity of Taq DNA polymerase to generate a real-time quantitative DNA analysis assay (7 , 8) . A nonextendable oligonucleotide hybridization probe with 5' fluorescent and 3' rhodamine (quench) moieties is present during the extension phase of the PCR. Degradation and release of the fluorescent moiety attributable to the 5' nuclease activity results in peak emission at 518 nm and is monitored every 8.5 s by a sequence detector. The increase in fluorescence is monitored during the complete amplification process (real-time). A relative standard curve representing four 4-fold dilutions of breast stock cDNA (1:2.5, 1:10, 1:40, and 1:160) was used for linear regression analysis of unknown samples. The expression of the housekeeping gene, cyclophilin 33A, was used to normalize for variances in input cDNA. The sequences of the PCR primer pairs and fluorogenic probe (5' to 3'), respectively, that were used for each gene are as follows: cyclophilin 33, GCTGCCTGTGCACTCATGAA, CAGTGCCATTGTGGTTTGTGA, and 6FAM-ATCACCGCCCTGGCACATGA-ACTG-TAMRA; apolipoprotein D, GAGAAGATCCCAACAACCTTTGA, TGATCTTTCCGTTTTCCATTAGTGA, and 6FAM-ATGGACGCTGCATCCAGGCCAACTA-TAMRA; heat shock factor 1, CCTGCAGGTTGTTCATAGTCAGAA, TCCGTCCATCCACTGTG-TGTATA, and 6FAM-ACACAACTGTCCCGTTCCCCGCTC-TAMRA; BRCA-1, GGCTATGCAAGG-GTCCCTTA, TGGTGGCGTTTAAATGGTTTT, and 6FAM-TCTCCCTTGGAAATCTGCCATGAGC-TAMRA; SWI/SNF, GGCTGGGAGGACTGGTGTT, TTTCCAAACCTGCCAGAAGTG, and 6FAM-AAGCCCTAGGCCCACCCTCCTCA-TAMRA; and -adrenergic receptor kinase 1, GGC-TCCTGTGCCCTTATTCAG, CTGCCAATGCCACTCTCTCA, and 6FAM-ACTCCCACTTCCCTGACACTGCGG-TAMRA. The fluorogenic probes are FAM and TAMRA.

Immunoperoxidase Staining.
Immunohistochemical staining of frozen tissue sections (8 µm) adjacent to those slides used for LCM were mounted on slides and fixed in 10% neutral buffered formalin for 8 min. The slides were preincubated with mouse serum (1:50 dilution) for 20 min at room temperature to block nonspecific binding and incubated with the anti-apolipoprotein D antibody 8CD6 (Signet Laboratories, Dedham, MA) at a 1:40 dilution for 20 min at room temperature. The slides were washed three times in PBS, incubated with PBS/0.3% H₂O₂ for 30 min, and washed three times in PBS. Sections were incubated with biotinylated anti-mouse antibody (Vector Laboratories, Burlingame, CA), washed in PBS, incubated with the ABC reagent (Vector Laboratories) for 1 h, washed, and developed according to the manufacturer’s recommendations. The tissue was postfixed in 4% formalin and counterstained with hematoxylin.

