ReviewCancer Biomarkers

Beyond PSA: The Next Generation of Prostate Cancer Biomarkers

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Science Translational Medicine  28 Mar 2012:
Vol. 4, Issue 127, pp. 127rv3
DOI: 10.1126/scitranslmed.3003180

Abstract

Since the introduction of serum prostate-specific antigen (PSA) screening 25 years ago, prostate cancer diagnosis and management have been guided by this biomarker. Yet, PSA has proven controversial as a screening assay owing to several inherent limitations. The next wave of prostate cancer biomarkers has emerged, introducing new assays in serum and urine that may supplement or, in time, replace PSA because of their higher cancer specificity. This expanding universe of biomarkers has been facilitated, in large part, by new genomic technologies that have enabled an unbiased look at cancer biology. Such efforts have produced several notable success stories that involve rapidly moving biomarkers from the bench to the clinic. However, biomarker research has centered on disease diagnostics, rather than prognosis and prediction, which would address disease management. The development of biomarkers to stratify risk of prostate cancer aggressiveness at the time of screening remains the greatest unmet clinical need in prostate cancer. We review the current state of prostate cancer biomarker research, including the PSA revolution, its impact on early cancer detection, the recent advances in biomarker discovery, and the future efforts that promise to improve clinical management of this disease.

The Age of Biomarkers

The introduction of biomarkers for disease diagnosis and management has revolutionized the practice of oncology. Biomarkers are molecules whose detection or evaluation provides information about a disease beyond the standard clinical parameters that are gathered by the clinician (http://www.cancer.gov/dictionary?cdrid=45618). Biomarkers can be proteins, metabolites, RNA transcripts, DNA, or epigenetic modifications of DNA, among other alterations. They can be detected in patient tissue samples, obtained either by biopsy or surgical resection, or noninvasively through the isolation of cells and/or molecules from bodily fluids, such as blood or urine. Although interest in biomarkers is increasing, controversies regarding what constitutes a robust biomarker and how to rigorously investigate biomarkers remain, and these subjects will be addressed later in this review.

• Broadly, there are seven common roles for biomarkers (1), which address specific clinical questions when managing cancer patients or patients suspected to have a malignancy:

• Disease disposition: What is a patient’s risk of developing cancer in the future?

• Screening: Does earlier detection of patients with cancer decrease mortality?

• Diagnostic: Who has cancer? What is the grade of the cancer?

• Prognostic: What clinical outcome is most likely if therapy is not administered?

• Predictive: Which therapy is most appropriate?

• Monitoring: Is therapy effective? Does the patient’s disease recur?

• Pharmacogenomic: Do genetics predict response to therapy or the risk for adverse reaction to the prescribed therapeutic dose?

A few successful examples of cancer biomarkers have emerged that illustrate these categories. For example, the Oncotype Dx gene expression assay (Genomic Health Inc.) serves as a multigene prognostic biomarker to help predict breast cancer recurrence (2). Amplification of the human epidermal growth factor receptor 2 (HER2) oncogene (3), mutation in v-raf murine sarcoma viral oncogene homolog B1 (BRAF) (4), and the presence of a fusion between the echinoderm microtubule-associated protein-like 4 (EML4) gene and the anaplastic lymphoma kinase (ALK) gene (EML4-ALK) (5) are predictive biomarkers for breast cancer (HER2), melanoma (BRAF), or lung cancer (EML4-ALK) that help identify which patients will most likely benefit from therapies targeted to those genetic aberrations (6). In terms of protein-based indicators, serum prostate-specific antigen (PSA) is commonly used for monitoring disease progression after hormonal therapy of hormone-naïve prostate cancer (7).

The ideal biomarker for clinical use should have three major characteristics: a safe and easy means of measurement, preferably noninvasively; high sensitivity, high specificity, and high positive and negative predictive values (PPV and NPV, respectively) for its intended outcome; and improves decision-making abilities in conjunction with clinicopathological parameters. Although a biomarker that performs well in several of the aforementioned categories would be ideal, the reality is that multiple biomarkers will be likely required for cancer to cover screening, diagnosis, prognosis, and prediction.

PSA as a Prostate Cancer Biomarker

Prostate cancer is the most common noncutaneous cancer in men, with more than 200,000 prostate cancer diagnoses per year in the United States. The lifetime risk for a U.S. male to develop prostate cancer is about 1 in 6, although the risk of dying from prostate cancer is only 1 in 35 (8). This discrepancy between prostate cancer incidence and lethality has led to widespread scrutiny of prostate cancer patient management, particularly for low-grade, low-stage (indolent) disease (9).

