Clinical Validation of IsoPSA™, a Single Parameter, Structure Based Assay for Improved Detection of High Grade Prostate Cancer.

PURPOSE: Current prostate specific antigen markers to detect prostate cancer are limited by low specificity for high grade disease. IsoPSA™ is a blood based, structure focused assay which predicts risk by partitioning the isoforms of prostate specific antigen that are linked to cancer in an aqueous 2-phase reagent system. We validated the clinical performance of this assay for identifying high grade disease in a new contemporary biopsy cohort. MATERIALS AND METHODS: We performed a multicenter prospective validation in 271 men scheduled for prostate biopsy at a total of 7 academic and community centers who were enrolled between May 2017 and March 2018. Blood samples were obtained for assay prior to biopsy. The discrimination power of the assay to detect high grade prostate cancer (Gleason 7 or greater) was evaluated by ROC analysis and compared to prior results. Clinical performance was further improved by comparison with multiparametric magnetic resonance imaging-ultrasound vs transrectal ultrasound guided biopsies. RESULTS: The assay AUC was 0.784 for high grade vs low grade cancer/benign histology, which was superior to the AUCs of total prostate specific antigen and percent free prostate specific antigen. If 1,000 patients were biopsied, the assay would have reduced the number of unnecessary biopsies from 705 to 402 (43%) with only 22 missed high grade cancers, of which 7 would have been Gleason sum 4 + 3 or higher. Subset analysis of multiparametric magnetic resonance imaging guided biopsy produced a substantial improvement of the AUC to 0.831. CONCLUSIONS: Validation of the structure based IsoPSA assay demonstrated statistical concordance with previously reported results and verified its superior performance vs concentration based prostate specific antigen and the free-to-total prostate specific antigen ratio. The assay improvement in detecting high grade prostate cancer using multiparametric magnetic resonance imaging-ultrasound guided biopsy may help define a new diagnostic paradigm.

Analytical applications of partitioning in aqueous two-phase systems: Exploring protein structural changes and protein-partner interactions in vitro and in vivo by solvent interaction analysis method.

This review covers the fundamentals of protein partitioning in aqueous two-phase systems (ATPS). Included is a review of advancements in the analytical application of solute partitioning in ATPS over the last two decades, with multiple examples of experimental data providing evidence that phase-forming polymers do not interact with solutes partitioned in ATPS. The partitioning of solutes is governed by the differences in solute interactions with aqueous media in the two phases. Solvent properties of the aqueous media in these two phases may be characterized and manipulated. The solvent interaction analysis (SIA) method, based on the solute partitioning in ATPS, may be used for characterization and analysis of individual proteins and their interactions with different partners. The current state of clinical proteomics regarding the discovery and monitoring of new protein biomarkers is discussed, and it is argued that the protein expression level in a biological fluid may be not the optimal focus of clinical proteomic research. Multiple examples of application of the SIA method for discovery of changes in protein structure and protein-partner interactions in biological fluids are described. The SIA method reveals new opportunities for discovery and monitoring structure-based protein biomarkers.

Solvent interaction analysis as a proteomic approach to structure-based biomarker discovery and clinical diagnostics.

Proteins have several measurable features in biological fluids that may change under pathological conditions. The current disease biomarker discovery is mostly based on protein concentration in the sample as the measurable feature. Changes in protein structures, such as post-translational modifications and in protein-partner interactions are known to accompany pathological processes. Changes in glycosylation profiles are well-established for many plasma proteins in various types of cancer and other diseases. The solvent interaction analysis method is based on protein partitioning in aqueous two-phase systems and is highly sensitive to changes in protein structure and protein-protein- and protein-partner interactions while independent of the protein concentration in the biological sample. It provides quantitative index: partition coefficient representing changes in protein structure and interactions with partners. The fundamentals of the method are presented with multiple examples of applications of the method to discover and monitor structural protein biomarkers as disease-specific diagnostic indicators.

High throughput characterization of structural differences between closely related proteins in solution.

