Correlation of protein expression, Gleason score and DNA ploidy in prostate cancer

Comparison Between Amide Proton Transfer Magnetic Resonance Imaging Using 3-Dimensional Acquisition and Diffusion-Weighted Imaging for Characterization of Prostate Cancer: A Preliminary Study

OBJECTIVE

This study aimed to compare diagnostic performance for tumor detection and for assessment of tumor aggressiveness in prostate cancer (PC) between amide proton transfer magnetic resonance imaging (MRI) with 3-dimensional acquisition (3DAPT) and diffusion-weighted imaging.

METHODS

The subjects were 23 patients with 27 pathologically proven PCs who underwent 3T multiparametric MRI. With reference to the pathology findings, 2 readers in consensus identified the location of PC on multiparametric MRI and measured APT signal intensity (APT SI [%]) and mean apparent diffusion coefficient (ADC) of the benign region and each PC lesion.

RESULTS

The mean ADC showed a significant difference between benign regions and PC lesions (0.74 ± 0.15 vs 1.37 ± 0.21, P < 0.001), whereas APT SI did not ( P = 0.091). Lesion APT SI was significantly higher and lesion ADC was significantly lower in PCs with Gleason group (GG) ≥3 than in PCs with GG ≤2 (3.37 ± 1.30 vs 1.78 ± 0.67, P < 0.001, and 0.71 ± 0.18 vs 0.79 ± 0.10, P = 0.038, respectively). The APT SI was significantly higher in GG3 than in GG1, in GG3 than in GG2, and in GG4 than in GG2 ( P = 0.009, P = 0.001, and P = 0.006, respectively). The area under the curve for separating tumor lesions and benign regions was 0.601 for 3DAPT and 0.983 for ADC ( P < 0.001). The area under the curve for separating tumors with GG ≤2 from tumors with GG ≥3 was 0.912 for 3DAPT and 0.734 for ADC ( P = 0.172).

CONCLUSIONS

In patients with PC, it might be preferable to use ADC to discriminate benign from malignant tissue and use APT SI for assessment of tumor aggressiveness.

Proteome Profiling of 1 to 5 Spiked Circulating Tumor Cells Isolated from Whole Blood Using Immunodensity Enrichment, Laser Capture Microdissection, Nanodroplet Sample Processing, and Ultrasensitive nanoLC-MS

Proteome profiling of circulating tumor cells (CTCs) can provide crucial insight into disease progression and the role of CTCs in tumor metastasis. We describe an integrated workflow to measure global protein expression in 1-5 spiked CTCs enriched from whole blood by immunodensity gradient centrifugation. Enriched CTCs were purified and collected by laser capture microdissection, prepared using a recently developed nanodroplet-based processing platform (nanoPOTS), and finally analyzed by ultrasensitive nanoLC-MS/MS. The workflow was capable of identifying an average of 164 and 607 protein groups from samples comprising 1 and 5 LNCaP cells, respectively, that were isolated from human whole blood. A panel of prostate cancer-specific proteins were identified and quantified, which was used to differentiate between spiked CTCs and white blood cells.

Quantitative Mass Spectrometry-Based Proteomic Profiling for Precision Medicine in Prostate Cancer

Prostate cancer (PCa) is one of the most frequently diagnosed cancer among men in the western societies. Many PCa patients bear tumors that will not threat their lives if left untreated or if treatment is delayed. Our inability for early identification of these patients has resulted in massive overtreatment. Therefore, there is a great need of finding biomarkers for patient stratification according to prognostic risk; as well as there is a need for novel targets that can allow the development of effective treatments for patients that progress to castration-resistant PCa. Most biomarkers in cancer are proteins, including the widely-used prostate-specific antigen (PSA). Recent developments in mass spectrometry allow the identification and quantification of thousands of proteins and posttranslational modifications from small amounts of biological material, including formalin-fixed paraffin-embedded tissues, and biological fluids. Novel diagnostic and prognostic biomarkers have been identified in tissue, blood, urine, and seminal plasma of PCa patients, and new insights in the ethology and progression of this disease have been achieved using this technology. In this review, we summarize these findings and discuss the potential of this technology to pave the way toward the clinical implementation of precision medicine in PCa.