Unlike most cancers, prostate cancer management has long used biomarkers. The first of these, prostatic acid phosphatase (PAP), was noted in the 1930s to be elevated in the serum of men with metastatic prostate cancer, and for nearly 50 years, PAP was investigated as a clinical marker for disease progression (10). In the 1980s, PAP was rapidly replaced by PSA, a secreted protein first studied in the late 1970s as a product of the prostate gland (11). PSA is encoded by the prostate-specific gene kallikrein 3 (KLK3), a member of the tissue kallikrein family—a gene family of serine proteases that also includes KLK2 and KLK4 (12). Mature PSA is the result of two proteolytic cleavages of two inactive precursor peptides, pre-proenzyme PSA (pre-proPSA) and proPSA. In its final form, PSA is secreted into semen (12). Under normal conditions, only low levels of PSA can be detected in blood, and the increase of serum PSA in prostate cancer can represent abnormalities in prostate gland architecture and vascularization, although the exact mechanism is unclear (7).

Initial reports suggested a role for PSA as a biomarker for monitoring the progression of patients already diagnosed with prostate cancer or for recurrence after curative therapy for organ-confined disease (Fig. 1). In a landmark study, Stamey et al. performed the first large-scale analysis of serum PSA as a prostate cancer biomarker, convincingly demonstrating that PSA was more sensitive than PAP for monitoring the disease (13). They showed that PSA level increased with advancing clinical stage and was useful for detecting disease recurrence after therapy (13). Subsequent studies shifted the focus of PSA toward early detection of prostate cancer. In 1986, the U.S. Food and Drug Administration (FDA) approved PSA as an adjunctive test to the digital rectal exam (DRE) for the detection of prostate cancer in men older than 50. In 1991, Catalona and colleagues demonstrated that the combination of a serum PSA measurement of ≥4.0 ng/ml with other clinical findings, such as the results of a DRE, improved detection of prostate cancer in a prospective study (14). Several groups confirmed that PSA was useful as a screening test for prostate cancer (15).

Fig. 1

PSA clinical course and biomarker uses. An increase in PSA levels suggests the presence of prostate cancer and can inform disease management decisions. Several types of PSA measurements can be used, including total PSA, complexed (cPSA) and free PSA (fPSA), PSA doubling time (PSADT) and velocity (PSAV), and PSA density. This plot illustrates the clinical course of a hypothetical patient with recurrent prostate cancer, in which disease recurred after curative therapy. Hormonal therapy in this example led to castrate-resistant prostate cancer (CRPC), in which the cancer became refractory to conventional hormonal therapies. The bottom segment of the plot indicates the type of biomarkers applicable for measurement during progression.

CREDIT: Y. GREENMAN/SCIENCE TRANSLATIONAL MEDICINE

Impact of PSA on Diagnosis and Treatment: More Harm than Good?

Between 1985 and 1995, prostate cancer incidence doubled in the United States, from about 55 to 110 cases per 100,000 men (16, 17). This was accompanied by an increase in invasive procedures for prostate cancer treatment; radical prostatectomy rates were nearly sixfold higher in 1990 than in 1984 (18). These major shifts in the detection and treatment of prostate cancer have been attributed to the use of PSA as a screening test, coupled with improvements in the safety of the radical prostatectomy procedure (19).

The introduction of PSA into the prostate cancer community also led to its widespread use as a screening test among asymptomatic men. Subsequently, the proportion of men with metastatic prostate cancer at the time of diagnosis decreased markedly, a major feat that altered disease management (16, 20). More men were being diagnosed with prostate cancer, with most having indolent disease. More men with benign prostatic conditions, such as inflammation or hyperplasia, were also being biopsied. PSA therefore enabled the early detection of many latent prostate cancers, most of which may have never led to harm (16). This discrepancy between decreasing disease aggressiveness and increasing treatment has led to widespread criticism that prostate cancer is now an “overdiagnosed” and “overtreated” cancer. Most indolent tumors are unlikely to cause significant symptoms or mortality, and it is estimated that up to 50% of new prostate cancer diagnoses detect a tumor that was unlikely to surface clinically in the absence of PSA screening (9). A subsequent analysis by Draisma et al. suggested an overdiagnosis rate of 20 to 42% (21).

