Mass Spectrometry-based Expression Profiling of Clinical Prostate Cancer

Tissue-based quantitative proteomics to screen and identify the potential biomarkers for early recurrence/metastasis of esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is the eighth cause of cancer-related deaths worldwide. To screen potential biomarkers associated with early recurrence/metastasis (R/M) of ESCC patients after radical resection, ESCC patients were analyzed by a comparative proteomics analysis using iTRAQ with RPLC-MS to screen differential proteins among R/M groups and adjacent normal tissues. The proteins were identified by qRT-PCR, Western blotting, and tissue microarray. The protein and mRNA expression difference of PHB2 between tumor tissues of ESCC patients and adjacent normal tissues, ESCC patients with and without metastasis, four ESCC cell lines and normal esophageal epithelial cells were inspected using immunohistochemical staining, qRT-PCR, and Western blotting. The EC109 and TE1 cells were used to establish PHB2 knockdown cell models, and their cell proliferation and invasion ability were determined by cell counting method, Transwell® assay. Thirteen proteins were selected by cutoff value of 0.67 fold for underexpression and 1.5-fold for overexpression. Seven proteins were confirmed to be associated with R/M among the 13 proteins. The potential biomarker PHB2 for early recurrence/metastasis of ESCC was identified. PHB2 expression was related to the OS of ESCC patients (P = 0.032) and had high levels in the tumor tissues and human cell lines of ESCC (P = 0.0002). Also, the high PHB2 expression promoted the metastasis of ESCC (P = 0.0075), suggesting high PHB2 expression was a potential prognostic biomarker. Experiments showed that PHB2 could significantly promote the proliferation and cell invasion ability of human ESCC cell lines and the knockdown of PHB2 suppressed the phosphorylation level of AKT, as well as the expression of MMP9 and RAC1. PHB2 could predict the early metastasis of ESCC patients.

Label-free electrochemical aptasensing of the human prostate-specific antigen using gold nanospears

Gold nanospears were electrodeposited with the assistance of arginine as a soft template and precise selection of experimental parameters. The nanospears were then employed as a transducer to immobilize an aptamer of prostate-specific antigen (PSA) and fabrication of a label-free electrochemical aptasensor. The aptasensor was employed for the detection of PSA with a linear concentration range of 0.125-200ngmL(-1) and a limit of detection of 50pgmL(-1). The aptasensor was successfully applied to detect PSA in blood serum samples of healthy and patient persons.

A label-free electrochemiluminescence immunosensor based on EuPO4 nanowire for the ultrasensitive detection of Prostate specific antigen

EuPO4 nanowire, which exhibited strong and stable cathodic electrochemiluminescence (ECL) activity, was used for the first time to fabricate an immunosensor for the detection of prostate specific antigen (PSA). EuPO4 has some inherent excellent properties such as long luminescence lifetime, narrow emission band, high quantum yield and low toxicity. Based on these properties, a novel label-free ECL immunosensor was developed using EuPO4 as a sensing matrix. Chitosan solution was used to disperse EuPO4 nanowires and the amino groups on chitosan enabled the covalent attachment of capture antibodies. After the modification of the electrode surface with EuPO4 nanowires, anti-PSA was then immobilized on them, forming a label-free immunosensing interface. The specific binding of PSA on the electrode inhibited the ECL reaction of EuPO4 nanowires with the coreactant due to the steric hindrance effect (Deng et al., 2013). Under the optimum conditions, a good linear relationship between ECL intensity and the logarithm of PSA concentration was obtained in the range of 0.0005-80 ng/mL with a detection limit of 177.33 fg/mL. The proposed ECL immunosensor showed good stability, acceptable selectivity and reproducibility.

