5A-Androstane-3A,17B-Diol Activates Pathway That Resembles The

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Prostate Cancer and Prostatic Diseases (2004) 7, 364–374 & 2004 Nature Publishing Group All rights reserved 1365-7852/04 $30.00 www.nature.com/pcan 5a-Androstane-3a,17b-diol activates pathway that resembles the epidermal growth factor responsive pathways in stimulating human prostate cancer LNCaP cell proliferation

RA Zimmerman1, I Dozmorov2, EH Nunlist1, Y Tang2,XLi1, R Cowan1, M Centola2, MB Frank2, DJ Culkin1 & H-K Lin1* 1Department of Urology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA; and 2Arthritis and Immunology Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, USA

5a-Androstane-3a,17b-diol (3a-diol) is considered to have no androgenic effects in androgen target organs unless it is oxidized to 5a-dihydrotestosterone (5a-DHT). We used microarray and bioinformatics to identify and compare 3a-diol and 5a- DHT responsive gene in human prostate LNCaP cells. Through a procedure called ‘hypervariable determination’, a similar set of 30 responsive genes involving signal transduction, transcription regulation, and cell proliferation were selected in 5a-DHT-, 3a-diol-, and epidermal growth factor (EGF)-treated samples. F-means cluster and networking procedures showed that the responsive pattern of these genes was more closely related between 3a-diol and EGF than between 5a-DHT and 3a-diol treatments. We conclude that 3a-diol is capable of stimulating prostate cell proliferation by eliciting EGF-like pathway in conjunction with androgen receptor pathway. Prostate Cancer and Prostatic Diseases (2004) 7, 364–374. doi:10.1038/sj.pcan.4500761 Published online 28 September 2004

Keywords: 5a-dihydrotestosterone; 5a-androstane-3a;17b-diol; androgen receptor; epidermal growth factor; microarray analysis

Introduction can be reduced to weaker androgens, including 5a- androstane-3a,17b-diol (3a-diol) and 5a-androstane- Androgens are responsible for development, growth, 3b,17b-diol (3b-diol), through the action of 3a-hydro- differentiation, and function of the prostate. 5a-Dihy- xysteroid dehydrogenase (3a-HSD) and 3b-HSD, respec- drotestosterone (5a-DHT), the most potent androgen in tively. Of these two pathways, prostatic 3a-HSDs play the prostate with high affinity toward the androgen dominant roles in 5a-DHT reduction.5,6 À10 1 receptor (AR; Kd ¼ 10 M), modulates androgen re- 3a-Diol has a low binding affinity toward the AR with 2,3 À6 7 sponsive gene (ARG) expression, and has been Kd ¼ 10 M, and it is believed that 3a-diol must first be implicated in the development of prostatic diseases oxidized to 5a-DHT by 3a-HSD,8 retinol dehydrogenase including benign prostatic hyperplasia (BPH) and (RoDH),9 11-cis-retinol dehydrogenase (RDH5),10 or 4 prostate cancer (PCa). Within the prostate, 5a-DHT mitochondrial L-3-hydroxyacyl coenzyme A dehydrogenase (ERAB, 17b-HSD type 10)11 before exerting its androgenic effects. However, increasing evidence sug- *Correspondence: H-K Lin, Department of Urology, University of gests that 3a-diol may be an important androgen in Oklahoma Health Sciences Center, 920 Stanton L Young Blvd, the induction of prostatic hyperplasia through yet WP3150, Oklahoma City, OK 73104, USA. E-mail: [email protected] 12 13 Received 12 May 2003; revised 4 December 2004; accepted 7 June undefined pathways, including cAMP, in a castrated 8,14,15 16 2004; published online 28 September 2004 dog model, in virilizing rat urogenital tract, and in 3a-diol regulated gene expression in LNCaP cells RA Zimmerman et al prostate formation in marsupial animals.17 We have (10À11–10À8 M), or EGF (0.1–10 ng/ml) stimulation. Con- 365 shown that 3a-diol can stimulate similar levels of cell trol wells did not receive the androgens or EGF. Cell proliferation without eliciting comparable AR trans- number was determined using an XTT cell proliferation activation activities as compared to those of 5a-DHT kit (Roche, Indianapolis, IN, USA). Results were normal- in human PCa LNCaP cells.18 Furthermore, 3a-diol- ized to day 0 and are presented as fold induction after regulated gene expression can be distinguished from treatment. 5a-DHT responsive genes in prostate cells. These results cDNA-based membrane array hybridization To identify suggest that 3a-diol may activate pathways other than transcription profiling in LNCaP cells following andro- classical AR trans-activation to stimulate prostate cell gens and EGF stimulation, cDNA-based membrane proliferation.18 arrays were used as previously reported.18 Briefly, Expression of epidermal growth factor (EGF) has been between 0 and 48 h after stimulation, total RNA was demonstrated in epithelial cells of normal and diseased extracted from LNCaP cells treated with 10À12 M5a- prostate.19,20 EGF exerts a mitogenic effect on human DHT, 10À11 M3a-diol, or 1 ng/ml EGF using Trizol prostate epithelium in vitro.21,22 EGF also induces AR (Invitrogen). Equal amounts of total RNA were pooled trans-activation in the absence of androgen.23 Although it from three repeats for each time point. Radiolabeled is widely suspected that EGF- and EGF receptor (EGFR)- probes were prepared from reverse transcription using activated signaling pathways may play a major role in 5 mg of the pooled total RNA from each time point in the the progression from androgen-dependent to androgen- presence of gene-specific CDS primers (Clontech), independent PCa, complex interactions between andro- dNTPs, 35 mCi [a-32P]dATP, and 200 U Superscript II gens and EGF remain unclear in the prostate gland. reverse transcriptase (Invitrogen) in a total of 10 mlat In this study, we used cDNA-based microarrays to 421C for 1 h. The radiolabeled cDNAs were purified, and further refine our understanding of the temporal regula- radioactivity was determined with LS 6000IC scintilla- tion of gene expression in LNCaP cells in response to tion counter (Beckman Coulter). 5a-DHT, 3a-diol, and EGF stimulation. Based on bioin- Array membrane hybridization and washing steps formatics analysis of similarities and connectivity of also followed procedures described by Nunlist et al.18 commonly expressed genes in LNCaP cells exposed to Washed membranes were then exposed to phosphor- androgens and EGF, our results showed that associations imaging screens, and images were captured through the of 3a-diol responsive genes bear a closer resemblance to Cyclone storage phosphoimager system (Packard the EGF responsive patterns than those in response to 5a- BioScience) and interpreted using OptiQuant image DHT stimulation. Our results also suggested that 3a-diol analysis software (Packard BioScience). For a complete may also exert its androgenic effects via a pathway list of genes present in these arrays, see http:// similar to growth factor pathway in LNCaP cells. www.clontech.com/atlas/genelists/index.html.