Treatment of indolent cancer may cause a patient more harm than good. Biopsies and prostate cancer treatments have been associated with psychological distress, loss of bodily function, pain, and suffering for patients (22). Side effects of radiotherapy and radical prostatectomy, including sexual dysfunction, urinary incontinence, and impaired bowel/rectal function, occur in 25 to 50% of patients, adding to a patient’s distress (23). Rarely, treatment of prostate cancer directly contributes to a life-threatening adverse event (24).

Although mortality from prostate cancer has been decreasing since the mid-1990s, it is unclear to what extent PSA screening may be responsible. The two largest prospective screening trials to date—the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) in the United States and the European Randomized Study of Screening for Prostate Cancer (ERSPC) in Europe—failed to demonstrate a concordant benefit in overall patient survival from PSA screening (25, 26). At best, the ERSPC trial demonstrated that PSA screening modestly decreased prostate cancer–related mortality. However, to prevent 1 death from prostate cancer, a physician must screen 1410 men for serum PSA and treat 48 (26). In 2002, the U.S. Preventive Services Task Force (USPSTF) deemed the evidence to be insufficient to recommend routine use of PSA as a screening test among men younger than age 75 (http://www.uspreventiveservicestaskforce.org/uspstf/uspsprca.htm). The USPSTF reviewed the available evidence again in 2011 and, in a draft report, concluded that population benefit from PSA screening was inconclusive, recommending against PSA-based prostate cancer screening at any age (this draft is currently in the public forum for feedback) (27).

A Biomarker with Limitations

The screening test performance characteristics of PSA are variable. First, its specificity and sensitivity range from 20 to 40% and 70 to 90%, respectively, depending on the PSA cutoff values used (for example, 3 versus 4 ng/ml) (28). The area under the curve (AUC) of the receiver operating characteristic (ROC) curve is between 0.55 and 0.70 for the ability of PSA to identify patients with cancer, where a score of 1.0 is perfect discrimination and 0.5 is a coin toss (28). One of the major reasons for such poor specificity is the fact that several noncancerous events may elevate the level of PSA. Indeed, infection, trauma, and benign prostatic hyperplasia (BPH) are more common causes of elevated serum PSA than cancer (7, 12, 28) (Fig. 2). BPH is present in more than 50% of men >50 years, thus confounding PSA as a cancer biomarker (8, 12). Because of this high false-positive rate, PSA-based screening for prostate cancer demonstrates a PPV of only 25 to 40% (29). Conversely, about 15% of men with a low level of PSA (<4.0 ng/ml) have prostate cancer, and 15% of these display advanced Gleason scores, a prognostic histopathological score given to prostate cancers (30, 31).

Fig. 2

Future challenges and unmet needs in prostate cancer biomarker research. Current clinical practice relies on PSA and its derivatives to help diagnose and manage prostate cancer. However, new prostate cancer biomarkers should be targeted to addressing unmet clinical needs in prostate cancer management, including risk indicators for disease with low PSA values (<10 ng/ml), prognostic markers to distinguish indolent from aggressive disease, and biomarkers for metastatic cancer.

CREDIT: Y. HAMMOND/SCIENCE TRANSLATIONAL MEDICINE

There have been many efforts to improve the performance of the PSA test, such as normalizing PSA to the size of the gland (PSA “density”) (13, 32, 33) or monitoring the dynamics of PSA change in serum (PSA velocity and doubling time) (3438). In addition, assays measuring alternative molecular traits of PSA have also gained attention, including free and complexed PSA (fPSA and cPSA, respectively) (3942), and isoforms of the PSA protein (proPSA, most commonly) (43, 44).

Among these, cPSA and fPSA have been considered adjunctive tests to total serum PSA rather than replacement assays (Fig. 1). cPSA measurements exploit the molecular interactions of PSA mainly with α1-antichymotrypsin (ACT) in the blood (40). Conversely, fPSA measures the percentage of total serum PSA not bound to ACT. fPSA decreases in prostate cancer, making it useful to distinguish men with BPH from men with cancer. An fPSA of less than 25%, when combined with total PSA, has been shown to improve the sensitivity and specificity of the latter and to reduce unnecessary biopsies (39, 42). The percentage of fPSA has thus gained FDA approval for use when patients have a total PSA in the 4 to 10 ng/ml “gray zone.” Furthermore, combined measurement of [−2]proPSA (a peptide precursor to mature PSA) with fPSA may help diagnose early prostate cancers with a PSA of 2 to 10 ng/ml (43, 44). fPSA has several drawbacks, such as its instability if sample processing occurs more than 24 hours after collection (45). The percentage of fPSA may also increase after DRE or biopsy procedures (46), thus restricting its use in those settings.