Supervised multi-view canonical correlation analysis (sMVCCA): integrating histologic and proteomic features for predicting recurrent prostate cancer

In this work, we present a new methodology to facilitate prediction of recurrent prostate cancer (CaP) following radical prostatectomy (RP) via the integration of quantitative image features and protein expression in the excised prostate. Creating a fused predictor from high-dimensional data streams is challenging because the classifier must 1) account for the "curse of dimensionality" problem, which hinders classifier performance when the number of features exceeds the number of patient studies and 2) balance potential mismatches in the number of features across different channels to avoid classifier bias towards channels with more features. Our new data integration methodology, supervised Multi-view Canonical Correlation Analysis (sMVCCA), aims to integrate infinite views of highdimensional data to provide more amenable data representations for disease classification. Additionally, we demonstrate sMVCCA using Spearman's rank correlation which, unlike Pearson's correlation, can account for nonlinear correlations and outliers. Forty CaP patients with pathological Gleason scores 6-8 were considered for this study. 21 of these men revealed biochemical recurrence (BCR) following RP, while 19 did not. For each patient, 189 quantitative histomorphometric attributes and 650 protein expression levels were extracted from the primary tumor nodule. The fused histomorphometric/proteomic representation via sMVCCA combined with a random forest classifier predicted BCR with a mean AUC of 0.74 and a maximum AUC of 0.9286. We found sMVCCA to perform statistically significantly (p < 0.05) better than comparative state-of-the-art data fusion strategies for predicting BCR. Furthermore, Kaplan-Meier analysis demonstrated improved BCR-free survival prediction for the sMVCCA-fused classifier as compared to histology or proteomic features alone.

Label-free electrochemical detection of prostate-specific antigen based on nucleic acid aptamer

We report a label-free aptasensor to make direct detection of prostate specific antigen (PSA, a biomarker of prostate cancer) using a quinone-containing conducting copolymer acting as redox transducer and grafting matrix for immobilization of the short aptamer strands. It is shown that capture of PSA generates a current decrease (signal-off) measured by Square Wave Voltammetry. This current decrease is specific for PSA above a limit of quantification in the ng mL(-1) range. The change in current is used to determine the PSA-aptamer dissociation constant K(D), of ca. 2.6 nM. To consolidate the proof of concept, a heterogeneous competitive exchange with a complementary DNA strand which breaks PSA-aptamer interactions is studied. This double-check followed by a current increase provides full assurance of a perfectly specific recognition.

Application of two-dimensional gel-based mass spectrometry to functionally dissect resistance to targeted cancer therapy

PURPOSE

The majority of gastric cancers are diagnosed at advanced stages, characterized by robust therapy resistance. The oncoprotein hypoxia-inducible factor 1 (HIF-1) is associated with therapy resistance, partly via activation of the DNA damage response. We have noted a robust ability of gastric cancer cells to functionally compensate the loss of HIF-1 in vitro. The purpose of this study was to identify molecular pathways that underlie this compensation.

EXPERIMENTAL DESIGN

We performed 2DE to compare the nuclear proteome of wild-type and HIF-1-deficient gastric cancer cells. Differently expressed protein spots were identified via MS). After bioinformatic evaluation, functional validation of selected identified pathways was performed.

RESULTS

2DE displayed a total of 2523 protein spots, from which 87 were identified as regulated by HIF-1. Seventy of the identified spots were different proteins and 17 were isoforms. Bioinformatic analyses revealed that a significant amount of the identified proteins were related to cellular survival pathways. Specifically, members of the proteasome pathway were found upregulated upon loss of HIF-1. Combined inhibition of HIF-1 and the proteasome inflicted significant DNA damage, supporting the hypothesis that the proteasome is of functional importance to compensate the loss of HIF-1.

CONCLUSIONS AND CLINICAL RELEVANCE

Our data show robust and functional changes of the nuclear proteome upon inactivation of the HIF-1 oncoprotein in gastric cancer cells. We propose that 2DE-MS represents a useful tool to functionally dissect resistance mechanisms to targeted therapy and to identify novel targets for antiproliferative combination therapy.

Why have so few proteomic biomarkers "survived" validation? (Sample size and independent validation considerations)

Proteomic biomarker discovery has led to the identification of numerous potential candidates for disease diagnosis, prognosis, and prediction of response to therapy. However, very few of these identified candidate biomarkers reach clinical validation and go on to be routinely used in clinical practice. One particular issue with biomarker discovery is the identification of significantly changing proteins in the initial discovery experiment that do not validate when subsequently tested on separate patient sample cohorts. Here, we seek to highlight some of the statistical challenges surrounding the analysis of LC-MS proteomic data for biomarker candidate discovery. We show that common statistical algorithms run on data with low sample sizes can overfit and yield misleading misclassification rates and AUC values. A common solution to this problem is to prefilter variables (via, e.g. ANOVA and or use of correction methods such as Bonferonni or false discovery rate) to give a smaller dataset and reduce the size of the apparent statistical challenge. However, we show that this exacerbates the problem yielding even higher performance metrics while reducing the predictive accuracy of the biomarker panel. To illustrate some of these limitations, we have run simulation analyses with known biomarkers. For our chosen algorithm (random forests), we show that the above problems are substantially reduced if a sufficient number of samples are analyzed and the data are not prefiltered. Our view is that LC-MS proteomic biomarker discovery data should be analyzed without prefiltering and that increasing the sample size in biomarker discovery experiments should be a very high priority.

