Accurate diagnosis of prostate cancer by combining Proclarix with magnetic resonance imaging.

OBJECTIVES: To assess of the clinical performance of Proclarix® (a novel Conformité Européenne [CE]-marked biomarker test aiding in the identification of clinically significant prostate cancer [csPCa]) alone or in combination with multiparametric magnetic resonance imaging (mpMRI) to predict csPCa (International Society of Urological Pathology Grade Group ≥2). PATIENTS AND METHODS: The study included blood samples from 721 men undergoing mpMRI followed by biopsy at University College London, London, and Vall d'Hebron University Hospital, Barcelona. Samples were tested blindly. The Proclarix-MRI model combining prostate volume, Proclarix and mpMRI results was trained using the UCL cohort (n = 159) and validated in the Vall d'Hebron cohort (n = 562). Its diagnostic performance was established in correlation to biopsy outcome and compared to available clinical parameters and risk calculators. RESULTS: Clinical performance of the Proclarix-MRI model in the validation cohort did not significantly differ from the training cohort and resulted in a sensitivity for csPCa of 90%, 90% negative predictive value and 66% positive predictive value. The Proclarix-MRI score's specificity (68%) was significantly (P < 0.001) better than the MRI-European Randomized study of Screening for Prostate Cancer risk score (51%), Proclarix (27%) or mpMRI (28%) alone. In addition, Proclarix by itself was found to be useful in the MRI Prostate Imaging-Reporting and Data System (PI-RADS) score 3 subgroup by outperforming prostate-specific antigen density in terms of specificity (25% vs 13%, P = 0.004) at 100% sensitivity. CONCLUSION: When combined with mpMRI and prostate volume, Proclarix reliably predicted csPCa and ruled out men with no or indolent cancer. A large reduction of two thirds of unneeded biopsies was achieved. Proclarix can further be used with high confidence to reliably detect csPCa in men with an indeterminate PI-RADS score 3 mpMRI. Despite these encouraging results, further validation is needed.

New strategy for the identification of prostate cancer: The combination of Proclarix and the prostate health index.

OBJECTIVES: Prostate health index (PHI) and, more recently, Proclarix have been proposed as serum biomarkers for prostate cancer (PCa). In this study, we aimed to evaluate Proclarix and PHI for predicting clinically significant prostate cancer (csPCa). PATIENTS AND METHODS: Proclarix and PHI were measured using samples of 344 men from two different centers. All patients underwent prostate biopsy, and among those, 188 men with PCa on biopsy had an additional radical prostatectomy (RP). All men had a prostate-specific antigen (PSA) between 2 and 10 ng/ml. Evaluation of area under the curve (AUC) and performance at predefined cut-offs of Proclarix and PHI risk scores as well as the linear combination thereof was performed to predict csPCa. PSA density was used as an independent comparator. RESULTS: The cohort median age and PSA were 65 (interquartile range [IQR]: 60-71) and 5.6 (IQR: 4.3-7.2) ng/ml, respectively. CsPCa was diagnosed in 161 (47%) men based on the RP specimen. ROC analysis showed that Proclarix and PHI accurately predicted csPCa with no significant difference (AUC of 0.79 and 0.76, p = 0.378) but significantly better when compared to PSA density (AUC of 0.66, p < 0.001). When using specific cut-offs, Proclarix (cut-off 10) revealed higher specificity and positive predictive value than PHI (cut-off 27) at similar sensitivities. The combination of Proclarix and PHI provided a significant increase in the AUC (p ≤ 0.007) compared to the individual tests alone and the highest clinical benefit was achieved. CONCLUSION: Results of this study show that both Proclarix and PHI accurately detect the presence of csPCa. The model combining Proclarix and PHI revealed the synergistic effect and improved the diagnostic performance of the individual tests.

Use of MS-GUIDE for identification of protein biomarkers for risk stratification of patients with prostate cancer.

