Molecular Alterations in Primary Prostate Cancer after Androgen Ablation Therapy

Carcinoma of the prostate accounts for approximately one third of all cancers diagnosed in men in the United States and remains the second most common cause of cancer death in this group (American Cancer Society Cancer Facts & Figures 2004; http://www.cancer.org/docroot/STT/stt_0.asp). The survival and growth of prostate cancer cells is initially dependent on the presence of androgens, and virtually all prostate cancer patients respond when first treated with androgen ablation. However, resistance to hormone blockade ultimately results in the recurrence of highly aggressive and metastatic prostate cancer that is androgen independent (1). Androgen-independent prostate cancer (AIPC) is therefore clinically defined as the progression of the disease under hormonal ablation.

Although several hypothesized mechanisms exist for the development of AIPC (reviewed in ref. 2), and recent studies using prostate cancer models have shed additional light on the process (3), our understanding of the disease in patients remains incomplete at the molecular level, and the key genes involved are still largely unknown. Expression analysis of prostate cancer before and after hormone therapy may identify genes and pathways that are critical to its progression. Over the past few years, numerous studies have been published on the molecular profiles of human prostate cancer tissue. Several of these have included metastatic lesions (4–9); however, few have analyzed androgen-independent tumor cells from the site of the primary lesion. As an example, Holzbeierlein et al. compared the gene expression profiles of normal prostate, primary tumors before and during hormone therapy, and metastatic tumors, three of which were androgen independent (8). Although the androgen-independent tumor group was small, they identified a pattern of gene expression, independent of treatment and metastatic status, unique to AIPC. Studies that delineate the molecular profile of androgen-independent tumors may be uniquely valuable in designing therapeutic interventions.

In the present study, we directly compared the gene expression profiles of 10 androgen-independent tumor biopsies, taken from the primary site of prostate cancer, with 10 primary, untreated prostate tumors. Each sample was microdissected to eliminate gene expression changes that could derive from cell types other than tumor. Expression patterns were evaluated with respect to metabolic pathways, gene ontologies, and genomic alterations.

Tissue specimens. Androgen-dependent prostate carcinoma specimens were obtained from patients undergoing prostatectomy as first-line therapy at either Catholic University in Santiago, Chile (cases 1-9) or University of North Carolina (case 10). The tumors were excised and one section was frozen, whereas the remainder was processed for diagnosis. A total of 16 frozen cases were collected, anonymized, and transferred to the National Cancer Institute. Ten of the samples had sufficient tumor for inclusion in the study. The tumors were evaluated by two pathologists (J.W.G. and R.F.C.) and assigned Gleason scores of 5 (n = 2), 6 (n = 4), 7 (n = 1), 8 (n = 2), and 9 (n = 1). For the comparison group of AIPC, we retrospectively obtained baseline, primary-site biopsies from patients who had participated in an institutional review board–approved National Cancer Institute phase II trial studying docetaxel and thalidomide in metastatic AIPC. We examined a total of 82 snap-frozen biopsies from 30 cases, of which 23 cases contained cancer. We then selected the 10 cases with the highest amount of tumor and quality RNA preservation. All patients from whom the androgen-independent samples were obtained met at least one of the following variables for clinically progressing, androgen-independent disease: two consecutively rising prostate-specific antigen levels (prostate-specific antigen ≥ 5.0), at least one new lesion on bone scan, or progressive measurable disease. In addition, in the absence of surgical castration, a serum testosterone of <50 ng/mL and continuance on gonadotropin-releasing hormone antagonist was required. Clinical details, including treatment history before androgen-independent disease, for each of the androgen-independent cases are provided in Table 1.

