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Tumor Biology |
Departments of Pathology [E. L., V. R., W. L. G.], Epidemiology and Biostatistics [J. S., A. S.], Urology [P. S.], and Genitourinary Oncology Service, Department of Medicine [H. S.], Memorial Sloan-Kettering Cancer Center, New York, New York 10021, and Department of Medicine, Joan and Sanford I. Weill College of Medicine of Cornell University, New York, New York 10021 [H. S.]
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ABSTRACT |
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 Top ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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INTRODUCTION |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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198,000 individuals in 2001 (1)
. In recent years, there has been a dramatic increase in the proportion of patients diagnosed with tumors that are seemingly confined to the gland. This is the result of increased public awareness and the more widespread application of detection strategies based on measurements of the level of prostate-specific antigen in the blood (2)
. Early disease is clinically heterogeneous because many patients have an indolent course that does not significantly affect an individual patient’s survival. In contrast, once metastatic disease is documented on an imaging study, the majority of patients die from their tumors as opposed to other causes. This has led us to consider the disease as a series of "states" that include clinically localized tumors, and those that have metastasized, as a framework to assess the clinical and biological factors that are associated with specific phenotypes and outcomes (3)
. Understanding the biological basis for this clinical heterogeneity is critical to assess prognosis, to select therapy, and to assess treatment effects. Tumor metastasis is the most clinically significant event in prostate cancer patients. Development of metastases requires that a cancer cell must complete a series of steps involving complex interactions between tumor cells and the host (4) . Cells from primary tumors must detach, invade stromal tissue, and penetrate vessels by which they disseminate. They must survive in the circulation to reach a secondary site in which they arrest because of physical size or binding to specific tissues. To form clinically significant tumors, metastatic cells must proliferate in the new microenvironment and recruit a blood supply. Those tumor cells growing at metastatic sites are then continually selected for growth advantage. This is a complex and dynamic process that is expected to involve alterations in many genes and transcriptional programs. Identification of genes, gene expression profiles, and biological pathways that contribute to metastasis will be of significant benefit to improved tumor classification and therapy. To address this question, we performed a genome-wide expression analysis and identified genes differentially expressed in primary and metastatic prostate cancers. These genes reflect the distinct clinical phenotype of these two cohorts, and provide insight into the biology of prostate cancer progression.
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MATERIALS AND METHODS |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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. Samples were snap-frozen in liquid nitrogen and stored at -80°C. Each sample was examined histologically using H&E-stained cryostat sections. Cells of interest were manually dissected from the frozen block, trimming away other tissues. Care was taken to remove nonneoplastic tissues from tumor samples. All of the studies were conducted under MSKCC Institutional Review Board-approved protocols.
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Data Analysis.
For oligonucleotide arrays, scanned image files were visually inspected for artifacts and analyzed using Microarray Suite v4.0 (Affymetrix). Differential expression was evaluated using several measures. Final ranking to obtain genes uniformly and strongly differentially expressed was determined by the following approach (fully described in supplementary information).4
The expression data set was first filtered to include only those probe sets detecting genes with mean expression values that differed by at least 3-fold between the two groups (absolute base-ten logarithm of the ratio of the means 
0.4771). Probes were then ranked based on the relative magnitude of the difference (t test) between the means of the two sample sets. Genes with expression differences likely attributable to contaminating nonneoplastic tissues were removed from the ranking. Datasets used for hierarchical clustering were normalized by standardizing each gene and array to mean = 0 and variance = 1. Hierarchical clustering and result display was performed using Cluster and TreeView software (5)
. Gene expression that was attributable to nonneoplastic tissues was identified by comparison with expression levels in nonneoplastic prostate samples (supplementary information). Specific genes corresponding to Unigene clusters were identified by GenBank accession number of the clone used to produce the oligonucleotide probe set and annotated through review of internet resources.5
Immunohistochemistry.
Multitissue blocks of formalin-fixed, paraffin-embedded tissue corresponding to the samples used in this analysis were prepared using a tissue arrayer (Beecher Instruments, Silver Spring, MD). The blocks contained three representative 0.6 mm cores from diagnostic areas for each case. Immunohistochemical detection of Ki67 (mib1; Dako, Carpinteria, CA; 1:200) was carried out with standard streptavidin-biotin peroxidase methodology using formalin-fixed, paraffin-embedded tissue, and microwave antigen retrieval as described previously (6)
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Quantitative Reverse Transcriptase-PCR.