BACKGROUND: Non-invasive liquid biopsies could complement current pathological nomograms for risk stratification of prostate cancer patients. Development and testing of potential liquid biopsy markers is time, resource, and cost-intensive. For most protein targets, no antibodies or ELISAs for efficient clinical cohort pre-evaluation are currently available. We reasoned that mass spectrometry-based prescreening would enable the cost-effective and rational preselection of candidates for subsequent clinical-grade ELISA development. METHODS: Using Mass Spectrometry-GUided Immunoassay DEvelopment (MS-GUIDE), we screened 48 literature-derived biomarker candidates for their potential utility in risk stratification scoring of prostate cancer patients. Parallel reaction monitoring was used to evaluate these 48 potential protein markers in a highly multiplexed fashion in a medium-sized patient cohort of 78 patients with ground-truth prostatectomy and clinical follow-up information. Clinical-grade ELISAs were then developed for two of these candidate proteins and used for significance testing in a larger, independent patient cohort of 263 patients. RESULTS: Machine learning-based analysis of the parallel reaction monitoring data of the liquid biopsies prequalified fibronectin and vitronectin as candidate biomarkers. We evaluated their predictive value for prostate cancer biochemical recurrence scoring in an independent validation cohort of 263 prostate cancer patients using clinical-grade ELISAs. The results of our prostate cancer risk stratification test were statistically significantly 10% better than results of the current gold standards PSA alone, PSA plus prostatectomy biopsy Gleason score, or the National Comprehensive Cancer Network score in prediction of recurrence. CONCLUSION: Using MS-GUIDE we identified fibronectin and vitronectin as candidate biomarkers for prostate cancer risk stratification.

Thrombospondin 1 and cathepsin D improve prostate cancer diagnosis by avoiding potentially unnecessary prostate biopsies.

OBJECTIVES: To investigate and further validate if two novel cancer-related glycoproteins, discovered by a genetic-guided proteomics approach, can distinguish benign disease from prostate cancer (PCa) in men with enlarged prostates. PATIENTS AND METHODS: A retrospective study was performed that included men with a total prostate-specific antigen (PSA) concentration of 2.0-10 ng/mL, negative digital rectal examination and enlarged prostate (volume ≥35 mL). Serum samples were collected between 2011 and 2016 at a single centre from 474 men before they underwent prostate biopsy. Serum concentrations of thrombospondin 1 (THBS1) and cathepsin D (CTSD) glycoproteins were combined with the percentage of free PSA to total PSA ratio (%fPSA) to predict any or significant cancer at biopsy. RESULTS: The multivariable logistic regression model including THBS1, CTSD and %fPSA discriminated among biopsy-positive and biopsy-negative patients in the validation set with an area under the curve (AUC) of 0.86 (P < 0.001, 95% confidence interval (CI) 0.82-0.91), while %fPSA alone showed an AUC of 0.64 (P < 0.001, 95% CI 0.57-0.71). At 90% sensitivity for PCa, the specificity of the model was 62%, while %fPSA had a specificity of 23%. For high grade (Gleason score ≥ 7 in prostatectomy specimen) PCa, the specificity was 48% at 90% sensitivity, with an AUC of 0.83, (P < 0.001, 95% CI 0.77 to 0.88). Limitations of the study include the retrospective set-up and single-centre cohort. CONCLUSIONS: A model combining two cancer-related glycoproteins (THBS1 and CTSD) and %fPSA can improve PCa diagnosis and may reduce the number of unnecessary prostate biopsies because of its improved specificity for PCa when compared to %fPSA alone.

N-glycoprotein SRMAtlas: a resource of mass spectrometric assays for N-glycosites enabling consistent and multiplexed protein quantification for clinical applications.

