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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.]
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ABSTRACT |
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 Top ABSTRACT IntroductionMaterials and MethodsResults and DiscussionREFERENCES |
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Introduction |
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TopABSTRACTIntroductionMaterials and MethodsResults and DiscussionREFERENCES |
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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.
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Materials and Methods |
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TopABSTRACTIntroductionMaterials and MethodsResults and DiscussionREFERENCES |
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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.
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Results and Discussion |
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TopABSTRACTIntroductionMaterials and MethodsResults and DiscussionREFERENCES |
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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:
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. Overall, the alterations in gene expression ranged from approximately -40- to 8-fold; the differential expression data for highly expressed genes was readily assessed visually using the pseudo-color overlay image generated by the Pathways software as shown in Fig. 2A
. A partial list of genes that are differentially expressed in invasive and metastatic cells is shown in Table 1
. The differentially expressed genes demonstrate a broad range of functional activity. Although many of these genes have been implicated in various aspects of tumor biology, few have been demonstrated to be associated with breast cancer and include apolipoprotein D (9)
, annexin I (10
, 11)
, tissue factor (12
, 13)
, RANTES (14
, 15)
, and BRCA1 (16
, 17)
. Overall, with the exception of BRCA1, our in vivo transcript data generated through the combined use of LCM and high-density cDNA array analysis are consistent with that reported in the literature. It is tempting to speculate on the potential role of many of the other differentially expressed genes. For example, 53BP2, which is down-regulated in both invasive and metastatic cells as compared with normal breast epithelium (Fig. 2B)
, has been demonstrated to bind bcl-2 and p53 and to impede cell cycle progression at G2-M (18)
. Additionally, the protein BAF60, a component of the SWI/SNF complex, is up-regulated in both invasive and metastatic cells (Fig. 2B)
. Interestingly, the SWI/SNF complex has been demonstrated to enhance nuclear receptor-mediated transcriptional activation, including that associated with the estrogen receptor and retinoic acid receptor (19)
. However, what role, if any, these genes or any other gene in Table 1
may play in the pathogenesis of breast cancer remains to be seen and will be the subject of further investigation.
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demonstrates that the differential expression pattern and the quantitative expression level of each of the five genes as determined by RTQ-PCR were similar to those observed with cDNA arrays in 8 of 10 expression data points, confirming the reliability of our array expression profile data. Our observed correlation between the cDNA array and RTQ-PCR data are consistent with that observed by others (4
, 23
, 24)
.
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. This result further supports the reliability of our expression data and demonstrates the cellular specificity of the apolipoprotein gene expression. Overall, the RTQ-PCR and immunohistochemistry results support the feasibility of our microarray experimental protocol as a means to assess in vivo transcript expression profiles. 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.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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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.
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TopABSTRACTIntroductionMaterials and MethodsResults and DiscussionREFERENCES |
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 T. Reimer, D. Koczan, B. Gerber, D. Richter, H.J. Thiesen, and K. Friese Microarray analysis of differentially expressed genes in placental tissue of pre-eclampsia: up-regulation of obesity-related genes Mol. Hum. Reprod., July 1, 2002; 8(7): 674 - 680. [Abstract] [Full Text] [PDF]
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K. Miura, E. D. Bowman, R. Simon, A. C. Peng, A. I. Robles, R. T. Jones, T. Katagiri, P. He, H. Mizukami, L. Charboneau, et al. Laser Capture Microdissection and Microarray Expression Analysis of Lung Adenocarcinoma Reveals Tobacco Smoking- and Prognosis-related Molecular Profiles Cancer Res., June 1, 2002; 62(11): 3244 - 3250. [Abstract] [Full Text] [PDF]