Materials and methods Microarray data analysis procedure Reagents and chemicals LNCaP human PCa cell line was Normalization Gene expression profiles among differ- obtained from American Type Culture Collection (ATCC ent experiments were normalized to their background and adjusted to each other using the procedure described # CRL-1740). 5a-DHT and 3a-diol were purchased from 24 Sigma-Aldrich (St Louis, MO, USA). [a-32P]dATP in detail elsewhere. In brief, the procedure assumed (3000 Ci/mmol, 10 mCi/ml) was acquired from Amer- that signals (S) from nonexpressed genes were normally sham Pharmacia (Piscataway, NJ, USA). RPMI 1640 distributed. The mean (S0) and standard deviation (SD0) medium, OPTI-MEM, penicillin–streptomycin, and EGF of these nonexpressed genes were calculated using an were purchased from Invitrogen (Carlsbad, CA, USA). iterative nonlinear curve fitting procedure and used as background parameters for normalization using Fetal bovine serum (FBS) was acquired from Atlanta 0 Biologicals (Norcross, GA, USA). Charcoal–dextran- the formula S ¼ (SÀS0)/SD0. After normalization, treated (CD) FBS was obtained from HyClone (Logan, ‘expressed’ genes were selected as genes not associated UT, USA). Atlas Human 1.2 Array and Atlas Human with a representative homogenous family of background Cancer 1.2 Array were obtained from BD Biosciences level values and used for subsequent profile adjustment Clontech (Palo Alto, CA, USA). and comparison. We performed normalization between arrays for genes expressed above background by robust regression Cell culture analysis. Potential outliers were identified, and their contributions to the calculations were downweighted in LNCaP cells were maintained at 371C and 5% CO2 an iterative manner in order to diminish their influences. in RPMI 1640 medium supplemented with 10% FBS, Expression profiles of control and experimental groups 5 U/ml penicillin, and 5 mg/ml streptomycin. Cells were were then rescaled to a common standard: the averaged passaged every 3–5 days or whenever cells reach 70–80% profile of the control group. confluence. A group of similarly expressed genes from control Cell proliferation assay LNCaP cell proliferation in experiments, denoted ‘reference group’, was identified response to androgens and EGF stimulation was deter- and used for selection of variable genes and F-clustering mined as previously described.18 Briefly, 2 Â 103 cells in procedure (below). The reference group consisted of RPMI 1640 containing 10% FBS were seeded in each well genes from the control samples whose expression levels of 96-well plates for adherence followed by serum were above the background level in at least one time deprivation and 5a-DHT (10À14–10À11 M), 3a-diol point and had very low variability as determined by

Prostate Cancer and Prostatic Diseases 3a-diol regulated gene expression in LNCaP cells RA Zimmerman et al

366 F-test. The residuals of the reference group approximated genes for visual inspection. Genes were presented in a normal distribution based on the Kolmogorov–Smirnov the same order in all three figures, and changes in criterion. color indicated expression variation between different treatments.