Protein biomarkers have the potential to transform medicine as they are clinically used to diagnose diseases, stratify patients, and follow disease states. Even though a large number of potential biomarkers have been proposed over the past few years, almost none of them have been implemented so far in the clinic. One of the reasons for this limited success is the lack of technologies to validate proposed biomarker candidates in larger patient cohorts. This limitation could be alleviated by the use of antibody-independent validation methods such as selected reaction monitoring (SRM). Similar to measurements based on affinity reagents, SRM-based targeted mass spectrometry also requires the generation of definitive assays for each targeted analyte. Here, we present a library of SRM assays for 5568 N-glycosites enabling the multiplexed evaluation of clinically relevant N-glycoproteins as biomarker candidates. We demonstrate that this resource can be utilized to select SRM assay sets for cancer-associated N-glycoproteins for their subsequent multiplexed and consistent quantification in 120 human plasma samples. We show that N-glycoproteins spanning 5 orders of magnitude in abundance can be quantified and that previously reported abundance differences in various cancer types can be recapitulated. Together, the established N-glycoprotein SRMAtlas resource facilitates parallel, efficient, consistent, and sensitive evaluation of proposed biomarker candidates in large clinical sample cohorts.

Cancer genetics-guided discovery of serum biomarker signatures for diagnosis and prognosis of prostate cancer.

A key barrier to the realization of personalized medicine for cancer is the identification of biomarkers. Here we describe a two-stage strategy for the discovery of serum biomarker signatures corresponding to specific cancer-causing mutations and its application to prostate cancer (PCa) in the context of the commonly occurring phosphatase and tensin homolog (PTEN) tumor-suppressor gene inactivation. In the first stage of our approach, we identified 775 N-linked glycoproteins from sera and prostate tissue of wild-type and Pten-null mice. Using label-free quantitative proteomics, we showed that Pten inactivation leads to measurable perturbations in the murine prostate and serum glycoproteome. Following bioinformatic prioritization, in a second stage we applied targeted proteomics to detect and quantify 39 human ortholog candidate biomarkers in the sera of PCa patients and control individuals. The resulting proteomic profiles were analyzed by machine learning to build predictive regression models for tissue PTEN status and diagnosis and grading of PCa. Our approach suggests a general path to rational cancer biomarker discovery and initial validation guided by cancer genetics and based on the integration of experimental mouse models, proteomics-based technologies, and computational modeling.

Evaluation of Proclarix in the diagnostic work-up of prostate cancer.

Objectives: The use of multiparametric magnetic resonance imaging (mpMRI) has been widely adopted in the diagnostic work-up for suspicious prostate cancer (PCa) and is recommended in most current guidelines. However, mpMRI lesions are often indeterminate and/or turn out to be false-positive on prostate biopsy. The aim of this work was to evaluate Proclarix, a biomarker test for the detection of relevant PCa, regarding its diagnostic value in all men before biopsy and in men with indeterminate lesions on mpMRI (PI-RADS 3) during work-up for PCa. Materials and Methods: Men undergoing mpMRI-targeted and systematic biopsy of the prostate were prospectively enrolled. The Proclarix test was evaluated for the detection accuracy of clinically significant PCa (csPCa) defined as Grade Group ≥ 2 and its association to mpMRI results. Further, Proclarix's performance was also tested when adapted to prostate volume (Proclarix density) and performance compared to PSA density (PSAD). Results: A total of 150 men with a median age of 65 years and median PSA of 5.8 ng/mL were included in this study. CsPCa was diagnosed in 65 (43%) men. Proclarix was significantly associated with csPCa and higher PI-RADS score (p < 0.001). At the pre-defined cut-off of 10%, Proclarix's sensitivity for csPCa was 94%, specificity 19%, negative predictive value 80% and positive predictive value 47%. Proclarix density showed the highest AUC for the detection of csPCa of 0.77 (95%CI: 0.69-0.85) compared to PSA, PSAD and Proclarix alone. Proclarix was able to identify all six csPCa in men with PI-RADS 3 lesions (n = 28), whereas PSAD missed two out of six. At optimized cut-offs, Proclarix density outperformed PSAD by potentially avoiding 41% of unnecessary biopsies. Conclusion: Proclarix demonstrates high sensitivity in detecting csPCa but may still result in unnecessary biopsies. However, Proclarix density was able to outperform PSAD and Proclarix and was found to be useful in men with PI-RADS 3 findings by safely avoiding unnecessary biopsies without missing csPCa.