PSA dynamics, namely, velocity (PSAV) and doubling time (PSADT), have prognostic value (47) (Fig. 2). PSAV is defined as the change in PSA concentration per year, with a high PSAV being strongly associated with prostate cancer and a ninefold elevated risk of cancer-related death after prostatectomy (34, 35, 48). PSADT is defined as the time necessary for the serum PSA level to double. PSADT is most commonly used to monitor disease progression after curative therapy for organ-confined disease, because an increasing PSA level indicates the presence of residual tumor cells. A more rapid PSADT (<10 months) is associated with reduced survival (36, 37). In rare cases, disease may recur in the absence of an elevated PSA (49). Nevertheless, neither PSADT nor PSAV has been shown to improve over a PSA measurement for prostate cancer screening (38).

Design, Interpretation, and Challenges for Prostate Cancer Biomarkers

One of the important lessons learned from the popularization of PSA as a screening test is that biomarker development requires a priori deliberation of the intended role. The PSA test was initially developed to monitor prostate cancer recurrence, yet widespread screening of asymptomatic men has resulted in a net overdiagnosis and overtreatment of indolent disease (9, 21). As a result, clinicians should discuss the limitations of the PSA test with patients and also inform them that treatment for prostate cancer may not always be beneficial. Hindsight begs the question: What is the best path to validate a new biomarker for clinical application? The National Cancer Institute’s Early Detection Research Network (EDRN) has headed a response to this question with their unique biomarker discovery and validation infrastructure as well as their standardized prospective-specimen-collection, retrospective-blinded-evaluation (PRoBE) approach to biomarker validation (50). These responses are described below with respect to biomarker design, interpretation, and challenges in analysis and validation.

Design

A general model for biomarker development consists of five phases (Table 1) (50, 51): (i) biomarker discovery, (ii) clinical assay development, (iii) retrospective studies to clarify target populations, (iv) prospective screening studies to determine efficacy, and (v) analysis of biomarker impact in terms of cost-effectiveness and patient compliance.

Table 1

Questions, challenges, and considerations in biomarker discovery and validation. The five phases are based on (50, 51).

View this table:

The problem with biomarker development lies not in the general framework outlined above, but poor adherence to it. Perhaps the greatest shortcoming of many failed biomarker trials is that independent groups have been unable to generate concordant results (52).

A major implication of this framework is that the time required from the initial discovery and retrospective studies to clinical adoption of a biomarker is lengthy, generally a decade or more. In effect, the framework describes an adapted version of phase 1/2/3 clinical trials, where the idea is to establish sequential levels of evidence—from discovery to retrospective to prospective studies—that show use of the biomarker. Ultimately, biomarker studies for prostate cancer are unlikely to be evaluated in terms of overall patient survival or progression-free survival, because these clinical trials may take decades to evaluate for a new biomarker. The only practical means to potentially assess such endpoints is to create large repositories for a range of biospecimens, including tissue, blood, and urine, based on ongoing screening and therapeutic trials (53, 54). Here, the EDRN has initiated efforts to collect thousands of biospecimens for cancer biomarker research (http://edrn.nci.nih.gov/), and such repositories will enable large-scale evaluation of new biomarkers in a relatively more rapid time frame (years as opposed to decades).

Interpretation

Statistical analysis is a core consideration in biomarker studies. Any interpretation must first determine that the study is designed with sufficient power to evaluate the desired endpoints (Table 1). Then, a classic biomarker analysis evaluates the sensitivity and specificity, often using an ROC curve. However, Shaw et al. argued that standard ROC curves are not always appropriate analyses, especially in the context of prostate cancer screening (55), because new prostate cancer biomarkers are usually combined with PSA measurement. Hence, studies become biased when they cannot evaluate the performance of the secondary (new) marker in the absence of the first (PSA) marker; for example, if a patient is PSA-negative. Therefore, a “relative” ROC (rROC) curve may be more appropriate, in which the relative true- and false-positive rates—but not their absolute true- and false-positive rates—are evaluated (55). Promising new prostate cancer biomarkers should therefore be evaluated both in combination with and independent of PSA status. Toward the latter, one approach is to move the research out of the urologist’s office and into the primary care setting, where men could be screened for a new biomarker before PSA testing and DRE. Another option would be to design clinical trials that include biopsies of men with an abnormal measurement of a new biomarker, even if the PSA test and DRE results suggest only a low risk for cancer.