Selection of hypervariable (HV) genes Genes expressed above background in at least one time point were tested Networking of the genes based on partial correlation for the high variability as described in detail else- 24–26 coefficients Correlation coefficients can be used as a where. Briefly, residuals were calculated as expres- measure of gene coexpression within clusters; however, sions in each time point minus their average over time not every pair of highly correlated genes are functionally course for identified genes. Standard deviation (s.d.) of interconnected. Rather, they could be under a common the residuals of each gene was compared with s.d. of influence of separate genes (such as common regulatory). reference group by F-test with threshold adjusted to The partial correlation coefficient with excluded influ- p ¼ 1/N, where N is the number of expressed genes. The ences from other genes is a better characteristic of the HV genes were selected as having low probability to functional interconnection.28 belong to the family of stable genes of the reference 25 To elicit a casual inference of genes, we used a group, po1/N, and were used for F-means clustering. procedure (described in detail elsewhere26) applying a partial correlation coefficient. The partial correlation of variables x and y with respect to z was defined as an F-means clustering analysis of gene expression dynamics x–y correlation after removing the effects of z or 2 2 1/2 Genes with expression above background in at least one prxy,z ¼ (rxyÀrxzryz)/((1Àrxz)(1Àryz)) . This analysis was time point in any of the three treatments were selected as performed in a pairwise manner for all genes shown to HV genes and used in clustering. To avoid the influence be related by cluster analysis. A program written in of low-intensity genes whose expression was presented Matlab (MathWorks Inc.) was used to calculate the by large negative values in logarithmic coordinates, their partial correlation for each element of a group of genes expressions were substituted by random values within whose intercorrelation was above the threshold of t1. 0–0.1 ranges. We estimated the proximity of the genes’ Results were presented in a square matrix. For gene a, profiles over time course by calculating their deviations each element of the matrix Xij is the partial correlation of from each other in each time point and comparing the a with gene i without the influence of gene j. Genes s.d. of these deviations with internal standard of stability whose correlation did not change below the thresholds t2 (s.d. of the residuals of the reference group). We without the influence of a third gene were considered to determined a parameter (termed ‘connectivity’) for each be truly connected. The thresholds t1 and t2 used to of these HV genes. Connectivity was defined as the determine the significant interconnections were calcu- number of genes whose dynamic behavior of expression lated from a Monte Carlo simulation on data of the same during the time course varied from the expression mean and s.d. as the experimental data to exclude pattern of a given gene within ranges of the reference random connections. This refinement of assessing gene group (based on an F criterion). A 0.0005 threshold for inter-relationships provides a means of identifying genes the F-test was used to diminish the portion of false- that were likely to be directly related, and, therefore, positive selections. functionally related, as opposed to cluster and correla- HV genes of the control group were sorted by their tion analysis, which identifies genes that are simply connectivity and the clustering process was started with correlated in their behavior. genes of higher connectivity. Genes of the highest connectivity were used as the seed for the first cluster, and all genes with connection to this seed composed RT-PCR Total RNA was extracted from these 5a-DHT- cluster 1. Among the genes not belonging to the first (10À12 M), 3a-diol- (10À11 M), or EGF- (1 ng/ml) treated cluster, the gene of the highest connection became the LNCaP cells using Trizol (Invitrogen), and first-strand seed of the second cluster and all genes connected to it cDNAs were reverse transcribed from 2.5 mg of the total composed the second cluster. This process was repeated RNA in the presence of random hexamers (Roche), 20 mM recursively until the last gene had been clustered. This each of dNTPs, and 200 U of MMLV reverse transcriptase method was developed to maximize the size of the (Invitrogen) in a total of 50 mlat421C for 2 h. QPCR clusters so that comprehensive global group dynamics reactions were performed by mixing 1 ml of first-strand could be revealed. In this statistical model, genes cDNAs, gene-specific 50 and 30 primers (Table 1), and appearing in more than one cluster were likely to be 15 ml of QuantiTectt SYBRs Green PCR kit (QIAGEN) in functional links among these clusters, and genes with a total of 30 ml. Reactions were performed by heat zero connectivity were dissimilar from any other gene. activation at 941C for 15 min, followed by cycling To find common expression patterns between different through 941C for 30 s, 50–551C for 1 min, and 721C for experiments, data from all three treatments were 1 min in iCycler (Bio-Rad) for a total of 40 cycles. All PCR grouped for clustering analysis and then segregated for reactions were run in duplicate and repeated at least further analysis. An identical cluster designation in all three times. The amplified products were quantified three treatments indicated similar dynamic behavior. using the threshold value (CT), which is associated with Genes of the same clusters were then sorted by their the exponential growth of the PCR products for each connectivity. Matrices of correlation coefficients were gene product calculated by the BioRad iCycler iQ system calculated for genes in a pairwise fashion and presented program (Bio-Rad). No template controls in which no as a graphical correlation mosaic output26,27 showing RNA samples were reverse transcribed failed to produce patterns of correlated and noncorrelated behavior of CT values at least three cycles higher than those reversely

Prostate Cancer and Prostatic Diseases 3a-diol regulated gene expression in LNCaP cells RA Zimmerman et al 367 Table 1 Sequences of oligonucleotide primer pairs used for QRT-PCR Gene name (accession number) Primer pairs sequence Product size (bp) Annealing temperature (oC) b-actin F 50-CTT CTA GGC GGA CTA TGA C-30 178 55 R50-ACT GCT GTC ACC TTC ACC GTT CCA GT-30