Potential Urinary Protein Biomarker Candidates for the Accurate Detection of Prostate Cancer among Benign Prostatic Hyperplasia Patients

Globally, Prostate cancer (PCa) is the most frequently occurring non-cutaneous cancer, and is the second highest cause of cancer mortality in men. Serum prostate specific antigen (PSA) has been the standard in PCa screening since its approval by the American Food & Drug Administration (FDA) in 1994. Currently, PSA is used as an indicator for PCa - patients with a serum PSA level above 4ng/mL will often undergo prostate biopsy to confirm cancer. Unfortunately fewer than ~30% of these men will biopsy positive for cancer, meaning that the majority of men undergo invasive biopsy with little benefit. Despite PSA's notoriously poor specificity (33%), there is still a significant lack of credible alternatives. Therefore an ideal biomarker that can specifically detect PCa at an early stage is urgently required. The aim of this study was to investigate the potential of using deregulation of urinary proteins in order to detect Prostate Cancer (PCa) among Benign Prostatic Hyperplasia (BPH). To identify the protein signatures specific for PCa, protein expression profiling of 8 PCa patients, 12 BPH patients and 10 healthy males was carried out using LC-MS/MS. This was followed by validating relative expression levels of proteins present in urine among all the patients using quantitative real time-PCR. This was followed by validating relative expression levels of proteins present in urine among all the patients using quantitative real time-PCR. This approach revealed that significant the down-regulation of Fibronectin and TP53INP2 was a characteristic event among PCa patients. Fibronectin mRNA down-regulation, was identified as offering improved specificity (50%) over PSA, albeit with a slightly lower although still acceptable sensitivity (75%) for detecting PCa. As for TP53INP2 on the other hand, its down-regulation was moderately sensitive (75%), identifying many patients with PCa, but was entirely non-specific (7%), designating many of the benign samples as malignant and being unable to accurately identify more than one negative.

MALDI MS profiling of post-DRE urine samples highlights the potential of β-microseminoprotein as a marker for prostatic diseases

BACKGROUND

To use spectra acquired by matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS) from pre- and post-digital rectal examination (DRE) urine samples to search for discriminating peaks that can adequately distinguish between benign and malignant prostate conditions, and identify the peaks' underlying biomolecules.

METHODS

Twenty-five participants with prostate cancer (PCa) and 27 participants with a variety of benign prostatic conditions as confirmed by a 10-core tissue biopsy were included. Pre- and post-DRE urine samples were prepared for MALDI MS profiling using an automated clean-up procedure. Following mass spectra collection and processing, peak mass and intensity were extracted and subjected to statistical analysis to identify peaks capable of distinguishing between benign and cancer. Logistic regression was used to combine markers to create a sensitive and specific test.

RESULTS

A peak at m/z 10,760 was identified as β-microseminoprotein (β-MSMB) and found to be statistically lower in urine from PCa participants using the peak's average areas. By combining serum prostate-specific antigen (PSA) levels with MALDI MS-measured β-MSMB levels, optimum threshold values obtained from Receiver Operator characteristics curves gave an increased sensitivity of 96% at a specificity of 26%.

CONCLUSIONS

These results demonstrate that with a simple sample clean-up followed by MALDI MS profiling, significant differences of MSMB abundance were found in post-DRE urine samples. In combination with PSA serum levels, obtained from a classic clinical assay led to high classification accuracy for PCa in the studied sample set. Our results need to be validated in a larger multicenter prospective randomized clinical trial.