Laser capture microdissection and RNA isolation. Each of the 20 frozen tissue blocks was recut into 8-μm thick sections onto glass slides and stored at −80°C. Each section was individually removed from storage and immediately stained as follows: 70% ethanol for 15 seconds, deionized water for 10 seconds, Mayer's hematoxylin (Sigma-Aldrich, St. Louis, MO) for 15 seconds, deionized water and bluing solution (Sigma-Aldrich) for 10 seconds each, and eosin (Sigma-Aldrich) for 5 seconds followed by dehydration for 10 seconds each in increasing concentrations of ethanol. Finally, the tissue was completely dehydrated in xylenes for 20 seconds. Cells from each case were microdissected by laser capture microdissection (10) with the PixCell IIe according to the manufacturer's protocol (Arcturus Engineering, Inc., Mountain View, CA), and total RNA was isolated with the PicoPure RNA Isolation kit (Arcturus Engineering). The samples were subjected to DNase treatment for 15 minutes, and RNA quality and quantity were assessed with the Bioanalyzer 2100 (Agilent Technologies, Inc., Palo Alto, CA) and Degradometer software version 1.2 (http://www.dnaarrays.org; ref. 11).

RNA amplification, microarray sample synthesis, and hybridization. RNA was amplified by modifying a previously established protocol that combines the RiboAmp (Arcturus Engineering) and Affymetrix (Affymetrix, Inc., Santa Clara, CA) systems (12), resulting in biotin-labeled antisense cRNA. Total RNA was used for amplification, because this approach has been shown to introduce less bias than when mRNA is used (13). Total RNA (1-10 ng) from each sample was subjected to two rounds of linear amplification with the RiboAmp HS kit. Antisense RNA (2 μg) from the second round of amplification was then used to synthesize double-stranded cDNA with the regular RiboAmp kit, because the RiboAmp HS kit has a maximum RNA input capacity of only 250 ng. The resulting cDNA was then used as template for the synthesis of antisense cRNA labeled with biotinylated UTP and CTP by in vitro transcription using the BioArray High-Yield RNA Transcript Labeling kit (Enzo Life Sciences, Inc., Farmingdale, NY). In total, each sample underwent three rounds of amplification, which has been shown previously to decrease the average distribution size of antisense RNA without affecting reproducibility on oligonucleotide arrays (14). Each labeled sample (15 μg) was then fragmented according to protocol (Affymetrix) and hybridized to Human Genome U133A GeneChip arrays for 16 hours. Microarrays were washed and stained using the “EuKGE-WS2v4” protocol and then scanned using the Affymetrix GeneChip Scanner 3000. The raw microarray data were uploaded to the Gene Expression Omnibus public repository (http://www.ncbi.nlm.nih.gov/geo/; Gene Expression Omnibus series no. GSE2443).

Data filtering and normalization, clustering, and statistical analysis. Expression profile data were prepared for analysis using Microarray Analysis Suite version 5.0 software (Affymetrix), setting the scaling of all probe sets on all chips to a constant value of 1,000. The data were then filtered to include only those probe sets having “present” or “marginal” calls (detection P < 0.065) in at least 10% of the samples. The global expression patterns were studied by principal component analysis considering one dimension for each gene on the array (Partekpro 5.0 software, Partek, Inc., St. Charles, MO). Principal component analysis determines a set of principal components as linear combinations of original dimensions such that the first principal component is in the direction of highest variance of the distribution, the next principal component is in the direction of highest of remaining variance, and so on (15). Eigen analysis of correlation matrix was used. A projection on the first three principal component's covering highest variance permits dimension reduction of multidimensional data for graphical visualization. In this three-dimensional plot, a point represents a tissue sample, whereas the close clustering of points indicates similar gene expression patterns.

Analysis of gene ontology representation. Genes showing significant differential expression were categorized by their ontologies using Expression Analysis Systematic Explorer software (16). The number of genes assigned to each ontology term was compared with the total population on the microarray to identify the probability of overrepresentation of each ontology. Overrepresentation analysis calculated for each of the gene ontology terms provided an Expression Analysis Systematic Explorer score, which was the upper boundary of the distribution of Jackknife Fisher exact probabilities. The gene ontologies having an Expression Analysis Systematic Explorer score of <0.05 were considered significantly overrepresented.