PASSEL: the PeptideAtlas SRMexperiment library.

Public repositories for proteomics data have accelerated proteomics research by enabling more efficient cross-analyses of datasets, supporting the creation of protein and peptide compendia of experimental results, supporting the development and testing of new software tools, and facilitating the manuscript review process. The repositories available to date have been designed to accommodate either shotgun experiments or generic proteomic data files. Here, we describe a new kind of proteomic data repository for the collection and representation of data from selected reaction monitoring (SRM) measurements. The PeptideAtlas SRM Experiment Library (PASSEL) allows researchers to easily submit proteomic data sets generated by SRM. The raw data are automatically processed in a uniform manner and the results are stored in a database, where they may be downloaded or browsed via a web interface that includes a chromatogram viewer. PASSELenables cross-analysis of SRMdata, supports optimization of SRMdata collection, and facilitates the review process of SRMdata. Further, PASSELwill help in the assessment of proteotypic peptide performance in a wide array of samples containing the same peptide, as well as across multiple experimental protocols.

Comprehensive description of the N-glycoproteome of mouse pancreatic ß-cells and human islets.

Cell surface N-glycoproteins provide a key interface of cells to their environment and therapeutic entry points for drug and biomarker discovery. Their comprehensive description denotes therefore a formidable challenge. The ß-cells of the pancreas play a crucial role in blood glucose homeostasis, and disruption of their function contributes to diabetes. By combining cell surface and whole cell capturing technologies with high-throughput quantitative proteomic analysis, we report on the identification of a total of 956 unique N-glycoproteins from mouse MIN6 ß-cells and human islets. Three-hundred-forty-nine of these proteins encompass potential surface N-glycoproteins and include orphan G-protein-coupled receptors, novel proteases, receptor protein kinases, and phosphatases. Interestingly, stimulation of MIN6 ß-cells with glucose and the hormone GLP1, known stimulators of insulin secretion, causes significant changes in surface N-glycoproteome expression. Taken together, this ß-cell N-glycoproteome resource provides a comprehensive view on the composition of ß-cell surface proteins and expands the scope of signaling systems potentially involved in mediating responses of ß-cells to various forms of (patho)physiologic stress and the extent of dynamic remodeling of surface N-glycoprotein expression associated with metabolic and hormonal stimulation. Moreover, it provides a foundation for the development of diabetes medicines that target or are derived from the ß-cell surface N-glycoproteome.

PROPOSe: A Real-life Prospective Study of Proclarix, a Novel Blood-based Test to Support Challenging Biopsy Decision-making in Prostate Cancer.

BACKGROUND: Prostate-specific antigen (PSA)-based detection of prostate cancer (PCa) often leads to negative biopsy results or detection of clinically insignificant PCa, more frequently in the PSA range of 2-10 ng/ml, in men with increased prostate volume and normal digital rectal examination (DRE). OBJECTIVE: This study evaluated the accuracy of Proclarix, a novel blood-based diagnostic test, to help in biopsy decision-making in this challenging patient population. DESIGN, SETTING, AND PARTICIPANTS: Ten clinical sites prospectively enrolled 457 men presenting for prostate biopsy with PSA between 2 and 10 ng/ml, normal DRE, and prostate volume ≥35 cm3. Transrectal ultrasound-guided and multiparametric magnetic resonance imaging (mpMRI)-guided biopsy techniques were allowed. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Serum samples were tested blindly at the end of the study. Diagnostic performance of Proclarix risk score was established in correlation to systematic biopsy outcome and its performance compared with %free PSA (%fPSA) and the European Randomised Study of Screening for Prostate Cancer (ERSPC) risk calculator (RC) as well as Proclarix density compared with PSA density in men undergoing mpMRI. RESULTS AND LIMITATIONS: The sensitivity of Proclarix risk score for clinically significant PCa (csPCa) defined as grade group (GG) ≥2 was 91% (n = 362), with higher specificity than both %fPSA (22% vs 14%; difference = 8% [95% confidence interval {CI}, 2.6-14%], p = 0.005) and RC (22% vs 15%; difference = 7% [95% CI, 0.7-12%], p = 0.028). In the subset of men undergoing mpMRI-fusion biopsy (n = 121), the specificity of Proclarix risk score was significantly higher than PSA density (26% vs 8%; difference = 18% [95% CI, 7-28%], p < 0.001), and at equal sensitivity of 97%, Proclarix density had an even higher specificity of 33% [95% CI, 23-43%]. CONCLUSIONS: In a routine use setting, Proclarix accurately discriminated csPCa from no or insignificant PCa in the most challenging patients. Proclarix represents a valuable rule-out test in the diagnostic algorithm for PCa, alone or in combination with mpMRI. PATIENT SUMMARY: Proclarix is a novel blood-based test with the potential to accurately rule out clinically significant prostate cancer, and therefore to reduce the number of unneeded biopsies.