MAPK3 (X60188) F 50-ATC TGG TCT GTG GGC TGC ATT CTG GC-30 340 55 R50-CTG GCT CAT CCG TCG GGT CAT AGT AC-30

AR (M20132) F 50-GCA TGG TGA GCA GAG TGC CCT ATC C-30 267 55 R50-ACA GGT ACT TCT GTT TCC CTT CAG CG-30

CTNNB1 (X87838) F 50-GGG TCC TCT GTG AAC TTG CTC AGG AC-30 402 55 R50-CTG GAT AGT CAG CAC CAG GGT GGT G-30

RB1 (M15400) F 50-CTG GCA GAA ATG ACT TCT ACT CGA ACA C-30 262 50 R50-TTT CAA GTG GCT TAG GAC TCA CCC AAA C-30

transcribed samples. Melting point analysis was per- 7 formed to ensure that single species of PCR product was No treatment generated in each reaction. b-Actin was also amplified 6 -12 α and used for normalization. Results were calculated as 10 M 5 -DHT 10-11 M 3α-diol DCT, the difference between the gene of interest and the 5 b-actin gene mean thresholds, and expressed as expres- 1 ng/ml EGF sion levels relative to b-actin.29 4

3 Fold induction Results 2

Stimulation of human prostate cancer LNCaP cell 1 proliferation by androgens and EGF 0 We have previously shown a similar level of LNCaP cell 0 1 2 3 4 5 6 7 proliferation when cells were treated with 10À12 M5a- Time after Treatment (Day) À11 DHT and 10 M3a-diol. However, 5a-DHT is a more Figure 1 Androgens- and EGF-stimulated LNCaP cell proliferation. potent androgen in activating AR trans-activation activ- LNCaP cells were seeded in each well of 96-well plates at a concentration of ity. EGF (0.1–10 ng/ml) also has similar mitogenic 2 Â 103 cells/well, and subjected to serum deprivation followed by 5a-DHT, efficiency in stimulating LNCaP cell proliferation. Over 3a-diol, or EGF stimulation. Cell proliferation was determined using XTT a period of 6 days, the concentrations used to treat assay everyday for a period of 6 days. Data were calculated as absorbance at days following androgens or EGF stimulation divided by the absorbance LNCaP cells for identification and comparison of determined at the day of stimulation, and presented as fold induction in responsive genes among androgens and EGF stimulated absorbance following the stimulation. A similar temporal change in cell a similar level of cell proliferation (Figure 1). proliferation was selected for microarray analysis.

Identification of genes regulated by androgens and To perform clustering analysis, data for all three EGF treatment using associative analysis treatments were put together with each gene presented three times, once for every treatment. By doing so, the To identify genes that respond to androgens and EGF identical clustering designation of the genes indicated stimulation, LNCaP cells were treated with 10À12 M5a- similarity of time-course expression between genes or DHT, 10À11 M3a-diol, or 1 ng/ml EGF in serum-deficient between different treatments. The clusters were enum- medium. Changes of gene expression between untreated erated in the order of their size, with cluster 1 as the and treated samples indicated cellular response to largest one. A total of 30 genes were identified as exogenous stimulation. A measurement for the internal responsive to all three treatments, their cluster designa- fluctuation and instrumental errors was calculated from tion is listed in Table 2. the untreated samples as described by Dozmorov and Among all three treatment groups, expressions of Centola24 and termed ‘reference group’. The expression several heat shock proteins (HSP; HSPs 27 (X54079), 40 of each gene was compared with the reference group to (D49547), 70 (X51757), and 75 (U12595)), heat shock measure their temporal variations. Genes that had higher transcription factor 1 (HSF1; M64673), and cell cycle variation across time points than the variation observed regulators (cyclins B1 (CCNB1; M25753), G1 (CCNG1; in the reference group were characterized as HV genes. U47413), and H (CCNH; U11791)) were identified and HV genes whose expression changed over time for 5a- partitioned into the two most common clusters, indicat- DHT-, 3a-diol-, and EGF-treated LNCaP cells were ing that these genes were similarly regulated by 5a-DHT, selected for further analysis. 3a-diol, and EGF. These results suggested that 5a-DHT,

Prostate Cancer and Prostatic Diseases 3a-diol regulated gene expression in LNCaP cells RA Zimmerman et al 368 Table 2 Networked genes Order in mosaic GenBank Gene symbol Description 3a-Diol EGF 5a-DHT