Identification of potential chromosomal deletion regions. The normalized and filtered data set was subjected to Differential Gene Locus Mapping (DIGMAP) analysis as described previously (17). Briefly, the gene locations were first mapped through the Gene Annotation Project database. UniGene clusters and their genomic locations in the data set were annotated, and information from this step was used to generate a DIGMAP source file that was used in the subsequent analysis. Next, a viewer program (DIGMAPviewer) read the DIGMAP source file, and DIGMAP partitioned the microarray data into subsets by chromosome number and subchromosomal locations. A graphical presentation was generated using a heat map to represent each data point with a colored cell that quantitatively reflected the original differential expression value. Genomic regions exhibiting differential gene expression were marked as differential flag regions by visual inspection of the graphical displays.

Technical variables. To assess the reliability and reproducibility of the protocol, each sample was evaluated following each step (Table 2). The amount of dissection was similar for most samples; however, the needle biopsies provided less RNA than the whole tumors, an average of 8.7 versus 38.6 ng, respectively. This may have been due to a lower density of cells per shot in the biopsies compared with the whole tumors. The Bioanalyzer electropherograms for all samples in the study showed dominant 18S and 28S ribosomal peaks and no obvious degradation peaks. Two recent studies showed that consistency in RNA quality, rather than undiminished integrity, is the critical determinant for avoiding artifactual differential expression (11, 18). However, these studies also showed that the 28S/18S ratio (provided by the Bioanalyzer software) is not a reliable indicator of RNA quality. Thus, we chose to additionally employ a quantitative measurement of RNA degradation to ensure that the RNA from the two tumor groups was of similar quality. The Degradometer software (11) uses the ratio of the average value of all degradation peak signals to the 18S peak signal multiplied by 100 to calculate an objective, quantitative degradation factor and showed no significant difference between the two sample groups (an average of 20.0 for the biopsies and 21.8 for the whole tumors), although six of the needle biopsies had insufficient concentrations for the calculation of a degradation factor.

The technical variables for microarray hybridization showed high consistency among all samples with respect to background and noise, with all values well within the manufacturer's recommended maximums of 200 and 5, respectively. Further, the scale factors, which can indicate skewing of the data between groups, potentially introducing error into differential expression comparisons, showed no statistically significant difference. The manufacturer's recommended maximum value for the 3′/5′ ratios for housekeeping genes glyceraldehyde-3-phosphate dehydrogenase and β-actin is 3.0, which typically assumes high quality, unamplified RNA. The protocol employed here resulted in much higher ratios averaging 7.5 and 9.6 for glyceraldehyde-3-phosphate dehydrogenase and 32.2 and 26.9 for β-actin. Similar observations have been reported by others using amplified RNA (12), as each round of amplification shortens RNA transcript lengths, eventually resulting in the loss of 5′ regions and increasing the ratio of signal between the 3′ and 5′ probe sets. The percentage of probe sets called present was not statistically different between the two tumor groups and was within the manufacturer's recommended range expected for human tissue.

Finally, we examined the possibility that gene expression differences could result from the genetic variation between the patients, because the samples were derived from patients in two separate countries (i.e., the United States and Chile). One of the samples in the androgen-dependent group originated within the United States (AD-10), whereas the rest derived from Chile, yet this tumor clustered well within the androgen-dependent tumor group when analyzed by hierarchical clustering (data not shown), indicating no obvious difference on this account.

Differential gene expression between tumor groups. Unsupervised principal component analysis based on the largest three principal components revealed separate clustering of the androgen-dependent and androgen-independent tumor groups along principal component 2 (9.35% variance) as shown in Fig. 1. This indicates the presence of a large number of genes (∼1,000 if the variance is equal for all genes) distinguishing the two tumor groups. In general, the androgen-dependent tumors clustered more tightly together, whereas the androgen-independent tumors, although predominantly separate from the androgen-dependent tumors, clustered more loosely. This likely represents the relative clinical similarity of the tumors within each group, with the androgen-dependent tumors being from newly diagnosed, untreated patients and the androgen-independent tumors being from patients having undergone various treatments (i.e., radiation, orchiectomy, flutamide, or combinations) while progressing to androgen-independent disease status. We found no correlation between treatment histories and gene expression profiles in this study.