On the development of plasma protein biomarkers.

The development of plasma biomarkers has proven to be more challenging than initially anticipated. Many studies have reported lists of candidate proteins rather than validated candidate markers with an assigned performance to a specific clinical objective. Biomarker research necessitates a clear rational framework with requirements on a multitude of levels. On the technological front, the platform needs to be effective to detect low abundant plasma proteins and be able to measure them in a high throughput manner over a large amount of samples reproducibly. At a conceptual level, the choice of the technological platform and available samples should be part of an overall clinical study design that depends on a joint effort between basic and clinical research. Solutions to these needs are likely to facilitate more feasible studies. Targeted proteomic workflows based on SRM mass spectrometry show the potential of fast verification of biomarker candidates in plasma and thereby closing the gap between discovery and validation in the biomarker development pipeline. Biological samples need to be carefully chosen based on well-established guidelines either for candidate discovery in the form of disease models with optimal fidelity to human disease or for candidate evaluation as well-designed and annotated clinical cohort groups. Most importantly, they should be representative of the target population and directly address the investigated clinical question. A conceptual structure of a biomarker study can be provided in the form of several sequential phases, each having clear objectives and predefined goals. Furthermore, guidelines for reporting the outcome of biomarker studies are critical to adequately assess the quality of the research, interpretation and generalization of the results. By being attentive to and applying these considerations, biomarker research should become more efficient and lead to directly translatable biomarker candidates into clinical evaluation.

Analysis of cell surface proteome changes via label-free, quantitative mass spectrometry.

We present a mass spectrometry-based strategy for the specific detection and quantification of cell surface proteome changes. The method is based on the label-free quantification of peptide patterns acquired by high mass accuracy mass spectrometry using new software tools and the cell surface capturing technology that selectively enriches glycopeptides exposed to the cell exterior. The method was applied to monitor dynamic protein changes in the cell surface glycoproteome of Drosophila melanogaster cells. The results led to the construction of a cell surface glycoprotein atlas consisting of 202 cell surface glycoproteins of D. melanogaster Kc167 cells and indicated relative quantitative changes of cell surface glycoproteins in four different cellular states. Furthermore we specifically investigated cell surface proteome changes upon prolonged insulin stimulation. The data revealed insulin-dependent cell surface glycoprotein dynamics, including insulin receptor internalization, and linked these changes to intracellular signaling networks.

A novel serum biomarker quintet reveals added prognostic value when combined with standard clinical parameters in prostate cancer patients by predicting biochemical recurrence and adverse pathology.