Database-augmented Mass Spectrometry Analysis of Exosomes Identifies Claudin 3 as a Putative Prostate Cancer Biomarker

In prostate cancer and other malignancies sensitive and robust biomarkers are lacking or have relevant limitations. Prostate specific antigen (PSA), the only biomarker widely used in prostate cancer, is suffering from low specificity. Exosomes offer new perspectives in the discovery of blood-based biomarkers. Here we present a proof-of principle study for a proteomics-based identification pipeline, implementing existing data sources, to exemplarily identify exosome-based biomarker candidates in prostate cancer.Exosomes from malignant PC3 and benign PNT1A cells and from FBS-containing medium were isolated using sequential ultracentrifugation. Exosome and control samples were analyzed on an LTQ-Orbitrap XL mass spectrometer. Proteomic data is available via ProteomeXchange with identifier PXD003651. We developed a scoring scheme to rank 64 proteins exclusively found in PC3 exosomes, integrating data from four public databases and published mass spectrometry data sets. Among the top candidates, we focused on the tight junction protein claudin 3. Retests under serum-free conditions using immunoblotting and immunogold labeling confirmed the presence of claudin 3 on PC3 exosomes. Claudin 3 levels were determined in the blood plasma of patients with localized (n = 58; 42 with Gleason score 6-7, 16 with Gleason score ≥8) and metastatic prostate cancer (n = 11) compared with patients with benign prostatic hyperplasia (n = 15) and healthy individuals (n = 15) using ELISA, without prior laborious exosome isolation. ANOVA showed different CLDN3 plasma levels in these groups (p = 0.004). CLDN3 levels were higher in patients with Gleason ≥8 tumors compared with patients with benign prostatic hyperplasia (p = 0.012) and Gleason 6-7 tumors (p = 0.029). In patients with localized tumors CLDN3 levels predicted a Gleason score ≥ 8 (AUC = 0.705; p = 0.016) and did not correlate with serum PSA.By using the described workflow claudin 3 was identified and validated as a potential blood-based biomarker in prostate cancer. Furthermore this workflow could serve as a template to be used in other cancer entities.

Prostate cancer proteomics: Current trends and future perspectives for biomarker discovery

The clinical and fundamental research in prostate cancer - the most common urological cancer in men - is currently entering the proteomic and genomic era. The focus has switched from one single marker (PSA) to panels of biomarkers (including proteins involved in ribosomal function and heat shock proteins). Novel genetic markers (such as Transmembrane protease serine 2 (TMPRSS2)-ERG fusion gene mRNA) or prostate cancer gene 3 (PCA3) had already entered the clinical practice, raising the question whether subsequent protein changes impact the evolution of the disease and the response to treatment. Proteomic technologies such as MALDI-MS, SELDI-MS, i-TRAQ allow a qualitative/quantitative analysis of the proteome variations, in both serum and tumor tissue. A new trend in prostate cancer research is proteomic analysis of prostasomes (prostate-specific exosomes), for the discovery of new biomarkers. This paper provides an update of novel clinical tests used in research and clinical diagnostic, as well as of potential tissue or fluid biomarkers provided by extensive proteomic research data.

Prostate Cancer Associated Lipid Signatures in Serum Studied by ESI-Tandem Mass Spectrometryas Potential New Biomarkers

Prostate cancer (PCa) is one amongst the most common cancersin western men. Incidence rate ofPCa is on the rise worldwide. The present study deals with theserum lipidome profiling of patients diagnosed with PCa to identify potential new biomarkers. We employed ESI-MS/MS and GC-MS for identification of significantly altered lipids in cancer patient’s serum compared to controls. Lipidomic data revealed 24 lipids are significantly altered in cancer patinet’s serum (n = 18) compared to normal (n = 18) with no history of PCa. By using hierarchical clustering and principal component analysis (PCA) we could clearly separate cancer patients from control group. Correlation and partition analysis along with Formal Concept Analysis (FCA) have identified that PC (39:6) and FA (22:3) could classify samples with higher certainty. Both the lipids, PC (39:6) and FA (22:3) could influence the cataloging of patients with 100% sensitivity (all 18 control samples are classified correctly) and 77.7% specificity (of 18 tumor samples 4 samples are misclassified) with p-value of 1.612×10⁻⁶ in Fischer’s exact test. Further, we performed GC-MS to denote fatty acids altered in PCa patients and found that alpha-linolenic acid (ALA) levels are altered in PCa. We also performed an in vitro proliferation assay to determine the effect of ALA in survival of classical human PCa cell lines LNCaP and PC3. We hereby report that the altered lipids PC (39:6) and FA (22:3) offer a new set of biomarkers in addition to the existing diagnostic tests that could significantly improve sensitivity and specificity in PCa diagnosis.