To identify the specific genes that were differentially expressed between the two tumor groups, the normalized data were filtered to remove all probe sets not called present in at least 20% of the samples, which resulted in 10,041 probe sets remaining. Two-sample Student's t testing identified 256 probe sets showing differential expression at P < 0.005 between the two groups (see the Supplementary Material for the unabridged list). These probe sets represented 239 genes, as several genes were identified by multiple probe sets. Approximately 61.3% of the genes showed down-regulation in the androgen-independent tumor cells, whereas 38.7% showed up-regulation. Many of the genes are involved in processes of carcinogenesis, such as angiogenesis, cell adhesion and the microenvironment, cell death including apoptosis, hormone response, oxidative stress and cancer cell metabolism, key signaling pathways, and metastasis. A list of selected differentially expressed genes is presented in Table 3.

Identification of overly represented gene ontologies. The list of 256 probe sets representing the genes showing significant differential expression was analyzed for overrepresentation of specific gene ontologies using the Expression Analysis Systematic Explorer software. Two major biological groups were identified (Table 4, top). The first group included genes that are involved in ribonucleoprotein complexes, are structural constituents of ribosomes, or are otherwise involved in protein biosynthesis. The genes in this group predominantly (20 of 31) showed lower expression in AIPC. The second group included genes involved in the extracellular matrix (ECM) and cell adhesion molecule activity and showed predominantly (15 of 19) increased expression. In addition, by a review of literature for the differentially expressed genes, we found the signaling pathway of the proinflammatory cytokine interleukin-6 (IL-6) and genes whose expression related to IL-6 to be overrepresented (Table 4, bottom).

Identification of potential chromosomal deletion regions. A total number of 7,002 distinct UniGene clusters and their genomic locations were annotated from the data set. A graphical presentation was generated using a heat map to show the quantitative differential expression for each probe set at its chromosomal location. The 20 samples were clustered according to similarity in differential expression patterns. Five of the androgen-independent tumors (AI-1, AI-2, AI-5, AI-6, and AI-7) and six of the androgen-dependent tumors (AD-1, AD-3, AD-4, AD-5, AD-7, and AD-8) were included for subsequent analysis due to the similar expression pattern within the samples of each group and because chromosomal deletions typically occur in only a subset of any given tumor type. Based on these remaining samples, nine differential flag regions showed concordant down-regulation in the androgen-independent samples (Table 5), representing regions of potential chromosomal deletion. To estimate the known significance of each region in prostate cancer, literature searches using PubMed (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi) were done to identify the prevalence of each region in the cancer research literature and those specific to prostate. Regions 1p36, 8p21, and 16q21 showed the highest degree of known significance in prostate cancer. The 16q21-16q24.3 region typified the differential flag regions in this study and is presented in Fig. 2.

The gene lists for each region were compared with the list of 239 genes identified in the initial analysis of differential expression. Each region contained one or more genes that were identified by both analyses as follows: 3p, one gene; 4q, one gene; 6p, two genes; 8p, one gene; 11p, three genes; 11q, two genes; and 16q, eight genes. The common genes in region 16q21-24.3 are indicated in Fig. 2.