Partitioning of a protein in an aqueous two-phase system (ATPS) is governed by interactions of the protein with aqueous media in the two phases. Here we describe how partitioning of proteins in a set of ATPS of different compositions can be used to quantify differences between 3D structures of closely related proteins. We also provide perspective on practical applications of the technology when comparative analysis of the higher-order structure of proteins is desired.

The Single-parameter, Structure-based IsoPSA Assay Demonstrates Improved Diagnostic Accuracy for Detection of Any Prostate Cancer and High-grade Prostate Cancer Compared to a Concentration-based Assay of Total Prostate-specific Antigen: A Preliminary Report.

BACKGROUND: IsoPSA is a serum-based assay that predicts prostate cancer (PCa) risk by partitioning isoforms of prostate-specific antigen (PSA) with an aqueous two-phase reagent. OBJECTIVES: To determine the diagnostic accuracy of IsoPSA in identifying the presence or absence of PCa and the presence of high-grade disease in a contemporary biopsy cohort. DESIGN, SETTING, AND PARTICIPANTS: Multicenter prospective study of 261 men scheduled for prostate biopsy at five academic and community centers in the USA enrolled between August 2015 and December 2016. INTERVENTION: Performance of the IsoPSA assay. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Discrimination power was evaluated using receiver operating characteristic (ROC) analysis. The outcome of the IsoPSA assay was transformed into risk probability using logistic regression. Decision curve analysis (DCA) was used to compare the net benefit of IsoPSA against other clinical protocols. RESULTS AND LIMITATIONS: The overall prevalence was 53% for any PCa and 34% for high-grade PCa. The area under the ROC curve was 0.79 for any cancer versus none and 0.81 for high-grade PCa versus low-grade PCa/benign histology. In this preliminary study, DCA revealed a superior net benefit of IsoPSA against no biopsy, all biopsy, and the modified Prostate Cancer Prevention Trial Risk Calculator 2.0. At a cutoff selected to recommend biopsy, IsoPSA demonstrated a 48% reduction in false-positive biopsies; at a cutoff selected to identity men at low risk of high-grade disease, there was a 45% reduction in the false-positive rate. CONCLUSION: The structure-based IsoPSA assay outperformed concentration-based PSA measurement, and provided a net benefit against other protocols. Once validated, clinical use of IsoPSA could significantly reduce unnecessary biopsies while identifying patients needing treatment. PATIENT SUMMARY: The IsoPSA assay outperformed prostate-specific antigen in predicting the overall risk of prostate cancer and the risk of clinically significant cancer in a preliminary study. The IsoPSA assay could assist in determining the need for prostate biopsy for patients.

Protein crystallization: virtual screening and optimization.

Advances in genomics have yielded entire genetic sequences for a variety of prokaryotic and eukaryotic organisms. This accumulating information has escalated the demands for three-dimensional protein structure determinations. As a result, high-throughput structural genomics has become a major international research focus. This effort has already led to several significant improvements in X-ray crystallographic and nuclear magnetic resonance methodologies. Crystallography is currently the major contributor to three-dimensional protein structure information. However, the production of soluble, purified protein and diffraction-quality crystals are clearly the major roadblocks preventing the realization of high-throughput structure determination. This paper discusses a novel approach that may improve the efficiency and success rate for protein crystallization. An automated nanodispensing system is used to rapidly prepare crystallization conditions using minimal sample. Proteins are subjected to an incomplete factorial screen (balanced parameter screen), thereby efficiently searching the entire "crystallization space" for suitable conditions. The screen conditions and scored experimental results are subsequently analyzed using a neural network algorithm to predict new conditions likely to yield improved crystals. Results based on a small number of proteins suggest that the combination of a balanced incomplete factorial screen and neural network analysis may provide an efficient method for producing diffraction-quality protein crystals.

Relative hydrophobicity and lipophilicity of drugs measured by aqueous two-phase partitioning, octanol-buffer partitioning and HPLC. A simple model for predicting blood-brain distribution.