Proteomics analysis of malignant and benign prostate tissue by 2D DIGE/MS reveals new insights into proteins involved in prostate cancer

BACKGROUND

The key to a more effective diagnosis, prognosis, and therapeutic management of prostate cancer (PCa) could lie in the direct analysis of cancer tissue. In this study, by comparative proteomics analysis of PCa and benign prostate hyperplasia (BPH) tissues we attempted to elucidate the proteins and regulatory pathways involved in this disease.

METHODS

The samples used in this study were fresh surgical tissues with clinically and histologically confirmed PCa (n = 19) and BPH (n = 33). We used two dimensional difference in gel electrophoresis (2D DIGE) coupled with mass spectrometry (MS) and bioinformatics analysis.

RESULTS

Thirty-nine spots with statistically significant 1.8-fold variation or more in abundance, corresponding to 28 proteins were identified. The IPA analysis pointed out to 3 possible networks regulated within MAPK, ERK, TGFB1, and ubiquitin pathways. Thirteen of the identified proteins, namely, constituents of the intermediate filaments (KRT8, KRT18, DES), potential tumor suppressors (ARHGAP1, AZGP1, GSTM2, and MFAP4), transport and membrane organization proteins (FABP5, GC, and EHD2), chaperons (FKBP4 and HSPD1) and known cancer marker (NME1) have been associated with prostate and other cancers by numerous proteomics, genomics or functional studies. We evidenced for the first time the dysregulation of 9 proteins (CSNK1A1, ARID5B, LYPLA1, PSMB6, RABEP1, TALDO1, UBE2N, PPP1CB, and SERPINB1) that may have role in PCa. The UBE2N, PSMB6, and PPP1CB, involved in cell cycle regulation and progression were evaluated by Western blot analysis which confirmed significantly higher abundances of UBE2N and PSMB6 and significantly lower abundance of PPP1CB in PCa.

CONCLUSION

In addition to the identification of substantial number of proteins with known association with PCa, the proteomic approach in this study revealed proteins not previously clearly related to PCa, providing a starting point for further elucidation of their function in disease initiation and progression.

Molecular diagnosis of prostate cancer: are we up to age?

Prostate cancer (PCa), a highly heterogeneous disease, is the one of the leading cause of morbidity and mortality in the developed countries. Historically used biomarkers such as prostatic acid phosphatase (PAP), serum prostate-specific antigen (PSA), and its precursor have not stood the challenge of sensitivity and specificity. At present, there is need to re-evaluate the approach to diagnose and monitor PCa. To this end, molecular markers that can accurately identify men with PCa at an early stage, and those who would benefit from early therapeutic intervention, are the need of the hour. There has been unprecedented progress in the development of new PCa biomarkers through advancements in proteomics, tissue DNA and protein/RNA microarray, identification of microRNA, isolation of circulating tumor cells, and tumor immunohistochemistry. This review will examine the current status of prostate cancer biomarkers with emphasis on emerging biomarkers by evaluating their diagnostic and prognostic potentials.

Spondin-2, a secreted extracellular matrix protein, is a novel diagnostic biomarker for prostate cancer

PURPOSE

SPON2 belongs to the F-spondin family of secreted extracellular matrix proteins. It is deregulated in some tumors, including prostate cancer. In this prospective study we assessed the role of serum SPON2 as a biomarker for prostate cancer diagnosis as well as any association between SPON2 levels and clinicopathological features. We also compared the diagnostic performance of this biomarker to that of serum sarcosine, and percent free-to-total and total prostate specific antigen.

MATERIALS AND METHODS

SPON2 was measured using a sandwich enzyme linked immunosorbent assay in serum samples from 286 patients with prostate cancer and 68 with no evidence of malignancy, as confirmed by 10 to 12-core ultrasound guided prostate biopsy. Nonparametric statistical tests and ROC analysis were done to assess the diagnostic performance of SPON2 vs the other biomarkers.