In the present study, the gene expression profiles of newly diagnosed, androgen-dependent primary prostate tumors were compared with those of prostate biopsies from patients who progressed to develop metastases and were treated with hormone ablation therapy. This latter group of clinical specimens represents a unique and precious resource, as very few patients undergo surgical procedures after establishment of advanced disease. The study examined one step in the overall progression of prostate cancer, specifically the effect of androgen ablation therapy on primary tumor cells. This differs from an analysis of metastatic, rapidly growing tumor cells, and it is important to consider primary androgen-independent tumor cells and metastatic androgen-independent tumors as separate entities with regards to hormone therapy. Both are androgen independent; however, the primary lesions grow slowly and “persist” without androgens, whereas the metastatic lesions grow rapidly and significantly expand the tumor burden of patients. This distinction is consequential as the androgen-independent primary tumor expression data set may reveal molecular changes more closely associated with “effective androgen ablation therapy” as opposed to those related with treatment failure and subsequent clinical breakthrough.

The expression array analysis generated a large amount of interesting data and identified many individual genes that were differentially regulated between androgen-independent and androgen-dependent primary-site prostate tumor cells. These included genes involved in angiogenesis, apoptosis, oxidative stress, and hormone response. All of the differentially expressed genes are of potential interest for follow-up studies, and some have been identified previously to have potential as clinical biomarkers. However, to better understand the functional themes related to androgen withdrawal, we searched for patterns of gene expression using gene ontology analysis. Two main groups that differed between androgen-independent and androgen-dependent tumors were identified: those associated with ribosomes and protein synthesis and those associated with cell adhesion and the ECM. Although aggressive tumors are generally expected to have higher expression and activity of the protein synthesis machinery, we found the converse, with the majority of these genes showing lower expression in androgen-independent cells. The second dominant ontological group, genes associated with cell adhesion and ECM, showed a nearly uniform increased expression in the androgen-independent cells. This was also unexpected, because the literature shows a mixture of up-regulation and down-regulation of ECM and adhesion molecules during prostate cancer progression. Thus, androgen blockade initially seems to act in clinical prostate samples, at least in part, by facilitating a more normal phenotype through the reversal of two critical cancer-related activities: increased protein synthesis (19) and decreased adhesion. Review of the literature indicates that this is not without precedent. In a recent study, Patriarca et al. described up-regulation of E-cadherin and α/β-catenin in prostate tumors after hormonal ablation and suggested that a more differentiated phenotype results after the treatment (20). It has also been recently suggested that telomerase expression patterns are reverted toward a normal phenotype after hormone ablation, particularly in high-grade tumors (21). Moreover, a review of protein expression changes after androgen deprivation therapy showed decreased proliferation markers (22), which seems to agree with our findings of a generalized decrease in protein synthesis.

It is important to note, however, that several of the up-regulated adhesion and ECM genes are associated with endothelial cells (i.e., VWF, PECAM1 (CD31), COL5A2, COL15A1, LAMB1, FN1, THBS1, and SPARC; refs. 23–25). In addition, other genes that we found to be up-regulated in AIPC, including JAK1, CDH11, and TIE2/TEK, have been found previously to be overexpressed in microvascular endothelial cells (24) or endothelial morphogenesis (23, 25). The origin of this gene expression is puzzling, because endothelial cells were excluded during microdissection and because tumors treated with androgen deprivation have been shown to display decreased microvessel density (22). Prostate tumor cells may participate in vasculogenic mimicry, whereby tumor cells themselves express endothelial-associated markers and form vasculogenic networks both in vitro and in vivo (26), which could account for the higher expression of these genes in AIPC.