Relative hydrophobicity and lipophilicity of 63 compounds with known permeability through the blood-brain barrier (BBB) was examined by partitioning in aqueous dextran-poly(ethylene glycol) two-phase system and octanol-buffer system, and by gradient RP-HPLC at pH 7.4. Combination of the relative hydrophobicity estimates, N(CH(2)) obtained by aqueous two-phase partitioning and the lipophilicity (logD(exp) or logD(HPLC)) values obtained by the shake-flask technique or HPLC technique allows one to differentiate between compounds capable of crossing the BBB and those that cannot. A simple model for predicting blood-brain distribution is proposed.

Relative hydrophobicity and lipophilicity of beta-blockers and related compounds as measured by aqueous two-phase partitioning, octanol-buffer partitioning, and HPLC.

Partitioning of 15 beta-blockers and structurally related compounds was examined in aqueous dextran-PEG two-phase systems and octanol-buffer systems at pH from 2.0 up to 12.5. The same compounds were examined by gradient RP-HPLC at pH 2.0, 7.4, and 11.0. The differences between the hydrophobic character of the phases in all three systems at different pH values were characterized using a homologous series of dinitrophenyl-amino acids by measuring the free energy of transfer of a methylene group. Estimates of the relative hydrophobicity, N(CH(2)), and lipophilicity, logD, of the compounds obtained by the three techniques employed were compared. The data indicate that while similar pH profiles for a given compound were established by all these techniques, the information provided is different. It is suggested that the combination of the two descriptors, logD and N(CH(2)), may be useful for quantitative structure-activity relationship analysis of the biological activities involving distribution and/or transport of chemical compounds in biological systems.

Solvatochromic relationship: prediction of distribution of ionic solutes in aqueous two-phase systems.

Partition ratios of several ionic compounds in 20 different polymer/polymer aqueous two-phase systems (ATPS) containing 0.15 M NaCl in 0.01 M phosphate buffer, pH 7.4, were determined. The differences between the electrostatic properties of the phases in all the ATPS were estimated from partitioning of the homologous series of dinitrophenylated-amino acids. Also the solvatochromic solvent parameters characterizing the solvent dipolarity/polarizability (π*), solvent hydrogen-bond donor acidity (α), and solvent hydrogen-bond acceptor basicity (ß) of aqueous media were measured in the coexisting phases of the ATPS. The solute-specific coefficients for the compounds examined were determined by the multiple linear regression analysis using the modified linear solvation energy relationship equation. The minimal number of ATPS necessary for determination of the coefficients was established and 10 ATPS were selected as a reference ATPS set. The solute-specific coefficients values obtained with this reference set of ATPS were used to predict the partition ratios for the compounds in 10 ATPS not included in the reference set. The predicted partition ratios values were compared to those determined experimentally and found to be in good agreement. It is concluded that the presented model of solute-solvent interactions as the driving force for solute partitioning in polymer/polymer ATPS describes experimental observations with 90-95% accuracy.

Effect of salt additives on partition of nonionic solutes in aqueous PEG-sodium sulfate two-phase system.

Partition of 12 nonionic organic compounds in aqueous PEG-8000-Na(2)SO(4) two-phase system was examined. Effects of four salt additives (NaCl, NaSCN, NaClO(4), and NaH(2)PO(4)) in the concentration range from 0.027 up to ca. 1.9 M on binodal curve of PEG-sulfate two-phase system and solute partitioning were explored. It was found that different salt additives at the relatively high concentrations display different effects on both phase separation and partition of various nonionic solutes. Analysis of the results indicates that the PEG-Na(2)SO(4) ATPS with the up to 0.215 M NaCl concentration may be viewed as similar to the ATPS without NaCl in terms of the Collander equation's predictive ability of the partitioning behavior of nonionic compounds. All ATPS with each of the salt additive used at the concentration of 0.027 M may be viewed as similar to each other as the Collander equation holds for partition coefficients of nonionic solutes in these ATPS. Collander equation is valid also for the compounds examined in the ATPS with additives of NaSCN and NaClO(4) at the concentrations up to 0.215 M. The observed similarity between these ATPS might be explained by the similar effects of these two salts on the water structure. At concentrations of the salt additives exceeding the aforementioned values, different effects of salt additives on partitioning of various nonionic solutes are displayed. In order to explain these effects of salt additives it is necessary to examine the intensities of different solute-solvent interactions in these ATPS within the framework of the so-called Linear Solvation Energy Relationship (LSER) model.