The objective was to determine the prognostic utility of a new biomarker combination in prostate cancer (PCa) patients undergoing Radical Prostatectomy (RP). Serum samples and clinical data of 557 men who underwent RP for PCa with pathological stage (pT) <3 at Martini Clinic (Hamburg, Germany) were used for analysis. Clinical Grade Group and clinical stage was determined using biopsy samples while tumor marker concentrations were measured in serum using immunoassays. The prognostic utility of the proposed marker combination was assessed using Cox proportional hazard regression and Kaplan-Meier analysis. The performance was compared to the Cancer of the Prostate Risk Assessment (CAPRA) score in the overall cohort and in a low-risk patient subset. A multivariable model comprising fibronectin 1, galectin-3-binding protein, lumican, matrix metalloprotease 9, thrombospondin-1 and PSA together with clinical Grade Group (GG) and clinical stage (cT) was created. The proposed model was a significant predictor of biochemical recurrence (BCR) (HR 1.29 per 5 units score, 95%CI 1.20-1.38, p<0.001). The Kaplan-Meier analysis showed that the proposed model had a better prediction for low-risk disease after RP compared to CAPRA (respectively 5.0% vs. 9.1% chance of BCR). In a pre-defined low risk population subset, the risk of BCR using the proposed model was below 5.2% and thus lower when compared to CAPRA = 0-2 (9%), GG<2 (7%) and NCCN = low-risk (6%) subsets. Additionally, the proposed model could significantly (p<0.001) discriminate patients with adverse pathology (AP) events at RP from those without. In conclusion, the proposed model is superior to CAPRA for the prediction of BCR after RP in the overall cohort as well as a in a pre-defined low risk patient population subset. It is also significantly associated with AP at RP.

Development and validation of a novel multivariate risk score to guide biopsy decision for the diagnosis of clinically significant prostate cancer.

Objectives: Selecting patients suspected of having prostate cancer (PCa) for a prostate biopsy remains a challenge. Prostate-specific antigen (PSA)-based testing is hampered by its low specificity that often leads to negative biopsy results or detection of clinically insignificant cancers, especially in the 2-10 ng/mL range. The objective was to evaluate a novel diagnostic test called Proclarix incorporating thrombospondin-1 and cathepsin D alongside total and free PSA as well as age for predicting clinically significant PCa. Patients and methods: The test was developed following a retrospective study design using biobanked samples of 955 men from two reference centres. A multivariate approach was used for model development followed by validation to discriminate significant (grade group ≥2) from insignificant or no cancer at biopsy. The test specificity, positive predictive value (PPV) and negative predictive value (NPV) at a fixed sensitivity of 90% were compared to percent free PSA (%fPSA) alone. The number of avoidable prostate biopsies deemed to be representative of clinical utility was also assessed. Results: In the targeted patient population, the test displayed increased diagnostic accuracy compared to %fPSA alone. Application of the established model on 955 patients at a fixed sensitivity of 90% for significant disease resulted in a specificity of 43%, NPV of 95% and a PPV of 25%. This is in comparison to a specificity of 17%, NPV of 89% and PPV of 19% for %fPSA alone and had the potential to reduce the total number of biopsies needed to identify clinically significant cancer. Further, the test score correlated with significance of cancer assessed on prostate biopsy. Conclusions: The Proclarix test can be used as an aid in the decision-making process if to biopsy men in this challenging patient population. The use of the test could reduce the number of biopsies performed avoiding invasive procedures, anxiety, discomfort, pain and complications.

Analytical performance of thrombospondin-1 and cathepsin D immunoassays part of a novel CE-IVD marked test as an aid in the diagnosis of prostate cancer.

The Prostate Specific Antigen (PSA) test suffers from low specificity for the diagnosis of Prostate Cancer (PCa). We originally discovered two cancer-related proteins thrombospondin-1 (THBS1) and cathepsin D (CTSD) using a mass-spectrometry-based proteomics approach. The two serum proteins were shown to improve the diagnosis of high-grade PCa. Thus, we developed quantitative ELISAs for the determination of their concentration in human serum. Here we report their analytical performance in terms of limit of detection, specificity, precision, linearity and interferences, which were determined based on CLSI guidelines. Further, we investigated the influence of pre-analytical factors on concentration measurements. For this, blood from 4-6 donors was collected in different tubes and stored at room temperature for different times prior to centrifugation at different centrifugal forces and temperatures. Stability of THBS1 and CTSD under different storage temperatures was also evaluated. Our results show that the assays are specific, linear and sensitive enough to allow measurement of clinical samples. Precision in terms of repeatability and total within-laboratory coefficient of variation (CV) are 5.5% and 8.1% for THBS1 and 4.3% and 7.2% for CTSD, respectively. Relative laboratory-to-laboratory differences were -6.3% for THBS1 and -3% for CTSD. Both THBS1 and CTSD were stable in serum samples, with 80-120% recoveries of concentrations across donors, sample preparation and storage. In conclusion, the ELISAs as part of the novel commercial in vitro diagnostic test Proclarix are suitable for the use in clinical practice. THBS1 and CTSD can be accurately measured for their intended use independent of the lot and laboratory when conditions consistent with routine practice for PSA sampling and storage are used.