Predicting response to hormonal therapy and survival in men with hormone sensitive metastatic prostate cancer

Androgen deprivation is the cornerstone of the management of metastatic prostate cancer. Despite several decades of clinical experience with this therapy there are no standard predictive biomarkers for response. Although several candidate genetic, hormonal, inflammatory, biochemical, metabolic biomarkers have been suggested as potential predictors of response and outcome, none has been prospectively validated nor has proven clinical utility to date. There is significant heterogeneity in the depth and duration of hormonal response and in the natural history of advanced disease; therefore to better optimize/individualize therapy and for future development, identification of biomarkers is critical. This review summarizes the current data on the role of several candidate biomarkers that have been evaluated in the advanced/metastatic disease setting.

Application of proteomic technologies for prostate cancer detection, prognosis, and tailored therapy

Prostate cancer affects 3 in 10 men over the age of 50 years, and, unfortunately, the clinical course of the disease is poorly predicted. At present, there is no means that can distinguish indolent from aggressive/metastatic tumors. Thus, a personalized clinical approach could be helpful in diagnosing clinically relevant disease and guiding appropriate patient therapy. Individualized medicine requires a deep knowledge of the molecular mechanisms underpinning prostate cancer carcinogenesis. Proteomics may be the most powerful way to uncover biomarkers of detection, prognosis, and prediction, as proteins do the work of the cell and represent the majority of the diagnostic markers and drug targets today. Proteomic technologies are rapidly advancing beyond the two-dimensional gel separation techniques of the past to new types of mass spectrometry and protein microarray analyses. Biological fluids and tissue-cell proteomes from men with prostate cancer are being explored to identify diagnostic and prognostic biomarkers and therapeutic targets using these new proteomic approaches. Traditional and novel proteomic technology and their application to prostate cancer studies in translational research will be presented and discussed in this review. Proteomics coupled with powerful nanotechnology-based biomarker discovery approaches may provide a new and exciting opportunity for body fluid-borne biomarker discovery and characterization. While innovative mass spectrometry technology and nanotrap could be applied to improve the discovery and measurement of biomarkers for the early detection of prostate cancer, the use of tissue proteomic tools such as the reverse-phase protein microarray may provide new approaches for personalization of therapies tailored to each tumor's unique pathway activation network.

Extraction of proteins from formalin-fixed, paraffin-embedded tissue using the Qproteome extraction technique and preparation of tryptic peptides for liquid chromatography/mass spectrometry analysis

This unit provides a robust, reliable, and easy-to-use kit-based method for extraction of intact, non-degraded proteins from formalin-fixed, paraffin-embedded (FFPE) tissue, and their subsequent use for analysis by liquid chromatography/mass spectrometry (LC/MS). After deparaffinization, proteins are extracted from unstained sections of FFPE rat liver tissue. After a simple cleanup step using organic extraction, the sample is transferred into a buffer optimized for trypsin digestion of the extracted proteins. Subsequently, LC/MS is used to identify the proteins that gave rise to the tryptic peptides. Comparing formalin-fixed and frozen tissues, good correlation is observed in the mass spectrometric pattern attributable to the tryptic peptides and number of identified proteins. Since FFPE tissues are generally available in clinical practice, this method can be used to analyze biomarkers in different pathological situations (e.g., healthy vs. diseased). The method can also be used for protein extraction from fresh-frozen tissue.

Peptide and protein imaging mass spectrometry in cancer research

MALDI mass spectrometry is able to acquire protein profiles directly from tissue that can describe the levels of hundreds of distinct proteins. MALDI imaging MS can simultaneously reveal how each of these proteins varies in heterogeneous tissues. Numerous studies have now demonstrated how MALDI imaging MS can generate different protein profiles from the different cell types in a tumor, which can act as biomarker profiles or enable specific candidate protein biomarkers to be identified. MALDI imaging MS can be directly applied to patient samples where its utility is to accomplish untargeted multiplex analysis of the tissue's protein content, enabling the different regions of the tissue to be differentiated on the basis of previously unknown protein profiles/biomarkers. The technique continues to rapidly develop and is now approaching the cusp whereby its potential to provide new diagnostic/prognostic tools for cancer patients can be routinely investigated. Here the latest methodological developments are summarized and its application to a range of tumors is reported in detail. The prospects of MALDI imaging MS are then described from the perspectives of modern pathological practice and MS-based proteomics, to ensure the outlook addresses real clinical needs and reflects the real capabilities of MS-based proteomics of complex tissue samples.