Solvent properties governing protein partitioning in polymer/polymer aqueous two-phase systems.

Distribution coefficients of various proteins were measured in aqueous Dextran-Ficoll, Dextran-PES, and Ficoll-PES two-phase systems, containing 0.15M NaCl in 0.01 M phosphate buffer, pH 7.4. The acquired data were combined with data for the same proteins in different systems reported previously and known solvatochromic solvent properties of the systems to characterize the protein-solvent interactions. The relative susceptibilities of proteins to solvent dipolarity/polarizability, solvent hydrogen bond acidity, solvent hydrogen bond basicity, and solvent ability to participate in ion-ion and ion-dipole interactions were characterized. These parameters, which are representative of solute-solvent interactions, adequately described the partitioning of the proteins in each system. It was found that the relative susceptibilities of proteins to solvent dipolarity/polarizability are interrelated with their relative susceptibilities to solvent hydrogen bond acidity and solvent hydrogen bond basicity similarly to those established previously for small nonionic organic compounds.

Prostate-specific antigen/solvent interaction analysis: a preliminary evaluation of a new assay concept for detecting prostate cancer using urinary samples.

OBJECTIVE: To provide preliminary clinical performance evaluation of a novel prostate cancer (CaP) assay, prostate-specific antigen/solvent interaction analysis (PSA/SIA) that focused on changes to the structure of PSA. METHODS: Two-hundred twenty-two men undergoing prostate biopsy for accepted clinical criteria at 3 sites (University Hospitals Case Medical Center in Cleveland, Cleveland Clinic, and Veterans Administration Boston Healthcare System) were enrolled in institutional review board-approved study. Before transrectal ultrasound-guided biopsy, patients received digital rectal examination with systematic prostate massage followed by collection of urine. The PSA/SIA assay determined the relative partitioning of heterogeneous PSA isoform populations in urine between 2 aqueous phases. A structural index, K, whose numerical value is defined as the ratio of the concentration of all PSA isoforms, was determined by total PSA enzyme-linked immunosorbent assay and used to set a diagnostic threshold for CaP. Performance was assessed using receiver operating characteristic (ROC) analysis with biopsy as the gold standard. RESULTS: Biopsies were pathologically classified as case (malignant, n=100) or control (benign, n=122). ROC performance demonstrated area under the curve=0.90 for PSA/SIA and 0.58 for serum total PSA. At a cutoff value of k=1.73, PSA/SIA displayed sensitivity=100%, specificity=80.3%, positive predictive value=80.6%, and negative predictive value=100%. No attempt was made in this preliminary study to further control patient population or selection criteria for biopsy, nor did we analytically investigate the type of structural differences in PSA that led to changes in k value. CONCLUSION: PSA/SIA provides ratiometric information independently of PSA concentration. In this preliminary study, analysis of the overall structurally heterogeneous PSA isoform population using the SIA assay showed promising results to be further evaluated in future studies.

Efficient protein crystallization.

High-throughput molecular biology and crystallography advances have placed an increasing demand on crystallization, the one remaining bottleneck in macromolecular crystallography. This paper describes three experimental approaches, an incomplete factorial crystallization screen, a high-throughput nanoliter crystallization system, and the use of a neural net to predict crystallization conditions via a small sample (approximately 0.1%) of screening results. The use of these technologies has the potential to reduce time and sample requirements. Initial experimental results indicate that the incomplete factorial design detects initial crystallization conditions not previously discovered using commercial screens. This may be due to the ability of the incomplete factorial screen to sample a broader portion of "crystallization space," using a multidimensional set of components, concentrations, and physical conditions. The incomplete factorial screen is complemented by a neural network program used to model crystallization. This capability is used to help predict new crystallization conditions. An automated, nanoliter crystallization system, with a throughput of up to 400 conditions/h in 40-nl droplets (total volume), accommodates microbatch or traditional "sitting-drop" vapor diffusion experiments. The goal of this research is to develop a fully-automated high-throughput crystallization system that integrates incomplete factorial screen and neural net capabilities.