Similarities and Differences of Blood N-Glycoproteins in Five Solid Carcinomas at Localized Clinical Stage Analyzed by SWATH-MS.

Cancer is mostly incurable when diagnosed at a metastatic stage, making its early detection via blood proteins of immense clinical interest. Proteomic changes in tumor tissue may lead to changes detectable in the protein composition of circulating blood plasma. Using a proteomic workflow combining N-glycosite enrichment and SWATH mass spectrometry, we generate a data resource of 284 blood samples derived from patients with different types of localized-stage carcinomas and from matched controls. We observe whether the changes in the patient's plasma are specific to a particular carcinoma or represent a generic signature of proteins modified uniformly in a common, systemic response to many cancers. A quantitative comparison of the resulting N-glycosite profiles discovers that proteins related to blood platelets are common to several cancers (e.g., THBS1), whereas others are highly cancer-type specific. Available proteomics data, including a SWATH library to study N-glycoproteins, will facilitate follow-up biomarker research into early cancer detection.

Direct selection of antibodies from complex libraries with the protein fragment complementation assay.

The aim of the present study was to develop the protein fragment complementation assay (PCA) for the intracellular selection of specific binding molecules from the fully synthetic HuCAL antibody library. Here, we describe the first successful selections of specific antibodies by PCA, and we discuss the opportunities and limitations of this approach. First, we enriched an antibody specific for the capsid protein D of bacteriophage lambda (gpD) by ten successive rounds of competitive liquid culture selection. In an independent approach, we selected a specific antibody for the c-Jun N-terminal kinase 2 (JNK2) in a single-step selection setup. In order to obtain specific antibodies in only a single PCA selection round, the selection system was thoroughly investigated and several strategies to reduce the amount of false positives were evaluated. When expressed in the cytoplasm of Escherichia coli, the PCA-selected scFv antibody fragments could be purified as soluble and monomeric proteins. Denaturant-induced unfolding experiments showed that both antibody fragments are stable molecules, even when the disulfide bonds are reduced. Furthermore, antigen-specificity of the PCA-selected antibody fragments is demonstrated by in vivo and in vitro experiments. As antigen binding is retained regardless of the antibody redox state, both PCA-selected antibody fragments can tolerate the loss of disulfide bridge formation. Our results illustrate that it is possible to select well-expressed, stable, antigen-specific, and intracellular functional antibodies by PCA directly.

Development and clinical testing of individual immunoassays for the quantification of serum glycoproteins to diagnose prostate cancer.

Prostate Cancer (PCa) diagnosis is currently hampered by the high false-positive rate of PSA evaluations, which consequently may lead to overtreatment. Non-invasive methods with increased specificity and sensitivity are needed to improve diagnosis of significant PCa. We developed and technically validated four individual immunoassays for cathepsin D (CTSD), intercellular adhesion molecule 1 (ICAM1), olfactomedin 4 (OLFM4), and thrombospondin 1 (THBS1). These glycoproteins, previously identified by mass spectrometry using a Pten mouse model, were measured in clinical serum samples for testing the capability of discriminating PCa positive and negative samples. The development yielded 4 individual immunoassays with inter and intra-variability (CV) <15% and linearity on dilution of the analytes. In serum, ex vivo protein stability (<15% loss of analyte) was achieved for a duration of at least 24 hours at room temperature and 2 days at 4°C. The measurement of 359 serum samples from PCa positive (n = 167) and negative (n = 192) patients with elevated PSA (2-10 ng/ml) revealed a significantly improved accuracy (P <0.001) when two of the glycoproteins (CTSD and THBS1) were combined with %fPSA and age (AUC = 0.8109; P <0.0001; 95% CI = 0.7673-0.8545). Conclusively, the use of CTSD and THBS1 together with commonly used parameters for PCa diagnosis such as %fPSA and age has the potential to improve the diagnosis of PCa.

