Carcinoma of the prostate is the second leading cause of male cancer-related death in the United States. Better indicators of prostate cancer presence and progression are needed to avoid unnecessary treatment, predict disease course, and develop more effective therapy. Numerous molecular markers have been described in human serum, urine, seminal fluid, and histological specimens that exhibit varying capacities to detect prostate cancer and predict disease course. However, to date, few of these markers have been adequately validated for clinical use. The purpose of this review is to examine the current status of these markers in prostate cancer and to assess the diagnostic potential for future markers from identified genes and molecules that display loss, mutation, or alteration in expression between tumor and normal prostate tissues. In this review we cite 91 molecular markers that display some level of correlation with prostate cancer presence, disease progression, cancer recurrence, prediction of response to therapy, and/or disease-free survival. We suggest criteria to consider when selecting a marker for further development as a clinical tool and discuss five examples of markers (chromogranin A, glutathione S-transferase π 1, prostate stem cell antigen, prostate-specific membrane antigen, and telomerase reverse transcriptase) that fulfill some of these criteria. Finally, we discuss how to conduct evaluations of candidate prostate cancer markers and some of the issues involved in the validation process.

Carcinoma of the prostate is the second leading cause of male cancer-related death in the United States, and it is estimated that in 2003 there were approximately 220,900 new cases and 28,900 deaths from this disease (1). Since the introduction of serum prostate-specific antigen (PSA) screening of asymptomatic populations, prostate cancer incidence rates have increased dramatically, as has the number of men undergoing radical prostatectomy and radiation therapy for this disease (1, 2). However, false positives for PSA continue to be a significant problem resulting in unnecessary biopsies, and the value of broad-based PSA testing with regard to predicting surgical cures has recently come into question (3).

Currently, there are no markers that differentiate clinically relevant from clinically benign disease. Better indicators of prostate cancer presence and progression are needed to avoid unnecessary treatment, predict disease course, and develop more effective therapy. A variety of putative prostate cancer markers have been described in human serum, urine, seminal fluid, and histological specimens. These markers exhibit varying capacities to detect prostate cancer and to predict disease course. These markers are distinct from chromosomal aberrations that have been associated with prostate cancer, which will not be dealt with here (4).

The purpose of this review is to examine the current status of markers in prostate cancer and to assess the diagnostic potential for future markers from identified genes and molecules that display loss, mutation, or alteration in expression between tumor and normal prostate tissues. To date, few of these markers have achieved widespread clinical utility. If we are to improve on the treatment of prostate cancer in the 21st century, we must identify and develop markers that are more clinically informative for this disease and that will allow risk-based individualization of therapy.

The first documented case of prostate cancer was reported by Langstaff in 1817 (5). One hundred eighteen years later, in 1935, prostatic acid phosphatase (PAP) levels were identified in the ejaculate of men, thus linking this enzyme to the prostate (6). Subsequent studies showed high PAP concentrations in primary and metastatic prostate cancer tissues and in human serum, making it the first candidate marker for the diagnosis of prostate cancer (7, 8). Reductions in serum PAP levels were found to occur in response to antiandrogen therapy, whereas increasing serum levels were associated with treatment failure and relapse (9, 10). However, whereas serum PAP levels were elevated in a significant number of men with metastatic disease (8), fewer than 20% of men with localized prostate cancer exhibited abnormal enzyme levels (11, 12). Meticulous sample collection and preparation were required because both platelets and leukocytes are contaminating sources of acid phosphatases (13) and because PAP activity is rapidly lost at room temperature (14). Development of a radioimmune assay for PAP in 1975 provided some improvement in test sensitivity (15), but the sensitivity levels were still inadequate for detection of early-stage disease. Therefore, it was clear that a more sensitive and robust indicator of disease presence would be required to detect prostate cancer in its earlier stages, when cure is more likely.

PSA is a kallekrein-like serine protease that was first described in 1971 (16). PSA is secreted from prostate epithelial cells and encoded by an androgen-responsive gene located on chromosome 19q13.3–13.4 (17). The main function of PSA is to liquefy human semen through its proteolytic action (18). PSA was initially thought to be a prostate-specific protein; however, subsequent investigations demonstrated that PSA is secreted in small quantities from a number of other normal male tissues and even some female tissues (19, 20). PSA was first detected in the serum of prostate cancer patients in 1980 (19), and a normal PSA serum concentration limit of 4 ng/ml for men was subsequently established (20). A serum level above 4 ng/ml was taken as an indicator of the possible presence of prostate cancer and served as the trigger for further clinical evaluation. Eventually, a number of studies enrolling large numbers of men over the age of 50 years suggested that quantitation of serum PSA was a useful diagnostic tool for detecting the presence of prostate cancer, particularly when combined with digital rectal examination (21, 22, 23, 24). However, other studies have called into question the sensitivity and specificity of the PSA test (25, 26, 27, 28). One problem is that serum PSA levels can be elevated as a result of conditions other than prostate cancer, such as benign prostatic hypertrophy (BPH) and prostatitis. As a result, false positives are a significant problem for the PSA test and can lead to unnecessary biopsies and other interventions. Of greater concern, 20–30% of men with prostate cancer have serum PSA levels in the normal range, resulting in undiagnosed disease (22, 23, 24). A recent study by Stamey et al.(3) has concluded that preoperative serum PSA levels do not correlate with cancer volume or the Gleason grade of radical prostatectomy specimens. This study also showed a poor correlation between preoperative serum PSA levels in the 2–9 ng/ml range and prostate cancer cure rates. Despite the drawbacks and criticisms cited here, PSA is currently the best clinical marker available for prostate cancer and the only one approved by the United States Food and Drug Administration for both posttreatment monitoring of disease recurrence and, when combined with digital rectal examination, evaluation of asymptomatic men (29, 30).

