Advances in proteomic prostate cancer biomarker discovery

J O U R NA L OF PR O TE O MI CS 7 3 ( 2 01 0 ) 1 8 3 9–1 8 5 0

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Review

Advances in proteomic prostate cancer biomarker discovery Young Ah Goo, David R. Goodlett⁎ Department of Medicinal Chemistry, University of Washington, 1959 NE Pacific Street, Seattle, WA 98195, United States Institute for Systems Biology, 1441 N. 34th Street, Seattle, WA 98103, United States

AR TIC LE I N FO

ABS TR ACT

Keywords:

Prostate cancer is the most common non-cutaneous cancer in men in the United States. For

Biomarker

reasons largely unknown, the incidence of prostate cancer has increased in the last two

Proteomics

decades, in spite or perhaps because of a concomitant increase in serum prostate-specific

Prostate cancer

antigen (PSA) screening. While PSA is acknowledged not to be an ideal biomarker for

Systems biology

prostate cancer detection, it is however widely used by physicians due to lack of an alternative. Thus, the identification of a biomarker(s) that can complement or replace PSA represents a major goal for prostate cancer research. Screening complex biological specimens such as blood, urine, and tissue to identify protein biomarkers has become increasingly popular over the last decade thanks to advances in proteomic discovery methods. The completion of human genome sequence together with new development in mass spectrometry instrumentation and bioinformatics has been a major driving force in biomarker discovery research. Here we review the current state of proteomic applications as applied to various sample sources including blood, urine, tissue, and “secretome” for the purpose of prostate cancer biomarker discovery. Additionally, we review recent developments in validation of putative markers, efforts at systems biology approach, and current challenges of proteomics in biomarker discovery. © 2010 Published by Elsevier B.V.

Contents 1. 2.

Introduction . . . . . . Biological specimens of 2.1. Blood . . . . . . 2.2. Urine . . . . . . 2.3. Prostatic fluids .

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Abbreviations: BPH, benign prostatic hyperplasia; CD, cluster designation; EDRN, Early Detection Research Network; EPS, expressedprostatic secretion; HGPIN, high-grade prostatic intraepithelial neoplasia; LCM, laser capture microdissection; MRM, multiple reaction monitoring; MudPIT, Multidimensional Protein Identification Technology; PAcIFIC, Peptide Acquisition Independent From Ion Count; PICA, peptide ion current area; PTM, post-translational modification; PSA, prostate-specific antigen; SELDI-TOF, Surface Enhanced Laser Desorption Ionization-Time of Flight. ⁎ Corresponding author. Box 357610, Medicinal Chemistry, University of Washington, Seattle, WA 98195, United States. Tel.: +1 206 616 4586; fax: +1 206 685 3252. E-mail address: [email protected] (D.R. Goodlett). 1874-3919/$ – see front matter © 2010 Published by Elsevier B.V. doi:10.1016/j.jprot.2010.04.002

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2.4. Prostatic tissue. . . . . . 2.5. Secreted proteins . . . . 2.5.1. Glycoproteins . . . . . 3. Other considerations . . . . . . 3.1. Quantification . . . . . . 3.2. Metabolomics . . . . . . 3.3. Global systems approach 4. Challenges and perspectives . . References. . . . . . . . . . . . . . .

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Introduction

Prostate cancer is the most common non-cutaneous cancer in men in the United States [1]. For reasons largely unknown [2], incidence of prostate cancer, even when corrected for new widespread testing of serum prostate-specific antigen (PSA), has increased in the last two decades. Regardless, the causes of prostate cancer are unknown, though ethnicity, genetic factors, and diet have been causally implicated [3,4]. Current prostate cancer diagnosis uses a combination of serum PSA levels, Gleason grade, prostate palpation, and a biopsy, but unfortunately these tests are inadequate for predicting if/how cancers will spread [5]. Despite the fact that serum PSA concentration remains the most widely used test for prostate cancer screening, PSA level is not specific for prostate cancer but also is affected by age or other prostatic conditions such as benign prostatic hyperplasia (BPH) or prostatitis. Approximately 15% of men with a PSA level below 4.0 ng/ml will have prostate cancer and even for men with a PSA level in between 4.0 and 10 ng/ml there is only about a 25% chance of having prostate cancer. Not until serum PSA levels exceed 10 ng/ml, does the chance of having prostate cancer increase to over 50% (http://www.cancer.org/). Since the use of early detection tests for prostate cancer became fairly common in the 1990s, the mortality rate associated with prostate cancer has dropped. However, it is not clear if the decrease is a direct result of prevalent screening or due to improvements in treatment. The standard treatment for aggressive prostate cancer involves disruption of androgen receptor signaling by surgical or pharmacological castration and has been used as a front-line treatment since the 1940s, resulting in tumor regression in 75% of cases [6]. For most of these patients, the tumor subsequently acquires androgen independence after a median duration of 12–15 months and progresses until a fatal outcome [7]. Identification of marker(s) associated with multi-stage prostate cancer will provide greater scientific understanding of possible causes and underlying mechanisms, and important insights needed for improving treatment. Since the term "proteomics" was first introduced in the late 1990s, mass spectrometry (MS)-based proteomics has been an essential tool for clinical biology and for the emerging field of systems biology to better understand complex human diseases. Proteomics has achieved expansive growth in recent years and is still rapidly growing fueled by innovative experimental approaches, improvements in sensitivity, resolution, and accuracy of mass analyzers. There are a handful of descriptive review articles discussing different types of mass analyzers

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and technologies used in current cancer proteomics research [8–14]. More recent developments employ relatively specific and homogeneous cells obtained by laser capture microdissection (LCM), seminal fluid, urine, and prostate-specific secreted proteins with the hope to better understand molecular changes in the cancer microenvironment by analyzing samples that are more proximal to where cancers arise. Here we review recent advancement in prostate cancer proteomics research with special emphasis on the type of samples and MS technologies used.