Structural markers in prostate cancer serum imaged ex vivo using cryogenic transmission electron microscopy

We used cryogenic transmission electron microscopy (cryo-TEM) to identify differences in macromolecular structures present in the serum from healthy individuals (HI) and prostate cancer (PCa) patients and show that these differences are potential markers for disease. Using a murine orthotopic model of human PCa, we determined that some of these structural markers in serum are associated with the tumour burden. These findings signify the potential of this nanoscale ex vivo imaging technology of body fluids as a platform for discovering early markers of PCa and other diseases.

Profiling of protein interaction networks of protein complexes using affinity purification and quantitative mass spectrometry

Protein-protein interactions are important for nearly all biological processes, and it is known that aberrant protein-protein interactions can lead to human disease and cancer. Recent evidence has suggested that protein interaction interfaces describe a new class of attractive targets for drug development. Full characterization of protein interaction networks of protein complexes and their dynamics in response to various cellular cues will provide essential information for us to understand how protein complexes work together in cells to maintain cell viability and normal homeostasis. Affinity purification coupled with quantitative mass spectrometry has become the primary method for studying in vivo protein interactions of protein complexes and whole organism proteomes. Recent developments in sample preparation and affinity purification strategies allow the capture, identification, and quantification of protein interactions of protein complexes that are stable, dynamic, transient, and/or weak. Current efforts have mainly focused on generating reliable, reproducible, and high confidence protein interaction data sets for functional characterization. The availability of increasing amounts of information on protein interactions in eukaryotic systems and new bioinformatics tools allow functional analysis of quantitative protein interaction data to unravel the biological significance of the identified protein interactions. Existing studies in this area have laid a solid foundation toward generating a complete map of in vivo protein interaction networks of protein complexes in cells or tissues.

Differential proteomics of the plasma of individuals with sepsis caused by Acinetobacter baumannii

This study examines alterations in the plasma proteome in ten adults affected by sepsis caused by Acinetobacter baumannii as compared to paired healthy controls. 2-DE profiles of plasma from patients and paired healthy donors, depleted of the six most abundant proteins, were analysed by the DIGE technique. Protein spot detection and quantification were performed with the Differential In-gel Analysis and Biological Variation Analysis modules of the DeCyder() software. Differentially expressed proteins were identified by mass spectrometry (MALDI-TOF/TOF) after colloidal Coomassie blue staining. Almost 900 spots were detected on a unique 2-D gel by the DIGE technique. A total of 269 protein spots of differential abundance were shown to be statistically significant (2.5-fold) with p values of p< or =0.01 (135 spots) and p< or =0.05 (134 spots) as determined by the t test. Seventy-one spots were submitted to mass spectrometry and about 30% could be successfully identified. This multiplex approach significantly reduced experimental variability, allowing for the confident detection of small differences in protein levels. Results include differentially expressed lipoproteins as well as proteins belonging to inflammatory/coagulation pathways and the kallikrein-kinin system. These data improves the knowledge for future developments in sepsis diagnosis, staging and therapy.

Sample preparation and fractionation for proteome analysis and cancer biomarker discovery by mass spectrometry

Sample preparation and fractionation technologies are one of the most crucial processes in proteomic analysis and biomarker discovery in solubilized samples. Chromatographic or electrophoretic proteomic technologies are also available for separation of cellular protein components. There are, however, considerable limitations in currently available proteomic technologies as none of them allows for the analysis of the entire proteome in a simple step because of the large number of peptides, and because of the wide concentration dynamic range of the proteome in clinical blood samples. The results of any undertaken experiment depend on the condition of the starting material. Therefore, proper experimental design and pertinent sample preparation is essential to obtain meaningful results, particularly in comparative clinical proteomics in which one is looking for minor differences between experimental (diseased) and control (nondiseased) samples. This review discusses problems associated with general and specialized strategies of sample preparation and fractionation, dealing with samples that are solution or suspension, in a frozen tissue state, or formalin-preserved tissue archival samples, and illustrates how sample processing might influence detection with mass spectrometric techniques. Strategies that dramatically improve the potential for cancer biomarker discovery in minimally invasive, blood-collected human samples are also presented.