At the direction of the United States Congress and spearheaded by the National Cancer Institute, support for basic and translational research in prostate cancer has expanded dramatically since 1992. This has resulted in an avalanche of data, much of it attempting to correlate various gene and protein markers with prostate cancer presence, progression, or disease-free survival. Some of these markers have also been proposed as potential therapeutic targets for prostate cancer treatment. However, to date, none of these candidate markers has been adequately validated for clinical use, and no replacement for PSA is visible on the scientific horizon.

Table 1 provides information on 91 genes and their encoded proteins, all of which have a potential role in prostate carcinogenesis and/or progression. All display some level of correlation with one or more of the following factors: presence of prostate cancer, disease progression, cancer recurrence, prediction of response to therapy, or disease-free survival. Information on these markers was accumulated through literature searches using PubMed and from the GeneCards database of human genes, their products, and their involvement in diseases (31). Evidence for the association of a specific marker with human prostate cancer range from a single publication, as in the case of UROC 28, to thousands of publications, as in the case of PSA. In light of the rapid pace of new marker discovery through the use of comprehensive DNA expression analysis and proteomics, there are no doubt candidate markers missing from this list. However, we have made Table 1 as current as possible, and we hope that it will serve as a resource for the prostate cancer research community. In Table 1, we present 89 proteins (the transcripts for DD3 and PCGEM1 do not contain open reading frames) that have been correlated with some aspect of prostate cancer presence or progression in one or more studies. They are listed in alphabetical order and categorized according to their subcellular location: nucleus; cytoplasm; plasma membrane; cytoskeleton; mitochondria; microsomal membrane; endoplasmic reticulum; lysosome; or secreted. The latest information on chromosomal location and molecular weight is included, along with the most common biochemical function of the protein and its major cellular function. In cases in which the biochemical and/or cellular functions of the proteins remain to be determined, the word “unknown” appears in the appropriate column. Alterations for some of these markers, such as p53 and telomerase reverse transcriptase (TERT), can be associated with specific pathways that clearly impact tumor growth and progression; for others, such as DD3 and PC-1, the causal connection is less clear. This is the case for many of the markers presented in Table 1. Whereas an informative marker need not have a specific function in disease progression (PSA is a good example), such a function is useful for understanding the molecular mechanisms of tumor progression and for developing targeted therapeutic interventions.

The markers displayed in Table 1 represent a wide array of biochemical and cellular functions. These functions include those of transcription factor, protease, kinase, phosphatase, protease inhibitor, cyclin-dependent kinase inhibitor, cytokine, reverse transcriptase, racemase, reductase, synthase, hydrolase, RNase, molecular chaperone, nuclear matrix, membrane scaffolding, and an assortment of other binding and permeability control proteins. There are also 9 proteins with unknown or poorly defined biochemical functions.

The question is, which of these 91 molecules, if any, are candidates for advancement “from the laboratory bench to the clinic?” This is a broad question that really has a number of parts. First, on what basis do we select from this growing list of candidate diagnostic markers those to pursue in large-scale validation studies designed to prove clinical usefulness? Second, how should these validation studies be conducted and evaluated? Third, what evidence is required to demonstrate that a new marker provides a defined “value added” to the existing methods of prostate cancer detection and for determining the likelihood of disease progression and recurrence and/or response to a given therapy? Fourth, how can we successfully standardize a putative clinical assay to ensure accurate, consistent results across a broad spectrum of research and/or clinical laboratories? This review will focus on the first two questions because addressing them is a prerequisite for moving on to questions 3 and 4.

What criteria do we consider when selecting one or more of these potential markers for further development as a clinical tool, and will any of the 89 proteins and 2 transcripts presented in Table 1 satisfy these criteria? The most important item regarding the selection of a candidate marker is the quality of scientific and clinical data supporting its potential utility. These include scientific studies relating the functional role of the gene/protein to the biology of the disease and clinical data linking the candidate marker with disease presence, alterations in stage, response to therapy, and/or overall survival. The marker should be measurable by a robust, reproducible, widely available assay that provides useful information that is readily interpretable by the clinician. The ideal candidate for an early detection or disease monitoring marker would be one that is prostate specific; detectable in an easily accessible biological fluid such as human serum, urine, or prostatic fluid; and able to distinguish between normal, BPH, prostatic intraepithelial neoplasia, and cancerous prostate tissues. In addition, the marker should have sufficiently convincing clinical correlation data from several different laboratories before it is brought forward for large-scale evaluation. Whereas it is unreasonable to expect that any single potential diagnostic marker by itself will be able to fulfill all of these criteria, which molecules in Table 1 are the most promising candidates to become clinically useful diagnostic or monitoring markers, and on what basis should we make our selection?

For a marker to be useful for diagnosis and monitoring of disease, it must be demonstrated that the marker correlates with an outcome of interest, such as disease progression, recurrence, or survival. Analyses should be multivariate and should show that the marker(s) predict the outcome of interest independently of the usually available characteristics, such as stage or grade. These assessments should be conducted on a set of cases with adequate outcome data and a sufficient number of events to allow statistical significance to be evaluated. The introduction of tissue microarrays promises to streamline this process considerably.