	Results and Discussion

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Microdissection and cDNA Array Analysis.
Although the combined use of LCM and cDNA arrays provides a unique opportunity to study gene expression of subpopulations of cells in their native (in vivo) tissue environment, such technologies have not been applied to clinical cancer specimens. To demonstrate the feasibility of integrating and applying these technologies to such specimens, we examined the differential gene expression between normal and malignant breast epithelial cells in a single clinical breast cancer specimen. Normal breast epithelial cells, invasive cells, and metastatic breast cancer cells (1 x 10⁵ cells from each target population) were cleanly captured by LCM (Fig. 1) , and total RNA was isolated. One-third of the isolated RNA was set aside for quantitative PCR validation studies (see below), and the remaining two-thirds were used for the generation of radiolabeled probe. This fraction of the RNA from each target population was divided in two, and a total of six (two for each target cell type) independent radiolabeling reactions were performed. To ensure that the cDNA products are proportional to the initial gene expression profile, we generated a ³³P-labeled microarray probe by oligo(dT) direct reverse transcription of total RNA, followed by second-strand cDNA synthesis. Probe derived from each target cell was simultaneously hybridized to two different microarray nylon filters, (designated GF211 and CBGF; see "Materials and Methods" for filter designs) containing a combined total of 8084 cDNAs. To avoid system variability that may be associated with the use of different filters, we performed sequential hybridizations on the same set of filters with each of the different cDNA probes. Furthermore, each hybridization to these two GeneFilters was performed in duplicate. Each hybridized filter was scanned with a phosphorimager, the resulting tiff file images were obtained, and comparative analysis was performed with Pathways custom image analysis software. The quality of the microarray hybridizations is demonstrated with the CBGF in which the same GeneFilter was sequentially hybridized with probes derived from normal, invasive, and metastatic cells (Fig. 2A) ; comparable data were obtained with the GF211 filters. To determine whether a hybridization signal corresponding to a particular cDNA is reproducible between GeneFilters, we compared duplicate gene expression profiles for each of three different cell types (normal, invasive, and metastatic cells). With this approach, <0.15% variability in gene expression was observed between normalized duplicate hybridizations:

View larger version (124K): [in this window] [in a new window]   Fig. 1. LCM from H&E-stained sections (8 µm) of normal breast epithelium and invasive and metastatic breast cancer. The convoluted morphologically normal breast epithelium (A) is selectively procured and transferred to film (D). Invasive breast cancer cells are interspersed among desmoplastic stroma (B), and metastatic breast cancer cells in an axillary lymph node (C) are captured and transferred to film (E and F, respectively). B, arrow, normal desmoplastic stromal cells; C, arrow, residual normal lymphoid tissue.

View larger version (72K): [in this window] [in a new window]   Fig. 2. A, differential gene expression monitored with the use of cDNA Genefilter arrays. A CBGF containing over 5100 genes expressed in breast tissue (2384 named genes and 2800 ESTs) was sequentially hybridized with cDNA probes corresponding to laser microdissected RNA from normal, invasive, and metastatic cells. Insets I and II, Pathways pseudo-color overlay of a portion of the same array corresponding to normal (red) versus invasive (green) and normal (red) versus metastatic (green) cells, respectively. Red spots, genes that are differentially expressed in the normal cells; green spots, genes differentially expressed in either invasive (I) or metastatic (II) cells; yellow spots, genes displaying equal levels of expression in both comparisons. B, representative hybridization patterns for a subset of differentially expressed genes. These spots represent the actual hybridization for each gene and have not been subjected to any normalization process. The differential hybridization can be assessed among normal (N), invasive (IN), and metastatic cells (MET) as compared with the hybridizations for the cyclophilin B, 33A, and 33C genes.

View larger version (63K): [in this window] [in a new window]   Fig. 3. A, comparative ratio analysis by RTQ-PCR and cDNA array hybridization of a subset of genes differentially expressed in invasive and metastatic tissues. , microarray ratios; , RTQ-PCR ratios. The real-time data represent the average of two independent experiments, and the array data are the average of duplicate hybridization comparisons. Rec., receptor; Apo-D, apolipoprotein D; HSF, heat shock factor. B, representative fields of apolipoprotein D immunohistochemical staining of normal breast cells (a), invasive cells (b), and metastatic cells (c). Consecutive tissue sections corresponding to those used for LCM (cDNA arrays and RTQ-PCR) were immunoperoxidase stained (with an antibody specific to apolipoprotein D) and hematoxylin counterstained.

Although two studies, one of which also included the use of LCM, have reported the use of cDNA arrays to study gene expression in tissues, our approach has several novel features.

(a) a single microarray profile in our study reflects gene expression that corresponds to a specific population of epithelial cells independent of contaminating stromal cells. By contrast, previous studies used genetic material derived from (nondissected) bulk tissue specimens that are composed of both malignant and normal cells (25) . Therefore, each individual microarray profile from bulk tissue reflects gene expression that corresponds to malignant cells as well as to many different types of contaminating normal cells in the cancer specimen.