1 M63167 AKT1 v-akt murine thymoma viral oncogene homolog 1 1 1 1 2 M33336 PRKAR1A protein kinase, cAMP-dependent, regulatory, I alpha 1 1 1 3 U66879 BAD BCL2-antagonist of cell death 2 1 1 4 U11791 CCNH cyclin H 2 2 2 5 M20132 AR androgen receptor 1 1 11 6 M25753 CCNB1 cyclin B1 2 2 2 7 U47413 CCNG1 cyclin G1 2 2 2 8 X54079 HSP27 heat shock 27 kDa protein 1 2 2 2 9 M64673 HSF1 heat shock transcription factor 1 1 1 1 10 M98343 EMS1 ems1 sequence 1 1 1 11 X07767 PRKACA protein kinase, cAMP-dependent, catalytic, alpha 1 1 1 12 X74594 RBL2 retinoblastoma-like 2 (p130) 1 1 1 13 D49547 HSP40 heat shock 40 kDa protein 1 1 1 1 14 X51757 HSP70 heat shock 70 kDa protein 6 1 1 1 15 M14694 TP53 tumor protein p53 3 2 5 16 NM_005417 SRC C-src proto-oncogene 3 3 13 17 X04602 IL6 interleukin 6 3 3 3 18 X86779 FASTK Fas-activated serine/threonine kinase 3 2 2 19 M15400 RB1 retinoblastoma 1 (including osteosarcoma) 3 2 4 20 AF084530 DMP1 cyclin D-binding Myb-like protein 4 4 2 21 M95712 BRAF v-raf murine sarcoma viral oncogene homolog B1 3 2 3 22 X79981 CDH5 cadherin 5, VE-cadherin 3 2 2 23 AF019770 PLAB prostate differentiation factor 2 2 2 24 U12595 HSP75 heat shock protein 75 2 2 2 25 M34181 PRKACB protein kinase, cAMP-dependent, catalytic, beta 4 4 8 26 M65066 PRKR1B protein kinase, cAMP-dependent, regulatory, I beta 4 4 12 27 M73077 GRLF1 glucocorticoid receptor DNA binding factor 1 1 1 1 28 M99487 FOLH1 folate hydrolase (prostate-spec. membrane antigen) 2 2 2 29 X60188 MAPK3 mitogen-activated protein kinase 3, ERK1 5 2 2 30 X87838 CTNNB1 catenin (cadherin-associated protein), beta 1 5 5 2

Clusters are obtained for all groups simultaneously: cluster designation is the same for all three treatments. Clusters are numerated in accordance with their size, starting with #1 for the largest cluster. Genes with no clustering designation in any one treatment are not shown. The same gene symbols are used in Figures 3–5.

3a-diol, and EGF regulate some downstream pathways shown as bright orange-red colors (Figure 2c). In the in a similar fashion. Among 5a-DHT, 3a-diol, and EGF other two treatments, due to their change in expression treatments, 3a-diol/EGF, 5a-DHT/EGF, or 3a-diol/5a- in response to different treatments, the same ordered DHT pairs each had 23, 21, and 18 genes of common genes have different cluster identity and different cluster assignments, correspondingly. Several genes’ correlation between each other as manifested by expression patterns including PRKACB (M34181), their color variation in the correlation mosaic PRKR1B (M65066), AR (M20132), TP53 (M14694), and (Figure 2b and d). SRC (NM_005417) had different clustering designations, Across all three treatments, the majority of the 30 specifically to be 4/4/8, 4/4/12, 1/1/11, 3/2/5, and genes were clustered into two main groups with several 3/3/13 for their cluster assignments following 3a-diol/ minor groups. Comparison of the expression dynamics EGF/5a-DHT treatment. This indicates partitioning of the 5a-DHT- and 3a-diol-treated LNCaP cells (Figure between 3a-diol/EGF and 5a-DHT treatment, with 2b and c) showed that between them, while the gene slightly higher similarity between 3a-diol/EGF pair than cluster borders were still maintained, variations in between 5a-DHT/EGF or 3a-diol/5a-DHT pair. The correlation for several genes—such as RBL2 (X74594), existence of different expression patterns among regula- DMP1 (AF084530), GRLF1 (M73077), and MAPK3 tory molecules such as AR, PRKACB, PRKR1B, TP53, (X60188)—changed from positive to negative correla- and SRC suggested that there are differentially activated tions or vice versa. Likewise, the gene cluster borders and regulatory pathways among these three stimuli. correlation mosaic patterns of the 3a-diol- and EGF- treated LNCaP cells were maintained with some changes in gene correlations, such as PRKAR1A (M33336), AR, Dynamics of the HV genes CCNG1, RB1, and PRKR1B, between these two treatments (Figure 2c and d). Except for AR and PRKR1B, The selected 30 genes were presented in a novel genes showing strong correlation with other genes in 5a- approach to reveal their inter- and intra-cluster relation- DHT- and EGF-treated samples also remained strongly ships. First, the genes were sorted based on their correlated in the 3a-diol treatment, while genes of weak connectivity within each cluster in 3a-diol-treated sam- correlation with other genes in 5a-DHT- or EGF-treated ple. Then, the same order was kept in both 5a-DHT- and cells exhibited positive correlation in 3a-diol-treated EGF-treated samples. Inter-relationship between genes cells. As shown in the correlation mosaics, both 3a-diol was estimated by their correlation coefficients and and 5a-DHT androgens and EGF pathways share presented in a correlation mosaic. The genes in one significant common gene expression patterns while cluster were presented consecutively in 3a-diol treatment maintaining their own specificity. 3a-Diol resembles as a distinct square, and their positive correlation is EGF more than 5a-DHT.