Quantitative reverse transcriptase-PCR was performed using the LightCycler thermal cycler system (Roche Diagnostics, Basel, Switzerland). Fifty to 100 ng of total RNA were used as template with the LightCycler RNA SYBR Green I system (2.7x concentration; Roche Diagnostics) according to the manufacturer’s instructions. A typical protocol included reverse transcription at 61°C for 20 min and a denaturation step at 95°C for 2 min followed by 45 cycles with 95°C denaturation for 5 s, 60°C annealing for 5 s, and 72°C extension for 8 s. Detection of the fluorescent product was performed at the end of the extension period. A melting curve analysis was performed at the end of the PCR at 95° for 5 s, 65° for 15 s, and a final denaturation at 95° for 0 s. Negative controls were run to confirm that the samples were not cross-contaminated. Data were analyzed with the LightCycler analysis software. A standard curve was created by serial dilution of the appropriate RNA template. Primers for TAGLN were: forward, 5'-GTCATTGGCCTTCAGATG-3', and reverse, 5'-ACACCTCAAAGCTTG-3'; for MSMB: forward, 5'-TGTTTCTACACCTGTGGG-3', and reverse, 5'-TGATAGGCATGGCTACAC-3'; and for 18S RNase: forward, 5'-GACATTGACCTCACCAAG-3', and reverse, 5'-ATCTTCTTCAGTCGCTCC-3'.
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RESULTS |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Identification of Differentially Expressed Genes.
An unsupervised analysis of gene expression in all 32 prostate cancers based on the hybridization results for the U95A array revealed a strong tendency for primary and metastatic tumors to have distinct expression profiles based on an average linkage hierarchical clustering algorithm (Fig. 1)
. This analysis revealed several groups of genes with distinct expression profiles for these two major tumor subdivisions. As expected, some of the gene expression differences that distinguished primary and metastatic tumors were contributed by the small amount of contaminating nonneoplastic prostate tissue present in the primary tumor samples (Fig. 1
, bottom panel). These were further confirmed by a comparison with expression data from nonneoplastic prostate (see supplementary information). The remaining gene expression differences are expected to be intrinsic to tumor cells and reflect biological distinctions.
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. The entire list of 3,436 differentially expressed sequences is available in the supplementary material.
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. The functional attributes of differentially expressed genes are expected to reflect biological differences between early-stage and advanced tumors. In keeping with this concept, 26 of the 100 most highly ranked genes are believed to play a role in some aspect of cell cycle regulation, DNA replication and repair, or mitosis including many genes, such as RFC5, TOP2A, RFC4 and MAD2L1, that are known to be up-regulated in highly proliferative cells (7)
. This finding correlated with the increased proliferation index of metastatic tumors used for gene expression analysis based on the immunohistochemical assessment of MKI67 (Fig. 2)
. Fifteen of the 100 highly ranked genes correspond to products potentially involved in signaling and signal transduction and 9 others may contribute to cell adhesion, cell migration, or extracellular matrix. These include HMMR, which encodes an extracellular matrix-binding protein believed to play a role in cell motility through the RAS-ERK signaling pathway (8)
, and INPP4A, the substrates of which are intermediates in pathways regulated through the AKT proto-oncogene (9)
.
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Validation.
Gene expression values that were determined by oligonucleotide array were validated in several ways. Some gene transcripts were represented by more than one probe set, and each detected similar relative levels of expression. An analysis of Cancer Genome Anatomy Project, serial analysis of gene expression, and other published data verified expression in prostate sample for many genes (10, 11, 12, 13, 14)
. Some differentially expressed genes were selected for the measurement of transcript levels using a quantitative reverse transcriptase-PCR technique in the same samples used for microarray-based expression analysis. The results were in good agreement with the relative levels of expression as determined by oligonucleotide arrays (examples shown in Fig. 3
). We also performed immunohistochemical analysis of tumor samples and established that protein expression of some gene products correlated with mRNA levels [e.g., MKI67 (Fig. 2)
and PSA, AR (data not shown)]. Several of the genes that we identified as highly differentially expressed have been previously implicated in progression of cancer and help to validate our experimental approach. In addition to those mentioned above, other examples include STK11 (15)
, PTTG1 (16)
, STK15 (17)
, and MYBL2 (18)
. STK15 was overexpressed in metastatic prostate cancers and has previously been implicated in aggressive prostate cancer (17)
.