Monitoring sensitivity and specificity is standard practice in biomarker studies, but this may not be sufficient to evaluate efficacy. These metrics measure the proportion of individuals, either positive or negative for the test, that have been detected accurately. But PPV and NPV are more clinically informative statistics than sensitivity and specificity, because they report a confidence in the relative value of a positive or negative test. Even with reasonable sensitivity and specificity, a test may actually have a low PPV. For PSA, even when the sensitivity is set reasonably high (4 ng/ml cutoff value), the resulting PPV is only ~25% (29).

Challenges and common errors

Biomarker studies are often fraught with systematic errors in their design and execution (Table 1) (56), which has resulted in widespread failure of initially “promising” biomarker trials (52, 57). In the literature, there are five common errors that render many biomarker studies ineffectual: lack of a robust assay protocol for reproducibility; biased comparison groups in the study (case versus controls); unclear or inappropriate clinical role of the biomarker; statistically underpowered study size; and inappropriate statistical analyses, including overfitting of data. These errors can be made at any stage of the biomarker development process, but most frequently, they occur in preclinical stages and the weakness of the biomarker is later revealed in larger trials (52). This is often because many preclinical models poorly reflect human disease, including cell lines (58). Murine prostate cancer models are similarly challenged by the fact that the mouse genome lacks a homolog to the human PSA (KLK3) gene and other aspects of human prostate biology (59).

Of these, the lack of a clear clinical role and inappropriate statistical methods are particularly germane to our discussion. First, the clinical role of newly discovered biomarkers is often only vaguely defined—if at all—leading to poorly executed clinical studies (51, 60, 61). A biomarker, by definition, is used for only a specific patient population for a specific clinical purpose, such as prognosis. Extension of a biomarker beyond its intended context is unlikely to provide clinical use. PSA screening trials were commissioned decades after PSA was introduced into clinical practice as a prostate cancer screening test, only to conclude that PSA screening offers negligible benefit at the population level (25, 26). To avoid such complications in biomarker development, a specific role for a candidate biomarker should be clearly defined through rigorous retrospective evaluation of the biomarker in clinically annotated biospecimen repositories.

The statistical analysis of biomarker trials is also challenging, and there is a concern that biomarker studies too often suffer from overfitting the data for an individual data set (5052, 56). This could lead to positive results for a single trial that are unable to be reproduced independently. Cross-validation of the statistical analysis is important, but it is only a partial solution until validation in independent cohorts occurs. A biomarker is not considered “validated” until independent research groups at multiple sites have demonstrated concordant results in separate trials.

Another issue that complicates biomarker studies is the bias that is introduced through selective reporting of data. This “nonreporting” bias tends to mask negative reports, whereas peer-reviewed literature tends to be more positive (50, 62). For instance, Kyzas et al. note that published articles show a significant association of p53 mutations with clinical outcome in head and neck squamous cell cancers, whereas unpublished data or data located in large, unwieldy supplemental files were markedly less positive (56). This issue of transparency may be best addressed during the peer review process, where journals can promote thorough evaluation of manuscripts by allowing longer periods of time for review of manuscripts with large amounts of supplementary material. The scientific community, too, can promote a culture of transparency by encouraging open-access electronic data sharing, and academic institutions can work to foster new investigators without undue research pressure, as has been comprehensively discussed elsewhere (63).

Biomarker Discovery in Prostate Cancer

A common theme in prostate cancer biomarker development is the desirability of noninvasive assays to replace biopsy as the diagnostic “gold standard.” Invasive biopsy procedures present an increased risk of adverse events, such as bleeding and sepsis, and are associated with a 15 to 20% false-negative rate (64, 65), perhaps owing to inefficient sampling, where normal tissue is biopsied with diseased tissue. Noninvasive biomarkers in serum and urine have the potential to improve the standard tissue biopsy procedure, although they cannot provide direct histopathological or spatiotemporal information. Hence, supplementing PSA measurements with noninvasive urine-based analyses may improve clinical practice in the near future.

The most crucial studies will focus on addressing current gaps in prostate cancer biomarker development, including prognostic and predictive biomarkers (Fig. 2). Development of new biomarkers that only identify more prostate cancer cases does not address this discrepancy. It follows, then, that the identification and validation of novel biomarkers to “rule out” aggressive prostate cancer at the point of screening is the greatest unmet clinical need, because this may reduce unnecessary interventions—such as biopsy or therapy—that may cause more harm than good.

