Androgen deprivation therapy for advanced prostate cancer is often effective, but not curative. Molecular pathways mediating the therapeutic response and those contributing to the subsequent hormone-refractory cell growth remain poorly understood. Here, cDNA microarray analysis of human CWR22 prostate cancer xenografts during the course of androgen deprivation therapy revealed distinct global gene expression profiles in primary, regressing and recurrent tumors. Elucidation of the genes involved in the transition between these states implicated specific molecular mechanisms in therapy failure and tumor progression. First, we identified a set of androgen-responsive genes whose expression decreased during the therapy response, but was then systematically restored in the recurrent tumors. In addition, altered expression of genes that encode known targets of rapamycin or that converge on the PI3K/AKT/FRAP pathway was observed in the recurrent tumors. Further suggestion for the involvement of these genes in hormone-refractory prostate cancer came from the observation that cells established from the recurrent xenografts were strongly inhibited in vitro by rapamycin. The results of this functional genomic analysis suggest that the combined effect of re-expression of androgen-responsive genes as well as the activation of rapamycin-sensitive signaling may drive prostate cancer progression, and contribute to the failure of androgen-deprivation therapy.
Analysis of the molecular effects of cancer therapies in vivo can help us to understand why certain therapies are effective and how therapy-failure and drug resistance may take place. Repeated sampling of specimens from human prostate cancer patients undergoing androgen-deprivation therapy is not possible. Therefore, we decided to explore the molecular events underlying human prostate cancer progression in the xenograft model system CWR22 (Pretlow et al., 1993; Wainstein et al., 1994; Cheng et al., 1996; Nagabhushan et al., 1996; Bubendorf et al., 1999) using functional genomics techniques. cDNA microarrays containing 6605 genes were applied to the analysis of CWR22 prostate cancer xenografts in nude mice before treatment (n=4), during androgen withdrawal (n=5), and after the development of recurrence (n=3). Multi-dimensional scaling (MDS) (Khan et al., 1998) was used to visualize global differences in gene expression profiles between the xenografts. MDS analysis revealed three distinct clusters of gene expression profiles corresponding to the three stages of tumor progression in the model system (Figure 1). This suggests that there are systematic global differences in gene expression patterns in androgen-dependent primary tumors, tumors that undergo regression, and those that grow independently of androgens. These differences should provide clues to the molecular mechanisms involved.
We then applied statistical tools to define those genes and signaling pathways that may contribute to the observed global differences in gene expression. Gene clustering using temporal templates (Figure 2b) was used to identify genes that were systemically associated with therapy response. We identified 604 genes that changed by at least twofold during therapy (Figures 1 and 2a). A cluster of 59 genes was further identified based on a decrease of gene expression by at least threefold during androgen withdrawal therapy (Figure 2c). These highly castration-repressed transcripts included at least eight known androgen-responsive genes such as cell cycle regulators ODC1, PCNA, and c-fos (Figure 2d) as well as other proliferation-associated genes including BUB1, BUB3, CDC2 delta T, CDC20, CDC28, CDC45L, CDKN3, CENPF, CKS2, MAD2L1, NEK2, STK12, and UBCH10. These castration-repressed genes (subsequently referred to as androgen-responsive genes) define possible effector and signaling genes downstream of the androgen receptor that may be involved in mediating the cessation of cell proliferation and induction of tumor regression.
The level of expression of these strongly castration-repressed transcripts was then analysed in the recurrent tumors. Virtually all of these genes (57/59, 96.6%) were re-expressed in the recurrent tumors, as defined by a level of expression reaching at least 70% of that seen in primary untreated tumors. In a few cases, expression levels in the recurrent tumors exceeded those observed in the primary tumors. For example, the FKBP5 gene, which encodes for a steroid receptor interacting immunophilin (Nair et al., 1997), was one of the most strongly repressed (ratio <0.2) genes during therapy (Figures 2c and 3c). In contrast, FKBP5 expression in the recurrent tumors was twofold higher than in the primary tumors (Figures 2c and 3c), as also suggested by another study (Amler et al., 2000). The restoration of androgen-responsive genes was validated in duplicate experiments on an independent cDNA microarray containing 8152 genes. In this data set, mRNA levels of the 465 most abundant 3495 transcripts (13.3%) changed during therapy. Of these 465 androgen-responsive genes, 94.8% had expression levels that were similar in the recurrent and primary tumors. These results therefore indicate that the restoration of expression of androgen-repressed transcripts is a systematic feature of recurrent prostate cancers. This provides unbiased, genomic-level support to the hypothesis that the reactivation of androgen-responsive genes in the absence of a ligand is involved in the growth of androgen-independent tumors (Culig et al., 1994; Gregory et al., 1998; Craft and Sawyers, 1998; Craft et al., 1999; Abreu-Martin et al., 1999; Peterziel et al., 1999). The gene expression profiles do not indicate at which level of androgen regulation this reactivation takes place and what the specific molecular mechanisms may be. The virtually universal restoration of androgen-responsive genes suggests that the reactivation could be taking place at the level of the androgen receptor itself. The reactivation of androgen-responsive genes may therefore explain part of the global gene expression changes seen in the recurrent tumors. However, in the MDS analysis (Figure 1), the recurrent tumors assumed a unique gene expression state that was different from the primary tumors and that could not be solely explained by the restoration of androgen-responsive genes.