2. Biological specimens of prostate cancer research 2.1.

Blood

Human plasma is the primary clinical specimen representing the largest reservoir of human proteins available as biomarkers for disease diagnosis and therapeutic action [15]. Despite more than 10 orders of magnitude in concentration dynamic range between the most abundant protein (albumin) and the rarest proteins, recent advances in multidimensional proteomic techniques promise identification and quantification of more clinically measurable proteins. More than 100 proteomic studies using human plasma or serum with a focus on prostate cancer research have been reported to date in PubMed (http://www.ncbi.nlm.nih.gov/pubmed/). These efforts led to a list of potential protein biomarkers some of which have been partly characterized, but most remain to be validated with a statistically meaningful study cohort size. We will review select studies that carried out putative biomarker identification followed by a large-scale validation attempt. In 2008 McLerran and colleagues reported a 3-stage validation process of putative serum biomarkers identified via surface enhanced laser desorption ionization-time of flight (SELDI-TOF). This study was the result of a rigorous validation effort undertaken by the National Cancer Institute early detection research network (EDRN). The validation study was specifically targeted at evaluation of a previously published EDRN study of spectral peaks for the detection of prostate cancer [16–18]. In the first stage of the study a group of six separate institutions first demonstrated that SELDI-TOF mass spectrometry instruments and protocols could be standardized and used to classify previously studied prostate cancer patient and control sera using known spectral features. A decision algorithm was then developed by analyzing serum

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samples from patients with prostate cancer (n = 181), BPH (n = 143) and normal controls (n = 220). A validation blinded test set from a separate, geographically diverse set of serum samples from 42 prostate cancer patients and 42 controls without prostate cancer were also included to test if the same algorithm could discriminate between cancer and non-cancer. Unfortunately, the decision algorithm was found to be unsuccessful in differentiating prostate cancer, BPH, and control samples due to a source of sample bias. The study uncovered possible sources of bias such as variability in sample storage time and variations in collection protocols [19]. In a second stage, in a companion article, they derived a new decision algorithm for classification of prostate cancer from a new retrospective sample cohort of 400 specimens. This new cohort was selected to minimize possible confounders identified in the first stage study. However, this approach once again failed to separate patients with prostate cancer from biopsy-negative controls, nor could it differentiate prostate cancer with low (<7) and high (≥7) Gleason scores. They concluded that putative biomarkers found in their previous studies [16–18] by SELDI-TOF MS based protein expression profiling were not generalizable and could not advance to the planned third stage prospective study [20]. These studies represent the most comprehensive, carefully designed, and multi-institutional efforts currently reported. A message taken from these studies was that the SELDI-TOF MS approach, described in this study, had no diagnostic value and it is unlikely that any mass spectrometry-based approach using unprocessed serum would be able to differentiate between prostate cancer and control. Thus, the authors emphasized the importance of standardized experimental protocols and uniform sample preparation processes in future studies. More recently, a higher resolution SELDI-qTOF instrument was used to identify biomarkers in pre-radical retropubic prostatectomy serum to try to predict the probability of prostate cancer recurrence following radical prostatectomy. In this study population, preoperative PSA alone had no independent power to predict recurrence. However, a combined model using two protein biomarkers, complement component 4a (C4a) and protein C inhibitor, demonstrated a statistically significant value for predicting prostate cancer recurrence in men who underwent radical retropubic prostatectomy [21]. Another study reports identification of two putative prostate cancer serum markers, pigment epithelium-derived factor (PEDF) and zinc-α2-glycoprotein (ZAG). These authors used immunoaffinity depletion of high abundance serum proteins and 2D-DIGE LC-tandem mass spectrometry (MS/MS) from a small cohort (N = 12) of patients with different Gleason scores. The two putative biomarkers underwent extensive validation in serum and tissue samples from the original cohort and also from a larger independent cohort of patients (n = 50). Notably, this study found that PEDF is a more accurate predictor of early stage prostate cancer [22]. PEDF was also implicated in an independent study, which found PEDF to be significantly downregulated in serum from prostate cancer patients (n = 11). This observation suggests a valuable prognostic indicator of prostate cancer may be decreased expression of PEDF in high-grade prostatic intraepithelial neoplasia (HGPIN) [23].

2.2.