One approach to identifying predictive biomarkers is to focus on genomic disease signatures, such as loss of the phosphatase and tensin homolog (PTEN) tumor suppressor or gain of ETS transcription factor gene fusions, which influence the biological characteristics of an individual cancer. For example, PTEN loss activates the phosphoinositide 3-kinase (PI3K) pathway, which inhibits androgen receptor (AR) signaling and causes resistance to AR-based therapies (66). Treatment of PTEN-null mouse tumors with combined pharmacologic inhibition of PI3K and AR signaling has led to tumor regression (66). Clinically, PTEN deletion is associated with poor outcome and hormone-refractory disease in prostate cancer (67). Therefore, PTEN deletion may be both prognostic and predictive of response to therapy. Similarly, gene fusions between transmembrane protease, serine 2 (TMPRSS2) and the transcription factor v-ets erythroblastosis virus E26 oncogene homolog (ERG) (Fig. 2), which occur in about 50% of prostate cancers, may predict for tumor sensitivity to poly(adenosine diphosphate–ribose) polymerase 1 (PARP1) inhibition (68) and may add prognostic information detailing more aggressive disease, especially in conjunction with PTEN deletion (69, 70).

The Next Generation of Biomarkers

PSA has persisted in clinical practice owing in large part to the public’s demand for cancer screening [see review by Colditz et al. in this issue (20)]. Indeed, PSA remains an inexpensive, sensitive biomarker for disease detection and monitoring progression and recurrence after curative therapy of local disease (7, 35). Thus, newly discovered prostate cancer biomarkers will most likely retain PSA as a primary clinical tool in conjunction with other tests (Fig. 3), unless head-to-head comparisons prove otherwise.

Fig. 3

Advances in prostate cancer biomarker uses. The emerging clinical paradigm for prostate cancer biomarkers includes the combined application of imaging modalities and molecular biomarkers found in serum, urine, and tissue. This paradigm combines the recent technological advances that have improved imaging technologies, such as magnetic resonance imaging, as well as advances in molecular biology that have enabled the robust detection of transcriptomic, proteomic, and genomic biomarkers noninvasively in patient serum and urine. These new molecular assays, including urine RNA biomarkers and the serum detection of CTCs and exosomes, also compliment traditional tissue-based metrics such as Gleason grading.

CREDIT: Y. HAMMOND/SCIENCE TRANSLATIONAL MEDICINE

Since the adoption of PSA, advances in DNA sequence and RNA transcriptome profiling, such as microarrays and whole-genome sequencing, have enabled detailed dissections of cancer biology at a level previously unattainable (71, 72). As a result, biomarker research has shifted to use these “-omics” methods, populating the prostate cancer literature with discoveries based on profiling tumors for aberrations in DNA, RNA, or epigenetic DNA methylation states. Tissue biomarkers and imaging-based technologies have been developed as well, including transrectal ultrasound, computed tomography, magnetic resonance imaging, and positron emission tomography (Fig. 3). These have been reviewed elsewhere (7376) and will not be discussed comprehensively here. However, it is important to consider their role in disease diagnosis and staging, especially regarding the detection of metastases by imaging. Here, we focus on the discovery and characterization of emerging biomarker assays for prostate cancer, including both blood- and urine-based diagnostics (Fig. 3).

PCA3

The most prominent biomarker emerging as a non-PSA–based diagnostic test for prostate cancer is prostate cancer antigen 3 (PCA3) (Fig. 2). PCA3 is a long noncoding RNA that has been shown to be elevated in >90% of prostate cancer tissues, but not in normal or BPH tissues (77, 78). The high sensitivity and specificity of PCA3 in tissues have led to its evaluation as a noninvasive biomarker, where numerous assays have been developed to detect it in patient urine samples, which contain cells shed from the prostate during urination (Fig. 3). Over the past decade, several iterations of PCA3 urine tests have also emerged (79) and, currently, a clinical-grade assay based on transcription-mediated amplification is available (79).

Urine PCA3 measurements add to the diagnostic information obtained from the PSA test, with higher AUC values of 0.66 to 0.72, compared to 0.54 to 0.63 for serum PSA alone (80). Unlike PSA, PCA3 levels are independent of prostate size (81). Sensitivities for urine PCA3 levels range from 47 to 69%, with most between 58 and 69%, although it is difficult to directly compare the studies because of different analysis platforms, different criteria for enrolling patients (for example, serum PSA concentrations), and relatively small patient cohorts of several hundred men rather than thousands (80). Although PCA3 is a robust biomarker, these differences in methodology illustrate the inherent challenges of biomarker research and development. In addition, combining a serum PSA value with a urine PCA3 analysis improves both measures, with the combination AUC of 0.71 to 0.75 (82). In 2012, PCA3 was approved by the FDA as a diagnostic test for prostate cancer in the setting of a previous negative prostate biopsy (http://www.accessdata.fda.gov/cdrh_docs/pdf10/p100033a.pdf).