(b) We generated probes directly without amplification to avoid possible representational bias that may be associated with amplification schemes, whereas Luo et al. (23) used a T7-based RNA amplification method to generate probes for their microarrays.

(c) By analyzing breast cancer progression, which reflects genetic alterations over time, we performed both spatial and temporal in vivo expression profiling. The previously mentioned studies performed expression profile analysis on tissues that were spatially but not temporally distinct (23 , 24) .

Concluding Remarks.
Using carefully controlled conditions, we demonstrated that in vivo subpopulations of malignant cells from multiple stages of breast cancer progression can be simultaneously screened for thousands of genes. We now report the feasibility of combining LCM and high-throughput cDNA arrays to study in vivo gene expression profiling, and we illustrate through the use of duplicate hybridizations, RTQ-PCR analysis, and immunohistochemistry that this approach produces reproducible and valid data. We believe that this in vivo functional genomic approach not only provides an evolving opportunity to rapidly and directly monitor in vivo gene expression in human breast cancer but also promises to provide novel insights into fundamental cancer biology. Furthermore, the application of this approach to clinical cancer specimens may provide a key step to rapid advances in cancer prevention, detection, diagnosis, and therapeutics.

	ACKNOWLEDGMENTS

 
We thank D. Haber, D. Krizman, L. Smith, and I. Stamenkovic for helpful discussions and Bruce Kaynor for technical assistance. Special thanks to B. Smith for providing tissue samples.

	FOOTNOTES

 
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

¹ This work was supported by a grant from the Massachusetts Department of Public Health Breast Cancer Program, a collaborative grant from the Dana-Farber/Partners Cancer Care Women’s Cancer Program, and Grant IRG-173H from the American Cancer Society (to D. C. S.).

² To whom requests for reprints should be addressed, at Microarrays Department, Research Genetics, Inc., 2700 Memorial Parkway, Huntsville, AL 35801. E-mail: abdel{at}resgen.com

³ The abbreviations used are: LCM, laser capture microdissection; RTQ-PCR, real-time quantitative PCR; CBGF, Custom Breast GeneFilter; EST, expressed sequence tag; FAM, 6-carboxyfluorescein; TAMRA, 6-carboxytetramethylrhodamine.

Received 7/ 8/99. Accepted 10/ 4/99.

	REFERENCES

Top ABSTRACT Introduction Materials and Methods Results and Discussion REFERENCES

 