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Urine

Analyzing urine samples has become increasingly popular in recent years due to the non-invasive nature of sample collection and reduced complexity of the proteome over serum. Recently, urine has been extensively analyzed by proteomics revealing that more than 1500 proteins constitute the normal urine proteome [24]. A large-scale urinary proteomics study was recently carried out to distinguish among BPH, HGPIN, and prostate cancer from 407 patient samples by using MALDI-TOF. Using this strategy, 71.2% specificity and 67.4% sensitivity in discriminating prostate cancer vs. BPH were reported. Uromodulin and semenogelin I (SEMG1) isoform b preproprotein were proposed as two biomarkers that can help enable distinction of prostate cancer and BPH [25]. Although it is not specific for the prostate cancer, a highly specific biomarker pattern for urothelial carcinoma was identified, and validated in a large group of patients with different urological disorders. In this study, polypeptide mass signature patterns from urine samples of 46 patients with urothelial carcinoma and 33 healthy volunteers were compared to establish a model for predicting the presence of cancer. The model was refined further by use of 366 urine samples obtained from other healthy volunteers and patients with malignant and non-malignant genitourinary disease. The study identified a diagnostic urothelial carcinoma pattern of 22 polypeptide masses. Prediction models based on these polypeptides were able to correctly differentiate urine samples of urothelial carcinoma from healthy volunteers with a combined sensitivity and specificity of 100%. Correct identification of patients with urothelial carcinoma from those with other malignant and non-malignant genitourinary disease was greater than 86%. A prominent polypeptide from the diagnostic pattern for urothelial carcinoma was identified as fibrinopeptide A, a known biomarker of ovarian cancer and gastric cancer [26]. In a separate study by Theodorescu and colleagues used capillary electrophoresis– mass spectrometry (CE–MS) to identify single polypeptides and patterns of polypeptides specific for prostate cancer in human urine [27]. Urine from 21 patients with benign pathology (BP), 26 with prostate cancer, and 41 healthy controls was used to define potential biomarkers. Nine polypeptides were selected that enabled correct classification of the prostate cancer patients vs. BP and control with 92% sensitivity and 96% specificity. They later examined an additional 474 samples from patients with renal disease enrolled in other studies and found that 14 (3%) had polypeptide pattern indicating that they harbor clinically undetected prostate cancer. CE–MS uses an on-line separation method to distinguish analytes by their differences in electrophoretic mobilities and mass/charge (m/z). It is a complementary separation method to the much more common LC–MS method, which uses hydrophobicity and m/z that has extremely high separation capacity. From CE–MS one obtains information similar to LC–MS; e.g. electrophoretic mobility instead of retention time and molecular signature of ions present in the sample. Several ionization methods have been used for CE–MS including electrospray ionization, ion spray or pneumatically assisted electrospray ionization, and off-line coupling of CE with matrix-assisted laser desorption ionization [28]. The CE–MS has been mostly

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used in the field of biological and biochemical studies [29]. While separation capacity can be better than other techniques, sensitivity remains an issue and has prevented many from adopting the method for routine analysis. Another limitation is variation in electrophoretic mobility of analytes that can be solved by reference to an internal standard. Despite these limitations Theodorescu and colleagues used CE–MS to great advantage in their study [27] and other surely will do so in the future.

2.3.

Prostatic fluids

Prostatic fluids are another source of biologic fluid from which proteomics may be carried out. This is an appealing fluid because it is normally secreted by the prostate as a result of standard digital rectal prostate examination and may also be collected in voided urine post-examination. The two prostatic fluids that are currently used for clinical proteomics studies are seminal plasma and expressed-prostatic secretion (EPS) fluids. Prostatic fluids are expected to be rich in proteins originating directly from the prostatic acini with minimal urine contamination. These fluids are derived mainly from the prostate epithelium, testes, and seminal vesicles. An earlier study of voided urine after prostatic massage was carried out using 2-DE MALDI-TOF MS from six prostate cancer patients and six age-matched patients with BPH. This pilot study identified urinary calgranulin B/MRP-14 as a potential novel marker for prostate cancer [30]. In a later study, four proteins, PSA, ZAG, prostatic acid phosphatase (PAP), and progastricsin (PG) that are over expressed in the seminal plasma of prostate carcinoma patients were identified by 2-DE and MALDI-TOF MS from pooled (n = 10) seminal plasma. The proteins were later purified as part of a structurebased drug design study [31]. In another report a post-prostatic massage urine specimen bank consisting of 57 samples obtained from prostate cancer patients and 56 samples from BPH subjects was created and protein profiles were analyzed by SELDI-TOF MS. 49 mass peaks that were significantly upregulated and 23 peaks that were significantly down-regulated in prostate cancer samples compared with peaks obtained from BPH were identified. Among these, a peak at 10,788 m/z was significant throughout all four rounds of assays and proposed as a prostate cancer diagnostic marker. The authors discussed use of the 10,788 peak together with 72 other peaks for better diagnostic markers [32]. Most recently, a study described characterization and comparison of expression levels for two glycoproteins, PSA and PAP, in expressedprostatic secretion fluid to the serum PSA level. Although the study found no correlation between PSA and PAP levels in EPS and serum PSA using a small cohort, the results were promising enough for the authors to propose a larger study cohort to better define concentration ranges across disease states [33]. Prostasomes are membranous vesicles that are secreted by the prostate gland and incorporated into the seminal plasma. The physiological role of prostasomes involves improvement of sperm motility and protection against attacks from the female immune defense during the passage to the egg [34]. While prostasomes research has traditionally been conducted by electron microscopy observations, there is a large-scale

proteomics profiling of prostasomes. This work used LC-ESIMS/MS coupled with a gas phase fractionation (GPF) to identify the protein contents of prostasomes from five normal individuals. The study revealed the presence of 139 proteins that could be categorized as enzymes, transport/structural proteins, GTP proteins, chaperone proteins, signal transduction proteins, and of course some unannotated proteins [35]. More recently Poliakov and colleagues reported structural heterogeneity of seminal prostasomes suggesting their functional diversity. The study also proposed that prostasomes may originate from multiple organs in addition to the prostate. Proteomics was also carried out to identify 440 prostasome proteins of which 304 were assigned with two or more peptides, from 1-DE gel bands by LC–MS/MS [36].