Another set of genes was identified which showed differential expression between the primary and recurrent tumors. Based on the mean gene expression ratios from six recurrent and four primary tumors, expression levels of 104 of the 3495 informative genes (3.0%) were significantly (twofold or more) increased, and those of 60 genes (1.7%) decreased in the recurrent tumors (Figure 1). Figure 3a shows 30 genes (out of a total of 164 differentially expressed genes) that were most systematically altered in the recurrent tumors. Among these 164 genes were several genes coding for proteins that either converged on the PI3K/AKT/FRAP pathway or represented direct targets of macrolide drugs (such as rapamycin and FK506). Also, as highlighted in Figure 3b, several genes that were androgen-responsive and re-expressed in the recurrent tumors met these criteria and suggested involved of rapamycin-sensitive signaling in hormone-refractory tumors.
These findings led us to hypothesize that rapamycin-sensitive gene products and signaling pathways may play a role in androgen-independent growth in the recurrent tumors. To further evaluate this hypothesis, we studied the effects of rapamycin and FK506 on the growth and viability of a cell line established from the recurrent CWR22R xenografts. Rapamycin is a known inhibitor of the PI3K/AKT/FRAP pathway (Kunz et al., 1993; Brunn et al., 1996; Sekulic et al., 2000). Death of the hormone-independent CWR22R cells was observed at very low doses of rapamycin (IC50 ∼0.1 nM) (Figure 3d), whereas hormone-responsive LNCap prostate cancer cell lines exhibited partial inhibition, even at high doses (Figure 3d). The results are based on two different cell lines that are not isogenic and may have other differences contributing to the observed effects. However, taken together with the global-scale gene expression studies, the data from this in vitro sensitivity testing indicate that further studies are warranted to explore rapamycin as a candidate drug for the treatment of hormone refractory prostate cancers. Cancer cells exhibit greater than a 1000-fold (IC50 ranging from <1 nM to >10 mM) variability in their sensitivity to rapamycin, possibly reflecting mechanisms of intrinsic resistance (Hosoi et al., 1998). Cancer cells that have activated genes and pathways that signal through the PI3K/AKT/FRAP pathway may be particularly sensitive. For example, IGF-1 receptor activation is associated with the efficacy of rapamycin treatment in childhood sarcomas (Dilling et al., 1994). Several growth factors and related genes that we observed to be overexpressed in the recurrent prostate cancers relative to the primary tumors (such as HGF, VEGFC, FGF2, IGFBP3, PDGFA, LTBP4, GFR, PGF, IPTKB, CDS1, and FKHL13, data not shown) could have similarly contributed to the activation of the PI3K/AKT/FRAP pathway and alterations in the rapamycin target expression.
We found that FK506 treatment did not have any effect on the growth of either the CWR22R or LNCaP cells (Figure 3d). Since FK506 and rapamycin bind many of the same intracellular targets, their different biological effects may be informative in elucidating those molecular pathways that are most critical for progression of prostate cancer. Rapamycin and FK506 both bind to FKBP12 (FK506-binding protein 12) (Sabers et al., 1995; Liu et al., 1991). Rapamycin-FKBP12, but not the FK-506-FKBP12 complex inhibits FRAP (FKBP-Rapamycin Associated Protein), a member of the phosphoinositide-3-kinase related kinases regulating translation following mitogenic activation of the PI3K/AKT/FRAP pathway. In contrast, FK506, but not rapamycin, inhibits calcineurin activity (Liu et al., 1991). This suggests that of the many known and unknown targets of rapamycin and FK506, FRAP and the activity of the PI3K/AKT pathway is a more likely candidate than calcineurin as a drug target in hormone-refractory prostate cancer. However, elucidation of the specific pathways inhibited by the rapamycin is important to uncover the molecular mechanisms of the observed effects and to identify critical targets of growth and survival signaling in hormone-refectory prostate cancer.