1. Bonner R. F., Emmert-Buck M., Cole K., Pohida T., Chuaqui R., Goldstein S., Liotta L. A. Laser capture microdissection: molecular analysis of tissue. Science (Washington DC), 278: 1481-1482, 1997.[Free Full Text]
2. Emmert-Buck M. R., Bonner R. F., Smith P. D., Chuaqui R. F., Zhuang Z., Goldstein S., Weissw R. A., Liotta L. A. Laser capture microdissection. Science (Washington DC), 274: 998-1001, 1996.[Abstract/Free Full Text]
3. Schena M., Shalon D., Davis R. W., Brown P. O. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science (Washington DC), 270: 467-470, 1995.[Abstract/Free Full Text]
4. Iyer V. R., Eisen M. B., Ross D. T., Schuler G., Moore T., Lee J. C. F., Trent J. M., Staudt L. M., Hudson J., Jr., Boguski M. S., Lashkari D., Shalon D., Botstein D., Brown P. O. The transcriptional program in the response of human fibroblasts to serum. Science (Washington DC), 283: 83-87, 1999.[Abstract/Free Full Text]
5. Holstege F. C., Jennings E. G., Wyrick J. J., Lee T. I., Hengartner C. J., Green M. R., Golub T. R., Lander E. S., Young R. A. Dissecting the regulatory circuitry of an eukaryotic genome. Cell, 95: 717-728, 1998.[Medline]
6. Douglas F., McClure K., Kazlauskas A., Lander E. S. Diverse signaling pathways activated by growth factor receptors induce broadly overlapping, rather than independent, sets of genes. Cell, 97: 727-741, 1999.[Medline]
7. Heid C. A., Stevens J., Livak K. J., Williams P. M. Real time quantitative PCR. Genome Res., 6: 986-994, 1996.[Abstract/Free Full Text]
8. Gelmini S., Orlando C., Sestini R., Vona G., Pinzani P., Ruocco L., Pazzagli M. Quantitative polymerase chain reaction-based homogeneous assay with fluorogenic probes to measure c-erbB-2 oncogene amplification. Clin. Chem., 43: 752-758, 1997.[Abstract/Free Full Text]
9. Dietz-Itza I., Vizoso F., Merino A. M., Sanchez L. M., Tolivia J., Fernandez J., Ruibal A., Lopez-Otin C. Expression and prognostic significance of apolipoprotein D in breast cancer. Am. J. Pathol., 144: 310-320, 1994.[Abstract]
10. Ahn S. H., Sawada H., Ro J. Y., Nicolson G. L. Differential expression of annexin I in human mammary ductal epithelial cells in normal and benign and malignant breast tissues. Clin. Exp. Metastasis, 15: 151-156, 1997.[Medline]
11. Pencil S. D., Toth M. Elevated levels of annexin I protein in vitro and in vivo in rat and human mammary adenocarcinoma. Clin. Exp. Metastasis, 16: 113-121, 1998.[Medline]
12. Contrino J., Hair G., Kreutzer D. L., Rickles F. R. In situ detection of tissue factor in vascular endothelial cells: correlation with the malignant phenotype of human breast disease. Nat. Med., 2: 209-215, 1996.[Medline]
13. Zhou J. N., Ljungdahl S., Shoshan M. C., Swedenborg J., Linder S. Activation of tissue-factor gene expression in breast carcinoma cells by stimulation of the RAF-ERK signaling pathway. Mol. Carcinog., 21: 234-243, 1998.[Medline]
14. Michie C. A., Tantscher E., Schall T., Rot A. Physiological secretion of chemokines in human breast milk. Eur. Cytokine Netw., 9: 123-129, 1998.[Medline]
15. Youngs S. J., Ali S. A., Taub D. D., Rees R. C. Chemokines induce migrational responses in human breast carcinoma cell lines. Int. J. Cancer, 71: 257-266, 1997.[Medline]
16. Miki Y., Swensen J., Shattuck-Eidens D., Futreal P. A., Harshman K., Tavtigian S., Liu Q., Cochran C., Bennett L. M., Ding W., Bell R., Rosenthal J., Hussey C., Tran T., McClure M., Frye C., Hattier T., Phelps R., Haugen-Strano A., Katcher H., Yakumo K., Gholami Z., Shaffer D., Stone S., Bayer S., Wray C., Bogden R., Dayananth P., Ward J., Tonin P., Narod S., Bristow P. K., Norris F. H., Helvering L., Morrison P., Rosteck P., Lai M., Barrett J. C., Lewis C., Neuhausen S., Cannon-Albright L., Goldgar D., Wiseman R., Kamb A., Skolnick M. H. A strong candidate for the breast and ovarian cancer susceptibility gene BRCA1. Science (Washington DC), 266: 66-71, 1994.[Abstract/Free Full Text]
17. Wilson C. A., Ramos L., Villasenor M. R., Anders K. H., Press M. F., Clarke K., Karlan B., Chen J. J., Scully R., Livingston D., Zuch R. H., Kanter M. H., Cohen S., Calzone F. J., Slamon D. J. Localization of human BRCA1 and its loss in high-grade, non-inherited breast carcinomas. Nat. Genet., 21: 236-240, 1999.[Medline]
18. Naumovski L., Cleary M. L. The p53-binding protein 53BP2 also interacts with Bc12 and impedes cell cycle progression at G2/M. Mol. Cell. Biol., 16: 3884-3892, 1996.[Abstract]
19. Chiba H., Muramatsu M., Nomoto A., Kato H. Two human homologues of Saccharomyces cerevisiae SWI2/SNF2 and Drosophila brahma are transcriptional coactivators cooperating with the estrogen receptor and the retinoic acid receptor. Nucleic Acids Res., 22: 1815-1820, 1994.[Abstract/Free Full Text]
20. Briggs M. D., Hoffman S. M., King L. M., Olsen A. S., Mohrenweiser H., Leroy J. G., Mortier G. R., Rimoin D. L., Lachman R. S., Gaines E., Cekleniak J. A., Knowlton R. G., Cohn D. H. Pseudoachondroplasia and multiple epiphyseal dysplasia due to mutations in the cartilage oligomeric matrix protein gene. Nat. Genet., 10: 330-336, 1995.[Medline]
21. Badino G. R., Novelli A., Girardi C., Di Carlo F. Evidence for functional -adrenoceptor subtypes in CG-5 breast cancer cells. Pharmacol. Res., 33: 255-260, 1996.[Medline]
22. Yang X., Dale E. C., Diaz J., Shyamala G. Estrogen dependent expression of heat shock transcription factor: implications for uterine synthesis of heat shock proteins. J. Steroid Biochem. Mol. Biol., 52: 415-419, 1995.[Medline]
23. Luo L., Salunga R. C., Guo H., Bittner A., Joy K. C., Galindo J. E., Xiao H., Rogers K. E., Wan J. S., Jackson M. R., Erlander M. G. Gene expression profiles of laser-captured adjacent neuronal subtypes. Nat. Med., 1: 117-122, 1999.
24. Schena M., Shalon D., Heller R., Chai A., Brown P. O., Davis R. W. Parallel human genome analysis: microarray-based expression monitoring of 1000 genes. Proc. Natl. Acad. Sci. USA, 93: 10614-10619, 1996.[Abstract/Free Full Text]
25. Welford S. M., Gregg J., Chen E., Garrison D., Sorensen P. H., Denny C. T., Nelson S. F. Detection of differentially expressed genes in primary tumor tissues using representational differences analysis coupled to microarray hybridization. Nucleic Acids Res., 26: 3059-3065, 1998.[Abstract/Free Full Text]