2.4.

Prostatic tissue

Direct analysis of prostate cancer tissue is another rich source of potential biomarkers. Analyzing a specific homogeneous cell type from tissue sections could reflect molecular changes that take place during canner onset or progression. Specific cell types can be isolated either by cell sorting or by LCM. Both methods provide enrichment of homogeneous cells from bulk tissues. Cell sorting can be done by use of specific cell surface cluster designation (CD) markers and sorted cells can be used directly or following cell culture for further investigation [37,38]. LCM has been very popular since early 2000 for isolating and studying homogenous cancer cell types of different states. Accordingly, considerable effort has been made toward technological improvements in recent studies [39,40]. One of the early studies produced protein profiles of patient-matched, normal prostatic epithelial cells and invasive adenocarcinoma cells from one subject resolved by 2-DE. This study found a number of differentially expressed proteins including cytoskeletal protein, tropomyosin β, and the metabolic enzyme, thioredoxin peroxidase [41]. Multiple studies have used SELDI-MS to study LCM isolated cells. One such large study employed 1500 microdissected, patient-matched normal, prostatic intraepithelial neoplastic, frankly invasive, and endothelial cells and demonstrated clinically diagnostic patterns could be generated [42]. Another study isolated pure populations of cells from the prostate tissue and identified a protein with an average m/z of 24,782.56 ± 107.27 that was correlated with the presence of prostate carcinoma. The authors designated this protein Prostate cancer-24, and its expression was detected in 16 of 17 (94%) prostate carcinoma specimens, but not in paired normal cells. In addition, this protein was not expressed in any of the 12 BPH specimens that were assayed [43]. A similar study was done on isolated basal cells, which produce signals for growth and differentiation of normal secretory epithelial cells in the human prostate. Loss of basal cell function may have a permissive role in progression of prostate intraepithelial neoplasia into invasive carcinoma. They identified several protein peaks selectively expressed in microdissected basal cells [40]. In spite of the prior mentioned limitations, SELDI-MS has been particularly popular in clinical research because it does not require prior sample fractionation of complex biological

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mixtures, has good reproducibility, and provides highthroughput analysis. Despite its popularity, the biggest challenge of SELDI-MS has been a limitation in protein identification from detected protein diagnostic patterns. The failure has been blamed on the SELDI technique, but in large part rests with the coupling of SELDI to inappropriate MS platforms incapable of making protein identifications from the generated precursor ion scans. Coupling to higher resolution QTOF platforms should make SELDI much more effective in clinical investigations.

2.5.

Secreted proteins

Cell–cell communication and signaling is a major component of normal prostate development. These signaling factors are most likely extracellular proteins, such as secreted/shed or membrane-associated proteins. Secreted proteins from adjacent disease-relevant cancer cells can be targeted for biomarker discovery. In a recent study, conditioned media from three human prostate cancer cell lines of different origin—PC3 (bone metastasis), LNCaP (lymph node metastasis), and 22Rv1 (localized to prostate)—were characterize by LC–MS/MS to identify secreted proteins that could serve as novel prostate cancer biomarkers. follistatin (FST), chemokine ligand 16 (CXCL16), pentraxin 3 (PTX3), and spondin 2 (SPON2) were suggested as putative biomarkers and subject to further validation using 42 serum samples from patients with or without prostate cancer. Each of these three candidate biomarkers showed a significant difference in up-regulation in patients with prostate cancer. A positive correlation with increasing PSA levels and candidate levels was also observed which suggested an association with prostate cancer progression [44]. In an earlier study, quantitative proteomics that incorporated the isotope coded affinity tag (ICAT) reagents and LC–MS/MS was carried out to identify secreted and cell surface proteins from neoplastic prostate epithelium. LNCaP cells grown under androgen-stimulated and -starved conditions were compared and a total of 517 unique quantifiable proteins were identified. Twenty-seven percent of these were assigned as secreted proteins. Among identified proteins, 52 proteins were found to be regulated by androgen suggesting that androgen-mediated release of proteins may occur through the activation of proteolytic enzymes [45].

2.5.1.