A mass spectrometric-derived cell surface protein atlas.

Cell surface proteins are major targets of biomedical research due to their utility as cellular markers and their extracellular accessibility for pharmacological intervention. However, information about the cell surface protein repertoire (the surfaceome) of individual cells is only sparsely available. Here, we applied the Cell Surface Capture (CSC) technology to 41 human and 31 mouse cell types to generate a mass-spectrometry derived Cell Surface Protein Atlas (CSPA) providing cellular surfaceome snapshots at high resolution. The CSPA is presented in form of an easy-to-navigate interactive database, a downloadable data matrix and with tools for targeted surfaceome rediscovery (http://wlab.ethz.ch/cspa). The cellular surfaceome snapshots of different cell types, including cancer cells, resulted in a combined dataset of 1492 human and 1296 mouse cell surface glycoproteins, providing experimental evidence for their cell surface expression on different cell types, including 136 G-protein coupled receptors and 75 membrane receptor tyrosine-protein kinases. Integrated analysis of the CSPA reveals that the concerted biological function of individual cell types is mainly guided by quantitative rather than qualitative surfaceome differences. The CSPA will be useful for the evaluation of drug targets, for the improved classification of cell types and for a better understanding of the surfaceome and its concerted biological functions in complex signaling microenvironments.

Metformin in chemotherapy-naive castration-resistant prostate cancer: a multicenter phase 2 trial (SAKK 08/09).

BACKGROUND: There is evidence linking metformin to improved prostate cancer (PCa)-related outcomes. OBJECTIVE: To evaluate treatment with metformin in patients with castration-resistant PCa (CRPC) and the effect of the treatment on progression-free survival (PFS) and PSA doubling time (PSA DT). DESIGN, SETTING, AND PARTICIPANTS: Forty-four men with progressive metastatic CRPC from 10 Swiss centers were included in this single-arm phase 2 trial between December 2010 and December 2011. INTERVENTION: Patients received metformin 1000 mg twice daily until disease progression. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The primary end point was the absence of disease progression at 12 wk. Simon two-stage optimal design was applied. With a 5% significance level and 90% power, 44 patients were required to test PFS at 12 wk ≤ 15% (H0) compared with ≥ 35% (H1). RESULTS AND LIMITATIONS: Thirty-six percent of patients were progression-free at 12 wk, 9.1% were progression-free at 24 wk, and in two patients a confirmed ≥ 50% prostate-specific antigen (PSA) decline was demonstrated. In 23 patients (52.3%) we observed a prolongation of PSA DT after starting metformin. The homeostatic model assessment index fell by 26% from baseline to 12 wk, indicating an improvement in insulin sensitivity. There was a significant change in insulin-like growth factor-1 and insulin-like growth factor binding protein 3 from baseline to 12 wk. Sample size and lack of a control arm are the limitations of this trial; analyses are therefore exploratory. CONCLUSIONS: Treatment with metformin is safe in nondiabetic patients, and it yields objective PSA responses and may induce disease stabilization. The activity of metformin in PCa, along with its low cost, favorable toxicity profile, and positive effect on metabolic parameters, suggests that further investigation of metformin as therapy for patients with PCa is of interest. PATIENT SUMMARY: In this trial we assessed the use of the diabetes mellitus drug metformin in patients with advanced prostate cancer. We found disease stabilization and prolongation of prostate-specific antigen doubling time in some patients as well as effects on metabolic parameters. TRIAL REGISTRATION: This study is registered with ClinicalTrials.gov with the identifier NCT01243385. PREVIOUS PRESENTATION: The study was presented at ESMO 2012 (abstract 1460).