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				S. Melov and A. Hubbard Microarrays as a Tool to Investigate the Biology of Aging: A Retrospective and a Look to the Future Sci. Aging Knowl. Environ., October 20, 2004; 2004(42): re7 - re7. [Abstract] [Full Text] [PDF]

				M Lacroix, R-A Toillon, and G Leclercq Stable 'portrait' of breast tumors during progression: data from biology, pathology and genetics Endocr. Relat. Cancer, September 1, 2004; 11(3): 497 - 522. [Abstract] [Full Text] [PDF]

				S. Shimizu, H. Nakashima, K. Masutani, Y. Inoue, K. Miyake, M. Akahoshi, Y. Tanaka, K. Egashira, H. Hirakata, T. Otsuka, et al. Anti-monocyte chemoattractant protein-1 gene therapy attenuates nephritis in MRL/lpr mice Rheumatology, September 1, 2004; 43(9): 1121 - 1128. [Abstract] [Full Text] [PDF]

				S. Zareparsi, A. Hero, D. J. Zack, R. W. Williams, and A. Swaroop Seeing the Unseen: Microarray-Based Gene Expression Profiling in Vision Invest. Ophthalmol. Vis. Sci., August 1, 2004; 45(8): 2457 - 2462. [Full Text] [PDF]

				J. Tian, K. Ishibashi, and J. T. Handa The expression of native and cultured RPE grown on different matrices Physiol Genomics, April 13, 2004; 17(2): 170 - 182. [Abstract] [Full Text] [PDF]

				J. Spychala, E. Lazarowski, A. Ostapkowicz, L. H. Ayscue, A. Jin, and B. S. Mitchell Role of Estrogen Receptor in the Regulation of Ecto-5'-Nucleotidase and Adenosine in Breast Cancer Clin. Cancer Res., January 15, 2004; 10(2): 708 - 717. [Abstract] [Full Text] [PDF]

				S. C. M. Tsoi, J. M. Cale, I. M. Bird, and H. H. Kay cDNA Microarray Analysis of the Gene Expression Profiles in Human Placenta: Up-Regulation of the Transcript Encoding Muscle Subunit of Glycogen Phosphorylase in Preeclampsia Reproductive Sciences, December 1, 2003; 10(8): 496 - 502. [Abstract] [PDF]