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DISCUSSION |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Few prior studies have used high-throughput gene expression analysis to study prostate cancer metastases. One reason is that well-preserved surgical tissue samples of metastatic prostate cancer are rare, which limits the availability of appropriate samples. Oligonucleotide arrays with 6800 probe sets were used to compare expression in three metastases to eight primary tumors (18) . Five of the nine genes found to be commonly differentially expressed in that study also have average expression values for our sample sets that are in agreement with that data. In addition, two (EGR2 and EGR3) were differentially expressed at least 3-fold in the present study. In the other published analysis of metastatic tumors (12) , the hierarchical clustering of cDNA array data was used to identify coordinately expressed groups of genes that were specifically differentially expressed in metastatic versus primary prostate cancer. Two of the genes in clusters that were overexpressed in metastatic prostate carcinomas (MTA1 and MYBL2) were also overexpressed in the present study. In fact, MYBL2 met our stringent criteria for strongly and commonly overexpressed genes in metastases. In a related study to identify genes associated with aggressive primary tumors, Singh et al. (19) identified five genes that were commonly used in models to predict the recurrence of carcinoma after radical prostatectomy. Two of the genes that were overexpressed in recurrent cases (HOXC6 and PDGFRA) were also overexpressed in our samples of metastatic carcinoma, and one gene overexpressed in nonrecurrent cases (SIAT1) was more strongly expressed in primaries, although none achieved the 3-fold difference filter used here. The partial agreement between these studies is encouraging in that, despite very different methodologies, genes that participate in the process of metastasis are being identified.
The predicted function of these differentially expressed genes provides a glimpse into the biology of prostate cancer progression. Although functional assignment is based on the limited published data, it is encouraging that many differentially expressed genes reflect biological distinctions and functional pathways previously implicated in aggressive disease. Included are cell cycle regulators and DNA replication and repair proteins that may drive cell proliferation; transcriptional regulators believed to play a role in development and differentiation; and proteins that play a role in signal transduction, cell structure, and cell interactions with environmental factors. The distinct expression patterns of these genes help to validate their role in the clinical phenotype of aggressive metastatic disease.
Some of the specific genes that are differentially expressed may identify critical functional pathways. One example is the MYBL2 gene, which was overexpressed in many metastatic tumors. MYBL2 activates CDC2 gene expression in proliferating fibroblasts (20) . CDC2 is a catalytic subunit of a protein kinase complex that induces entry into mitosis. Cyclin E modulates the functional activity of these genes. All of them were overexpressed in our metastatic samples, which suggests that this pathway may be a critical component of cell cycle regulation in many metastatic prostate cancers. Identifying these genes in specific tumor samples may, therefore, provide diagnostic information and serve as a therapeutic target.
In addition to the limited number of genes discussed here, our analysis identified hundreds of poorly characterized EST clusters that likely represent novel genes of unknown function that were highly differentially expressed between primary and metastatic prostate cancers. The biological activity of these uncharacterized genes may be inferred from attributes of known genes with shared expression patterns. Therefore, many are likely to play important biological roles similar to those predicted for the known gene products discussed here. All of them warrant further study and may provide new insights into prostate cancer biology. Additional study of the complete molecular phenotype and patterns of variation will lead to a more in-depth understanding of the clinical heterogeneity of prostate cancers.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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1 Supported by NIH Grant U01 CA84999 (to W. L. G.). 