RESULTS

Median serum SPON2 was significantly higher in patients with prostate cancer than in those with no evidence of malignancy (77.5 vs 23.6 ng/ml, p<0.0001). ROC analysis showed a higher predictive value of SPON2 (AUC 0.952) than of serum sarcosine (AUC 0.674), percent free-to-total prostate specific antigen (AUC 0.806) and total prostate specific antigen (AUC 0.561). Moreover, patients with low grade prostate cancer had higher median SPON2 levels (p=0.001). Spearman rank correlation confirmed a negative association with Gleason score (rs=-0.29, p=0.0005).

CONCLUSIONS

We found evidence that SPON2 levels were significantly higher in patients with prostate cancer than in healthy individuals. Moreover, this biomarker had better diagnostic performance than serum sarcosine, and percent free-to-total and total prostate specific antigen. This greater accuracy was also present in a subset of patients with normal prostate specific antigen.

Peroxiredoxins 3 and 4 are overexpressed in prostate cancer tissue and affect the proliferation of prostate cancer cells in vitro

The present study aimed to investigate the proteome profiling of surgically treated prostate cancers. Hereto, 2D-DIGE and mass spectrometry were performed for protein identification, and data validation for peroxiredoxin 3 and 4 (PRDX3 and PRDX4) was accomplished by reverse phase protein arrays (RPPA). The Formal Concept Analysis (FCA) method was applied to assess whether the TMPRSS2-ERG gene fusion could influence the degree of overexpression of PRDX3 and PRDX4 in prostate cancer. Lastly, we performed an in vitro functional characterization of both PRDX3 and PRDX4 using the classical human prostate cancer cell lines DU145 and LNCaP. Reverse phase protein arrays verified that the overexpression of both PRDX3 and PRDX4 in tumor samples is negatively correlated with the presence of the TMPRSS2-ERG gene fusion. Functional characterization of PRDX3 and PRDX4 activity in PCa cell lines suggests a role of these members of the peroxiredoxin family in the pathophysiology of this tumor entity.

Genomic instability influences the transcriptome and proteome in endometrial cancer subtypes

BACKGROUND

In addition to clinical characteristics, DNA aneuploidy has been identified as a prognostic factor in epithelial malignancies in general and in endometrial cancers in particular. We mapped ploidy-associated chromosomal aberrations and identified corresponding gene and protein expression changes in endometrial cancers of different prognostic subgroups.

METHODS

DNA image cytometry classified 25 endometrioid cancers to be either diploid (n = 16) or aneuploid (n = 9), and all uterine papillary serous cancers (UPSC) to be aneuploid (n = 8). All samples were subjected to comparative genomic hybridization and gene expression profiling. Identified genes were subjected to Ingenuity pathway analysis (IPA) and were correlated to protein expression changes.

RESULTS

Comparative genomic hybridization revealed ploidy-associated specific, recurrent genomic imbalances. Gene expression analysis identified 54 genes between diploid and aneuploid endometrioid carcinomas, 39 genes between aneuploid endometrioid cancer and UPSC, and 76 genes between diploid endometrioid and aneuploid UPSC to be differentially expressed. Protein profiling identified AKR7A2 and ANXA2 to show translational alterations consistent with the transcriptional changes. The majority of differentially expressed genes and proteins belonged to identical molecular functions, foremost Cancer, Cell Death, and Cellular Assembly and Organization.

CONCLUSIONS

We conclude that the grade of genomic instability rather than the histopathological subtype correlates with specific gene and protein expression changes. The identified genes and proteins might be useful as molecular targets for improved diagnostic and therapeutic intervention and merit prospective validation.

The application of SELDI-TOF-MS in clinical diagnosis of cancers

Cancer diagnosis is important, and the early diagnosis of cancers could predict a more successful treatment. The proteomic studies emerged to be useful in combined analyses of samples from patients and provide more accurate diagnosis when compared to the single-factor-based diagnosis. In recent years, cancer detection with surface-enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF MS) is flourishing and brought significant progress in this area. This paper summarizes some recent results with this technique for cancer diagnosis.

Alternative Tests to Psa for Prostate Cancer Diagnosis

Prostate specific antigen (PSA) is still the most useful tool to select the population requiring prostate biopsy. The main downsides of PSA are an inadequate sensitivity to be used in screening and a low specificity for cancer detection.