				J. C. Sok, M. A. Kuriakose, V. B. Mahajan, A. N. Pearlman, M. D. DeLacure, and F.-A. Chen Tissue-Specific Gene Expression of Head and Neck Squamous Cell Carcinoma In Vivo by Complementary DNA Microarray Analysis Arch Otolaryngol Head Neck Surg, July 1, 2003; 129(7): 760 - 770. [Abstract] [Full Text] [PDF]

				X.-J. Ma, R. Salunga, J. T. Tuggle, J. Gaudet, E. Enright, P. McQuary, T. Payette, M. Pistone, K. Stecker, B. M. Zhang, et al. Gene expression profiles of human breast cancer progression PNAS, May 13, 2003; 100(10): 5974 - 5979. [Abstract] [Full Text] [PDF]

				J. W. Kim and X. W. Wang Gene expression profiling of preneoplastic liver disease and liver cancer: a new era for improved early detection and treatment of these deadly diseases? Carcinogenesis, March 1, 2003; 24(3): 363 - 369. [Abstract] [Full Text] [PDF]

				D. Porter, J. Lahti-Domenici, A. Keshaviah, Y. K. Bae, P. Argani, J. Marks, A. Richardson, A. Cooper, R. Strausberg, G. J. Riggins, et al. Molecular Markers in Ductal Carcinoma in Situ of the Breast Mol. Cancer Res., March 1, 2003; 1(5): 362 - 375. [Abstract] [Full Text] [PDF]

				A K Banerjee, P H Rabbitts, and J George Lung cancer * 3: Fluorescence bronchoscopy: clinical dilemmas and research opportunities Thorax, March 1, 2003; 58(3): 266 - 271. [Abstract] [Full Text] [PDF]

				M. Nakazono, F. Qiu, L. A. Borsuk, and P. S. Schnable Laser-Capture Microdissection, a Tool for the Global Analysis of Gene Expression in Specific Plant Cell Types: Identification of Genes Expressed Differentially in Epidermal Cells or Vascular Tissues of Maize PLANT CELL, March 1, 2003; 15(3): 583 - 596. [Abstract] [Full Text] [PDF]

				E. V. Schmidt Genes Involved in Breast Cancer Progression: Analysis of Global Changes in Gene Expression or Retroviral Tagging? Am. J. Pathol., December 1, 2002; 161(6): 1973 - 1977. [Full Text] [PDF]

				A. Adeyinka, E. Emberley, Y. Niu, L. Snell, L. C. Murphy, H. Sowter, C. C. Wykoff, A. L. Harris, and P. H. Watson Analysis of Gene Expression in Ductal Carcinoma in Situ of the Breast Clin. Cancer Res., December 1, 2002; 8(12): 3788 - 3795. [Abstract] [Full Text] [PDF]

				J. L. Dennis, J. K. Vass, E. C. Wit, W. N. Keith, and K. A. Oien Identification from Public Data of Molecular Markers of Adenocarcinoma Characteristic of the Site of Origin Cancer Res., November 1, 2002; 62(21): 5999 - 6005. [Abstract] [Full Text] [PDF]

				K. S. Wilson, H. Roberts, R. Leek, A. L. Harris, and J. Geradts Differential Gene Expression Patterns in HER2/neu-Positive and -Negative Breast Cancer Cell Lines and Tissues Am. J. Pathol., October 1, 2002; 161(4): 1171 - 1185. [Abstract] [Full Text] [PDF]

				S. M. Roth, R. E. Ferrell, D. G. Peters, E. J. Metter, B. F. Hurley, and M. A. Rogers Influence of age, sex, and strength training on human muscle gene expression determined by microarray Physiol Genomics, September 3, 2002; 10(3): 181 - 190. [Abstract] [Full Text] [PDF]

P. Sluka, L. O'Donnell, and P. G. Stanton Stage-Specific Expression of Genes Associated with Rat Spermatogenesis: Characterization by Laser-Capture Microdissection and Real-Time Polymerase Chain Reaction Biol Reprod, September 1, 2002; 67(3): 820 - 828. [Abstract] [Full Text] [PDF]