In conclusion, large-scale gene expression data as a function of therapy response and tumor progression in vivo may provide insights to the signaling pathways that could be exploited for therapeutic intervention in hormone-refractory prostate cancer, an often incurable and chemotherapy-resistant cancer. Our functional genomics study revealed two gene expression profiles that are operational during prostate cancer progression and endocrine therapy failure in vivo. These are the virtually uniform restoration of androgren-responsive signaling and the activation of genes associated with rapamycin sensitive signaling. Based on the results of this study, rapamycin is a candidate drug for pre-clinical and clinical evaluation in the treatment of hormone-refractory human prostate cancer.
Materials and methods
Xenografts and cell lines
CWR22 xenografts were derived from a primary human prostate carcinoma and can be serially transplanted in nude mice. The primary CWR22 xenografts undergo growth arrest and partial tumor regression after castration of the mouse host. After 3 to 10 months, androgen-independent tumor growth resumes in about half of the cases, reflecting the clinical course in patients. Fresh frozen tissue from CWR22 human prostate cancer xenografts (Pretlow et al., 1993) was obtained from 13 different mice at different stages of hormonal therapy and tumor progression (four primary untreated CWR22, five CWR22 therapy time points after 0.5, 2, 4, 8 and 16 days after castration, and four independent hormone-refractory CWR22R strains). LNCaP (ATCC) and CWR22R cell lines (kindly provided by Dr Jim Jacobberger's Laboratory at Case Western University) were cultured in RPMI1640 10% fetal bovine serum (Life Technologies, Rockville, MD, USA) at 37°C and 5% CO2. mRNA was extracted with the FastTrack 2.0 Kit (Invitrogen Corporation, Carlsbad, CA, USA).
Analysis of mRNA expression by cDNA microarrays
Custom cDNA microarrays were constructed consisting of 6605 to 8000 elements (sequence verified clones from Research Genetics, Huntsville, AL, USA), representing different (non-redundant) transcripts including 4032 to 7700 known (named) genes (Mousses et al., 2000). All xenografts were analysed at least twice. Either LNCap or CWR22R were used as a reference and labeled with Cy5. The reference cDNA was simultaneously hybridized with Cy3 labeled test specimens on a cDNA microarray as previously described (Mousses et al., 2000). Details of the fabrication of the microarray slides, image generation, and the software used for the ratio analysis, and bioinformatics, and supplemental methods are available at (Mousses et al., 2000) (http://www.nhgri.nih.gov/DIR/Microarray).
Template-based clustering algorithm
Template based gene clustering is a supervised form of clustering which we developed to identify the genes that respond to time course treatments based on their correlation to templates that represent the temporal kinetics of the expression profiles. Complete descriptions of template based gene clustering including methods, theory, and equations used, including the algorithm is available at (http://www.nhgri.nih.gov/DIR/Microarray).
Multidimensional scaling analysis of gene expression data has been described elsewhere (Khan et al., 1998; Mousses et al., 2000). Briefly, MDS is a method that represents measurements of similarity (between pairs of objects), as Euclidean distance between points in a dimension-reduced space. The dimension reduction procedure is to minimize the difference between the distance in higher-dimensional space and the approximate distance in the dimension-reduced (2-D or 3-D) space.
Drug treatment and cell viability
Exponentially growing LNCaP or CWR22R cells were trypsinized and plated at 0.5×105 or 1×105 cells/ml respectively in 96-well culture plates. After 24 h, cells were treated for 72 h with serial twofold dilutions of either FK-506 (Tacrolimus, Calbiochem Inc., San Diego, CA, USA) or rapamycin (Sirolimus) Sigma, St. Louis, MO, USA). DMSO was added to the control wells. The experiments were performed in triplicate. Cell viability was measured by the WST-8 assay (Dojindo Molecular Technologies Inc., Gaithersburg, MD, USA).