In the absence of these supportive data, even the most promising marker will not convince either the clinical or pharmaceutical communities that it is worth substantial investment for further evaluation. Based on an analysis of published reports regarding the candidate markers in Table 1, there are five markers that appear to have a significant volume of convincing supportive data, both biological and clinical, associated with them. There are several other candidate markers that have significant supportive data; however, for the purpose of this review, we will discuss these five as examples: chromogranin A (GRN-A); glutathione S-transferase π 1 (GSTP1); prostate stem cell antigen (PSCA); prostate-specific membrane antigen (PSMA); and TERT. Their selection in no way diminishes the potential importance of the other markers in Table 1. Each of the proteins listed in the table has different strengths and weaknesses as a clinical prostate cancer marker, and no doubt proteins other than the ones we focus on here will be brought forward for clinical evaluation in the future.

To choose a marker for diagnosis or prognosis of disease course to bring forward for large-scale clinical evaluation, it should fulfill several criteria. First, there should be a biological or therapeutic rationale for choosing the marker, or at least a consistent association with disease presence, disease characteristics such as stage, or disease aggressiveness. Second, there should be an assessment of the strength of marker association with disease outcome. Third, the marker should be assessed as an independent predictor in a multivariate analysis. The merits and disadvantages of each of the five candidate markers we have selected for scrutiny within the context of the above criteria are discussed below.

GRN-A.

GRN-A is a member of the granin family of proteins and acts as a prohormone, which, after proteolytic processing, results in the generation of multiple peptides with biological activity (32). GRN-A is stored in the dense core secretory granules of most endocrine and neuroendocrine cells and is a marker of neuroendocrine differentiation (33). Whereas serum levels of GRN-A do not accurately distinguish BPH from prostate cancer very well, they do correlate with tumor stage and grade. In addition, this marker has the capability to detect neuroendocrine cells and thus has the potential to identify androgen-independent disease. Serum GRN-A levels exhibit a well-documented rise in late-stage disease and demonstrate a wide prevalence range of 32–71%, depending on the study cited (34, 35, 36, 37, 38, 39, 40, 41, 42, 43). Studies involving GRN-A have been conducted in human serum using radioimmune assay or ELISA and in tissue using immunohistochemistry (IHC). Elevated serum levels of GRN-A appear to predict poor prognosis in cases of androgen-independent prostate cancer after endocrine therapy and may be an intermediate marker of early progression for this form of the disease and a possible predictor of early death (44, 45). One study used multivariate analysis to demonstrate a significant association between GRN-A positivity and death from prostate cancer (45). Prostate neuroendocrine cells do not contain androgen receptors or produce PSA; thus hormone-refractory disease could be detected earlier in a population of men with apparently normal PSA levels than is currently possible. Whereas GRN-A does not appear to be prognostic of disease recurrence after radical prostatectomy or radiation therapy (46, 47), one report links elevated serum levels to response to estramustine therapy (48). Two significant weakness of GRN-A as a marker are that not all prostate tumors contain neuroendocrine cells and that GRN-A is unable to detect very early stage disease. However, previous studies suggest that GRN-A is able to monitor treatment success, predict disease outcome, and predict prognosis in androgen-independent prostate cancer. There are statistically significant data suggesting that when combined with PSA, elevated GRN-A levels may effectively predict a poor prognosis after endocrine therapy (49). Taken together, this evidence makes GRN-A a good candidate for further clinical evaluation as a prognostic and/or treatment marker for prostate cancer.

GSTP1.

GSTP1 is a member of a large family of glutathione transferases that function to protect cells from oxidative insult (50); thus, the biological rationale for selecting this marker is its role in preventing damage to cells by neutralizing free radicals. This marker is also unique in its capacity to provide a facile methylation-based detection method for an important epigenetic phenomenon. GSTP1 has been extensively studied in prostate cancer, and its reduced expression, due predominantly to promotor hypermethylation, represents the most common epigenetic alteration associated with this disease. One study has shown that in prostate cancer cells, methylation of the GSTP1 gene is not confined to the promoter but is extensive throughout the CpG islands (51). Several studies have shown a high sensitivity for this marker to detect the presence of both prostatic intraepithelial neoplasia and prostate cancer, an ability to distinguish these from BPH, and a prevalence of methylation in the range of 60–80% in prostate cancer (51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61). In addition, several GSTP1 polymorphisms have shown a correlation with increased risk of disease development, although data regarding this ability are conflicting (62, 63, 64, 65, 66, 67). Strengths of GSTP1 as a clinical marker are the ability to quantitate the methylation status of the GSTP1 gene in biopsy/prostatectomy tissues and in cells derived from serum, urine, and seminal plasma and its high prevalence in this disease. Recent studies using quantitative real-time methylation-sensitive PCR demonstrate that GSTP1 methylation could be a sensitive marker for prostate cancer in men with clinically localized disease (51). In addition, there is no correlation between GSTP1 methylation status and PSA levels, making GSTP1 a potential early and independent marker for the disease. The ability of GSTP1 hypermethylation to distinguish between BPH and prostate cancer is well documented, and one recent study correlated methylation status with poor prognosis in 101 patients diagnosed with prostate cancer (68). However, whereas these results are statistically significant, they were not tested by multivariate analysis. Reversal of GSTP1 CpG island hypermethylation and gene reactivation in LNCaP prostate carcinoma cells can be achieved by procainamide treatment; however, no effect on tumor cell growth was observed in these studies (69). The strengths of GSTP1 methylation status, as cited above, and the possible availability in the near future of drugs that can reverse hypermethylation make it a good candidate for further evaluation as an early detection marker. If successfully validated, GSTP1 methylation testing of cells derived from serum and urine samples may have clinical usefulness for both early detection of prostate cancer and posttreatment monitoring of disease.

PSCA.