TMPRSS2-ERG

TMPRSS2-ERG gene fusions are one of the most common genetic events in prostate cancer and account for 90% of prostate cancer fusions (83). TMPRSS2-ERG fusions are specific for prostate cancer and can even be detected in precursor lesions, such as prostate intraepithelial neoplasia (PIN) (Fig. 2), if these lesions are proximal to, or contiguous with, regions of cancer (84). The detection of TMPRSS2-ERG RNA in patient urine has also been investigated (85, 86) (Fig. 3). Yet, TMPRSS2-ERG is absent in about 50% of cancers; therefore, its use lies in multiplexed assays with other biomarkers, such as PCA3 (85, 86). A study of more than 1300 men demonstrated that combined measurement of PCA3 and TMPRSS2-ERG in urine outperformed serum PSA for prostate cancer diagnosis, thus adding to available clinical information in the Prostate Cancer Prevention Trial (PCPT) risk estimates for predicting cancer (http://deb.uthscsa.edu/URORiskCalc/Pages/uroriskcalc.jsp) (86).

There has been some debate whether the presence of a TMPRSS2-ERG fusion is itself a prognostic biomarker when detected in tissues. Several groups have reported an association between TMPRSS2-ERG and aggressive prostate cancer (69, 86, 87); however, others have not observed this association (88, 89). One complication to these studies has been heterogeneity in the patient populations studied and the clinical outcomes evaluated. Quantitative levels of urine TMPRSS2-ERG appear to be associated with clinically significant prostate cancer based on Epstein criteria, which stratifies disease aggressiveness using PSA density and characteristics of the patient’s biopsy (Gleason score, the percent tumor observed, and number of cores with tumor) (86).

Despite the potential benefits of PCA3 and TMPRSS2-ERG, these biomarkers are currently adjunctive to PSA (Fig. 2), and head-to-head trials to determine whether these tests perform well in the absence of PSA screening are lacking. Furthermore, urine expression of PCA3 or TMPRSS2-ERG is determined relative to urine PSA mRNA (85, 86); thus, if the PSA transcript level is too low, the tests are not informative.

α-Methylacyl–coenzyme A racemase

Another biomarker nominated by RNA expression profiling is the enzyme α-methylacyl–coenzyme A racemase (AMACR), which has demonstrated high sensitivities and specificities, each >90% when tested as a diagnostic biomarker on prostate biopsy tissue (90). Low AMACR gene expression has also been correlated with metastasis and biochemical recurrence of prostate cancer (91). However, it is not specific to prostate cancer (92) and is also not suitable for noninvasive detection in urine (93), rendering it most useful as a tissue biomarker when prostate biopsy cores yield ambiguous pathological results.

Germline risk loci

In addition to profiling RNA and DNA, genomic analyses have recently uncovered single-nucleotide polymorphisms (SNPs) associated with prostate cancer, which may serve as germline indications of an individual’s risk for developing cancer. To date, more than 50 SNPs have been proposed as putative risk loci for prostate cancer, of which ~30 have been validated in multiple studies (94). Although each individual SNP is likely to contribute a minor degree to disease risk—thus making individual assays ineffective—combining multiple SNPs may yield more informative results. In a retrospective study, Zheng et al. defined a core set of five disease-associated SNPs that were then combined with family history to predict risk (up to 10-fold) for developing prostate cancer (95). Recently, rare SNP variants in homeobox B13 (HOXB13), an AR gene cofactor, have been implicated in familial predisposition to early-onset prostate cancer as well, although these variants occur at low prevalence in the general prostate cancer population (<1%) (96).

Other “-omic” biomarkers

High-throughput proteomics and metabolomics platforms have revealed serum protein and urine metabolite signatures in prostate cancer. One major advantage of profiling the human serum proteome is the vast dynamic range—greater than 10 logs (1010)—over which serum proteins can be accurately detected (97). Rosenzweig et al. used mass spectrometry to nominate serum protein signatures for predicting prostate cancer biochemical recurrence (98). Mass spectrometry has also been used to identify candidate serum proteins that may indicate response to chemotherapy (99). Profiling culture media from prostate cancer cell lines for secreted proteins has also identified several potential diagnostic proteins, yet to be validated in patients (100). Finally, a study of urine metabolites in prostate cancer patients has led to the identification of a series of metabolites that are elevated in aggressive forms of prostate cancer, including sarcosine, a metabolite of glycine (101).

