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The isolation and subsequent molecular analysis of extracellular vesicles (EVs) derived from patient samples is a widely used strategy to understand vesicle biology and to facilitate biomarker discovery. Expressed prostatic secretions in urine are a tumor proximal fluid that has received significant attention as a source of potential prostate cancer (PCa) biomarkers for use in liquid biopsy protocols. Standard EV isolation methods like differential ultracentrifugation (dUC) co-isolate protein contaminants that mask lower-abundance proteins in typical mass spectrometry (MS) protocols. Further complicating the analysis of expressed prostatic secretions, uromodulin, also known as Tamm-Horsfall protein (THP), is present at high concentrations in urine. THP can form polymers that entrap EVs during purification, reducing yield. Disruption of THP polymer networks with dithiothreitol (DTT) can release trapped EVs, but smaller THP fibres co-isolate with EVs during subsequent ultracentrifugation. To resolve these challenges, we describe here a dUC method that incorporates THP polymer reduction and alkaline washing to improve EV isolation and deplete both THP and other common protein contaminants. When applied to human expressed prostatic secretions in urine, we achieved relative enrichment of known prostate and prostate cancer-associated EV-resident proteins. Our approach provides a promising strategy for global proteomic analyses of urinary EVs.

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.

PurposeThe rs17632542 single nucleotide polymorphism (SNP) results in lower serum prostate specific antigen (PSA) levels which may further mitigate against its clinical utility as a prostate cancer biomarker. Post-digital rectal exam (post-DRE) urine is a minimally invasive fluid that is currently utilized in prostate cancer diagnosis. To detect and quantitate the variant protein in urine.Experimental designFifty-three post-DRE urines from rs17632542 genotyped individuals processed and analyzed by liquid chromatography/mass spectrometry (LC-MS) in a double-blinded randomized study. The ability to distinguish between homozygous wild-type, heterozygous, or homozygous variant is examined before unblinding.ResultsStable-isotope labeled peptides are used in the detection and quantitation of three peptides of interest in each sample using parallel reaction monitoring (PRM). Using these data, groupings are predicted using hierarchical clustering in R. Accuracy of the predictions show 100% concordance across the 53 samples, including individuals homozygous and heterozygous for the SNP.Conclusions and clinical relevanceThe study demonstrates that MS based peptide variant quantitation in urine could be useful in determining patient genotype expression. This assay provides a tool to evaluate the utility of PSA variant (rs17632542) in parallel with current and forthcoming urine biomarker panels.

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.

Bone metastasis is a frequent and incurable consequence of advanced prostate cancer (PC). An interplay between disseminated tumor cells and heterogeneous bone resident cells in the metastatic niche initiates this process. Melanoma differentiation associated gene-9 (mda-9/Syntenin/syndecan binding protein) is a prometastatic gene expressed in multiple organs, including bone marrow-derived mesenchymal stromal cells (BM-MSCs), under both physiological and pathological conditions. We demonstrate that PDGF-AA secreted by tumor cells induces CXCL5 expression in BM-MSCs by suppressing MDA-9-dependent YAP/MST signaling. CXCL5-derived tumor cell proliferation and immune suppression are consequences of the MDA-9/CXCL5 signaling axis, promoting PC disease progression. mda-9 knockout tumor cells express less PDGF-AA and do not develop bone metastases. Our data document a previously undefined role of MDA-9/Syntenin in the tumor and microenvironment in regulating PC bone metastasis. This study provides a framework for translational strategies to ameliorate health complications and morbidity associated with advanced PC. [ABSTRACT FROM AUTHOR]

Abstract Background Diabetic patients are at increased risk for bacterial infections; these studies provide new insight into the role of the host defense complement system in controlling bacterial pathogens in hyperglycemic environments. Methods The interactions of complement C3 with bacteria in elevated glucose were assayed for complement activation to opsonic forms, phagocytosis and bacterial killing. C3 was analyzed in euglycemic and hyperglycemic conditions by mass spectrometry to measure glycation and structural differences. Results Elevated glucose inhibited S. aureus activation of C3 and deposition of C3b and iC3b on the bacterial surface. S. aureus-generated C5a and serum-mediated phagocytosis by neutrophils were both decreased in elevated glucose conditions. Interestingly, elevated glucose increased the binding of unactivated C3 to S. aureus, which was reversible on return to normal glucose concentrations. In a model of polymicrobial infection, S. aureus in elevated glucose conditions depleted C3 from serum resulting in decreased complement-mediated killing of E. coli. To investigate the effect of differing glucose concentration on C3 structure and glycation, purified C3 incubated with varying glucose concentrations was analyzed by mass spectrometry. Glycation was limited to the same three lysine residues in both euglycemic and hyperglycemic conditions over one hour, thus glycation could not account for observed changes between glucose conditions. However, surface labeling of C3 with sulfo-NHS-biotin showed significant changes in the surface availability of seven lysine residues in response to increasing glucose concentrations. These results suggest that the tertiary structure of C3 changes in response to hyperglycemic conditions leading to an altered interaction of C3 with bacterial pathogens. Conclusions These results demonstrate that hyperglycemic conditions inhibit C3-mediated complement effectors important in the immunological control of S. aureus. Mass spectrometric analysis reveals that the glycation state of C3 is the same regardless of glucose concentration over a one-hour time period. However, in conditions of elevated glucose C3 appears to undergo structural changes.