Prostate Cancer and Prostatic Diseases 3a-diol regulated gene expression in LNCaP cells RA Zimmerman et al 369 b 30 28 26 a 24 1 AKT1 22 2 PRKAR1A 20 -0.8 -0.6 18 -0.4 3 BAD -0.2 16 0.0

4 CCNH Genes 14 0.2 0.4 12 0.6 5 AR 0.8 10 1.0 6 CCNB1 8 7 CCNG1 6 8 BSP27 4 2 9 HSF1 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 10 EMS1 Genes c 30 11 PRKACA 28 12 RBL2 26 24 13 HSP40 22 14 HSP70 20 18 15 TP53 16

16 SRC Genes 14 17 IL-6 12 10 18 FASTK 8 19 RB1 6 20 DMTF1 4 2 EGF 21 BRAF 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 22 CDH5 d Genes 30 23 PLAB 28 24 HSP75 26 24 25 PRKACB 22 26 PRKR1B 20 27 GRLF1 18 16

28 FOLH1 Genes 14 29 MAPK3 12 10 30 CTNNB1 8 6 4 2 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 Genes Figure 2 Mosaic of correlation coefficients for the variable genes in 5a-DHT-, 3a-diol-, and EGF-treated LNCaP cells. The identity of the genes is listed (a), and mosaics of correlation coefficients for 5a-DHT (b), 3a-diol (c), and EGF (d) for the 30 genes are presented. Genes were ordered according to their connectivity within each cluster in 3a-diol-treated samples in (c); two largest red squares with vague borders in (c) represent two main clusters from the 30 HV genes commonly regulated by 5a-DHT, 3a-diol, and EGF. Detailed information of genes along axis is listed in Table 2. Colors associated with different ranges of correlation coefficients are shown adjacent to (a).

Prostate Cancer and Prostatic Diseases 3a-diol regulated gene expression in LNCaP cells RA Zimmerman et al

370 Gene network construction BAD (U66879), HSF1, and HSP40 were more closely associated with GRLF1 through PRKAR1A expression in To determine the relationship among the 30 HV genes response to 5a-DHT stimulation. In contrast, expression given in Table 2 whose expressions were commonly of IL-6, BAD, HSF1, and HSP40 was more associated regulated by 5a-DHT, 3a-diol, and EGF, the associations of expression among these genes were evaluated using a novel networking method. For every treatment, the 30 AR GRLF1 genes were tested against each other through testing partial correlation with each other, as described in Materials and method, to establish their true connec- PRKACA HSP70 tions. Using Powerpoint graph features, we built a TP53 network based on the genes’ connections. The protein BAD nodes were arranged similarly to facilitate discrimina- RB1 PRKAR1A CDH5 tion. Both positive and negative correlations were PRKACB revealed on the graph. Although common sets of genes were identified to be regulated by 5a-DHT, 3a-diol, and BRAF DMP1 AKT1 EGF in LNCaP cells, significant differences existed in EMS1 ways these genes influence each other (Figures 3–5). The MAPK3 gene network identified in these treatments fell into two PRKR1B patterns anchored by AR and GRLF1. In 3a-diol- and FOLH1 EGF-treated LNCaP cells, AR and GRLF1 worked HSF1 PRKR1B CTNNB1 collaboratively and interconnected with PRKACA HSP75 (X07767) and HSP70 (Figures 3 and 4). In 5a-DHT-treated HSP40 IL6 samples, AR and GRLF1 were not connected by these PLAB two intermediate genes, but rather appeared as if they were in different pathways. AR was positively correlated SRC with CCNB1 expression, and was independent of GRLF1 CCNH and other intermediate genes like AKT1 (M63167) or DMP1 (AF084530), as found in 3a-diol- or EGF-treated HSP27 LNCaP cells. Furthermore, expressions of IL-6 (X04602), CCNB1

CCNG1

AR GRLF1 Figure 4 Gene interconnection in response to 3a-diol. All designations are the same as in Figure 3.

TP53 PRKACA HSP70 GRLF1

HSF1 AR

PRKAR1A CDH5 RB1 PRKACA HSP70 HSP40 IL6 RBL2 RB1 BAD IL6 BRAF DMP1 AKT1 EMS1 PRKAR1A PLAB MAPK3 SRC TP53 HSF1 PRKACB FOLH1 CDH5 BAD HSP40 EMS1 PRKR1B PRKR1B CTNNB1 DMP1 BRAF

HSP75 MAPK3

PRKR1B AKT1 PRKR1B FOLH1 PLAB

HSP75 CTNNB1 CCNH HSP27

CCNH CCNB1 CCNG1

Figure 3 Gene interconnection in response to EGF obtained by CCNB1 calculation of partial correlation coefficients. Genes are the same as shown in Table 2. Solid lines represent positive interconnections with an averaged partial correlation coefficient higher than 0.7, dashed lines interconnec- CCNG1 tions with a negative partial correlation coefficient with averaged value lower than 0.7, and gray lines interconnections duplicated in at least two Figure 5 Gene interconnection in response to 5a-DHT. All designations experimental groups. are the same as in Figure 3.