Further refinements of genomic technologies, such as next-generation transcriptome sequencing (RNA-Seq), promise to uncover additional biomarkers in an unbiased manner, including tissue-specific noncoding RNAs similar to PCA3 (71). Here, advances in computational biology and bioinformatics will help fish out cancer-specific indicators in a sea of omics data. In some cases, these high-throughput approaches can be used to define patient-specific biomarkers (“personalized medicine”) (102). To this end, Roychowdhury et al. used different sequencing approaches to define the genomic and transcriptomic aberrations in metastatic prostate cancer patients less than 4 weeks after biopsy, a timeframe comparable to the standard “washout” period required of patients who change between two clinical trials (103).

Circulating tumor cells

One area of expanding investigation is circulating tumor cells (CTCs) found in the bloodstream (Fig. 3). The number of CTCs present in whole blood can be a biomarker for cancer detection, and the cells themselves a source of molecular information, such as TMPRSS2-ERG, AR, and PTEN copy number status (104). An increased abundance of CTCs in the blood of castration-resistant prostate cancer patients has predicted worse overall survival (105, 106). However, detecting CTCs and extracting molecular information are currently labor-intensive and expensive, and it is yet unknown if CTC abundance in blood represents aggressive disease undergoing hematogenous spread or if they are simply cells that have dislodged from the bulk tumor.

Exosomes

Prostate-derived exosomes (also called prostatosomes) are small vesicles (50 to 150 nm in diameter) generated from internalized parts of the cellular membrane, which are subsequently secreted into the blood, semen, or urine (Fig. 3) (107). Prostate cancer patients exhibit increased numbers of exosomes in their serum compared to men with no disease, and elevated levels of exosomes may also correlate with increasing Gleason score (108). Prostate cancer RNA biomarkers, including PCA3 and TMPRSS2-ERG, can also be detected in urine-derived exosomes from prostate cancer patients (109). Although these efforts remain mainly research-oriented at this time, they provide promising future directions for biomarker research.

Future Directions

The era of PSA testing in prostate cancer has imparted lasting changes in the way we think about prostate cancer biology and clinical management. Although patients want to know earlier if they have prostate cancer, the high prevalence of latent cancers detected by PSA-based screening singularly argues for the use of adjunctive biomarkers that better refine disease risk. This PSA conundrum in prostate cancer has also led to evaluation of screening methodologies in other cancers, such as breast (110) and lung, where imaging technologies enable greater detection but also increase unnecessary procedures and health care costs (111113).

When used in the proper context, prostate cancer biomarkers of the future could avoid unnecessary biopsies, reduce the number of prostatectomies and radiotherapy, stratify organ-confined tumors (curable by surgery), monitor progression during “watchful waiting,” detect micrometastatic disease (below the limit of detection for imaging), and/or lower overall mortality from the disease. A more rational approach to biomarker discovery, combined with modern molecular science and bioinformatics, will eventually allow clinicians to better diagnose and target treatment for those patients who are most likely to benefit.

References and Notes

  1. Acknowledgments: We thank S. Roychowdhury and members of the Chinnaiyan lab for helpful discussions and comments on this manuscript. We further acknowledge the numerous labs, authors, and publications that we were unable to cite in this review owing to space restrictions. Funding: This work was supported by EDRN grants U01 CA 11275 (A.M.C. and M.A.R.) and 5U01 CA113913 (J.T.W.), Department of Defense grants PC100171 (A.M.C.) and PC094290 (J.R.P.), and NIH Prostate Specialized Program of Research Excellence grant P50CA69568 (A.M.C.). A.M.C. is supported by the Doris Duke Charitable Foundation Clinical Scientist Award, the Prostate Cancer Foundation, the American Cancer Society, and the Howard Hughes Medical Institute. J.R.P. is a Fellow of the University of Michigan Medical Scientist Training Program. A.M.C. is a Taubman Scholar of the University of Michigan. Competing interests: A.M.C. serves as an advisor to Gen-Probe Inc., who has developed diagnostic tests using PCA3 and TMPRSS2-ERG. The University of Michigan has licensed the development of TMPRSS2-ERG–based prostate cancer diagnostic assays to Gen-Probe, and A.M.C. and M.A.R. are named as coinventors. Gen-Probe was not involved in the writing or approval of this manuscript.
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