Abreu-Martin MT, Chari A, Palladino AA, Craft NA, Sawyers CL . 1999 Mol. Cell Biol. 19: 5143–5154
Amler LC, Agus DB, LeDuc C, Sapinoso ML, Fox WD, Kern S, Lee D, Wang V, Leysens M, Higgins B, Martin J, Gerald W, Dracopoli N, Cordon-Cardo C, Scher HI, Hampton GM . 2000 Cancer Res. 60: 6134–6141
Brunn GJ, Williams J, Sabers C, Wiederrecht G, Lawrence Jr JC, Abraham RT . 1996 EMBO J. 15: 5256–5267
Bubendorf L, Kolmer M, Kononen J, Koivisto P, Mousses S, Chen Y, Mahlamaki E, Schraml P, Moch H, Willi N, Elkahloun AG, Pretlow TG, Gasser TC, Mihatsch MJ, Sauter G, Kallioniemi OP . 1999 J. Natl. Cancer Inst. 91: 1758–1764
Cheng L, Sun J, Pretlow TG, Culp J, Yang NS . 1996 J. Natl. Cancer Inst. 88: 607–611
Craft N, Sawyers CL . 1998 Cancer Metastasis Rev. 17: 421–427
Craft N, Shostak Y, Carey M, Sawyers CL . 1999 Nat. Med. 5: 280–285
Culig Z, Hobisch A, Cronauer MV, Radmayr C, Trapman J, Hittmair A, Bartsch G, Klocker H . 1994 Cancer Res. 54: 5474–5478
Dilling MB, Dias P, Shapiro DN, Germain GS, Johnson RK, Houghton PJ . 1994 Cancer Res. 54: 903–907
Gregory CW, Hamil KG, Kim D, Hall SH, Pretlow TG, Mohler JL, French FS . 1998 Cancer Res. 58: 5718–5724
Hosoi H, Dilling MB, Liu LN, Danks MK, Shikata T, Sekulic A, Abraham RT, Lawrence Jr JC, Houghton PJ . 1998 Mol. Pharmacol. 54: 815–824
Khan J, Simon R, Bittner M, Chen Y, Leighton SB, Pohida T, Smith PD, Jiang Y, Gooden GC, Trent JM, Meltzer PS . 1998 Cancer Res. 58: 5009–5013
Kunz J, Henriquez R, Schneider U, Deuter-Reinhard M, Movva NR, Hall MN . 1993 Cell 73: 585–596
Liu J, Farmer Jr JD, Lane WS, Friedman J, Weissman I, Schreiber SL . 1991 Cell 66: 807–815
Mousses S, Bittner ML, Chen Y, Doughtery ER, Baxevanis A, Meltzer PS, Trent JM . 2000 Functional Genomics: Gene Expression Analysis by cDNA Microarrays Livesey FJ and Hunt SP (eds) Oxford University Press: Oxford pp 113–137
Nagabhushan M, Miller CM, Pretlow TP, Giaconia JM, Edgehouse NL, Schwartz S, Kung HJ, de Vere White RW, Gumerlock PH, Resnick MI, Amini SB, Pretlow TG . 1996 Cancer Res. 56: 3042–3046
Nair SC, Rimerman RA, Toran EJ, Chen S, Prapapanich V, Butts RN, Smith DF . 1997 Mol. Cell.Biol. 17: 594–603
Peterziel H, Mink S, Schonert A, Becker M, Klocker H, Cato AC . 1999 Oncogene 18: 6322–6329
Pretlow TG, Wolman SR, Micale MA, Pelley RJ, Kursh ED, Resnick MI, Bodner DR, Jacobberger JW, Delmoro CM, Giaconia JM, et al. 1993 J. Natl. Cancer Inst. 85: 394–398
Sabers CJ, Martin MM, Brunn GJ, Williams JM, Dumont FJ, Wiederrecht G, Abraham RT . 1995 J. Biol. Chem. 270: 815–822
Sekulic A, Hudson CC, Homme JL, Yin P, Otterness DM, Karnitz LM, Abraham RT . 2000 Cancer Res. 60: 3504–3513
Wainstein MA, He F, Robinson D, Kung HJ, Schwartz S, Giaconia JM, Edgehouse NL, Pretlow TP, Bodner DR, Kursh ED, Pretlow TG . 1994 Cancer Res. 54: 6049–6052
Supported in part by NIH grant CA57179 (to T Pretlow), CaPCURE (L Bubendorf) and Swiss National Science Foundation (L Bubendorf and U Wagner) and the Krebsliga beider Basel and Novartis Foundation (U Wagner).
About this article
The Journal of Steroid Biochemistry and Molecular Biology (2017)
S100P is Overexpressed in Squamous Cell and Adenosquamous Carcinoma Subtypes of Endometrial Cancer and Promotes Cancer Cell Proliferation and Invasion
Cancer Investigation (2016)
Breast Cancer Research and Treatment (2016)