Prostate Cancer and Prostatic Diseases 3a-diol regulated gene expression in LNCaP cells RA Zimmerman et al with AR expression in 3a-diol- and EGF-treated cells, parameter, denoted ‘connectivity’, to reveal the most 371 and had no direct connectivity with PRKAR1A in GRLF1 comprehensive global group dynamics of gene behavior. group. A slight difference of calculated connectivity of With the above-mentioned stringent statistical criteria, genes between 3a-diol and EGF stimulation may reflect the selected genes were solely attributed to experimental the presence of either divergent pathways between these treatments. two stimuli or experimental errors. These results Clustering based on the use of correlation coefficients suggested that connectivities among the responsive is useful and can be applied for selection of genes with genes were more closely related between 3a-diol and both similar and opposite behaviors. Genes under EGF than those between 3a-diol and 5a-DHT. common regulatory controls or connected through common intermediate elements could show very high similarity in expression. However, correlation coeffi- Differential regulation of gene expression cients provide information about gene coexpression and by androgens and EGF do not necessarily reflect functional interconnection of selected genes. The information about functional inter- We confirmed temporal regulation of AR, MAPK3, connection could be obtained from partial correlation CTNNB1, and RB1 transcript expression by 5a-DHT, coefficients. However, determination of a partial correla- 3a-diol, and EGF by RT-PCR analysis. The selected genes tion coefficient between two genes with extracted represented similar and differential responsive genes influence of all other genes is not practical, especially following androgens and EGF stimulation as listed in with the large data set obtained from arrays. For Table 2. Levels of these genes expression were normal- example, calculation based on the use of inverted ized to levels of b-actin expression at each time point covariance matrix cannot be used for genes with highly (Figure 6). Temporal changes in these gene expressions similar expression behavior, as explained by Toh and 28 confirmed the differential cluster distribution analyzed Horimoto. Preselection of some genes is necessary. In by bioinformatics. this report, we selected HV genes for the preprocess of networking. Calculation of partial correlations based on a stepwise examination of contribution of each indivi- dual gene was performed. Analysis of the influence of Discussion each single gene on the partial correlation coefficient of the examined pair enabled to reveal strong interconnec- We used statistically robust methods in this study for tions that were not susceptible to other genes’ influences, comparative analysis of the gene expression data. First, 24–27 and helped to identify interconnections that were using a statistically based normalization method, the influenced but not cancelled by such genes. In addition, technical noise represented as the ‘reference group’ was thresholds for the averaged (0.7) and minimal (0.35) used as an internal standard for selecting HV genes. partial correlations were chosen by comparing results Second, clustering of the HV genes was performed from experimental data to results obtained from a utilizing the F-test, and a threshold of 0.85 was used to simulation experiment. The simulation set was con- ensure that the cluster was meaningful. Different clusters structed by randomizing experimental gene expression of HV genes represented distinct expression patterns of values while maintaining the same mean and s.d. over all truly responsive genes. Third, F-clustering utilized a time points as the experimental data. The identified thresholds provide rational bases for defining connections among genes and, as such, significantly reduce the 10 5 number of false-positive connections obtained with AR MAPK3 previous methods, in which arbitrary thresholds were 8 4 5α-DHT used. α 6 3 -diol 3 EGF The application of clustering and gene expression 4 2 networks to the biology of the prostrate yielded several

-actin genes known to play important roles in the regulation of β 2 1 cellular growth and differentiation (see Table 2). It is 0 0 notable that many genes that are similarly expressed, 5 10 that is, within particular gene expression clusters, are CTNNB1 RB1 functionally related to one another. Cluster 1, for 4 8 example, contains various HSPs, protein kinases, 3 6 and receptors including AR. The stress-induced Ratio of gene expression/ 2 4 HSP70 and the HSP40 proteins in this cluster interact with AR30–32 and are co-chaperones involved in AR 1 2 stability.33 GRLF1, also in cluster 1, associates with the 0 0 promoter region of the glucocorticoid receptor (GR) gene 0 1 3 6 12 24 48 0 1 3 6 12 24 48 and acts to repress glucocorticoids-elevated GR tran- Time after treatment (hours) scription. Biochemical analysis suggests that GRLF1 Figure 6 Temporal regulation of genes analyzed by RT-PCR. MAPK3, interacts with specific sequence motif34 and, therefore, AR, CTNNB1, and RB1 representing similar and different cluster may direct androgen-specific responses in the model a a assignments following 5 -DHT, 3 -diol, and EGF stimulation were system described here. In addition, EMS1 restructures selected for RT-PCR confirmation. Results were calculated as ratios of these four genes’ expression normalized to the level of b-actin expression at actin microfilaments and is known to undergo each time point. A representative result was presented for each gene from at post-translational modification in carcinoma cells in least three repeats for each time point. response to EGF.35

Prostate Cancer and Prostatic Diseases 3a-diol regulated gene expression in LNCaP cells RA Zimmerman et al