Mining the archival formalin-fixed paraffin-embedded tissue proteome: opportunities and challenges

The significant potential of tissue-based proteomic biomarker studies can be restricted by difficulties in accessing samples in optimal fresh-frozen form. While archival formalin-fixed tissue collections with attached clinical and outcome data represent a valuable alternate resource, the use of formalin as a fixative which induces protein cross-linking, has generally been assumed to render them unsuitable for proteomic studies. However, this view has been challenged recently with the publication of several papers accomplishing variable degrees of heat-induced reversal of cross-links. Although still in its infancy and requiring the quantitative optimisation of several critical parameters, formalin-fixed tissue proteomics holds promise as a powerful tool for biomarker-driven translational research. Here, we critically review the current status of research in the field, highlighting challenges which need to be addressed for robust quantitative application of protocols to ensure confident high impact inferences can be made.

Biomedical nanotechnology for cancer

Nanotechnology may hold the key to controlling many devastating diseases. In the fight against the pain, suffering, and death due to cancer, nanotechnology will allow earlier diagnosis and even prevention of malignancy at premalignant stages, in addition to providing multimodality treatment not possible with current conventional techniques. This review discusses nanotechnology already used in diagnostic and therapeutic applications for cancer. Also addressed are theoretic and evolving uses of nanotechnology, including multifunctional nanoparticles for imaging and therapy, nanochannel implants for controlled release of drugs, nanoscale devices for evaluation of proteomics and genomics, and diagnostic techniques that take advantage of physical changes in diseased tissue.

Proteomics in clinical prostate research

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

Molecular biomarkers for cancer detection in blood and bodily fluids

Cancer is a major and increasing public health problem worldwide. Traditionally, the diagnosis and staging of cancer, as well as the evaluation of response to therapy have been primarily based on morphology, with relatively few cancer biomarkers currently in use. Conventional biomarker studies have been focused on single genes or discrete pathways, but this approach has had limited success because of the complex and heterogeneous nature of many cancers. The completion of the human genome project and the development of new technologies have greatly facilitated the identification of biomarkers for assessment of cancer risk, early detection of primary cancers, monitoring cancer treatment, and detection of recurrence. This article reviews the various approaches used for development of such markers and describes markers of potential clinical interest in major types of cancer. Finally, we discuss the reasons why so few cancer biomarkers are currently available for clinical use.

Investigation of molecular interaction within biological macromolecular complexes by mass spectrometry

The advent of electrospray ionization (ESI) and matrix-assisted laser desorption ionization (MALDI) has accelerated structural studies of biological macromolecular complexes. At present, mass spectrometry can provide accurate mass values not only of individual biological macromolecules but also of their assemblies. Furthermore, it can also give information on the interface sites of the biological macromolecular complexes. The present article focuses on the role of mass spectrometry in the investigation of biological molecular interactions, such as protein-protein, protein-DNA, and protein-ligand interactions, which play essential roles in various biological events.

Functional and quantitative proteomics using SILAC

Researchers in many biological areas now routinely characterize proteins by mass spectrometry. Among the many formats for quantitative proteomics, stable-isotope labelling by amino acids in cell culture (SILAC) has emerged as a simple and powerful one. SILAC removes false positives in protein-interaction studies, reveals large-scale kinetics of proteomes and - as a quantitative phosphoproteomics technology - directly uncovers important points in the signalling pathways that control cellular decisions.

Application of Genomic Technologies to Human Prostate Cancer

Prostate cancer is the most commonly diagnosed non-cutaneous malignancy in U.S. males and has a broad spectrum of clinical behavior ranging from indolent to lethal. Microarray technology has provided unprecedented opportunity to explore the genetic processes underlying prostate cancer by providing a comprehensive survey of a cell's transcriptional landscape. Prostate cancer, however, has posed significant challenges that have contributed to inconsistent results between studies and difficulty replicating findings. Despite these challenges, several important insights have been gained along with new clinical biomarkers of diagnosis and prognosis. Continued improvements in methods of tissue preparation, microarray technology and data analysis will overcome existing challenges and fuel future discoveries.