Glycoproteins

Protein glycosylation is a common post-translational modification (PTM) of proteins destined for the extracellular compartment. Carbohydrate moieties are linked either to serine/ threonine (O-linked glycosylation) or to asparagine (N-linked glycosylation) [46]. In particular, N-linked glycosylation is prevalent in proteins exported to the extracellular environment including the external side of plasma membrane proteins, secreted proteins, and proteins contained in body fluids [47]. Notably, many current clinical biomarkers and therapeutic targets are glycoproteins—Her2/neu (breast cancer) [48], PSA (prostate cancer), and CA125 (ovarian cancer) [49]. Alteration in glycosylation and the carbohydrate structure of proteins have been linked to cancer and other disease states [50,51]. Not surprisingly a number of studies have been conducted to

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identify secreted glycoproteins associated with prostate cancer pathogenesis. In a study by Liu and colleagues patient-matched cancer and non-cancer tissues were digested by collagenase and the cellfree supernatant was used to generate mass fingerprints of secreted proteins. A ProteinChip Array was used to generate SELDI-MS patterns from 43 primary prostate tumors, including 26 with matched non-cancer specimens revealing cancers of similar TNM (Tumor, Node, Metastasis) stages were more likely to have similar profiles. The authors then used quantitative glycopeptide capture of one specimen with MS/MS to identify a down regulation of tissue metalloproteinase inhibitor-1 in cancer [52]. In a separate study homogeneous prostate and bladder stromal cells were isolated from tissues and cell-free conditioned media following cell culture was used to identify secreted glycoproteins involved in organ-specific development. A label-free quantitative analysis of this LC–MS/MS data identified a number of potentially prostate-specific stromal signaling factors, cathepsin L, follistatin-related protein, neuroendocrine convertase, and tumor necrosis factor receptor. Their role in stromal/epithelial interaction and functional prostate development has been discussed [38]. The carbohydrate binding specificities of different lectins were used by Drake and colleagues to capture glycoproteins from pooled serum of BPH and prostate cancer sera (n = 5) followed by MS/MS analysis [53]. Their study identified a variant isoform of α-fetoprotein (AFP) in prostate cancer sera. This paper described alternative experimental strategies for integrating lectin-based methods into clinical expression profiling to achieve disease group-specific glycoproteins.

3.

Other considerations

3.1.

Quantification

Along with protein identification, protein quantification is another main component of the biomarker discovery process to determine changes in protein expression between disease and control state. Popular methods of proteomic quantification used in prostate cancer research are: 1) a combination of gel electrophoresis and mass spectrometry. In this approach 1-DE or 2-DE are used to distinguish differentially expressed proteins based on staining intensity of the gels and proteins identified by MS/MS. Protein identification may also be carried out via simple MALDI-TOF MS peptide mass fingerprinting; 2) stable isotope labeling of proteins introduces pairs of chemically, metabolically, or enzymatically identical “mass” tags that may be separated by MS and then corresponding proteins identified by MS/MS of the labeled peptides. Popular methods for introducing the stable isotope mass tags are isotope coded affinity tags (ICAT) [54], isobaric stable isotope labeling (iTRAQ) [55], and stable isotope labeling with amino acids in cell culture (SILAC) [56] (Fig. 1); 3) label-free quantification has recently become very popular for ease of use. In these studies protein quantity is inferred from the number of peptide spectra produced for all peptides from a given protein; and 4) More recently, it has been recognized that mass spectrometry can be utilized as quantification and validation tools using multiple reaction monitoring (MRM) with the advantage

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Fig. 1 – Stable isotope labeling for protein quantification currently used in prostate cancer research. A) SILAC (metabolic labeling). Cells grow in isotopically enriched culture media contain amino acids (15N or 13C) and metabolically incorporated during cell culture. Labeled cells are combined, purified and proteolyzed prior to MS and MS/MS to determine relative protein abundance and for protein identification. It is applicable only to cultured cells and cannot be used for tissues and other body fluids. B) ICAT (chemical labeling). Heavy and light affinity tags are labeled to cysteine residues of protein mixture. Labeled proteins are combined, proteolyzed, affinity separated, and relative protein abundance is determined. C) iTRAQ (chemical labeling). Labeling occurs at the peptide level after protein digestion. N-terminal and lysine residues of all peptides are labeled though acylation with reactive chemical compounds. It can multiplex up to 8 different samples. (4 labels shown here).

of allowing selectively monitoring a number of protein candidates in a data-directed mode [57]. Several studies using the 2-DE MS approach have already been reviewed in earlier sections [22,23,30,31,41]. However, we note that while quite useful for resolving protein isoforms— perhaps as many as 1000 may be resolved on a single 2-DE gel— this presents a problem that the same protein may be identified repeatedly. Unfortunately, it is not always the case that the PTMs that distinguish the isoforms and result in multiple “spots” for a single gene will be identified. There is thus a natural bias toward observation of the most abundant protein isoforms by 2DE-MS methods that may be limited to some extent by affinity removal prior to analysis. Finally, the time-intensive process of creating and analyzing 2-DE gels make them unsuitable for many laboratories wishing to conduct more routine high-throughput clinical analyses. In a study to understand the biochemical means of lycopene chemoprevention, protein expression patterns were compared between LNCaP and androgen-depleted LNCaP cells treated with lycopene. In this study 12 ICAT experiments were carried out to compare differential expression between placebo-treated vs. lycopene-treated LNCaP cells from three cellular fractions (membrane, soluble and nucleus) by µLC-ESI-MS/MS. One notable outcome was that a possible mechanism of lycopene chemoprevention is achieved by the stimulation of detoxification enzymes associated with the antioxidant response element [58]. Another study reported lower expression of actinin-4, a protein associated with cancer invasion and metastasis, by ICAT-LC–MS/MS in prostate cancer cells. Restoration of actinin-4 expression inhibited