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Z. Gu, R. Y. Lee, T. C. Skaar, K. B. Bouker, J. N. Welch, J. Lu, A. Liu, Y. Zhu, N. Davis, F. Leonessa, et al. Association of Interferon Regulatory Factor-1, Nucleophosmin, Nuclear Factor-{kappa}B, and Cyclic AMP Response Element Binding with Acquired Resistance to Faslodex (ICI 182,780) Cancer Res., June 1, 2002; 62(12): 3428 - 3437. [Abstract] [Full Text] [PDF]
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M. Ellis, N. Davis, A. Coop, M. Liu, L. Schumaker, R. Y. Lee, R. Srikanchana, C. G. Russell, B. Singh, W. R. Miller, et al. Development and Validation of a Method for Using Breast Core Needle Biopsies for Gene Expression Microarray Analyses Clin. Cancer Res., May 1, 2002; 8(5): 1155 - 1166. [Abstract] [Full Text] [PDF]
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 J.-H. Kim, S. J. Skates, T. Uede, K.-k. Wong, J. O. Schorge, C. M. Feltmate, R. S. Berkowitz, D. W. Cramer, and S. C. Mok Osteopontin as a Potential Diagnostic Biomarker for Ovarian Cancer JAMA, April 3, 2002; 287(13): 1671 - 1679. [Abstract] [Full Text] [PDF]
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S. Ramaswamy and T. R. Golub DNA Microarrays in Clinical Oncology J. Clin. Oncol., April 1, 2002; 20(7): 1932 - 1941. [Abstract] [Full Text] [PDF]
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 D. R. Laybutt, A. Sharma, D. C. Sgroi, J. Gaudet, S. Bonner-Weir, and G. C. Weir Genetic Regulation of Metabolic Pathways in beta -Cells Disrupted by Hyperglycemia J. Biol. Chem., March 22, 2002; 277(13): 10912 - 10921. [Abstract] [Full Text] [PDF]
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 D. R. Laybutt, H. Kaneto, W. Hasenkamp, S. Grey, J.-C. Jonas, D. C. Sgroi, A. Groff, C. Ferran, S. Bonner-Weir, A. Sharma, et al. Increased Expression of Antioxidant and Antiapoptotic Genes in Islets That May Contribute to {beta}-Cell Survival During Chronic Hyperglycemia Diabetes, February 1, 2002; 51(2): 413 - 423. [Abstract] [Full Text] [PDF]
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H. C. King and A. A. Sinha Gene Expression Profile Analysis by DNA Microarrays: Promise and Pitfalls JAMA, November 14, 2001; 286(18): 2280 - 2288. [Abstract] [Full Text] [PDF]
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 S. Tugendreich, E. Perkins, J. Couto, P. Barthmaier, D. Sun, S. Tang, S. Tulac, A. Nguyen, E. Yeh, A. Mays, et al. A Streamlined Process to Phenotypically Profile Heterologous cDNAs in Parallel Using Yeast Cell-Based Assays Genome Res., November 1, 2001; 11(11): 1899 - 1912. [Abstract] [Full Text] [PDF]
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 D. Tsavachidou, W. Podrzucki, J. Seykora, and S. L. Berger Gene Array Analysis Reveals Changes in Peripheral Nervous System Gene Expression following Stimuli That Result in Reactivation of Latent Herpes Simplex Virus Type 1: Induction of Transcription Factor Bcl-3 J. Virol., October 15, 2001; 75(20): 9909 - 9917. [Abstract] [Full Text] [PDF]
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 S. C. Mok, J. Chao, S. Skates, K.-k. Wong, G. K. Yiu, M. G. Muto, R. S. Berkowitz, and D. W. Cramer Prostasin, a Potential Serum Marker for Ovarian Cancer: Identification Through Microarray Technology J Natl Cancer Inst, October 3, 2001; 93(19): 1458 - 1464. [Abstract] [Full Text] [PDF]
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T. Betsuyaku, G. L. Griffin, M. A. Watson, and R. M. Senior Laser Capture Microdissection and Real-Time Reverse Transcriptase/ Polymerase Chain Reaction of Bronchiolar Epithelium after Bleomycin Am. J. Respir. Cell Mol. Biol., September 1, 2001; 25(3): 278 - 284. [Abstract] [Full Text] [PDF]
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 W.-K. Hofmann, S. de Vos, K. Tsukasaki, W. Wachsman, G. S. Pinkus, J. W. Said, and H. P. Koeffler Altered apoptosis pathways in mantle cell lymphoma detected by oligonucleotide microarray Blood, August 1, 2001; 98(3): 787 - 794. [Abstract] [Full Text] [PDF]
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I. E. Krop, D. Sgroi, D. A. Porter, K. L. Lunetta, R. LeVangie, P. Seth, C. M. Kaelin, E. Rhei, M. Bosenberg, S. Schnitt, et al. HIN-1, a putative cytokine highly expressed in normal but not cancerous mammary epithelial cells PNAS, July 24, 2001; (2001) 171138398. [Abstract] [Full Text] [PDF]
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D. A. Zajchowski, M. F. Bartholdi, Y. Gong, L. Webster, H.-L. Liu, A. Munishkin, C. Beauheim, S. Harvey, S. P. Ethier, and P. H. Johnson Identification of Gene Expression Profiles That Predict the Aggressive Behavior of Breast Cancer Cells Cancer Res., July 1, 2001; 61(13): 5168 - 5178. [Abstract] [Full Text] [PDF]
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J. Luo, D. J. Duggan, Y. Chen, J. Sauvageot, C. M. Ewing, M. L. Bittner, J. M. Trent, and W. B. Isaacs Human Prostate Cancer and Benign Prostatic Hyperplasia: Molecular Dissection by Gene Expression Profiling Cancer Res., June 1, 2001; 61(12): 4683 - 4688. [Abstract] [Full Text] [PDF]