Dynamic Profiling of the Post-translational Modifications and Interaction Partners of Epidermal Growth Factor Receptor Signaling after Stimulation by Epidermal Growth Factor Using Extended Range Proteomic Analysis (ERPA)

In a recent report, we introduced Extended Range Proteomic Analysis (ERPA), an intermediate approach between top-down and bottom-up proteomics, for the comprehensive characterization at the trace level (fmol level) of large and complex proteins. In this study, we extended ERPA to determine quantitatively the temporal changes that occur in the tyrosine kinase receptor, epidermal growth factor receptor (EGFR), upon stimulation. Specifically A 431 cells were stimulated with epidermal growth factor after which EGFR was immunoprecipitated at stimulation times of 0, 0.5, 2, and 10 min as well as 4 h. High sequence coverage was obtained (96%), and methods were developed for label-free quantitation of phosphorylation and glycosylation. A total of 13 phosphorylation sites were identified, and the estimated stoichiometry was determined over the stimulation time points, including Thr(P) and Ser(P) sites in addition to Tyr(P) sites. A total of 10 extracellular domain N-glycan sites were also identified, and major glycoforms at each site were quantitated. No change in the extent of glycosylation with stimulation was observed as expected. Finally potential binding partners to EGFR were identified based on changes in the amount of protein pulled down with EGFR as a function of time of stimulation. Many of the 19 proteins identified are known binding partners of EGFR. This work demonstrates that comprehensive characterization provides a powerful tool to aid in the study of important therapeutic targets. The detailed molecular information will prove useful in future studies in tissue.

Advances and challenges in liquid chromatography-mass spectrometry-based proteomics profiling for clinical applications

Recent advances in proteomics technologies provide tremendous opportunities for biomarker-related clinical applications; however, the distinctive characteristics of human biofluids such as the high dynamic range in protein abundances and extreme complexity of the proteomes present tremendous challenges. In this review we summarize recent advances in LC-MS-based proteomics profiling and its applications in clinical proteomics as well as discuss the major challenges associated with implementing these technologies for more effective candidate biomarker discovery. Developments in immunoaffinity depletion and various fractionation approaches in combination with substantial improvements in LC-MS platforms have enabled the plasma proteome to be profiled with considerably greater dynamic range of coverage, allowing many proteins at low ng/ml levels to be confidently identified. Despite these significant advances and efforts, major challenges associated with the dynamic range of measurements and extent of proteome coverage, confidence of peptide/protein identifications, quantitation accuracy, analysis throughput, and the robustness of present instrumentation must be addressed before a proteomics profiling platform suitable for efficient clinical applications can be routinely implemented.

Direct cancer tissue proteomics: a method to identify candidate cancer biomarkers from formalin-fixed paraffin-embedded archival tissues

Successful treatment of multiple cancer types requires early detection and identification of reliable biomarkers present in specific cancer tissues. To test the feasibility of identifying proteins from archival cancer tissues, we have developed a methodology, termed direct tissue proteomics (DTP), which can be used to identify proteins directly from formalin-fixed paraffin-embedded prostate cancer tissue samples. Using minute prostate biopsy sections, we demonstrate the identification of 428 prostate-expressed proteins using the shotgun method. Because the DTP method is not quantitative, we employed the absolute quantification method and demonstrate picogram level quantification of prostate-specific antigen. In depth bioinformatics analysis of these expressed proteins affords the categorization of metabolic pathways that may be important for distinct stages of prostate carcinogenesis. Furthermore, we validate Wnt-3 as an upregulated protein in cancerous prostate cells by immunohistochemistry. We propose that this general strategy provides a roadmap for successful identification of critical molecular targets of multiple cancer types.

Lectin capture strategies combined with mass spectrometry for the discovery of serum glycoprotein biomarkers

The application of mass spectrometry to identify disease biomarkers in clinical fluids like serum using high throughput protein expression profiling continues to evolve as technology development, clinical study design, and bioinformatics improve. Previous protein expression profiling studies have offered needed insight into issues of technical reproducibility, instrument calibration, sample preparation, study design, and supervised bioinformatic data analysis. In this overview, new strategies to increase the utility of protein expression profiling for clinical biomarker assay development are discussed with an emphasis on utilizing differential lectin-based glycoprotein capture and targeted immunoassays. The carbohydrate binding specificities of different lectins offer a biological affinity approach that complements existing mass spectrometer capabilities and retains automated throughput options. Specific examples using serum samples from prostate cancer and hepatocellular carcinoma subjects are provided along with suggested experimental strategies for integration of lectin-based methods into clinical fluid expression profiling strategies. Our example workflow incorporates the necessity of early validation in biomarker discovery using an immunoaffinity-based targeted analytical approach that integrates well with upstream discovery technologies.