Gene expression related to IL-6 and its signaling pathway was also a central theme represented in the data. There is an increasing body of evidence suggesting that IL-6 is involved in the progression of prostate cancer (27) and may even have utility as a diagnostic marker for predicting progression (28). IL-6 signaling involves activation of signal transducer and activator of transcription (STAT) proteins by the Janus kinases (JAK). Both JAK1 and STAT5B were differentially regulated in this study, indicating perturbation of this pathway in AIPC. We found previously that STAT5B was down-regulated in high-grade androgen-dependent tumors compared with moderate grade (29). Because STAT5B was even further down-regulated in the AIPC cells, it may have potential as both a marker for progression and a therapeutic target. In addition to IL-6 signaling, the down-regulation of IL-6 is likely related to the differential regulation of several of the other genes, including MCL1 (30), JUND and JUNB (31), and SDC2 (32). A potentially critical facet of IL-6 in prostate cancer is its ability to independently activate the androgen receptor (AR; reviewed in ref. 33). Thus, it seems logical that the expression of the androgen-responsive genes KLK2 and KLK3, and GADD45B and GADD45G, was lower in the AIPC cells in this study, because IL-6 was also decreased. Finally, neuroendocrine differentiation may be involved in the development of AIPC (28), and IL-6 has been shown to promote neuroendocrine differentiation in prostate cancer cells (34). Because the AIPC tumors showed differential expression of genes in the IL-6 pathway, we examined the data for the neuroendocrine markers synaptophysin and chromogranin A. We found a trend of increased chromogranin A expression (2.4-fold; P < 0.07) in the androgen-independent tumors, which concurs with others showing more significant increases in neuroendocrine differentiation in androgen-independent disease (35).

It is also important to compare the genes identified in this study with those hypothesized to be involved in the potential mechanisms for the development of androgen-independence. Chen et al. recently showed that, in seven pairs of xenograft tumors before and after the development of androgen independence, only the AR gene was differentially expressed. In the study presented here, AR expression was not significantly different between the two groups. However, because the data were analyzed for gene expression changes consistent among the tumors in each group, it is possible that increased AR expression was present in a subset of the tumors, as a nonsignificant trend of AR overexpression was present in the androgen-independent group. However, although the model presented by Chen et al. showed that overexpression of AR alone was sufficient for the development of androgen independence, there are numerous genes identified in the literature to be involved in this process. Feldman and Feldman (2) present a concise review of the potential mechanisms for the development of AIPC, and several of the genes identified in the study presented here may fit these postulated mechanisms. For example, the antioxidant enzyme superoxide dismutase-2 was down-regulated 9-fold in the androgen-independent tumors and has been shown to inversely correlate with prostate cancer progression in cell models (36) and in tissue (37), fitting the model of a decrease in protective enzymes that may cause an increase in the frequency of mutation. Another potential mechanism for the development of AIPC is the “outlaw pathway,” whereby the AR is stimulated by nonandrogen growth factors, and IL-6, discussed above, fits this model, as do several genes downstream of AR activation (e.g. KLK2, KLK3, and GADD45), which were differentially expressed. In addition, CDH11 has been shown previously to be up-regulated in hormone-refractory prostate cancer cell lines (38) and showed 3-fold higher expression in the AIPC cells in this study. BPAG1 also showed increased expression of ∼3-fold in the AIPC cells. BPAG1 is a hemidesmosome protein whose expression becomes up-regulated with the onset of invasive growth (39). Further studies examining the specific roles of the genes identified here in the development or maintenance of the androgen-independent phenotype are necessary.

Standard approaches to the analysis of microarray data, including our own as discussed above, cluster genes based on transcriptional profiles and thus overlook gene expression patterns of contiguous chromosomal regions. Using the newly developed DIGMAP approach, we identified nine genomic regions of interest in AIPC. Most of the regions appear from the literature to be frequent deletions in a variety of human cancers, and regions 1p36, 8p21, and 16q21 have additional significance in prostate cancer. These regions are generally hypothesized to include tumor suppressor genes or other genes required for maintenance of a normal or less aggressive phenotype. For example, chromosomal deletions at 16q have been correlated with more malignant grade tumors (40) and with tumors with poor clinical outcomes (41), which agrees with our findings that this region may be increasingly affected during progression to the androgen-independent state.

Overall, comparison of the expression data relative to the genome is intriguing. The androgen-independent tumor cells show decreased expression of several genes that map to distinct genomic regions, including known hotspots for prostate cancer. Mechanistically, this could occur via either DNA deletions and/or epigenetic phenomenon, such as gene promoter methylation. There are two possible implications of this finding. First, although androgen withdrawal therapy is effective at slowing the progression of prostate cancer clinically, it does not stop the continued progression of expression changes related to genomic alterations. Alternatively, there may be inherent differences in genomic status between patients where the majority will not recur after treatment (the androgen-dependent group in this study) and those that are clearly aggressive (the androgen-independent group). This is an enticing possibility, as it would suggest that, for prognostic purposes, the two patient groups could be stratified based on genome-related expression data.