372 F-means clustering analysis involves multiple steps, and diseased human prostate. Controversies exist be- including connectivity assignment, clustering assign- tween androgen and EGF interaction. It has been ment, and subsequent clustering process, starting with reported that 5a-DHT does not change intracellular the gene that has no connection to the established cluster levels of EGF.44 Although upregulation in EGFR has seeds but has highest connectivity. All genes having been detected when these cells were cultured in the connection to the new seed will make up the new cluster. presence of androgens,45,46 it has been shown that 5a- This process continues until all genes are assigned DHT-stimulated LNCaP cell proliferation is not inhibited clusters. Using this approach, some genes bearing by the addition of anti-EGFR neutralizing monoclonal. connectivity to different cluster seeds could belong to This suggests that autocrine activation of EGFR is absent different clusters. We chose to use the approach of in androgen stimulation.47 In contrast, it has been looking at all genes at every step instead of the regular reported that treating cultured human PCa cells with a clustering method approach, where once a gene cluster tyrosine kinase inhibitor, RG-13022 (tyrphostin), blocks designation is assigned, it is excluded from further EGF-induced and androgen-stimulated prostate cell consideration. Our approach reveals all possible inter- proliferation, suggesting that functional tyrosine kinase connections, and enables us to look at the global pathways are required for androgen-induced prostate interactions more easily. cell growth.48 Our bioinformatics analysis suggested that Functional connections for gene products in different 5a-DHT-elevated cell proliferation may not be mediated clusters have also been described. For example, the totally via increased EGF synthesis or EGFR activation, protein kinase AKT1, in cluster 1, is a downstream since connectivity analysis of 5a-DHT- and EGF-regu- effector in the EGFR pathway36 and is involved in AR- lated genes supports the presence of separate regulatory mediated signaling in LNCaP cells.37 IL-6, in cluster 3, pathway(s) activated by 5a-DHT and EGF in LNCaP reportedly upregulates AR-induced gene activation in cells. LNCaP cells.38 Recently, Yang et al39 confirmed this Currently, androgen ablative therapy is used for finding and reported that IL-6 can also inhibit AR trans- treating BPH and PCa. Antiandrogen treatment activation via downregulation of AKT1 in LNCaP cells. for BPH involves the use of the 5a-reductase type 2 We have confirmed differential regulation and utilization inhibitor finasteride.49 Although finasteride reduces of AR and AKT by 5a-DHT and 3a-diol (unpublished plasma 5a-DHT levels by 90%, it reduces prostatic results). These data support the hypothesis that multiple volume by only 30%, suggesting the existence of other ligands bind receptors in prostrate cells and result in the sources of 5a-DHT49 or other molecules responsible stimulation or repression of inter-related pathways in an for prostate enlargement. When bicalutamide (antian- attempt to maintain homeostasis in this tissue, or that drogen) is used as monotherapic agent in patients distinct signaling pathways are activated in distinct with nonmetastatic locally advanced PCa, bicalutamide prostatic cells. The identification of biologically relevant is as effective as castration in disease progression pathways reported here for this model supports the and overall survival.50 When bicalutamide is used as application of the novel statistical procedures utilized an adjuvant in conjunction to local standard therapy, here to array-based data. including radical prostatectomy, radiotherapy, or AR-mediated pathway is believed to regulate watchful waiting at the stage of localized or locally androgen-dependent proliferation in the prostate advanced, nonmetastatic PCa, a significant risk reduc- and PCa cells following 5a-DHT exposure. 3a-Diol tion of cancer progression and bone metastases is is reduced from 5a-DHT by 3a-HSDs and has low observed. However, disease progression is present affinity toward the AR. Although 3a-diol has long in more than 70% of the patients who receive bicaluta- been suspected to play a role in various physiological mide monotherapy49 and more than 30% of patients functions,17,40 the mechanism behind 3a-diol-mediated who receive combined therapy.51 These studies androgenic effects remains unclear.12 Our connectivity suggest that there is a pathway(s) other than AR trans- analysis revealed that commonly regulated genes are activation responsible for disease progression, or that ‘associated’ differently between 3a-diol- and 5a-DHT- there are hormonally independent cells in the diseased stimulated cells, and suggested that 3a-diol might prostate. In the presence of androgen blockades, the AR utilize, in addition to the AR pathway, a separate is not downregulated,52,53 and adrenal androgens, AR, pathway such as EGFR to stimulate prostate cell and other steroid receptors may stimulate mitogenic proliferation. Therefore, accumulation of 3a-diol in the growth factor signaling pathways to mediate cellular prostate through the actions of 3a-HSDs41,42 and 17b- proliferation.54–56 If 3a-diol truly serves as a ‘crosstalk’ HSD42,43 may be partially responsible for the pathogen- molecule between androgens and growth factors and esis of prostatic diseases. Upregulated expression of utilizes the EGF pathway for mediating prostate cell 3a-HSD transcripts41 in primary cultures of prostate growth, as suggested in our analysis, current strategies of cells derived from BPH and PCa and proteins (unpub- using 5a-reductase inhibitor and/or AR blockers for BPH lished results) in BPH and PCa tissue sections may and PCa treatment for androgen deprivation therapy account for 3a-diol accumulation in the diseased gland. need to be modified to provide maximal androgen It is possible that failure of initial treatment modality blockade. and further clinical progression of BPH and PCa may result from augmentation of both 5a-DHT and 3a-diol and imbalance of androgen-metabolizing enzymes in the diseased prostate. Acknowledgements In addition to steroid hormones, it has been widely accepted that polypeptide growth factors, including EGF, This work was supported by the NIH Grant DK54925 to are involved in the growth and development of normal HKL.

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