2 To whom requests for reprints should be addressed, at Department of Pathology, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY 10021. Phone: (212) 639-5905; Fax: (212) 639-4559; E-mail: geraldw{at}mskcc.org
3 The abbreviations used are: MSKCC, Memorial Sloan-Kettering Cancer Center; EST, expressed sequence tag.
4 Supplementary information for this article is available at Cancer Research Online (http://cancerres.aacrjournals.org).
5 Internet resources: Genecards, http://nciarray.nci.nih.gov/cards/; Locus Link, http://www.ncbi.nlm.nih.gov/LocusLink//index.html; Online Mendelian Inheritance in Man, http://neptune.nlm.nih.gov/entrez/query.fcgi?db = OMIM; Gene Ontology Browser, http://cgap.nci.nih.gov/Genes; and publications identified in PubMed, http://www.ncbi.nlm.nih.gov/PubMed/.
Received 3/20/02. Accepted 6/ 3/02.
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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R. Foley, D. Hollywood, and M. Lawler Molecular pathology of prostate cancer: the key to identifying new biomarkers of disease Endocr. Relat. Cancer, September 1, 2004; 11(3): 477 - 488. [Abstract] [Full Text] [PDF]
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P. L. Paris, A. Andaya, J. Fridlyand, A. N. Jain, V. Weinberg, D. Kowbel, J. H. Brebner, J. Simko, J.E. V. Watson, S. Volik, et al. Whole genome scanning identifies genotypes associated with recurrence and metastasis in prostate tumors Hum. Mol. Genet., July 1, 2004; 13(13): 1303 - 1313. [Abstract] [Full Text] [PDF]
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D. R. Rhodes, J. Yu, K. Shanker, N. Deshpande, R. Varambally, D. Ghosh, T. Barrette, A. Pandey, and A. M. Chinnaiyan Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression PNAS, June 22, 2004; 101(25): 9309 - 9314. [Abstract] [Full Text] [PDF]
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D. F. Dukers, J. C. van Galen, C. Giroth, P. Jansen, R. G.A.B. Sewalt, A. P. Otte, H. C. Kluin-Nelemans, C. J.L.M. Meijer, and F. M. Raaphorst Unique Polycomb Gene Expression Pattern in Hodgkin's Lymphoma and Hodgkin's Lymphoma-Derived Cell Lines Am. J. Pathol., March 1, 2004; 164(3): 873 - 881. [Abstract] [Full Text] [PDF]
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J. Lapointe, C. Li, J. P. Higgins, M. van de Rijn, E. Bair, K. Montgomery, M. Ferrari, L. Egevad, W. Rayford, U. Bergerheim, et al. Gene expression profiling identifies clinically relevant subtypes of prostate cancer PNAS, January 20, 2004; 101(3): 811 - 816. [Abstract] [Full Text] [PDF]
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J. Holzbeierlein, P. Lal, E. LaTulippe, A. Smith, J. Satagopan, L. Zhang, C. Ryan, S. Smith, H. Scher, P. Scardino, et al. Gene Expression Analysis of Human Prostate Carcinoma during Hormonal Therapy Identifies Androgen-Responsive Genes and Mechanisms of Therapy Resistance Am. J. Pathol., January 1, 2004; 164(1): 217 - 227. [Abstract] [Full Text] [PDF]
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D. K. Vanaja, J. C. Cheville, S. J. Iturria, and C. Y. F. Young Transcriptional Silencing of Zinc Finger Protein 185 Identified by Expression Profiling Is Associated with Prostate Cancer Progression Cancer Res., July 15, 2003; 63(14): 3877 - 3882. [Abstract] [Full Text] [PDF]
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F. G. Bottone Jr., J. M. Martinez, J. B. Collins, C. A. Afshari, and T. E. Eling Gene Modulation by the Cyclooxygenase Inhibitor, Sulindac Sulfide, in Human Colorectal Carcinoma Cells: POSSIBLE LINK TO APOPTOSIS J. Biol. Chem., July 3, 2003; 278(28): 25790 - 25801. [Abstract] [Full Text] [PDF]
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C. Yan, H. Wang, Y. Toh, and D. D. Boyd Repression of 92-kDa Type IV Collagenase Expression by MTA1 Is Mediated through Direct Interactions with the Promoter via a Mechanism, Which Is Both Dependent on and Independent of Histone Deacetylation J. Biol. Chem., January 17, 2003; 278(4): 2309 - 2316. [Abstract] [Full Text] [PDF]
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