Identification of mammalian cell lines using MALDI-TOF and LC-ESI-MS/MS mass spectrometry

Direct mass spectrometric analysis of complex biological samples is becoming an increasingly useful technique in the field of proteomics. Matrix-assisted laser desorption/ionization mass spectroscopy (MALDI-MS) is a rapid and sensitive analytical tool well suited for obtaining molecular weights of peptides and proteins from complex samples. Here, a fast and simple approach to cellular protein profiling is described in which mammalian cells are lysed directly in the MALDI matrix 2,5-dihydroxybenzoic acid (DHB) and mass analyzed using MALDI-time of flight (TOF). Using the unique MALDI mass spectral "fingerprint" generated in these analyses, it is possible to differentiate among several different mammalian cell lines. A number of techniques, including MALDI-post source decay (PSD), MALDI tandem time-of-flight (TOF-TOF), MALDI-Fourier transform ion cyclotron resonance (FTICR), and nanoflow liquid chromatography followed by electrospray ionization and tandem mass spectrometry (LC-ESI-MS/MS) were employed to attempt to identify the proteins represented in the MALDI spectra. Performing a tryptic digestion of the supernatant of the cells lysed in DHB with subsequent LC-ESI-MS/MS analysis was by far the most successful method to identify proteins.

Advances, Challenges, and Limitations in Serum-Proteome-Based Cancer Diagnosis

Recent advances in medicine have dramatically reduced the incidence and mortality of many cardiovascular, infectious, and certain neoplastic diseases; the overall mortality for most malignant solid tumors remains high. The poor prognosis in these cancers is due, in part, to the absence of adequate early screening tests, leading to delays in diagnosis. Three strategies have been applied to fight cancer: analysis of the molecular mechanisms involved in its pathogenesis and progression, improvement of early diagnosis, and the development of novel treatment strategies. There have been major advances in our understanding of cancer biology and pathogenesis and in the development of new (targeted) treatment modalities. However, insufficient progress has been made with respect to improving the methods for the early diagnosis and screening of many cancers. Therefore, cancer is often diagnosed at advanced stages, delaying timely treatment and leading to poor prognosis. Proteome analysis has recently been used for the identification of biomarkers or biomarker patterns that may allow for the early diagnosis of cancer. This tool is of special interest, since it allows for the identification of tumor-derived secretory products in serum or other body fluids. In addition, it may be used to detect reduced levels or loss of proteins in the serum of cancer patients that are present in noncancer individuals. These changes in the serum proteome may result from cancer-specific metabolic or immunological alterations, which are, at least partly, independent of tumor size or mass, thereby facilitating early discovery.

Discovery of Biomarker Candidates within Disease by Protein Profiling: Principles and Concepts†

Proteins and peptides present within clinical samples represent a valuable library of information regarding the ongoing processes within cells and tissues in health and disease. We have developed and validated novel technology applications that can be used to characterize the patterns of global protein expression in tissue and biofluids in either gel-based systems or by automated multidimensional nanocapillary liquid chromatography. Mass spectrophotometry platforms using MALDI MS and MS/MS or LTQ ion trap MS were capable of delivering sensitive and accurate identifications of hundreds of proteins contained in individual samples including individual forms of processing intermediates such as phospho peptides. The Systems Biology approach of integrating protein expression data with clinical data such as histopathology, clinical functional measurements, medical imaging scores, patient demographics, and clinical outcome provides a powerful tool for linking biomarker expression with biological processes that can be segmented and linked to disease presentation.

Integrating disease knowledge and technology to deliver protein targets and biomarkers into drug discovery projects

Biomarker discovery is dependent upon two disciplines: the field of clinical bioanalysis linked to disease aetiology and the application of high level technology platforms for identifying proteins/peptides in complex samples. However, diagnostic biomarker measurements require certain definitions of context that can only be achieved by combining protein science with clinical science. The evaluation of biomarkers requires careful attention to match (1) a specific biological question with (2) the appropriate clinical sample and (3) high resolution technology systems which link protein identities to clinical informatics.: