Identification of differentially expressed proteins in direct expressed prostatic secretions of men with organ-confined versus extracapsular prostate cancer

Increased α2,3-sialyl N-glycosylated prostate-specific membrane antigen (PSMA) in post-DRE urine is associated with high grade group prostate cancer

INTRODUCTION

Aberrant glycosylation of proteins is an important hallmark in multiple cancers. Prostate-specific membrane antigen (PSMA), a highly glycosylated protein with 10 N-linked glycosylation sites, is an Food and Drug Administration approved theranostic for prostate cancer. However, glycosylation changes in PSMA that are associated with prostate cancer disease progression have not been fully characterized.

METHODS

We investigated whether urinary PSMA sialylation correlate with high-grade prostate cancer. Urine samples were collected from men after digital rectal examination (DRE) before prostate biopsy. Lectin-antibody enzyme-linked immunoassay was used to quantify α2,3-sialyl PSMA in post-DRE urine samples from subjects with benign prostate tumors, Grade Group 1 prostate cancer and those with Grade Group ≥2 disease.

RESULTS

There are significant increases in α2,3-sialylated PSMA in patients with Grade Group ≥2 disease compared to benign (p = 0.0009) and those with Grade Group 1 disease (p = 0.0063). There were no significant differences in α2,3-sialyl PSMA levels between Grade Group 1 and benign prostate tumors (p = 0.7947).

CONCLUSIONS

Our study shows that there are significant differences in the abundance of α2,3-sialylated PSMA in post-DRE urines from disease stratified prostate cancer patients, and the increase is correlated with progression and disease severity. The detection of increased PSMA sialyation in post-DRE urines from patients with higher Grade Group ≥2 disease states provides novel untapped potential for the development of prognostic biomarkers for prostate cancer. Specifically, quantitation of α2,3-sialylated PSMA shows potential for discriminating between benign to intermediate grade disease, which is a significant clinical challenge in staging and risk stratification of prostate cancer.

Plasma-derived extracellular vesicle surface markers CD45, CD326 and CD56 correlate with the stage of osteoarthritis: a primary study of a novel and promising diagnostic tool of the disease

Recently, there is a growing interest in the research based on extracellular vesicles (EVs) which represent paracrine factors secreted by almost all cell types. Both, normal and pathological cells are able to release various types of EVs with different physiological properties, functions and compositions. EVs play an important role in intercellular communication, mechanism and tissue repair. Moreover, EVs could help not only in the treatment of diseases but also in their diagnostics. This work focused on the evaluation of the potential of EVs being used as biomarkers for the diagnosis of osteoarthritis (OA) based on a comparison of the composition of EVs separated from platelet-poor plasma (PPP) of healthy donors and OA patients at different stages of OA. OA is established as a complex syndrome with extensive impact on multiple tissues within the synovial joint. It is a chronic disease of musculoskeletal system that mainly affects the elderly. Depending on the use of the Kellgren-Lawrence classification system, there are four grades of OA which have a negative impact on patients' quality of life. It is very difficult to detect OA in its early stages, so it is necessary to find a new diagnostic method for its timely detection. PPP samples were prepared from whole blood. PPP-EVs were separated from 3 groups of donors-healthy control, early stage OA, end-stage OA, and their content was compared and correlated. EVs from PPP were separated by size exclusion chromatography and characterized in terms of their size, yield and purity by NTA, western blotting, ELISA and flow cytometry. Detection of surface markers expression in EVs was performed using MACSPlex approach. Inflammatory and growth factors in EVs were analysed using MAGPix technology. Our study confirmed significant differences between EVs surface markers of patients and healthy controls correlating with the age of donor (CD63, CD31 and ROR1) and stage of OA (CD45, CD326 and CD56), respectively. Circulating EVs have been under extensive investigation for their capability to predict OA pathology diagnosis as potential targets for biomarker discovery. Taken together, obtained results indicated that PPP-EVs surface markers could be used as potential biomarkers in the early diagnosis of OA.

The Latest Developments in Using Proteomic Biomarkers from Urine and Serum for Non-Invasive Disease Diagnosis and Prognosis

Due to diagnostic improvements, medical diagnostics is demanding non-invasive or minimally invasive methods. Non-invasively obtained body fluids (eg., Urine, serum) can replace cerebral fluid, amniotic fluid, synovial fluid, bronchoalveolar lavage fluid, and others for diagnostic reasons. Many illnesses are induced by perturbations of cellular signaling pathways and associated pathway networks as a result of genetic abnormalities. These disturbances are represented by a shift in the protein composition of the fluids surrounding the tissues and organs that is, tissue interstitial fluid (TIF). These variant proteins may serve as diagnostic "signatures" for a variety of disorders. This review provides a concise summary of urine and serum biomarkers that may be used for the diagnosis and prognosis of a variety of disorders, including cancer, brain diseases, kidney diseases, and other system diseases. The studies reviewed in this article suggest that serum and urine biomarkers of various illnesses may be therapeutically useful for future diagnostics. Correct illness management is crucial for disease prognosis, hence non-invasive serum and urine biomarkers have been extensively studied for diagnosis, subclassification, monitoring disease activity, and predicting treatment results and consequences.

Characterization of proteins, mRNAs, and miRNAs of circulating extracellular vesicles from prostate cancer patients compared to healthy subjects

Prostate cancer (PC) is the fifth leading cause of death in men globally. Measurement of the blood PSA level is still considered the gold-standard biomarker test for PC despite its high rate of delivering false positives and negatives that result in an inappropriate medical response, including overtreatment. We collected extracellular vesicles (EVs) from the blood plasma of PC patients with organ-confined, extracapsular-invading, and seminal vesicle-invading tumors and from healthy subjects. We examined the protein, mRNA, and miRNA content of these EVs using mass spectrometry (MS), a human PC PCR array, and a miScript miRNA PCR array, respectively. The proteomic analysis showed distinct groups of proteins that are differently expressed in each group of patients, as well as in healthy subjects. Samples from healthy subjects and each tumor type were used for both mRNA and miRNA arrays. The mRNA analysis showed distinct groups of mRNAs that were overexpressed in healthy or in one of the three tumor types but not in the EVs of the other groups. The miRNA analysis showed distinct groups of miRNAs as well. The fold of regulation in the expression of the identified mRNA and miRNA of each stage of the disease from healthy subjects showed that various mRNAs and miRNAs could discriminate the disease stage. Overall, our data suggest many molecular marker candidates for distinguishing between healthy subjects and PC patients using the cargo of circulating vesicles, as well as markers to discriminate between the different tumor types. Once verified, these markers might have a diagnostic value for PC.

Artificial Intelligence for Clinical Diagnosis and Treatment of Prostate Cancer

As medical science and technology progress towards the era of "big data", a multi-dimensional dataset pertaining to medical diagnosis and treatment is becoming accessible for mathematical modelling. However, these datasets are frequently inconsistent, noisy, and often characterized by a significant degree of redundancy. Thus, extensive data processing is widely advised to clean the dataset before feeding it into the mathematical model. In this context, Artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL) algorithms based on artificial neural networks (ANNs) and their types, are being used to produce a precise and cross-sectional illustration of clinical data. For prostate cancer patients, datasets derived from the prostate-specific antigen (PSA), MRI-guided biopsies, genetic biomarkers, and the Gleason grading are primarily used for diagnosis, risk stratification, and patient monitoring. However, recording diagnoses and further stratifying risks based on such diagnostic data frequently involves much subjectivity. Thus, implementing an AI algorithm on a PC's diagnostic data can reduce the subjectivity of the process and assist in decision making. In addition, AI is used to cut down the processing time and help with early detection, which provides a superior outcome in critical cases of prostate cancer. Furthermore, this also facilitates offering the service at a lower cost by reducing the amount of human labor. Herein, the prime objective of this review is to provide a deep analysis encompassing the existing AI algorithms that are being deployed in the field of prostate cancer (PC) for diagnosis and treatment. Based on the available literature, AI-powered technology has the potential for extensive growth and penetration in PC diagnosis and treatment to ease and expedite the existing medical process.

Site-Specific Intact N-Linked Glycopeptide Characterization of Prostate-Specific Membrane Antigen from Metastatic Prostate Cancer Cells

The composition of N-linked glycans that are conjugated to the prostate-specific membrane antigen (PSMA) and their functional significance in prostate cancer progression have not been fully characterized. PSMA was isolated from two metastatic prostate cancer cell lines, LNCaP and MDAPCa2b, which have different tissue tropism and localization. Isolated PSMA was trypsin-digested, and intact glycopeptides were subjected to LC-HCD-EThcD-MS/MS analysis on a Tribrid Orbitrap Fusion Lumos mass spectrometer. Differential qualitative and quantitative analysis of site-specific N-glycopeptides was performed using Byonic and Byologic software. Comparative quantitative analysis demonstrates that multiple glycopeptides at asparagine residues 51, 76, 121, 195, 336, 459, 476, and 638 were in significantly different abundance in the two cell lines (p < 0.05). Biochemical analysis using endoglycosidase treatment and lectin capture confirm the MS and site occupancy data. The data demonstrate the effectiveness of the strategy for comprehensive analysis of PSMA glycopeptides. This approach will form the basis of ongoing experiments to identify site-specific glycan changes in PSMA isolated from disease-stratified clinical samples to uncover targets that may be associated with disease progression and metastatic phenotypes.

Metabolite interactions in prostatic fluid mimics assessed by 1H NMR

INTRODUCTION

Molecular interactions in prostatic fluid are of biological interest and may affect MRI and MRS of the prostate. We investigated the existence of interactions between the major components of this fluid: spermine, citrate and myoinositol, metal ions, including zinc, and proteins.

MATERIALS AND METHODS

Solutions of 90 mM citrate, 18 mM spermine and 6 mM myo-inositol, mimicking expressed prostatic fluid, were investigated by 1H NMR using changes in T2 relaxation and chemical shift as markers for interactions.

RESULTS AND DISCUSSION

Adding to this metabolite mixture the ions Na⁺ , K⁺, Ca⁺⁺, Mg⁺⁺ and Zn⁺⁺, decreased the T2 relaxation times of citrate and spermine protons by factors of 3 and 2, respectively, with Zn++ causing the largest effect, indicating ion-metabolite interactions. The T2 of 18 mM spermine dropped by a factor of 2 upon addition with 90 mM citrate, but no effect on T2 was seen with myo-inositol pointing to a specific citrate-spermine interaction. Moreover, the T2 of citrate in the presence of spermine decreased by adding metal ions and increasing amounts of Zn⁺⁺, indicating complexation of citrate and spermine with metal ions, particularly with Zn. The addition of bovine serum albumin (BSA), as an index protein, substantially further decreased the T2 of spermine and citrate implying the formation of a transient spermine-metal ion-citrate-BSA complex. Finally, we found that the T2 of citrate in extracellular fluid of prostate cancer cells, as a mimic of fluid in cancerous prostates, decreased by adding fetal calf serum, indicating protein binding.

Liquid biopsy: a step closer to transform diagnosis, prognosis and future of cancer treatments

Over the past decade, invasive techniques for diagnosing and monitoring cancers are slowly being replaced by non-invasive methods such as liquid biopsy. Liquid biopsies have drastically revolutionized the field of clinical oncology, offering ease in tumor sampling, continuous monitoring by repeated sampling, devising personalized therapeutic regimens, and screening for therapeutic resistance. Liquid biopsies consist of isolating tumor-derived entities like circulating tumor cells, circulating tumor DNA, tumor extracellular vesicles, etc., present in the body fluids of patients with cancer, followed by an analysis of genomic and proteomic data contained within them. Methods for isolation and analysis of liquid biopsies have rapidly evolved over the past few years as described in the review, thus providing greater details about tumor characteristics such as tumor progression, tumor staging, heterogeneity, gene mutations, and clonal evolution, etc. Liquid biopsies from cancer patients have opened up newer avenues in detection and continuous monitoring, treatment based on precision medicine, and screening of markers for therapeutic resistance. Though the technology of liquid biopsies is still evolving, its non-invasive nature promises to open new eras in clinical oncology. The purpose of this review is to provide an overview of the current methodologies involved in liquid biopsies and their application in isolating tumor markers for detection, prognosis, and monitoring cancer treatment outcomes.

A Combined CRISP3 and SPINK1 Prognostic Grade in EPS-Urine and Establishment of Models to Predict Prognosis of Patients With Prostate Cancer

Background

Prostate cancer (PCa) is characterized by significant heterogeneity. Thus, novel prognostic indicators are required to improve prognosis and treatment.

Methods

Cysteine rich secretory protein 3 (CRISP3) and serine peptidase inhibitor Kazal type 1 (SPINK1) levels in expressed prostatic secretion (EPS)-urine collected during digital rectal examination of 496 patients histologically diagnosed with PCa were detected via enzyme-linked immunosorbent assay. A combined CRISP3 and SPINK1 prognostic grade (CSPG) was defined using cut-off values from receiver operating characteristic curves. Log-rank Kaplan-Meier survival curves investigated differences in prognosis between groups. Univariate and multivariate Cox analyses investigated the CSPG relationship with biochemical recurrence (BCR), cancer-specific survival (CSS), and overall survival (OS). Three prognostic models were developed and validated.

Conclusions

CRISP3 and SPINK1 levels increased with Gleason score progression, pathological T stage, and metastasis status. CSPG in EPS-urine, which was an effective independent prognostic variable, accurately predicted the prognosis of patients with PCa. Three clinical prognostic models using the CSPG for BCR, CSS, and OS were developed and validated.

Glycosylation Changes in Prostate Cancer Progression

Prostate Cancer (PCa) is the most commonly diagnosed malignancy and second leading cause of cancer-related mortality in men. With the use of next generation sequencing and proteomic platforms, new biomarkers are constantly being developed to both improve diagnostic sensitivity and specificity and help stratify patients into different risk groups for optimal management. In recent years, it has become well accepted that altered glycosylation is a hallmark of cancer progression and that the glycan structures resulting from these mechanisms show tremendous promise as both diagnostic and prognostic biomarkers. In PCa, a wide range of structural alterations to glycans have been reported such as variations in sialylation and fucosylation, changes in branching, altered levels of Lewis and sialyl Lewis antigens, as well as the emergence of high mannose “cryptic” structures, which may be immunogenic and therapeutically relevant. Furthermore, aberrant expression of galectins, glycolipids, and proteoglycans have also been reported and associated with PCa cell survival and metastasis. In this review, we discuss the findings from various studies that have explored altered N- and O-linked glycosylation in PCa tissue and body fluids. We further discuss changes in O-GlcNAcylation as well as altered expression of galectins and glycoconjugates and their effects on PCa progression. Finally, we emphasize the clinical utility and potential impact of exploiting glycans as both biomarkers and therapeutic targets to improve our ability to diagnose clinically relevant tumors as well as expand treatment options for patients with advanced disease.

On the Road to Accurate Protein Biomarkers in Prostate Cancer Diagnosis and Prognosis: Current Status and Future Advances

Prostate cancer (PC) is a leading cause of morbidity and mortality among men worldwide. Molecular biomarkers work in conjunction with existing clinicopathologic tools to help physicians decide who to biopsy, re-biopsy, treat, or re-treat. The past decade has witnessed the commercialization of multiple PC protein biomarkers with improved performance, remarkable progress in proteomic technologies for global discovery and targeted validation of novel protein biomarkers from clinical specimens, and the emergence of novel, promising PC protein biomarkers. In this review, we summarize these advances and discuss the challenges and potential solutions for identifying and validating clinically useful protein biomarkers in PC diagnosis and prognosis. The identification of multi-protein biomarkers with high sensitivity and specificity, as well as their integration with clinicopathologic parameters, imaging, and other molecular biomarkers, bodes well for optimal personalized management of PC patients.

Proteomic discovery of non-invasive biomarkers of localized prostate cancer using mass spectrometry

Prostate cancer is the second most frequently diagnosed non-skin cancer in men worldwide. Patient outcomes are remarkably heterogeneous and the best existing clinical prognostic tools such as International Society of Urological Pathology Grade Group, pretreatment serum PSA concentration and T-category, do not accurately predict disease outcome for individual patients. Thus, patients newly diagnosed with prostate cancer are often overtreated or undertreated, reducing quality of life and increasing disease-specific mortality. Biomarkers that can improve the risk stratification of these patients are, therefore, urgently needed. The ideal biomarker in this setting will be non-invasive and affordable, enabling longitudinal evaluation of disease status. Prostatic secretions, urine and blood can be sources of biomarker discovery, validation and clinical implementation, and mass spectrometry can be used to detect and quantify proteins in these fluids. Protein biomarkers currently in use for diagnosis, prognosis and relapse-monitoring of localized prostate cancer in fluids remain centred around PSA and its variants, and opportunities exist for clinically validating novel and complimentary candidate protein biomarkers and deploying them into the clinic.

Direct N-Glycosylation Profiling of Urine and Prostatic Fluid Glycoproteins and Extracellular Vesicles

Expressed prostatic secretions (EPS), also called post digital rectal exam urines, are proximal fluids of the prostate that are widely used for diagnostic and prognostic assays for prostate cancer. These fluids contain an abundant number of glycoproteins and extracellular vesicles secreted by the prostate gland, and the ability to detect changes in their N-glycans composition as a reflection of disease state represents potential new biomarker candidates. Methods to characterize these N-glycan constituents directly from clinical samples in a timely manner and with minimal sample processing requirements are not currently available. In this report, an approach is described to directly profile the N-glycan constituents of EPS urine samples, prostatic fluids and urine using imaging mass spectrometry for detection. An amine reactive slide is used to immobilize glycoproteins from a few microliters of spotted samples, followed by peptide N-glycosidase digestion. Over 100 N-glycan compositions can be detected with this method, and it works with urine, urine EPS, prostatic fluids, and urine EPS-derived extracellular vesicles. A comparison of the N-glycans detected from the fluids with tissue N-glycans from prostate cancer tissues was done, indicating a subset of N-glycans present in fluids derived from the gland lumens. The developed N-glycan profiling is amenable to analysis of larger clinical cohorts and adaptable to other biofluids.

A novel expressed prostatic secretion (EPS)-urine metabolomic signature for the diagnosis of clinically significant prostate cancer

OBJECTIVE

Significant efforts are currently being made to identify novel biomarkers for the diagnosis and risk stratification of prostate cancer (PCa). Metabolomics can be a very useful approach in biomarker discovery because metabolites are an important read-out of the disease when characterized in biological samples. We aimed to determine a metabolomic signature which can accurately distinguish men with clinically significant PCa from those affected by benign prostatic hyperplasia (BPH).

METHODS

We first performed untargeted metabolomics using ultrahigh-performance liquid chromatography tandem mass spectrometry on expressed prostatic secretion urine (EPS-urine) from 25 patients affected by BPH and 25 men with clinically significant PCa (defined as Gleason score ≥ 3 + 4). Diagnosis was histologically confirmed after surgical treatment. The EPS-urine metabolomic approach was then applied to a larger, prospective cohort of 92 consecutive patients undergoing multiparametric magnetic resonance imaging for clinical suspicion of PCa prior to biopsy.

RESULTS

We established a novel metabolomic signature capable of accurately distinguishing PCa from benign tissue. A metabolomic signature was associated with clinically significant PCa in all subgroups of the Prostate Imaging Reporting and Data System (PI-RADS) classification (100% and 89.13% of accuracy when the PI-RADS was in range of 1-2 and 4-5, respectively, and 87.50% in the more critical cases when the PI-RADS was 3).

CONCLUSIONS

A combination of metabolites and clinical variables can effectively help in identifying PCa patients that might be overlooked by current imaging technologies. Metabolites from EPS-urine should help in defining the diagnostic pathway of PCa, thus improving PCa detection and decreasing the number of unnecessary prostate biopsies.

The MYH9 Cytoskeletal Protein Is a Novel Corepressor of Androgen Receptors

In the progression of castration-resistant prostate cancer (CRPC), the androgen receptor (AR) that serves as a transcription factor becomes the most remarkable molecule. The transcriptional activity of AR is regulated by various coregulators. As a result, altered expression levels, an aberrant location or activities of coregulators promote the development of prostate cancer. We describe herein results showing that compared with androgen-dependent prostate cancer (ADPC) cells, AR nuclear translocation capability is enhanced in androgen-independent prostate cancer (AIPC) cells. To gain insight into whether AR coregulators are responsible for AR translocation capability, we performed coimmunoprecipitation (CO-IP) coupled with LC-MS/MS to screen 27 previously reported AR cofactors and 46 candidate AR cofactors. Furthermore, one candidate, myosin heavy chain 9 (MYH9), was identified and verified as a novel AR cofactor. Interestingly, the distribution of MYH9 was in both the cytoplasmic and nuclear compartments yet was enriched in the nucleus when AR was knocked down by AR shRNA, suggesting that the nuclear translocation of MYH9 was negatively regulated by AR. In addition, we found that blebbistatin, an inhibitor of MYH9, not only promoted AR nuclear translocation but also enhanced the expression of the AR target gene PSA, which indicates that MYH9 represses nuclear AR signaling. Taken together, our findings reveal that MYH9 appears to be a novel corepressor of AR plays a pivotal role in the progression of CRPC.

The Role of the Metzincin Superfamily in Prostate Cancer Progression: A Systematic-Like Review

Prostate cancer remains a leading cause of cancer-related morbidity in men. Potentially important regulators of prostate cancer progression are members of the metzincin superfamily of proteases, principally through their regulation of the extracellular matrix. It is therefore timely to review the role of the metzincin superfamily in prostate cancer and its progression to better understand their involvement in this disease. A systematic-like search strategy was conducted. Articles that investigated the roles of members of the metzincin superfamily and their key regulators in prostate cancer were included. The extracted articles were synthesized and data presented in tabular and narrative forms. Two hundred and five studies met the inclusion criteria. Of these, 138 investigated the role of the Matrix Metalloproteinase (MMP) subgroup, 34 the Membrane-Tethered Matrix Metalloproteinase (MT-MMP) subgroup, 22 the A Disintegrin and Metalloproteinase (ADAM) subgroup, 8 the A Disintegrin and Metalloproteinase with Thrombospondin Motifs (ADAMTS) subgroup and 53 the Tissue Inhibitor of Metalloproteinases (TIMP) family of regulators, noting that several studies investigated multiple family members. There was clear evidence that specific members of the metzincin superfamily are involved in prostate cancer progression, which can be either in a positive or negative manner. However, further understanding of their mechanisms of action and how they may be used as prognostic indicators or molecular targets is required.

PARK7 maintains the stemness of glioblastoma stem cells by stabilizing epidermal growth factor receptor variant III

PARK7 is involved in many key cellular processes, including cell proliferation, transcriptional regulation, cellular differentiation, oxidative stress protection, and mitochondrial function maintenance. Deregulation of PARK7 has been implicated in the pathogenesis of various human diseases, including cancer. Here, we aimed to clarify the effect of PARK7 on stemness and radioresistance of glioblastoma stem cells (GSCs). Serum differentiation and magnetic cell sorting of GSCs revealed that PARK7 was preferentially expressed in GSCs rather than differentiated GSCs. Immunohistochemical staining showed enhanced expression of PARK7 in glioma tissues compared to that in normal brain tissues. shRNA-mediated knockdown of PARK7 inhibited the self-renewal activity of GSCs in vitro, as evidenced by the results of neurosphere formation, limiting dilution, and soft-agar clonogenic assays. In addition, PARK7 knockdown suppressed GSC invasion and enhanced GSC sensitivity to ionizing radiation (IR). PARK7 knockdown suppressed expression of GSC signatures including nestin, epidermal growth factor receptor variant III (EGFRvIII), SOX2, NOTCH1, and OCT4. Contrarily, overexpression of PARK7 in CD133^- non-GSCs increased self-renewal activities, migration, and IR resistance, and rescued the reduction of GSC factors under shPARK7-transfected and serum-differentiation conditions. Intriguingly, PARK7 acted as a co-chaperone of HSP90 by binding to it, protecting EGFRvIII from proteasomal degradation. Knockdown of PARK7 increased the production of reactive oxygen species, inducing partial apoptosis and enhancing IR sensitivity in GSCs. Finally, PARK7 knockdown increased mouse survival and IR sensitivity in vivo. Based on these data, we propose that PARK7 plays a pivotal role in the maintenance of stemness and therapeutic resistance in GSCs.

RainBio: Proportional Visualization of Large Sets in Biology

Set visualization is a well-known task in information visualization. In biology, it is used for comparing visually sets of genes or proteins, typically using Venn diagrams. However, limitations of the Venn diagram are well-known: they are limited to 6 sets and difficult to read above 4. Many other set visualization techniques have been proposed, but they have never been widely used in biology. In this paper, we introduce RainBio, a technique for visualizing sets in biology and aimed at providing a global overview showing the size of the main intersections, in a proportional way, and the similarities between sets. We adapt rainbow boxes, a technique for visualizing small datasets, to the visualization of larger sets, using element aggregation and intersection clustering. We present the application of RainBio to three datasets, with 5, 6 and 12 sets. We also describe a small user study comparing RainBio with Venn diagrams, involving 30 students in biology. Results showed that RainBio led to significantly fewer errors on 6-set dataset, and that the majority of students preferred RainBio. RainBio is proposed as a web-based tool for up to 15 sets.

Prostate Cancer Biomarker Development: National Cancer Institute's Early Detection Research Network Prostate Cancer Collaborative Group Review

Prostate cancer remains the most common non-skin cancer and second leading cause of death among men in the United States. Although progress has been made in diagnosis and risk assessment, many clinical questions remain regarding early identification of prostate cancer and management. The early detection of aggressive disease continues to provide high curative rates if diagnosed in a localized state. Unfortunately, prostate cancer displays significant heterogeneity within the prostate organ and between individual patients making detection and treatment strategies complex. Although prostate cancer is common among men, the majority will not die from prostate cancer, introducing the issue of overtreatment as a major concern in clinical management of the disease. The focus of the future is to identify those at highest risk for aggressive prostate cancer and to develop prevention and screening strategies, as well as discerning the difference in malignant potential of diagnosed tumors. The Prostate Cancer Research Group of the National Cancer Institute's Early Detection Research Network has contributed to the progress in addressing these concerns. This summary is an overview of the activities of the group.See all articles in this CEBP Focus section, "NCI Early Detection Research Network: Making Cancer Detection Possible."

Proteomic Analysis of Exosomes for Discovery of Protein Biomarkers for Prostate and Bladder Cancer

Extracellular vesicles (EVs) are released by nearly all cell types as part of normal cell physiology, transporting biological cargo, including nucleic acids and proteins, across the cell membrane. In pathological states such as cancer, EV-derived cargo may mirror the altered state of the cell of origin. Exosomes are the smaller, 50–150 nanometer-sized EVs released from fusion of multivesicular endosomes with the plasma membrane. Exosomes play important roles in cell-cell communication and participate in multiple cancer processes, including invasion and metastasis. Therefore, proteomic analysis of exosomes is a promising approach to discover potential cancer biomarkers, even though it is still at an early stage. Herein, we critically review the advances in exosome isolation methods and their compatibility with mass spectrometry (MS)-based proteomic analysis, as well as studies of exosomes in pathogenesis and progression of prostate and bladder cancer, two common urologic cancers whose incidence rates continue to rise annually. As urological tumors, both urine and blood samples are feasible for noninvasive or minimally invasive analysis. A better understanding of the biological cargo and functions of exosomes via high-throughput proteomics will help provide new insights into complex alterations in cancer and provide potential therapeutic targets and personalized treatment for patients.

Recent advances in mass spectrometry based clinical proteomics: applications to cancer research

Cancer biomarkers have transformed current practices in the oncology clinic. Continued discovery and validation are crucial for improving early diagnosis, risk stratification, and monitoring patient response to treatment. Profiling of the tumour genome and transcriptome are now established tools for the discovery of novel biomarkers, but alterations in proteome expression are more likely to reflect changes in tumour pathophysiology. In the past, clinical diagnostics have strongly relied on antibody-based detection strategies, but these methods carry certain limitations. Mass spectrometry (MS) is a powerful method that enables increasingly comprehensive insights into changes of the proteome to advance personalized medicine. In this review, recent improvements in MS-based clinical proteomics are highlighted with a focus on oncology. We will provide a detailed overview of clinically relevant samples types, as well as, consideration for sample preparation methods, protein quantitation strategies, MS configurations, and data analysis pipelines currently available to researchers. Critical consideration of each step is necessary to address the pressing clinical questions that advance cancer patient diagnosis and prognosis. While the majority of studies focus on the discovery of clinically-relevant biomarkers, there is a growing demand for rigorous biomarker validation. These studies focus on high-throughput targeted MS assays and multi-centre studies with standardized protocols. Additionally, improvements in MS sensitivity are opening the door to new classes of tumour-specific proteoforms including post-translational modifications and variants originating from genomic aberrations. Overlaying proteomic data to complement genomic and transcriptomic datasets forges the growing field of proteogenomics, which shows great potential to improve our understanding of cancer biology. Overall, these advancements not only solidify MS-based clinical proteomics' integral position in cancer research, but also accelerate the shift towards becoming a regular component of routine analysis and clinical practice.

Multi-omics Biomarker Pipeline Reveals Elevated Levels of Protein-glutamine Gamma-glutamyltransferase 4 in Seminal Plasma of Prostate Cancer Patients

Seminal plasma, because of its proximity to prostate, is a promising fluid for biomarker discovery and noninvasive diagnostics. In this study, we investigated if seminal plasma proteins could increase diagnostic specificity of detecting primary prostate cancer and discriminate between high- and low-grade cancers. To select 147 most promising biomarker candidates, we combined proteins identified through five independent experimental or data mining approaches: tissue transcriptomics, seminal plasma proteomics, cell line secretomics, tissue specificity, and androgen regulation. A rigorous biomarker development pipeline based on selected reaction monitoring assays was designed to evaluate the most promising candidates. As a result, we qualified 76, and verified 19 proteins in seminal plasma of 67 negative biopsy and 152 prostate cancer patients. Verification revealed a prostate-specific, secreted and androgen-regulated protein-glutamine gamma-glutamyltransferase 4 (TGM4), which predicted prostate cancer on biopsy and outperformed age and serum Prostate-Specific Antigen (PSA). A machine-learning approach for data analysis provided improved multi-marker combinations for diagnosis and prognosis. In the independent verification set measured by an in-house immunoassay, TGM4 protein was upregulated 3.7-fold (p = 0.006) and revealed AUC = 0.66 for detecting prostate cancer on biopsy for patients with serum PSA ≥4 ng/ml and age ≥50. Very low levels of TGM4 (120 pg/ml) were detected in blood serum. Collectively, our study demonstrated rigorous evaluation of one of the remaining and not well-explored prostate-specific proteins within the medium-abundance proteome of seminal plasma. Performance of TGM4 warrants its further investigation within the distinct genomic subtypes and evaluation for the inclusion into emerging multi-biomarker panels.

Improved detection of prostate cancer using a magneto-nanosensor assay for serum circulating autoantibodies

Purpose

To develop a magneto-nanosensor (MNS) based multiplex assay to measure protein and autoantibody biomarkers from human serum for prostate cancer (CaP) diagnosis.

Materials and methods

A 4-panel MNS autoantibody assay and a MNS protein assay were developed and optimized in our labs. Using these assays, serum concentration of six biomarkers including prostate-specific antigen (PSA) protein, free/total PSA ratio, as well as four autoantibodies against Parkinson disease 7 (PARK7), TAR DNA-binding protein 43 (TARDBP), Talin 1 (TLN1), and Caldesmon 1 (CALD1) and were analyzed. Human serum samples from 99 patients (50 with non-cancer and 49 with clinically localized CaP) were evaluated.

Results

The MNS assay showed excellent performance characteristics and no cross-reactivity. All autoantibody assays showed a statistically significant difference between CaP and non-cancer samples except for PARK7. The most significant difference was the combination of the four autoantibodies as a panel in addition to the free/total PSA ratio. This combination had the highest area under the curve (AUC)– 0.916 in ROC analysis.

Conclusions

Our results suggest that this autoantibody panel along with PSA and free PSA have potential to segregate patients without cancer from those with prostate cancer with higher sensitivity and specificity than PSA alone.

Search for Novel Diagnostic Biomarkers of Prostate Inflammation-Related Disorders: Role of Transglutaminase Isoforms as Potential Candidates

Investigations on prostate inflammation-related disorders, including acute and chronic prostatitis, chronic pelvic pain syndrome, benign prostate hyperplasia (BPH), and prostate cancer (PCa), are still ongoing to find new, accurate, and noninvasive biomarkers for a differential diagnosis of those pathological conditions sharing some common macroscopic features. Moreover, an ideal biomarker should be useful for risk assessment of prostate inflammation progression to more severe disorders, like BPH or PCa, as well as for monitoring of treatment response and prognosis establishment in carcinoma cases. Recent literature evidence highlighted that changes in the expression of transglutaminases, enzymes that catalyze transamidation reactions leading to posttranslational modifications of soluble proteins, occur in prostate inflammation-related disorders. This review focuses on the role specifically played by transglutaminases 4 (TG4) and 2 (TG2) and suggests that both isoenzymes hold a potential to be included in the list of candidates as novel diagnostic biomarkers for the above-cited prostate pathological conditions.

Liquid Biopsy Potential Biomarkers in Prostate Cancer

Prostate cancer (PCa) is the second most common cancer in men worldwide with an incidence of 14.8% and a mortality of 6.6%. Shortcomings in comprehensive medical check-ups in low- and middle-income countries lead to delayed detection of PCa and are causative of high numbers of advanced PCa cases at first diagnosis. The performance of available biomarkers is still insufficient and limited applicability, including logistical and financial burdens, impedes comprehensive implementation into health care systems. There is broad agreement on the need of new biomarkers to improve (i) early detection of PCa, (ii) risk stratification, (iii) prognosis, and (iv) treatment monitoring. This review focuses on liquid biopsy tests distinguishing high-grade significant (Gleason score (GS) ≥ 7) from low-grade indolent PCa. Available biomarkers still lack performance in risk stratification of biopsy naïve patients. However, biomarkers with highly negative predictive values may help to reduce unnecessary biopsies. Risk calculators using integrative scoring systems clearly improve decision-making for invasive prostate biopsy. Emerging biomarkers have the potential to substitute PSA and improve the overall performance of risk calculators. Until then, PSA should be used and may be replaced whenever enough evidence has accumulated for better performance of a new biomarker.

Liquid biopsy of cancer: a multimodal diagnostic tool in clinical oncology

Over the last decades, the concept of precision medicine has dramatically renewed the field of medical oncology; the introduction of patient-tailored therapies has significantly improved all measurable outcomes. Liquid biopsy is a revolutionary technique that is opening previously unexpected perspectives. It consists of the detection and isolation of circulating tumor cells, circulating tumor DNA and exosomes, as a source of genomic and proteomic information in patients with cancer. Many technical hurdles have been resolved thanks to newly developed techniques and next-generation sequencing analyses, allowing a broad application of liquid biopsy in a wide range of settings. Initially correlated to prognosis, liquid biopsy data are now being studied for cancer diagnosis, hopefully including screenings, and most importantly for the prediction of response or resistance to given treatments. In particular, the identification of specific mutations in target genes can aid in therapeutic decisions, both in the appropriateness of treatment and in the advanced identification of secondary resistance, aiming to early diagnose disease progression. Still application is far from reality but ongoing research is leading the way to a new era in oncology. This review summarizes the main techniques and applications of liquid biopsy in cancer.

Nuclear magnetic resonance spectroscopy as a new approach for improvement of early diagnosis and risk stratification of prostate cancer

Prostate cancer (PCa) is the second most common male cancer worldwide and the fifth leading cause of death from cancer in men. Early detection and risk stratification is the most effective way to improve the survival of PCa patients. Current PCa biomarkers lack sufficient sensitivity and specificity to cancer. Metabolite biomarkers are evolving as a new diagnostic tool. This review is aimed to evaluate the potential of metabolite biomarkers for early detection, risk assessment, and monitoring of PCa. Of the 154 identified publications, 27 and 38 were original papers on urine and serum metabolomics, respectively. Nuclear magnetic resonance (NMR) is a promising method for measuring concentrations of metabolites in complex samples with good reproducibility, high sensitivity, and simple sample processing. Especially urine-based NMR metabolomics has the potential to be a cost-efficient method for the early detection of PCa, risk stratification, and monitoring treatment efficacy.

Systems pharmacology using mass spectrometry identifies critical response nodes in prostate cancer

In the United States alone one in five newly diagnosed cancers in men are prostate carcinomas (PCa). Androgen receptor (AR) status and the PI3K-AKT-mTOR signal transduction pathway are critical in PCa. After initial response to single drugs targeting these pathways resistance often emerges, indicating the need for combination therapy. Here, we address the question of efficacy of drug combinations and development of resistance mechanisms to targeted therapy by a systems pharmacology approach. We combine targeted perturbation with detailed observation of the molecular response by mass spectrometry. We hypothesize that the molecular short-term (24 h) response reveals details of how PCa cells adapt to counter the anti-proliferative drug effect. With focus on six drugs currently used in PCa treatment or targeting the PI3K-AKT-mTOR signal transduction pathway, we perturbed the LNCaP clone FGC cell line by a total of 21 treatment conditions using single and paired drug combinations. The molecular response was analyzed by the mass spectrometric quantification of 52 proteins. Analysis of the data revealed a pattern of strong responders, i.e., proteins that were consistently downregulated or upregulated across many of the perturbation conditions. The downregulated proteins, HN1, PAK1, and SPAG5, are potential early indicators of drug efficacy and point to previously less well-characterized response pathways in PCa cells. Some of the upregulated proteins such as 14-3-3 proteins and KLK2 may be useful early markers of adaptive response and indicate potential resistance pathways targetable as part of combination therapy to overcome drug resistance. The potential of 14-3-3ζ (YWHAZ) as a target is underscored by the independent observation, based on cancer genomics of surgical specimens, that its DNA copy number and transcript levels tend to increase with PCa disease progression. The combination of systematic drug perturbation combined with detailed observation of short-term molecular response using mass spectrometry is a potentially powerful tool to discover response markers and anti-resistance targets.

Metastatic prostate cancer is often treated with pharmacological agents to prevent the tumor from expanding; however, despite advances in drug development patients often die of the disease. An international research team lead by Ruedi Aebersold (ETH Zürich, Switzerland) and Chris Sander (Dana Faber Cancer Institute, Boston, USA) asked how prostate cancer cells adapt to pharmacological treatment on the molecular protein level and find a general response in their prostate cancer model. Next, they asked if similar changes are found in prostate cancer patients. Indeed, the same proteins upregulated in prostate cancer models are also upregulated in prostate cancer patients. Immediately, this has implications for patient treatment stratification and opens new avenues for drug developments in metastatic prostate cancer.

Targeted proteomics in urinary extracellular vesicles identifies biomarkers for diagnosis and prognosis of prostate cancer

Rapid and reliable diagnosis of prostate cancer (PCa) is highly desirable as current used methods lack specificity. In addition, identification of PCa biomarkers that can classify patients into high- and low-risk groups for disease progression at early stage will improve treatment decision-making. Here, we describe a set of protein-combination panels in urinary extracellular vesicles (EVs), defined by targeted proteomics and immunoblotting techniques that improve early non-invasive detection and stratification of PCa patients.We report a two-protein combination in urinary EVs that classifies benign and PCa patients (ADSV-TGM4), and a combination of five proteins able to significantly distinguish between high- and low-grade PCa patients (CD63-GLPK5-SPHM-PSA-PAPP). Proteins composing the panels were validated by immunohistochemistry assays in tissue microarrays (TMAs) confirming a strong link between the urinary EVs proteome and alterations in PCa tissues. Moreover, ADSV and TGM4 abundance yielded a high diagnostic potential in tissue and promising TGM4 prognostic power. These results suggest that the proteins identified in urinary EVs distinguishing high- and low grade PCa are a reflection of histological changes that may be a consequence of their functional involvement in PCa development. In conclusion, our study resulted in the identification of protein-combination panels present in urinary EVs that exhibit high sensitivity and specificity for PCa detection and patient stratification. Moreover, our study highlights the potential of targeted proteomic approaches–such as selected reaction monitoring (SRM)–as diagnostic assay for liquid biopsies via urinary EVs to improve diagnosis and prognosis of suspected PCa patients.

Prostate cancer proteomics: clinically useful protein biomarkers and future perspectives

INTRODUCTION

Although prostate cancer constitutes one of the most important, death-related diseases in the male population, there is still a need for identification of sensitive biomarkers that could precociously detect the disease and differentiate aggressive from indolent cancers, in order to decrease overtreatment. Proteomics research has improved understanding on mechanisms underlying tumorigenesis, cancer cells migration and invasion potential, and castration resistance. This review has focused on proteomic studies of prostate cancer published in the recent years, with a special emphasis on determination of biomarkers for cancer progression and diagnosis. Areas covered: Shotgun and targeted-proteomic studies of prostate cancer in different matrices are reviewed, i.e., prostate tissue, prostate cell lines, blood (serum and plasma), urine, seminal plasma, and exosomes. The most important biomarkers for cancer diagnosis and aggressiveness characterization are highlighted. Expert commentary: In general, results demonstrate alteration in cell cycle control, DNA repair, proteasomal degradation, and metabolic activity. However, these studies suffer from low reproducibility due to heterogeneity of the cancer itself, as well as to techniques utilized for protein identification/quantification. Downstream confirmatory studies in separate cohorts are warranted in order to demonstrate accuracy of these results.

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

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

Mass spectrometry based proteomics analyses in kallikrein-related peptidase research: implications for cancer research and therapy

INTRODUCTION

Kallikrein-related peptidases (KLKs) are a family of serine peptidases that are deregulated in numerous pathological conditions, with a multitude of KLK-mediated functional roles implicated in the progression of cancer. Advances in multidimensional mass spectrometry (MS)-based proteomics have facilitated the quantitative measurement of deregulated KLK expression in cancer, identifying certain KLKs, as well as their substrates, as potential cancer biomarkers. Areas covered: In this review, we discuss how these approaches have been utilized for KLK biomarker discovery and unbiased substrate determination in complex protein pools that mimic the in vivo extracellular microenvironment. Expert commentary: Although a limited number of studies have been performed, the quantity of information generated has greatly improved our understanding of the functional roles of KLKs in cancer progression. In addition, these data suggest additional means through which deregulated KLK expression may be targeted in cancer treatment, highlighting the potential therapeutic value of these state-of-the-art MS-based studies.

Hypoxia-Induced Changes in the Fibroblast Secretome, Exosome, and Whole-Cell Proteome Using Cultured, Cardiac-Derived Cells Isolated from Neonatal Mice

Cardiac fibroblasts (CFs) represent a major subpopulation of cells in the developing and adult heart. Cardiomyocyte (CM) and CF intercellular communication occurs through paracrine interactions and modulate myocyte development and stress response. Detailed proteomic analysis of the CF secretome in normal and stressed conditions may offer insights into the role of CF in heart development and disease. Primary neonatal mouse CFs were isolated and cultured for 24 h in 21% (normoxic) or 2% (hypoxic) O₂. Conditioned medium was separated to obtain exosomes (EXO) and EXO-depleted secretome fractions. Multidimensional protein identification technology was performed on secreted fractions. Whole cell lysate data were also generated to provide subcellular context to the secretome. Proteomic analysis identified 6163 unique proteins in total. Statistical (QSpec) analysis identified 494 proteins differentially expressed between fractions and oxygen conditions. Gene Ontology enrichment analysis revealed hypoxic conditions selectively increase expression of proteins with extracellular matrix and signaling annotations. Finally, we subjected CM pretreated with CF secreted factors to hypoxia/reoxygenation. Viability assays suggested altered viability due to CF-derived factors. CF secretome proteomics revealed differential expression based on mode of secretion and oxygen levels in vitro.

Characterization of Protein Content Present in Exosomes Isolated from Conditioned Media and Urine

Cells secrete biomolecules into the extracellular space as a way of intercellular communication. Secreted proteins can act as ligands that engage specific receptors-on the same cell, nearby cells, or distant cells-and induce defined signaling pathways. Proteins and other biomolecules can also be packaged as cargo molecules within vesicles that are released to the extracellular space (termed extracellular vesicles or EVs). A subclass of such EVs, exosomes have been shown to horizontally transfer information. In recent years, exosomes have sparked tremendous interest in biological research, both for the discovery of novel biomarkers and for the identification of signaling molecules, as part of their cargo. Although multiple methods have been described for the isolation of exosomes, described here is a simple differential centrifugation approach that is well suited for the isolation of exosomes from conditioned cell culture media and urine. Mass spectrometry provides an ideal method to comprehensively analyze the protein cargo of exosomes. © 2017 by John Wiley & Sons, Inc.

Evaluation of expressed prostatic secretion and serum using surface-enhanced Raman spectroscopy for the noninvasive detection of prostate cancer, a preliminary study

Surface-enhanced Raman spectroscopy (SERS) involving expressed prostatic secretion (EPS) and serum was investigated; the objective was to determine if this approach could distinguish prostate cancer from benign prostatic hyperplasia. A total of 120 SERS spectra for EPS and 96 spectra for serum were gathered from patients within a prospective contemporary biopsy cohort. Significant differences in spectra between prostate cancer and benign prostatic hyperplasia were tentatively assigned to component changes in EPS and serum samples. Principal component analysis and linear discriminate analysis were utilized to evaluate the spectral data for EPS and serum, to build diagnostic algorithms. The leave-one-out cross-validation method was used to validate the diagnostic algorithms; it revealed diagnostic sensitivities of 75% and 60%, specificities of 75% and 76.5%, and accuracies of 75% and 68% for EPS and serum, respectively. The results suggest that EPS and serum SERS analysis could be a potential tool for prostate cancer detection.

Integrative analysis to select cancer candidate biomarkers to targeted validation

Targeted proteomics has flourished as the method of choice for prospecting for and validating potential candidate biomarkers in many diseases. However, challenges still remain due to the lack of standardized routines that can prioritize a limited number of proteins to be further validated in human samples. To help researchers identify candidate biomarkers that best characterize their samples under study, a well-designed integrative analysis pipeline, comprising MS-based discovery, feature selection methods, clustering techniques, bioinformatic analyses and targeted approaches was performed using discovery-based proteomic data from the secretomes of three classes of human cell lines (carcinoma, melanoma and non-cancerous). Three feature selection algorithms, namely, Beta-binomial, Nearest Shrunken Centroids (NSC), and Support Vector Machine-Recursive Features Elimination (SVM-RFE), indicated a panel of 137 candidate biomarkers for carcinoma and 271 for melanoma, which were differentially abundant between the tumor classes. We further tested the strength of the pipeline in selecting candidate biomarkers by immunoblotting, human tissue microarrays, label-free targeted MS and functional experiments. In conclusion, the proposed integrative analysis was able to pre-qualify and prioritize candidate biomarkers from discovery-based proteomics to targeted MS.

Whole blood mRNA in prostate cancer reveals a four-gene androgen regulated panel

Due to increased sensitivity, the expression of circulating nucleotides is rapidly gaining popularity in cancer diagnosis. Whole blood mRNA has been used in studies on a number of cancers, most notably two separate studies that used whole blood mRNA to define non-overlapping signatures of prostate cancer that has become castration independent. Prostate cancer is known to rely on androgens for initial growth, and there is increasing evidence on the importance of the androgen axis in advanced disease. Using whole blood mRNA samples from patients with prostate cancer, we have identified the four-gene panel of FAM129A, MME, KRT7 and SOD2 in circulating mRNA that are differentially expressed in a discovery cohort of metastatic samples. Validation of these genes at the mRNA and protein level was undertaken in additional cohorts defined by risk of relapse following surgery and hormone status. All the four genes were downregulated at the mRNA level in the circulation and in primary tissue, but this was not always reflected in tissue protein expression. MME demonstrated significant differences in the hormone cohorts, whereas FAM129A is downregulated at the mRNA level but is raised at the protein level in tumours. Using published ChIP-seq data, we have demonstrated that this may be due to AR binding at the FAM129A and MME loci in multiple cell lines. These data suggest that whole blood mRNA of androgen-regulated genes has the potential to be used for diagnosis and monitoring of prostate cancer.

Exosomal proteins as prostate cancer biomarkers in urine: From mass spectrometry discovery to immunoassay-based validation

Exosomes have recently appeared as a novel source of non-invasive cancer biomarkers since tumor-specific molecules can be found in exosomes isolated from biological fluids. We have previously analyzed the proteome of urinary exosomes by mass spectrometry, and identified proteins differentially expressed in prostate cancer patients compared to healthy males. Since mass spectrometry is so far not commonly used in clinical laboratories, we have here investigated whether antibody-based methods such as Western blot or ELISA can be used to validate the use of the identified proteins as prostate cancer biomarkers. Western blot experiments designed to detect flotillin 2, TMEM256, Rab3B and LAMTOR1 showed that the level of these proteins was higher in urinary exosomes from prostate cancer patients compared to healthy males. Furthermore, a receiver operating characteristic curve of flotillin 2 in samples from 16 controls and 16 patients showed an area under the curve of 0.91, and 88% sensitivity at a threshold set to give 94% specificity. In addition, ELISA-based detection of flotillin 2 and PARK7 showed that the combination of these proteins was able to distinguish prostate cancer patients and healthy controls with 68% sensitivity and 93% specificity. Several promising biomarkers identified by mass spectrometry could not be evaluated by Western blot or ELISA due to their low exosomal amount and/or lack of good antibodies. In conclusion, our results show that several urinary exosomal proteins identified as prostate cancer biomarkers by mass spectrometry have a high diagnostic value also when analyzed by immunology-based methods, thus bringing these biomarkers closer to a potential clinical use.

The Role of Proteomics in Biomarker Development for Improved Patient Diagnosis and Clinical Decision Making in Prostate Cancer

Prostate Cancer (PCa) is the second most commonly diagnosed cancer in men worldwide. Although increased expression of prostate-specific antigen (PSA) is an effective indicator for the recurrence of PCa, its intended use as a screening marker for PCa is of considerable controversy. Recent research efforts in the field of PCa biomarkers have focused on the identification of tissue and fluid-based biomarkers that would be better able to stratify those individuals diagnosed with PCa who (i) might best receive no treatment (active surveillance of the disease); (ii) would benefit from existing treatments; or (iii) those who are likely to succumb to disease recurrence and/or have aggressive disease. The growing demand for better prostate cancer biomarkers has coincided with the development of improved discovery and evaluation technologies for multiplexed measurement of proteins in bio-fluids and tissues. This review aims to (i) provide an overview of these technologies as well as describe some of the candidate PCa protein biomarkers that have been discovered using them; (ii) address some of the general limitations in the clinical evaluation and validation of protein biomarkers; and (iii) make recommendations for strategies that could be adopted to improve the successful development of protein biomarkers to deliver improvements in personalized PCa patient decision making.

Targeted proteomics identifies liquid-biopsy signatures for extracapsular prostate cancer

Biomarkers are rapidly gaining importance in personalized medicine. Although numerous molecular signatures have been developed over the past decade, there is a lack of overlap and many biomarkers fail to validate in independent patient cohorts and hence are not useful for clinical application. For these reasons, identification of novel and robust biomarkers remains a formidable challenge. We combine targeted proteomics with computational biology to discover robust proteomic signatures for prostate cancer. Quantitative proteomics conducted in expressed prostatic secretions from men with extraprostatic and organ-confined prostate cancers identified 133 differentially expressed proteins. Using synthetic peptides, we evaluate them by targeted proteomics in a 74-patient cohort of expressed prostatic secretions in urine. We quantify a panel of 34 candidates in an independent 207-patient cohort. We apply machine-learning approaches to develop clinical predictive models for prostate cancer diagnosis and prognosis. Our results demonstrate that computationally guided proteomics can discover highly accurate non-invasive biomarkers.

Proteomic technologies are capable of identifying thousands of proteins in biological samples, but biomarker applications are lagging. Here the authors use Multiple Reaction Monitoring Mass Spectrometry to delineate peptide signatures that accurately distinguish between defined prostate cancer patient risk groups.

Prostasomes as a source of diagnostic biomarkers for prostate cancer

New biomarkers are needed to improve the diagnosis of prostate cancer. Similarly to healthy cells, prostate epithelial cancer cells produce extracellular vesicles (prostasomes) that can be isolated from seminal fluid, urine, and blood. Prostasomes contain ubiquitously expressed and prostate-specific membrane and cytosolic proteins, as well as RNA. Both quantitative and qualitative changes in protein, mRNA, long noncoding RNA, and microRNA composition of extracellular vesicles isolated from prostate cancer patients have been reported. In general, however, the identified extracellular vesicle-associated single-marker molecules or combinations of marker molecules require confirmation in large cohorts of patients to validate their specificity and sensitivity as prostate cancer markers. Complications include variable factors such as prostate manipulation and urine flux, as well as masking by ubiquitously expressed free molecules and extracellular vesicles from tissues other than the prostate. Herein, we propose that the most promising methods include comprehensive combinational screening for (mutant) RNA in prostasomes that are immunoisolated with antibodies targeting prostate-specific epitopes.

Bioinformatics analysis of differentially expressed proteins in prostate cancer based on proteomics data

We mined the literature for proteomics data to examine the occurrence and metastasis of prostate cancer (PCa) through a bioinformatics analysis. We divided the differentially expressed proteins (DEPs) into two groups: the group consisting of PCa and benign tissues (P&b) and the group presenting both high and low PCa metastatic tendencies (H&L). In the P&b group, we found 320 DEPs, 20 of which were reported more than three times, and DES was the most commonly reported. Among these DEPs, the expression levels of FGG, GSN, SERPINC1, TPM1, and TUBB4B have not yet been correlated with PCa. In the H&L group, we identified 353 DEPs, 13 of which were reported more than three times. Among these DEPs, MDH2 and MYH9 have not yet been correlated with PCa metastasis. We further confirmed that DES was differentially expressed between 30 cancer and 30 benign tissues. In addition, DEPs associated with protein transport, regulation of actin cytoskeleton, and the extracellular matrix (ECM)–receptor interaction pathway were prevalent in the H&L group and have not yet been studied in detail in this context. Proteins related to homeostasis, the wound-healing response, focal adhesions, and the complement and coagulation pathways were overrepresented in both groups. Our findings suggest that the repeatedly reported DEPs in the two groups may function as potential biomarkers for detecting PCa and predicting its aggressiveness. Furthermore, the implicated biological processes and signaling pathways may help elucidate the molecular mechanisms of PCa carcinogenesis and metastasis and provide new targets for clinical treatment.

Recent progress in urinary proteome analysis for prostate cancer diagnosis and management

Prostate cancer (PCa) is fifth leading cause of cancer-associated deaths in men worldwide. Although the application of the serum prostate-specific antigen (PSA) screening test resulted in an increase in the PCa diagnosed cases, it demonstrated a negligible benefit regarding the associated mortality. Treatment options vary, with active surveillance to be preferable for patients with low-risk PCa and therapy of advanced castration-resistant PCa to rely on α-emitters and cytotoxic chemotherapy. Although recent developments have led to the approval of novel drugs for the treatment of castration-resistant PCa, the optimal sequence and timing of medication have not been yet determined. New screening modalities could improve the discriminatory accuracy between tumors with favorable clinical prognosis. Implementation of proteomic-based biomarkers appears to be a promising improvement, which could enable a more accurate diagnosis, guide treatment and improve patient outcome. Reviewed here are urinary proteome-based approaches for detection of PCa and patient management.

Proteomics in pharmaceutical research and development

In the 20 years since its inception, the evolution of proteomics in pharmaceutical industry has mirrored the developments within academia and indeed other industries. From initial enthusiasm and subsequent disappointment in global protein expression profiling, pharma research saw the biggest impact when relating to more focused approaches, such as those exploring the interaction between proteins and drugs. Nowadays, proteomics technologies have been integrated in many areas of pharmaceutical R&D, ranging from the analysis of therapeutic proteins to the monitoring of clinical trials. Here, we review the development of proteomics in the drug discovery process, placing it in a historical context as well as reviewing the current status in light of the contributions to this special issue, which reflect some of the diverse demands of the drug and biomarker pipelines.

InteractiVenn: a web-based tool for the analysis of sets through Venn diagrams

BACKGROUND

Set comparisons permeate a large number of data analysis workflows, in particular workflows in biological sciences. Venn diagrams are frequently employed for such analysis but current tools are limited.

RESULTS

We have developed InteractiVenn, a more flexible tool for interacting with Venn diagrams including up to six sets. It offers a clean interface for Venn diagram construction and enables analysis of set unions while preserving the shape of the diagram. Set unions are useful to reveal differences and similarities among sets and may be guided in our tool by a tree or by a list of set unions. The tool also allows obtaining subsets' elements, saving and loading sets for further analyses, and exporting the diagram in vector and image formats. InteractiVenn has been used to analyze two biological datasets, but it may serve set analysis in a broad range of domains.

CONCLUSIONS

InteractiVenn allows set unions in Venn diagrams to be explored thoroughly, by consequence extending the ability to analyze combinations of sets with additional observations, yielded by novel interactions between joined sets. InteractiVenn is freely available online at: www.interactivenn.net .

InteractiVenn: a web-based tool for the analysis of sets through Venn diagrams

Background

Set comparisons permeate a large number of data analysis workflows, in particular workflows in biological sciences. Venn diagrams are frequently employed for such analysis but current tools are limited.

Results

We have developed InteractiVenn, a more flexible tool for interacting with Venn diagrams including up to six sets. It offers a clean interface for Venn diagram construction and enables analysis of set unions while preserving the shape of the diagram. Set unions are useful to reveal differences and similarities among sets and may be guided in our tool by a tree or by a list of set unions. The tool also allows obtaining subsets’ elements, saving and loading sets for further analyses, and exporting the diagram in vector and image formats. InteractiVenn has been used to analyze two biological datasets, but it may serve set analysis in a broad range of domains.

Conclusions

InteractiVenn allows set unions in Venn diagrams to be explored thoroughly, by consequence extending the ability to analyze combinations of sets with additional observations, yielded by novel interactions between joined sets. InteractiVenn is freely available online at: www.interactivenn.net.