cell proliferation by prostate cancer cell line 22RV1. Thus the decreased expression of actinin-4 in prostate cancer cells has been implicated in causing aberrations in the intracellular trafficking of various cell surface molecules and possibly contributing to carcinogenesis [59]. Yet other studies showed global proteome changes in LNCaP cells in response to the presence/absence of androgens by ICAT labeling and LC–MS/MS. Changes in the levels of proteins in response to androgens were detected including previously known androgen-regulated proteins [45,60]. A 2008 report used iTRAQ to study differences in protein expression between the poorly metastatic LNCaP cell line and its highly metastatic variant LNCaP-LN3 cell lines. This study found ten up-regulated and four down-regulated proteins in LNCaP-LN3 cells by standard LC–MS/MS analysis. Among these, tumor rejection antigen (gp96) was assessed by immunohistochemistry using prostate tissues from benign (n = 95), malignant (n = 66), and metastatic cases (n = 3) and found a statistically significant overexpression of gp96 expression in malignant [61]. Tissue specimens from 10 patients with benign BPH and 10 with prostate cancer were analyzed by iTRAQ and a hybrid quadrupole time-of-flight system (QqTOF). The study resulted in the reproducible identification of 30 up-regulated and another 35 down-regulated proteins between the BPH and prostate cancer specimens. A couple of up-regulated proteins in the prostate cancer specimens, α-methylacyl CoA racemase (AMACR) and prostate-specific membrane antigen (PSMA), were confirmed by immunohistochemical analysis and proposed as prostate cancer biomarkers [62]. Similar in concept to iTRAQ reagents are reagents known as tandem mass

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tags (TMT), which use isobaric chemical tags that enable concurrent identification and quantification of proteins in different samples by MS/MS [63]. The tag is composed of an amine-reactive NHS-ester group, a spacer arm and an MS/MS reporter with identical structure that covalently attach to the free amino termini of lysine residues of peptides. Currently up to a six protein samples may be combined and profiled by TMTs simultaneously, but this has not yet been done in the prostate cancer research. Another stable isotope labeling strategy, SILAC [56], makes use of in vivo incorporation of (12)C- and (13)C-labeled amino acids added to the growth media of separately cultured cell lines, resulted in either "light" or "heavy" labeled proteins, respectively. Using this method, Everley and co-workers compared the expression levels for more than 440 proteins in the microsomal fraction of benign and advanced metastatic prostate cell lines and found 60 proteins elevated in the highly metastatic cells. However further experiments will be required to narrow the list for predictive biomarkers [64]. Another popular MS method used for proteomic expression profiling, so-called label-free quantification because it eschews use of stable isotopes, has the advantage of allowing data on each sample to be acquired independently from all other samples after which analysis may be carried in silico between any pair of samples. These label-free methods consist of two basic types, which use either precursor ion data (i.e., MS survey scan) or tandem mass spectral data (i.e., MS/MS fragment ion scans) to estimate changes in relative abundance of proteins between samples. The MS1 based methods associate changes in relative protein abundance from direct measurement of peptide ion current areas [65–67]. The MS2 based methods estimate differences in relative protein expression by either accounting extent of protein sequence coverage or the number of MS/MS spectra generated, which is commonly referred to as spectral counting [68–70]. All these studies demonstrate the feasibility of label-free methods to reflect relative changes in protein abundance between samples. To date though there are only a few label-free quantification studies published in prostate cancer research. In a recent study, organ-specific secreted glycoproteins were identified by comparative analysis between bladder and prostate stromal cells using a calculated peptide ion current area (PICA) method [38]. Xu and colleagues used conditioned medium from a human metastatic prostate cancer cell line, PC-3ML, in which matrix metalloproteinase-9 (MMP-9) had been down-regulated by RNA interference and compared with that from the parental cells by measuring peptide ion peak intensities observed in low collision energy mode to identify new substrates to further understand how MMP-9 might contribute to tumor metastasis. Six of the identified proteins (amyloid-β precursor protein, collagen VI, leukemia inhibitory factor, neuropilin-1, prostate cancer cell-derived growth factor (PCDGF), and protease nexin-1 (PN-1)) were further tested in the conditioned media by immunoblotting. A strong correlation was found between label-free quantification results and those from immunoblotting [71]. More recently, the combination of a specific parent with unique fragment ions has been used to selectively monitor and quantify proteins via selected peptides. Targeted MS

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analysis using this MRM approach enhances the lower detection limit for a peptide, thus providing overall higher dynamic range of measurements [57]. With modern triple quadrupole mass spectrometers, a large number of precursorproduct transitions (up to 100) can be monitored during a single LC–MS/MS run making the method economically competitive with the simpler technology of Western blotting. To date many small molecule analytes such as drug metabolites, hormones, protein degradation products, and pesticides have been routinely measured using this approach, but the MRM approach has not yet been widely applied in the prostate cancer research. A couple of method development studies so far reported the use of MRM to detect PSA in low ng/ml range serum [72] and to monitor testosterone concentrations from human plasma [73].

3.2.

Metabolomics

Metabolomics is one of the latest biomarker discovery approaches to identify and separate metabolites using technology similar to proteomics. These small molecules are often the final products of biochemical activity in molecular pathways and so may be used within a perturbed biological system to distinguish normal from cancer. Metabolomics could potentially provide opportunity to develop diagnostic and prognostic evaluation to stratify patients to allow more suitable treatment [74]. One notable recent study reported the global metabolomic alterations that distinguish benign prostate, localized and metastatic prostate cancer. In this work Sreekumar and colleagues identified 1126 metabolites from various prostate cancer clinical samples including tissue, urine and plasma and demonstrated metabolomic profiles of different stages of cancer [75]. Most notably they reported sarcosine as a promising indicator of prostate cancer progression to metastasis. The MS based observation of sarcosine has been also confirmed by a series of in vivo experiments confirming a role of sarcosine in cancer cell invasion and aggressiveness.

3.3.

Global systems approach

Advances in mass spectrometric instrumentation and need for new and improved biomarker discovery have resulted in large-scale proteome data production, which requires innovative approaches to process, analyze, and visualize highthroughput data. A special need remains to integrate quantitative expression measurements into molecular, biochemical networks and to annotate putative or unknown protein functions in a large biological system. A new approach called systems biology has accelerated the integration of large and often disparate data sets for biomarker discovery [76]. Instead of analyzing individual components, an organism is viewed as an integrated and interacting network of genes, proteins and biochemical reactions of the small molecules from which constitute various forms of metabolomics. The “systems” approach to study of biological samples can produce a set of proteins (as opposed to single marker) involved in diseasespecific pathways, which can in turn provide a better understanding of prostate cancer pathogenesis. At the moment few studies have adopted this approach, which is due in part to wide ranges of large-scale data that must be generated

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and their subsequent integration to provide a global picture of disease-specific pathways and networks. One such recent study utilized six cancer cell lines from three different tissues, breast, bladder, and prostate, to study the secretome of these tissues. The study relied on mapping identified proteins into pathways via software that attempts to discover cellular signaling pathways. The results revealed that several secretome proteins might be interconnected via intracellular canonical pathways suggesting the use of relatively simple (from the perspective of proteome complexity) secretomes to discover pathway-based biomarkers. Although not in prostate cancer, when this strategy was applied to breast cancer, it appeared that the insulin growth factor (IGF) signaling and the plasminogen activating system may be differentially regulated in invasive breast cancer [77]. In order to better understand androgen-regulated proteins and their encoded functions, androgen-starved and androgen-treated LNCaP cells were analyzed by semi-quantitative multidimensional protein identification technology (MudPIT) ESI-MS/MS and quantitative iTRAQ MALDI-TOF MS/MS platforms. An enrichment-based pathway mapping of the androgen-regulated proteomic data sets revealed an impaired regulation of aminoacyl tRNA synthetases, indicating an increase in protein biosynthesis—a hallmark during prostate cancer progression [78]. Another study that used a similar systems approach was the aforementioned prostate and bladder stromal cell secretome study. Large quantitative datasets were used to identify active pathways involved in stromal/epithelial columniation and postulate that prostate stromal/epithelial signaling may be accomplished through activation of the ECM–receptor interaction, complement and coagulation cascades, focal adhesion and cell adhesion pathways [38].

4.

Challenges and perspectives

Many potentially promising biomarker candidates have been identified in prostate cancer research with the help of proteomics in recent years. However, most of these studies have been limited to the “discovery” stage with putative biomarkers still awaiting validation (Table 1) or they have already failed confirmation as true markers when subjected to larger follow-up validation studies. Additionally, the pathways and networks thought to be active in prostate cancer remain to be verified in a clinical setting [38,58,77,78]. These results demonstrate the ease of putative biomarker discovery coupled to difficulties in validation in cancer research. Some of these failures to validate or make sense of the data may simply be related to the highly heterogeneous nature of cancer and specimens from a genetically diverse population. However, as McLerran et al. and others have pointed out, experimental design, standardized sample collection, storage, and sample process protocol are also critical for successful biomarker discovery. Data collection and analysis are also big challenges since there is neither a standardized pipeline for proteomic data collection nor statistical analysis of data sets small or large. While proteomic data generation and analysis methods could be standardized for clinical research, this could only be accomplished by a coordinated effort from funding agencies and research institutions.

Table 1 – Putative prostate cancer biomarkers discussed in this review found from various biological specimens. All the markers are in Phase I, discovery and early refinement stage and require characterization, and retrospective studies to be validated as clinical biomarker. Note biomarker candidates identified by use of one study cohort are not listed. Serum/ plasma