PSCA is a glycosylphosphatidylinositol-anchored cell surface antigen that is found predominantly in prostate and may play a role in stem cell functions such as proliferation or signal transduction (70). Whereas the biological role of PSCA in prostate cancer is unclear, the marker is expressed predominantly in the prostate and has potential as a therapeutic target. Other strengths of PSCA as a prostate cancer marker include elevated PSCA expression levels in the majority of prostate cancers and a correlation between this elevation and higher Gleason grade and more advanced tumor stage (71, 72, 73, 74). Published studies also show a high correlation (64–94%) between increased PSCA expression and the presence of prostate cancer, with protein expression localized to both the basal and secretory cells (71, 72, 73, 74, 75). PSCA has been assayed by a variety of methods, including in situ hybridization, quantitative reverse transcription-PCR, and IHC, demonstrating a prevalence of 48–94% for prostate cancer (71, 72, 76). One IHC study demonstrated an association between increased PSCA expression and higher Gleason score, more advanced tumor stage, and progression to androgen-independent prostate cancer (72). However, extensive multivariate analysis to confirm these findings has yet to be performed. PSCA expression is maintained in androgen-independent prostate cancer, and PSCA is highly expressed in metastatic disease (71, 72, 73, 74, 75, 76). Whereas most of the studies performed to date have been on prostate tissue samples, there is at least one report of PSCA detection in peripheral blood (73). Another strength of this marker is its potential as a therapeutic target. Anti-PSCA monoclonal antibodies have been shown to inhibit tumor growth and metastasis formation of human xenografts grown in scid mice (76). This opens up the possibility for therapeutic treatment of human prostate cancers using immunotherapeutic regimens (76, 77, 78). In addition, PSCA is coamplified with the tumor progression factor and oncogene c-myc in locally advanced prostate cancers, suggesting a role for PSCA in the progression of this disease (74, 79). Three weaknesses of PSCA as a candidate for further development are the limited number of published studies supporting its value as a clinical marker, a need for better quantitation methods, and uncertainty as to whether analysis of PSCA levels adds information to the results of PSA testing. However, based on the available data and the value of PSCA as a therapeutic target, further evaluation of PSCA as a clinical prostate cancer marker should be performed to determine its utility.

PSMA.

Discovered in 1987, PSMA is a cell surface membrane protein and one of the most extensively studied prostate cancer markers cited in Table 1(80). PSMA is a type II integral membrane protein that displays multiple enzymatic activities (81, 82). The protein translocates from the cytosol in normal prostate to the plasma membrane in prostate cancer (83). The exact biological role of PSMA in the disease mechanism is unclear at this time; however, extensive data exist on its utility as a marker and therapeutic target. Numerous studies have shown that PSMA serum levels are elevated in primary prostate cancer and metastatic disease, that PSMA demonstrates a >90% prevalence in the disease, and that levels can be detected in both tumor tissue and serum using several antibodies (84, 85, 86, 87, 88, 89, 90, 91). PSMA has been detected in prostate tissues using IHC and Western analysis, in circulating prostate cancer cells by reverse transcription-PCR, and in serum using ELISA assays. One study using Western analysis demonstrated that in postprostatectomy patients, PSMA values are elevated in hormone-refractory tumors, suggesting that PSMA levels may correspond with poor clinical outcome (85). In another study (92), PSMA serum levels were found to increase with age and were significantly elevated in men over 50 years of age. To date, however, increased PSMA serum levels have not been convincingly linked to disease aggressiveness, and perhaps due to tumor differentiation status, some studies have shown that levels actually decrease in advanced disease (93). PSMA protein has also been shown to be up-regulated in prostate cancer patients after androgen deprivation therapy (94). Recent technological advances have allowed for the high-throughput assay of this marker in human serum using a protein chip, mass spectrometry platform (95). That study demonstrated significantly greater PSMA levels in men with prostate cancer than in those with BPH or with no evidence of disease. PSMA is moderately prostate specific and has been investigated as a target for immunotherapy using autologous dendritic cells (96). Efforts are also under way using the PSMA gene promoter to pursue gene therapy strategies by introducing cytotoxic agents into prostate cancer cells (97). A weakness of PSMA as a clinical marker for early diagnosis is that elevated serum levels have been observed in healthy males and females and in the serum of breast cancer patients (98). Another weakness is that serum levels of PSMA have been shown to increase with increasing age, which could be a confounding factor in a disease that most often occurs late in life. However, there is an abundance of data supporting the ability of PSMA to detect the presence of prostate cancer, and new technologies are being developed that allow quantitative high-throughput analysis of biological fluids. This argues in favor of further evaluation of this marker to determine whether or not it has clinical utility for prostate cancer detection or treatment monitoring or as a treatment target.

TERT.

The TERT gene encodes the reverse transcriptase component of telomerase that maintains the telomeric ends of chromosomes and has been associated with senescence and cancer (99). The TERT component is expressed in cells that exhibit telomerase activity and is undetectable in most benign tissues (100). The biological rationale for selecting TERT is the ability of TERT to confer cellular immortalization, a major step in the process of malignant transformation. Thus, this marker may provide a very sensitive means for detecting infiltrating cancer cells in benign tissue. A significant number of studies have been conducted to evaluate TERT or telomerase activity as a marker for prostate cancer (101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119). Published reports demonstrate that TERT activity levels exhibit a prevalence range of 63–94% for prostate cancer, and activity has been detected in some cases of high-grade prostatic intraepithelial neoplasia (100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111). TERT has been most often assayed by IHC or the telomeric repeat amplification protocol assay. Most studies find the marker consistently absent from normal prostate and the majority of BPH tissues. The highest TERT activity appears to correlate with poorly differentiated disease, and there is some evidence for a correlation with tumor stage and grade and patient mortality and disease recurrence (100, 104, 107, 108, 109). Whereas statistical significance has been demonstrated in some of these studies, the correlations have not been tested by multivariate analysis. TERT activity does not correlate with PSA level, making it a potential independent marker for prostate cancer. One study suggests that telomere length in tumor tissues correlates with survival and recurrence in prostate cancer patients (120). However, TERT also displays several weaknesses as a clinical marker for prostate cancer detection. TERT is not prostate specific, and in most studies, assays were conducted in prostate tissues, and thus required biopsy material before marker assay. However, several studies have now successfully used human urine, seminal fluid, and prostatic fluid to detect TERT activity (113, 114, 121). Although TERT activity levels appear to be independent of PSA, the “value added” of TERT for early detection, staging, or prognosis and the overall clinical utility of TERT remain to be fully uncovered. Further evaluation of TERT may reveal a niche for use of TERT as a supplement to PSA testing.

Recently, methodologies as diverse as positional cloning, differential display, comprehensive DNA expression analysis, and serum proteomic analysis have provided some very preliminary yet exciting prostate cancer marker candidates. Among these are Hepsin, a serine protease associated with cell growth and morphology (122, 123, 124); RNase L, a RNase involved in viral resistance and a candidate for the HPC1 gene (125, 126); ST7, a protein of unknown function (127, 128, 129); and EZH2, a homeotic protein that participates in the repression of gene expression (130, 131). In addition, proteomic analysis of serum from prostate cancer patients has shown promise for diagnostic and prognostic use for this disease (132). Candidate markers identified by these new technologies will require confirmation and correlation with disease formation or progression or with patient survival or response to therapy in additional human samples before they can be considered for validation studies.

To validate the clinical usefulness of any marker, it is important to first establish what the end point will be. This, in turn, will determine the study population that will be examined. The appropriate statistical design of the study will require information on prevalence and the postulated strength of the association of marker expression with the outcome of interest. These considerations will, in turn, determine both specificity and sensitivity of the marker. In some situations, an appropriate control population may be required to determine the specificity of the marker through the determination of false positive and negative rates. Finally, an appropriate sample collection, preparation, and assay method must be decided on. Multi-institutional clinical studies will also likely require the use of a central laboratory or shared controls and training sets of malignant samples to ensure accuracy and consistency. An example may serve to illustrate a reasonable evaluation process.

Suppose we wish to further evaluate a hypothetical marker, which we will call Optimal Marker in Prostate Carcinoma (OMPC), for clinical utility. Previous studies indicate a correlation between OMPC expression and metastatic disease. The clinical question to be addressed is whether this marker can predict which patients with early-stage disease will eventually develop metastatic disease despite local therapy. To accomplish this, we would want to evaluate a large enough number of cases of early-stage prostate cancer for which there is a minimum of 5 years of clinical follow-up data available to obtain a robust assessment of the strength of association between the marker and outcome. The method of OMPC analysis would be selected based on limit of detection, sources of variability, suitability for the samples that will be available, day-to-day and interobserver variability, cost, scalability, and optimum reagents (133). The procedure for scoring or interpreting the results of the assay will need to be optimized to reduce variability. If cut points are to be used, they should also be developed and tested for robustness, if necessary.

The optimized assay is then performed on a large number of early-stage prostate cancer cases, with the hypothetical result that all patients express the marker, but only 40% express at “high” levels as determined by our cut point. For illustrative purposes, say these high expressers have a 2.5× greater risk of developing metastatic disease after local therapy. Does OMPC predict outcome at least as well as or independently of the other known prognostic factors, Gleason score and PSA? Because all of the patients had early-stage cancers, a multivariate analysis including all of the significant prognostic factors would answer this question. If OMPC remained a strong prognostic factor for development of metastatic disease, with a hazard ratio of at least 2, and was independent of other prognostic factors, we would be more confident of its potential value. However, this would require verification in an independent cohort of patients with early-stage prostate cancer receiving a defined initial local treatment. Using an estimate of 40% high expression prevalence, we can calculate the size of the study necessary to determine whether the marker is associated with a hazard ratio of at least 2, with an 80% power to detect a minimum 2-fold effect on the likelihood of metastatic development (134). Whereas we have here addressed the analysis of a single marker, the strategy outlined would also apply to a genomic or proteomic “profile” as determined by comprehensive molecular analysis.

The development of novel and clinically relevant markers for prostate cancer diagnosis, prognosis, and prediction is essential to the optimal identification and treatment of this disease. With the advent of DNA expression analysis, tissue microarrays, and proteomic analysis, the list of potential prostate cancer markers grows daily. Sorting through these potential markers and bringing them from the laboratory environment into clinical use at the patient bedside will require a comprehensive pursuit and rigorous analysis. Many of the molecules cited in Table 1 have languished for years in a gray zone between usefulness as a clinical marker for prostate cancer and elimination from further consideration. As a research community, we must devise approaches that will ensure that we realize the next generation of clinically relevant prostate cancer markers.

We have here attempted to provide examples of potential prostate cancer markers that may be of clinical benefit in prostate cancer detection, prognosis, and/or prediction. We have also suggested one possible methodology for the clinical evaluation of these markers. The goal of this effort is not to dictate the optimal markers or the methodologies for their verification, but to provide by example a framework from which the general research community can work toward achieving the goal of bringing new prostate cancer markers forward for clinical use.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Requests for reprints: James V. Tricoli, Diagnostics Research Branch, Cancer Diagnosis Program, National Cancer Institute, 6130 Executive Boulevard, Executive Plaza North, Suite 6044, Rockville, MD 20852. Phone: (301) 496-1591; Fax: (301) 402-7819; E-mail: tricolij@mail.nci.nih.gov

Table 1

Potential prostate cancer markers

MarkerChromosome locusM                  r                  aSubcellular locationBiochemical functionBiological/cellular function
A2M 12p13.3-12.3 163 Secreted Protease inhibitor Protein carrier 
Akt-1 14q32.32 56 Nucleus/cytoplasm Protein kinase Apoptotic inhibition 
AMACR 5p13.2-q11 42 Mitochondria/peroxisome Racemase Stereoisomerization 
Annexin 2 1q21 11 Plasma membrane Calcium and lipid binding Membrane trafficking 
Bax 19q13.3-.4 21 Cytoplasm/membrane Bcl-2 binding Apoptosis 
Bcl-2 18q21.3 26 Mitochondrial membrane Membrane permeability Apoptosis 
Cadherin-1 16q22.1 97 Plasma membrane Catenin/integrin binding Cell adhesion 
Caspase 8 2q33-34 55 Cytoplasm Protease Apoptosis 
Catenin 5q31 100 Cytoskeleton Cadherin binding Cell adhesion 
Cav-1 7q31.1 20 Plasma membrane Scaffolding Endocytosis/signaling 
CD34 1q32 41 Plasma membrane Scaffolding Cell adhesion 
CD44 11p13 82 Plasma membrane Hyaluronate binding Cell adhesion 
Clar1 19q13.3-.4 34 Nucleus SH3 binding Unknown 
Cox-2 1q25.2-.3 69 Microsomal membrane Prostaglandin synthase Inflammatory response 
CTSB 8p23.1 38 Lysosome Protease Protein turnover 
Cyclin D1 11q13 34 Nucleus CDKb regulation Cell cycle 
DD3 9q21-22 Nucleus/cytoplasm Noncoding Unknown 
DRG-1 22q12.2 43 Cytoplasm GTP binding Cell growth/differentiation 
EGFR 7p12 134 Plasma membrane EGF binding Signaling 
EphA2 1p36 11 Plasma membrane Tyrosine kinase Signaling 
ERGL 15q22-23 57 Plasma membrane Lectin/mannose binding Unknown 
ETK/BMK Xp22.2 78 Cytoplasm Tyrosine kinase Signaling 
EZH2 7q36.1 85 Nucleus Transcription repressor Homeotic gene regulation 
Fas 11q13.3 23 Plasma membrane Caspase recruitment Apoptosis 
GDEP 4q21.1 4c Unknown Unknown Unknown 
GRN-A 14q32 50 Secretory granules Statin Endocrine function 
GRP78 9q33.3 72 Endoplasmic reticulum Multimeric protein assembly Cell stress response 
GSTP1 11q13 23 Cytoplasm Glutathione reduction DNA protection 
Hepsin 19q11-13.2 45 Plasma membrane Serine protease Cell growth/morphology 
Her-2/Neu 17q21.1 138 Plasma membrane Tyrosine kinase Signaling 
HSP27 7q11.23 23 Cytoplasm Chaperone Cell stress response 
HSP70 6p21.3 70 Cytoplasm Chaperone Cell stress response 
HSP90 11q13 63 Cytoplasm Chaperone Cell stress response 
Id-1 20q11.1 16 Nucleus Transcription factor Differentiation regulator 
IGF-1 12q22-23 17 Secreted IGFR ligand Signaling 
IGF-2 11p15.5 20 Secreted IGFR ligand Signaling 
IGFBP-2 2q33-34 35 Secreted IGF binding Signaling 
IGFBP-3 7p13-12 32 Secreted IGF binding Signaling/apoptosis 
IL-6 7p15.3 24 Secreted Cytokine B-cell differentiation 
IL-8 4q13.3 11 Secreted Cytokine Neutrophil activation 
KAI1 11p11.2 30 Plasma membrane CD4/CD8 binding Signaling 
Ki67 10q25-ter 358 Nucleus Nuclear matrix associated Cell proliferation 
KLF6 10p15 32 Nucleus Transcription factor B-cell development 
KLK2 19q13.41 29 Secreted Protease Met-Lys/Ser-Arg cleavage 
Maspin 18q21.3 42 Extracellular Protease inhibitor Cell invasion suppressor 
MSR1 8p22 50 Plasma membrane LDL receptor Endocytosis 
MXI1 10q25.2 26 Nucleus Transcription factor Myc suppression 
MYC 8q24.12-.13 49 Nucleus Transcription factor Cell proliferation 
NF-kappaB 10q24 97 Nucleus Transcription factor Immune response 
NKX3.1 8p21 26 Nucleus Transcription factor Cell proliferation 
OPN 4q22.1 35 Secreted Integrin binding Cell-matrix interaction 
p16 9p21 17 Nucleus CDK inhibitor Cell cycle 
p21 6p21.2 18 Nucleus CDK inhibitor Cell cycle 
p27 12p13.1-12 22 Nucleus CDK inhibitor Cell cycle 
p53 17p13.1 44 Nucleus Transcription factor Growth arrest/apoptosis 
PAP 3q21-23 45 Secreted Tyrosine phosphatase Signaling 
PART-1 5q12.1 Nucleus/cytoplasm Unknown Unknown 
PATE 11q24.2 14 Plasma membrane Unknown Unknown 
PC-1 5q35 32 Nucleus RNA binding Ribosome transport 
PCGEM1 2q32 Nucleus/cytoplasm Noncoding Cell proliferation/survival 
PCTA-1 1q42-43 36 Cytoplasm Unknown Cell adhesion 
PDEF 6p21.31 38 Nucleus Transcription factor PSA promoter binding 
PI3K p85 5q12-13 84 Cytoplasm Lipid kinase Signaling 
PI3K p110 1p36.2 120 Cytoplasm Lipid kinase Signaling 
PIM-1 6p21.2 36 Cytoplasm Protein kinase Cell differentiation/survival 
PMEPA-1 20q13.31-33 32 Plasma membrane NEDD4 binding Growth regulation 
PRAC 17q21.3 Nucleus Choline/ethanolamine kinase Unknown 
Prostase 19q13.3-.4 27 Secreted Serine protease ECM degradation 
Prostasin 16p11.2 36 Plasma membrane Serine protease Cell invasion suppressor 
MarkerChromosome locusM                  r                  aSubcellular locationBiochemical functionBiological/cellular function
A2M 12p13.3-12.3 163 Secreted Protease inhibitor Protein carrier 
Akt-1 14q32.32 56 Nucleus/cytoplasm Protein kinase Apoptotic inhibition 
AMACR 5p13.2-q11 42 Mitochondria/peroxisome Racemase Stereoisomerization 
Annexin 2 1q21 11 Plasma membrane Calcium and lipid binding Membrane trafficking 
Bax 19q13.3-.4 21 Cytoplasm/membrane Bcl-2 binding Apoptosis 
Bcl-2 18q21.3 26 Mitochondrial membrane Membrane permeability Apoptosis 
Cadherin-1 16q22.1 97 Plasma membrane Catenin/integrin binding Cell adhesion 
Caspase 8 2q33-34 55 Cytoplasm Protease Apoptosis 
Catenin 5q31 100 Cytoskeleton Cadherin binding Cell adhesion 
Cav-1 7q31.1 20 Plasma membrane Scaffolding Endocytosis/signaling 
CD34 1q32 41 Plasma membrane Scaffolding Cell adhesion 
CD44 11p13 82 Plasma membrane Hyaluronate binding Cell adhesion 
Clar1 19q13.3-.4 34 Nucleus SH3 binding Unknown 
Cox-2 1q25.2-.3 69 Microsomal membrane Prostaglandin synthase Inflammatory response 
CTSB 8p23.1 38 Lysosome Protease Protein turnover 
Cyclin D1 11q13 34 Nucleus CDKb regulation Cell cycle 
DD3 9q21-22 Nucleus/cytoplasm Noncoding Unknown 
DRG-1 22q12.2 43 Cytoplasm GTP binding Cell growth/differentiation 
EGFR 7p12 134 Plasma membrane EGF binding Signaling 
EphA2 1p36 11 Plasma membrane Tyrosine kinase Signaling 
ERGL 15q22-23 57 Plasma membrane Lectin/mannose binding Unknown 
ETK/BMK Xp22.2 78 Cytoplasm Tyrosine kinase Signaling 
EZH2 7q36.1 85 Nucleus Transcription repressor Homeotic gene regulation 
Fas 11q13.3 23 Plasma membrane Caspase recruitment Apoptosis 
GDEP 4q21.1 4c Unknown Unknown Unknown 
GRN-A 14q32 50 Secretory granules Statin Endocrine function 
GRP78 9q33.3 72 Endoplasmic reticulum Multimeric protein assembly Cell stress response 
GSTP1 11q13 23 Cytoplasm Glutathione reduction DNA protection 
Hepsin 19q11-13.2 45 Plasma membrane Serine protease Cell growth/morphology 
Her-2/Neu 17q21.1 138 Plasma membrane Tyrosine kinase Signaling 
HSP27 7q11.23 23 Cytoplasm Chaperone Cell stress response 
HSP70 6p21.3 70 Cytoplasm Chaperone Cell stress response 
HSP90 11q13 63 Cytoplasm Chaperone Cell stress response 
Id-1 20q11.1 16 Nucleus Transcription factor Differentiation regulator 
IGF-1 12q22-23 17 Secreted IGFR ligand Signaling 
IGF-2 11p15.5 20 Secreted IGFR ligand Signaling 
IGFBP-2 2q33-34 35 Secreted IGF binding Signaling 
IGFBP-3 7p13-12 32 Secreted IGF binding Signaling/apoptosis 
IL-6 7p15.3 24 Secreted Cytokine B-cell differentiation 
IL-8 4q13.3 11 Secreted Cytokine Neutrophil activation 
KAI1 11p11.2 30 Plasma membrane CD4/CD8 binding Signaling 
Ki67 10q25-ter 358 Nucleus Nuclear matrix associated Cell proliferation 
KLF6 10p15 32 Nucleus Transcription factor B-cell development 
KLK2 19q13.41 29 Secreted Protease Met-Lys/Ser-Arg cleavage 
Maspin 18q21.3 42 Extracellular Protease inhibitor Cell invasion suppressor 
MSR1 8p22 50 Plasma membrane LDL receptor Endocytosis 
MXI1 10q25.2 26 Nucleus Transcription factor Myc suppression 
MYC 8q24.12-.13 49 Nucleus Transcription factor Cell proliferation 
NF-kappaB 10q24 97 Nucleus Transcription factor Immune response 
NKX3.1 8p21 26 Nucleus Transcription factor Cell proliferation 
OPN 4q22.1 35 Secreted Integrin binding Cell-matrix interaction 
p16 9p21 17 Nucleus CDK inhibitor Cell cycle 
p21 6p21.2 18 Nucleus CDK inhibitor Cell cycle 
p27 12p13.1-12 22 Nucleus CDK inhibitor Cell cycle 
p53 17p13.1 44 Nucleus Transcription factor Growth arrest/apoptosis 
PAP 3q21-23 45 Secreted Tyrosine phosphatase Signaling 
PART-1 5q12.1 Nucleus/cytoplasm Unknown Unknown 
PATE 11q24.2 14 Plasma membrane Unknown Unknown 
PC-1 5q35 32 Nucleus RNA binding Ribosome transport 
PCGEM1 2q32 Nucleus/cytoplasm Noncoding Cell proliferation/survival 
PCTA-1 1q42-43 36 Cytoplasm Unknown Cell adhesion 
PDEF 6p21.31 38 Nucleus Transcription factor PSA promoter binding 
PI3K p85 5q12-13 84 Cytoplasm Lipid kinase Signaling 
PI3K p110 1p36.2 120 Cytoplasm Lipid kinase Signaling 
PIM-1 6p21.2 36 Cytoplasm Protein kinase Cell differentiation/survival 
PMEPA-1 20q13.31-33 32 Plasma membrane NEDD4 binding Growth regulation 
PRAC 17q21.3 Nucleus Choline/ethanolamine kinase Unknown 
Prostase 19q13.3-.4 27 Secreted Serine protease ECM degradation 
Prostasin 16p11.2 36 Plasma membrane Serine protease Cell invasion suppressor 
Table 1A

Continued

Prostein1q32.160aPlasma membraneUnknownUnknown
PSA 19q13.3-.4 71 Secreted Protease Semen liquification 
PSCA 8q24.2 13 Plasma membrane Unknown Unknown 
PSDR1 14q23-24.3 35a Nucleus/cytoplasm Dehydrogenase reductase Steroid metabolism 
PSGR 11p15 35 Plasma membrane Odorant receptor Unknown 
PSMA 11p11.2 84 Plasma membrane Folate hydrolase Cell stress response 
PSP94 10q11.23 13 Secreted FSH inhibitor Growth inhibition 
PTEN 10q23.3 47 Cytoplasm Protein/lipid phopatase Signaling 
RASSF1 3p21.31 33 Cytoplasm Ras binding Signaling 
RB1 13q14.2 106 Nucleus E2F-1 inactivation Cell cycle 
RNAseL 1q25.3 84 Cytoplasm/mitochondria RNAse Viral resistance 
RTVP-1 12q21.1 29 Plasma membrane Unknown Immune response/apoptosis 
ST7 7q31.2 60/85 Plasma membrane Unknown Cell proliferation 
STEAP 7q21.23 40 Plasma membrane Unknown Unknown 
TERT 5p15.33 127 Nucleus Reverse transcriptase Telomere synthesis 
TIMP 1 Xp11.3-.23 23 Secreted Protease inhibitor Cell adhesion 
TIMP 2 17q25 24 Secreted Protease inhibitor Cell adhesion 
TMPRSS2 21q22.3 54 Plasma membrane Serine protease Unknown 
TRPM2 8p21-12 52 Plasma membrane Calcium channel Ion flux 
Trp-p8 2q37.1 120 Plasma membrane Calcium channel Ion flux 
UROC28 6q23.3 17 Nucleus/cytoplasm Choline/ethanolamine kinase Unknown 
VEGF 6p12 27 Secreted VEGFR binding Angiogenesis 
Prostein1q32.160aPlasma membraneUnknownUnknown
PSA 19q13.3-.4 71 Secreted Protease Semen liquification 
PSCA 8q24.2 13 Plasma membrane Unknown Unknown 
PSDR1 14q23-24.3 35a Nucleus/cytoplasm Dehydrogenase reductase Steroid metabolism 
PSGR 11p15 35 Plasma membrane Odorant receptor Unknown 
PSMA 11p11.2 84 Plasma membrane Folate hydrolase Cell stress response 
PSP94 10q11.23 13 Secreted FSH inhibitor Growth inhibition 
PTEN 10q23.3 47 Cytoplasm Protein/lipid phopatase Signaling 
RASSF1 3p21.31 33 Cytoplasm Ras binding Signaling 
RB1 13q14.2 106 Nucleus E2F-1 inactivation Cell cycle 
RNAseL 1q25.3 84 Cytoplasm/mitochondria RNAse Viral resistance 
RTVP-1 12q21.1 29 Plasma membrane Unknown Immune response/apoptosis 
ST7 7q31.2 60/85 Plasma membrane Unknown Cell proliferation 
STEAP 7q21.23 40 Plasma membrane Unknown Unknown 
TERT 5p15.33 127 Nucleus Reverse transcriptase Telomere synthesis 
TIMP 1 Xp11.3-.23 23 Secreted Protease inhibitor Cell adhesion 
TIMP 2 17q25 24 Secreted Protease inhibitor Cell adhesion 
TMPRSS2 21q22.3 54 Plasma membrane Serine protease Unknown 
TRPM2 8p21-12 52 Plasma membrane Calcium channel Ion flux 
Trp-p8 2q37.1 120 Plasma membrane Calcium channel Ion flux 
UROC28 6q23.3 17 Nucleus/cytoplasm Choline/ethanolamine kinase Unknown 
VEGF 6p12 27 Secreted VEGFR binding Angiogenesis 
a

Molecular weight (in thousands) estimated from amino acid data.

b

CDK, cyclin-dependent kinase; EGF, epidermal growth factor; IGFR, insulin-like growth factor receptor; IGF, insulin-like growth factor; LDL, low-density lipoprotein; ECM, extracellular matrix; FSH, follicle-stimulating hormone; VEGFR, vascular endothelial growth factor receptor.

c

Data for Table 1 resourced from GeneCards database, Weizmann Institute of Science (31).

We thank Claudine Valmonte and Jamie Ritchey of The EMMES Corp. for invaluable assistance on the literature search. We also thank Drs. Arthur Brothman, James Jacobson, Gary Kelloff, Tracy Lugo, Alison Martin, Lisa McShane, Suresh Mohla, Judd Moul, Shiv Srivastava, Sheila Taube, and Magdalena Thurin for helpful comments on the manuscript.

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