So far, a limited value for PSA derivates (velocity, density, free, proisoforms and doubling time) has been recognised.

We present a short review of the literature describing a selection of the most promising alternatives to PSA being studied currently: PCA3, serum kallikreins, serum detectable prostate specific membrane antigen, the nuclear matrix protein EPCA, EPCA-2, prostatic acid phosphatase, urine detectable GSTP1, anti-AMACR antibodies, sarcosine, plasminogen activating urokinase, IGFBP, TGF beta 1, PSP94, IL6, plasmatic DNA, serum autoantibodies, neuroendocrine markers, proteomic analysis.

Multivariate meta-analysis of proteomics data from human prostate and colon tumours

Background

There is a vast need to find clinically applicable protein biomarkers as support in cancer diagnosis and tumour classification. In proteomics research, a number of methods can be used to obtain systemic information on protein and pathway level on cells and tissues. One fundamental tool in analysing protein expression has been two-dimensional gel electrophoresis (2DE). Several cancer 2DE studies have reported partially redundant lists of differently expressed proteins. To be able to further extract valuable information from existing 2DE data, the power of a multivariate meta-analysis will be evaluated in this work.

Results

We here demonstrate a multivariate meta-analysis of 2DE proteomics data from human prostate and colon tumours. We developed a bioinformatic workflow for identifying common patterns over two tumour types. This included dealing with pre-processing of data and handling of missing values followed by the development of a multivariate Partial Least Squares (PLS) model for prediction and variable selection. The variable selection was based on the variables performance in the PLS model in combination with stability in the validation. The PLS model development and variable selection was rigorously evaluated using a double cross-validation scheme. The most stable variables from a bootstrap validation gave a mean prediction success of 93% when predicting left out test sets on models discriminating between normal and tumour tissue, common for the two tumour types. The analysis conducted in this study identified 14 proteins with a common trend between the tumour types prostate and colon, i.e. the same expression profile between normal and tumour samples.

Conclusions

The workflow for meta-analysis developed in this study enabled the finding of a common protein profile for two malign tumour types, which was not possible to identify when analysing the data sets separately.

The effect of Gleason score on the predictive value of prostate-specific antigen doubling time

OBJECTIVE

To evaluate the influence of the pathological Gleason score on the predictive value of the prostate-specific antigen (PSA) doubling time (DT), as this variable predicts a patient's risk of disease progression both before and after definitive therapy for prostate cancer, and there is an inverse correlation between the Gleason score and PSA production.

PATIENTS AND METHODS

We evaluated all men treated with radical prostatectomy (RP) between 1990 and 1999 who did not receive neoadjuvant or adjuvant therapy. We identified 2296 patients who had multiple PSA values available before RP, and 1323 who had biochemical recurrence after RP and had at least two PSA values available before starting secondary therapy. Systemic progression and cancer-specific survival (CSS) rates were estimated using the Kaplan-Meier method and Cox proportional hazard regression models.

RESULTS

A PSA DT of <18 vs >18 months predicted a lower 10-year systemic progression-free survival for patients with tumours having a pathological Gleason score of <7 (98% vs 99%, P = 0.005), 7 (82% vs 91%, P = 0.003) and 8-10 (57% vs 73%, P = 0.042). A PSA DT after RP of <12 months was significantly associated with a lower 10-year systemic progression-free survival for patients with tumours having a Gleason score of <7 (77% vs 94%, P < 0.001) and 7 (61% vs 86%, P < 0.001), but not 8-10 (61% vs 75%, P = 0.11). The ability of PSA DT before and after RP to predict systemic progression and CSS decreased with increasing Gleason score.

CONCLUSIONS

The PSA DT remains associated with outcome both before and after RP across increasing pathological Gleason scores, although the predictive ability of PSA DT is diminished in Gleason 8-10 cancers.

Altered expression of 12S/MT-RNR1, MT-CO2/COX2, and MT-ATP6 mitochondrial genes in prostate cancer

BACKGROUND

Prostate cancer is one of the commonest cancers worldwide and is responsible for nearly 6% of all male cancer deaths. Despite this relevance, the mechanisms involved in the development and progression of this malignancy remain unknown. The involvement of polypeptides of the mitochondrial respiratory chain, the Krebs cycle and the glutathione antioxidant system in this type of cancer has been previously described, although no publication has focused on the expression of mitochondrial genes in the prostate of PCa patients.

METHODS

We have determined by reverse transcription-quantitative PCR (RT-qPCR) the relative amount of the transcripts of eight mitochondrial genes (MT-ND2, MT-ND4, MT-ND6, MT-CYB, 12S/MT-RNR1, 16S/MT-RNR2, MT-CO2/COX2, MT-ATP6), and four nuclear genes (COX11, GSR, CS, ACO2), all of them key players in the normal metabolism of mitochondria. Additionally we analyzed the expression of Cyclophilin A (PPIA).

RESULTS

We observed differential expression of mitochondrial 12S/MT-RNR1, MT-CO2/COX2, and MT-ATP6 transcripts in tumor samples when compared to their paired normal samples.

CONCLUSIONS

The amount of mitochondrial 12S/MT-RNR1, MT-CO2/COX2, and MT-ATP6 transcripts is significantly decreased in tumor samples when compared to their paired normal sample, suggesting that mitochondrial gene expression is altered in PCa.

Clinical proteomics: discovery of cancer biomarkers using mass spectrometry and bioinformatics approaches--a prostate cancer perspective

Prostate cancer (PCa) is an intractable disease, where diagnosis and clinical prediction of the disease course and response to treatment is compromised by the lack of objective and robust biomarker assays. In late stage metastatic disease, treatment options are limited, although it is recognized that some patients may benefit from immunotherapy and in particular vaccine therapy. However, research into biomarkers that correlate with the clinical outcome of immunotherapy has lagged behind vaccine development. Thus, proteomic tools are increasingly being utilized for the discovery of biomarkers which will allow us to make clinical decisions about patient treatment at an earlier stage and should aid in shortening the development time for vaccines. In this review we will summarize the various proteomic platforms used to investigate new biomarkers in PCa for better patient diagnosis, prognosis, patient stratification, treatment monitoring and clinical surrogate endpoints. We will discuss method limitations and highlight the key areas of research required for understanding the etiology of PCa.

Proteomics in clinical prostate research

The incidence of early prostate cancer (PCa) has increased rapidly in recent years. The majority of newly diagnosed PCa are in early tumor phase. Presently, we do not have adequate biomarkers to assess tumor aggressiveness in individual cases. Consequently, too many patients are given curatively intended treatment. An exploration of the human proteome may provide clinically useful markers. 2-DE has been successfully used for analysis of the protein phenotype using clinical samples. Proteins are separated according to size and charge, gels are compared by image analysis, protein spots of interest are excised, and proteins identified by MS. This method is exploratory and allows protein identification. However, low-abundance proteins are difficult to detect and 2-DE is currently too labor-intensive for routine use. In recent years, nongel based techniques, such as LC-MS, SELDI-MS, and protein arrays have emerged. They require smaller sample sizes and can be more automated than 2-DE. In this review, we describe studies of the protein expression of benign prostatic tissue and PCa, which is likely to serve as the first step in prognostic biomarker discovery. The prostate proteome is still far from a complete mapping which would enhance our understanding of PCa biology.

Proteomic analysis of proteins regulated by TRPS1 transcription factor in DU145 prostate cancer cells

The aim of the present study was to identify proteins differentially regulated by TRPS1 in human prostate cancer cells in order to better understand the role of TRPS1 in prostate cancer development. The proteomes of androgen-independent DU145 prostate cancer cells, that do not express TRPS1 and of genetically engineered DU145 cells that stable and inducible express recombinant TRPS1 protein, were compared. Using two-dimensional electrophoresis followed by mass spectrometric analysis, 13 proteins that were differentially expressed between these two cell lines were identified. These proteins represent a dominant reduction of expression of antioxidant proteins, including superoxide dismutase, protein disulfide isomerase A3 precursor, endoplasmin precursor and annexin A2. Furthermore, regulation was observed for mitochondrion-associated proteins, glycolytic enzymes, a cytoskeleton-associated protein, a nuclear protein and proteins involved in apoptosis. Our data indicate that overexpression of TRPS1 protein is correlated with reduced protein expression of certain antioxidants. This suggests a possible involvement of TRPS1 in oxidative stress, and possibly in apoptosis in androgen-independent DU145 prostate cancer cells.