Urine

PSA

UMOD

C4a PCI

FPA SEMG1-isoform b preproprotein

PEDF ZAG FST CXCL16 PTX3 SPON2 AFP

Seminal plasma Calgranulin B / MRP14 PSA ZAG

Prostatic tissue Gp96 AMACR PSMA

PAP PG

For now the community continues its disparate discovery and validation studies in the hope that global disease-specific protein biomarkers may be uncovered as well as stage-specific disease markers. The cancer proteome of blood, urine, tissue, and secretomes are complex biological systems involving not only a single mechanism to initiate or sustain cancer growth, but rather orchestrated by multiple signals from various cell types, the surrounding tissue microenvironment and the genetics/environment of the host, all of which impinge further complexity. In line with this observation, current studies indicate that it is unlikely that a single biomarker can achieve multi-stage cancer diagnosis or track treatment efficacy. Rather, identifying and utilizing combinations of biomarkers, especially ones that can outperform/complement PSA, will eventually allow accurate diagnosis and management of prostate cancer. Mass spectrometry has proven to be the single most valuable tool for proteomics. This is likely to continue until other technologies such as protein arrays become more robust and inexpensive at which point they may steal some of the quantitative workload [79,80]. However, until that point advances in mass spectrometric instrumentation and their proteomic applications will continue to grow as instruments with higher mass accuracy, resolution, dynamic range, and more accurate quantification capabilities are deployed. In the past decade, shotgun proteomics has been one of the mainstays of proteomics alongside 2-DE methods. There are many variations on the shotgun proteomic theme, but most involve protease digestion of a complex protein sample to make peptides that are in turn analyzed by MS/MS to identify the proteins from which they were derived (Fig. 2A). This peptide based approach circumvents the fundamental decrease in fragmentation efficiency that accompanies increasing molecular weight of proteins, which is one reason that so-called bottom-up approaches have proliferated throughout the field of proteomics [81,82]. One important limitation of standard bottom-up shotgun methods is due to requisite proteolysis of proteins to peptides only some of which are detected in the mass spectrometer while many others never are. This loss of

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Fig. 2 – Bottom-up or top-down proteomics approach. A) Bottom-up proteomics generally involve protease digestion of a complex protein sample to make peptide that can be analyzed by MS for protein identification following optional peptide fractionation to reduce complexity. B) In the top-down approach, intact protein of interests is isolated from a 1-DE or 2-DE gel. A complex protein mixture is optionally fractionated by enrichment or depletion methods. Intact proteins are subsequently applied to MS-based analyses without proteolysis. Advantage of top-down proteomic is to increase ability of detecting protein isoforms, PTMs, and genetic insertion/deletion events. The two approaches can be used to complement each other. C) PAcIFIC is a combination of methods designed to detect proteins at an order of magnitude better than the dynamic range of other proteomic methods. Protein samples can be enriched or depleted in the beginning but omit any further fractionations. It acquires MS/MS at every 15 m/z range until the considered precursor ion m/z range of 400–1400 is covered. PAcIFIC can be used for both top-down and bottom-up applications.

protein sequence information means that bottom-up shotgun proteomic experiments typically produce very low protein sequence coverage, which precludes mapping all PTMs as well as detection of genetic insertion/deletion events. Top-down proteomic methods that seek to circumvent this deficiency by analyzing whole proteins instead of peptides may provide some answers if they can be better coupled to the discovery process [83,84] (Fig. 2B). One significant advantage then of a perfected top-down proteomic pipeline would be mass spectrometric detection of protein isoforms much as in the case for 2-DE, but with the additional caveat of also characterizing the isoforms simultaneous to detection. Thus, there has been a great deal of interest in top-down proteomics from laboratories that specialize in bottom-up proteomics at least as a complimentary tool to characterize proteomes.

To conclude, one of the fundamental problems of most shotgun proteomic methods has been the coupling to datadependent methods for selecting peptides ions during LC–MS/ MS analysis. These automated routines are fantastic aids to making shotgun proteomics high-throughput, but their random component coupled to sample complexity that is well beyond the peak capacity of any LC–MS/MS method results in under-sampling of the proteins present no matter how many times the sample is analyzed. Recently, we described a dataindependent approach to complex mixture analysis that is systematic and thus more thorough than data-dependent methods [85]. This new bottom-up proteomic approach demonstrated that better sequence coverage and dynamic range detection could be easily achieved with current instrumentation by acquisition of MS/MS spectra at each m/z

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“channel” across the standard range of 400–1400 u. The MS/MSonly approach to peptide acquisition independent from ion count (PAcIFIC) even detects peptides hidden by chemical and electronic noise in the baseline none of which will never be detected by data-dependent methods (Fig. 2C). For example, the results of the PAcIFIC method applied to analysis of human plasma depleted of the top-7 most abundant proteins identified roughly three-times more proteins (i.e. 746 at FPR ≤ 0.5%) than comparable data-dependent methods and achieved better dynamic range (i.e. eight orders of magnitude at FPR ≤ 0.5%), without any fractionation other than immuno-depletion. Thus we are confident that technological improvements will continue to provide better proteome coverage of all human samples as well as prostate cancer samples. Finally, while human genome sequencing has provided the fuel for large-scale, high-throughput proteomics research and resulted in, in part, the emergence of the systems biology, new technologies will continue to provide leverage against the cancer challenge. New and innovative genome sequencing methods will facilitate rapid production of individual genomic blue print and thus individual proteomes [86]. The patientspecific system biology data will usher in a new era of personalized medicine that should, if managed properly, provide individualized diagnostics and therapies [87]. Similarly, prostate cancer biomarker discovery will soon take on a multidimensional and personalized aspect that integrates a person's own genome with their own transcriptomic, proteomic, and metabolomic data to provide predictive data coupled with a personalized cancer management therapy for clinicians to better manage patient outcomes.

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