However, an important caveat is that the androgen-independent and androgen-dependent tumor groups in this study differ in that, in addition to the status of androgen-dependence, one group became clinically aggressive and the other may not. Consequently, the expression differences could be due to this clinical behavior rather than to hormonal therapy and androgen independence. There are numerous studies in the prostate cancer literature that compare recurrent tumors with tumors before recurrence, without separating out the tumors that would never recur, and this complicates the conclusions that can be drawn. Identifying gene expression profiles that segregate the androgen-dependent tumor that will recur from those that will not is an area of great interest (42) and will make an important contribution to prostate cancer prognostics. Approaches that emphasize the identification of key groups of genes, such as the gene ontology analysis we present here, may shed light on the identification of recurrent tumors and how they respond to therapy.

These data provide a significant contribution to our knowledge of the molecular characteristics of AIPC; however, the potential weaknesses of the study warrant discussion. First, because the AIPC biopsy specimens contained relatively few tumor cells and most were completely exhausted in obtaining the RNA for microarray analysis, it was not possible to conduct traditional validation experiments, such as quantitative reverse transcription-PCR. However, the use of RNA amplification in conjunction with microarray analysis has been repeatedly validated in the literature (43–45), and the protocol we used provided high efficiency and little technical variation between the samples both within and between the tumor groups. In addition, in silico validation showed that several of the genes identified in this study [e.g., NOV (46) and MIF (47)] have been shown previously to be differentially expressed in androgen-independent cells or with increasing prostate tumor grade, and Table 3 (column 7) frequently cites a report validating the findings in this study. A second concern was that the differentially expressed genes could have resulted from the treatment that the AIPC patients had undergone rather than being a characteristic of AIPC itself. However, because the AIPC biopsies included in this study derived from patients who had undergone a diverse array of treatment, it was unlikely that treatment alone produced the consistent differential expression we identified. A third concern was that the differentially expressed genes could have resulted from the different processing that biopsies undergo compared with whole prostatectomies. However, the RNA isolated from all samples was of similar quality. Finally, the ratios of differential expression are modest in comparison with studies using little or no RNA amplification. However, RNA amplification dampens the variation of gene expression (13), which likely reduced the dynamic range of the data while still allowing for the statistically significant separation of gene expression between the two groups.

The various potential biases in the study, although addressed to the best of our ability, remain a source of concern. We were not able to obtain patient-matched, androgen-dependent and androgen-independent specimens, and processing of whole prostatectomies and core biopsies, although both frozen, are intrinsically different as we have studied previously (48). Thus, although the resulting data show significant corroboration in the literature regarding differential expression of individual genes, and the genes could be grouped by gene ontology analysis and regions of potential chromosomal deletion, it is still possible that the results were influenced by bias error. In spite of this, understanding androgen independence as it develops in patients is of critical importance and must be moved forward even with its inherent challenges. Thus, this initial study is a screening effort to identify potential biomarker and therapeutic candidates, some of which may be false positives. The design of subsequent validation studies will benefit by including such sample sets as those with larger numbers of cases, most of which will be formalin-fixed, patient-matched series and autopsy specimens.

In conclusion, this study defines the effects of androgen ablation therapy on the gene expression profile of primary prostate cancer cells that are resistant to treatment. These data establish the state of the transcriptome of a discrete and important step in the process of prostate cancer progression, beyond an untreated high-grade lesion yet before an androgen-independent metastatic lesion, and may be critical to developing intervention strategies for this advanced disease.

We thank Dr. David D. Roberts (Biochemical Pathology Section, Center for Cancer Research, National Cancer Institute) for valuable discussion.