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V. Luzzi, V. Holtschlag, and M. A. Watson Expression Profiling of Ductal Carcinoma in Situ by Laser Capture Microdissection and High-Density Oligonucleotide Arrays Am. J. Pathol., June 1, 2001; 158(6): 2005 - 2010. [Abstract] [Full Text] [PDF]
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O. Kitahara, Y. Furukawa, T. Tanaka, C. Kihara, K. Ono, R. Yanagawa, M. E. Nita, T. Takagi, Y. Nakamura, and T. Tsunoda Alterations of Gene Expression during Colorectal Carcinogenesis Revealed by cDNA Microarrays after Laser-Capture Microdissection of Tumor Tissues and Normal Epithelia Cancer Res., May 1, 2001; 61(9): 3544 - 3549. [Abstract] [Full Text]
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M. Matsushima-Nishiu, M. Unoki, K. Ono, T. Tsunoda, T. Minaguchi, H. Kuramoto, M. Nishida, T. Satoh, T. Tanaka, and Y. Nakamura Growth and Gene Expression Profile Analyses of Endometrial Cancer Cells Expressing Exogenous PTEN Cancer Res., May 1, 2001; 61(9): 3741 - 3749. [Abstract] [Full Text]
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J.-S. Chen, E. Coustan-Smith, T. Suzuki, G. A. Neale, K. Mihara, C.-H. Pui, and D. Campana Identification of novel markers for monitoring minimal residual disease in acute lymphoblastic leukemia Blood, April 1, 2001; 97(7): 2115 - 2120. [Abstract] [Full Text] [PDF]
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M. T. Hartsough, S. E. Clare, M. Mair, A. G. Elkahloun, D. Sgroi, C. K. Osborne, G. Clark, and P. S. Steeg Elevation of Breast Carcinoma Nm23-H1 Metastasis Suppressor Gene Expression and Reduced Motility by DNA Methylation Inhibition Cancer Res., March 1, 2001; 61(5): 2320 - 2327. [Abstract] [Full Text]
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 S E Wildsmith and F J Elcock Microarrays under the microscope Mol. Pathol., February 1, 2001; 54(1): 8 - 16. [Abstract] [Full Text]
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A. Nestl, O. D. Von Stein, K. Zatloukal, W.-G. Thies, P. Herrlich, M. Hofmann, and J. P. Sleeman Gene Expression Patterns Associated with the Metastatic Phenotype in Rodent and Human Tumors Cancer Res., February 1, 2001; 61(4): 1569 - 1577. [Abstract] [Full Text]
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 R. Reeves, D. D. Edberg, and Y. Li Architectural Transcription Factor HMGI(Y) Promotes Tumor Progression and Mesenchymal Transition of Human Epithelial Cells Mol. Cell. Biol., January 15, 2001; 21(2): 575 - 594. [Abstract] [Full Text]
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F. Bertucci, R. Houlgatte, A. Benziane, S. Granjeaud, J. Adelaide, R. Tagett, B. Loriod, J. Jacquemier, P. Viens, B. Jordan, et al. Gene expression profiling of primary breast carcinomas using arrays of candidate genes Hum. Mol. Genet., December 1, 2000; 9(20): 2981 - 2991. [Abstract] [Full Text] [PDF]
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S. M. Albelda and D. Sheppard Functional Genomics and Expression Profiling . Be There or Be Square Am. J. Respir. Cell Mol. Biol., September 1, 2000; 23(3): 265 - 269. [Full Text]
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K. Ono, T. Tanaka, T. Tsunoda, O. Kitahara, C. Kihara, A. Okamoto, K. Ochiai, T. Takagi, and Y. Nakamura Identification by cDNA Microarray of Genes Involved in Ovarian Carcinogenesis Cancer Res., September 1, 2000; 60(18): 5007 - 5011. [Abstract] [Full Text]
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 D. E. Palmer-Toy, D. A. Sarracino, D. Sgroi, R. LeVangie, and P. E. Leopold Direct Acquisition of Matrix-assisted Laser Desorption/Ionization Time-of-Flight Mass Spectra from Laser Capture Microdissected Tissues Clin. Chem., September 1, 2000; 46(9): 1513 - 1516. [Full Text] [PDF]
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 F. Fend and M. Raffeld Laser capture microdissection in pathology J. Clin. Pathol., September 1, 2000; 53(9): 666 - 672. [Abstract] [Full Text] [PDF]
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S Curran, J A McKay, H L McLeod, and G I Murray Laser capture microscopy Mol. Pathol., April 1, 2000; 53(2): 64 - 68. [Abstract] [Full Text]
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 A Schulze and J Downward Analysis of gene expression by microarrays: cell biologist's gold mine or minefield? J. Cell Sci., January 12, 2000; 113(23): 4151 - 4156. [Abstract] [PDF]
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I. E. Krop, D. Sgroi, D. A. Porter, K. L. Lunetta, R. LeVangie, P. Seth, C. M. Kaelin, E. Rhei, M. Bosenberg, S. Schnitt, et al. HIN-1, a putative cytokine highly expressed in normal but not cancerous mammary epithelial cells PNAS, August 14, 2001; 98(17): 9796 - 9801. [Abstract] [Full Text] [PDF]
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, 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.