Differential expression of protease activity in serum samples of prostate carcinoma patients with metastases

Description of an activity-based enzyme biosensor for lung cancer detection

BACKGROUND

Lung cancer is associated with the greatest cancer mortality as it typically presents with incurable distributed disease. Biomarkers relevant to risk assessment for the detection of lung cancer continue to be a challenge because they are often not detectable during the asymptomatic curable stage of the disease. A solution to population-scale testing for lung cancer will require a combination of performance, scalability, cost-effectiveness, and simplicity.

METHODS

One solution is to measure the activity of serum available enzymes that contribute to the transformation process rather than counting biomarkers. Protease enzymes modify the environment during tumor growth and present an attractive target for detection. An activity based sensor platform sensitive to active protease enzymes is presented. A panel of 18 sensors was used to measure 750 sera samples from participants at increased risk for lung cancer with or without the disease.

RESULTS

A machine learning approach is applied to generate algorithms that detect 90% of cancer patients overall with a specificity of 82% including 90% sensitivity in Stage I when disease intervention is most effective and detection more challenging.

CONCLUSION

This approach is promising as a scalable, clinically useful platform to help detect patients who have lung cancer using a simple blood sample. The performance and cost profile is being pursued in studies as a platform for population wide screening.

Blood-based diagnostics of Alzheimer's disease

Introduction: This review is focused on the methods used for biomarker discovery for Alzheimer's disease (AD) in blood rather than on the nature of the biomarkers themselves. Areas covered: All biomarker discovery programs explicitly rely on contrasts in phenotype as a basis for defining differences. In this review, we explore the basis of contrasting choices as a function of the type of biomarker (genetic, protein, metabolite, non-coding RNA, or pathogenic epitope). We also provide an overview of the capacity to identify pathogenic epitopes with our new platform called Aptamarkers. It is suggested that a pre-existing hypothesis regarding the pathophysiology of the disease can act as a constraint to the development of biomarkers. Expert opinion: Limiting putative biomarkers to those that have a postulated role in the pathophysiology of disease imposes an unrealistic constraint on biomarker development. The understanding of Alzheimer's disease would be accelerated by agnostic, non-hypothesis-based biomarker discovery methods. There is a need for more complex contrasts and more complex mathematical models.

Differentiating tumor heterogeneity in formalin-fixed paraffin-embedded (FFPE) prostate adenocarcinoma tissues using principal component analysis of matrix-assisted laser desorption/ionization imaging mass spectral data

RATIONALE

Many patients with adenocarcinoma of the prostate present with advanced and metastatic cancer at the time of diagnosis. There is an urgent need to detect biomarkers that will improve the diagnosis and prognosis of this disease. Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) is playing a key role in cancer research and it can be useful to unravel the molecular profile of prostate cancer biopsies.

METHODS

MALDI imaging data sets are highly complex and their interpretation requires the use of multivariate statistical methods. In this study, MALDI-IMS technology, sequential principal component analysis (PCA) and two-dimensional (2-D) peak distribution tests were employed to investigate tumor heterogeneity in formalin-fixed paraffin-embedded (FFPE) prostate cancer biopsies.

RESULTS

Multivariate statistics revealed a number of mass ion peaks obtained from different tumor regions that were distinguishable from the adjacent normal regions within a given specimen. These ion peaks have been used to generate ion images and visualize the difference between tumor and normal regions. Mass peaks at m/z 3370, 3441, 3447 and 3707 exhibited stronger ion signals in the tumor regions.

CONCLUSIONS

This study reports statistically significant mass ion peaks unique to tumor regions in adenocarcinoma of the prostate and adds to the clinical utility of MALDI-IMS for analysis of FFPE tissue at a molecular level that supersedes all other standard histopathologic techniques for diagnostic purposes used in the current clinical practice. Copyright © 2016 John Wiley & Sons, Ltd.

Multiplex profiling of tumor-associated proteolytic activity in serum of colorectal cancer patients

PURPOSE

The monitoring of tumor-associated protease activity in blood specimens has recently been proposed as new diagnostic tool in cancer research. In this paper, we describe the screening of a peptide library for identification of reporter peptides (RPs) that are selectively cleaved in serum specimens from colorectal cancer patients and investigate the benefits of RP multiplexing.

EXPERIMENTAL DESIGN

A library of 144 RPs was constructed that contained amino acid sequences of abundant plasma proteins. Proteolytic cleavage of RPs was monitored with MS. Five RPs that were selectively cleaved in serum specimens from tumor patients were selected for further validation in serum specimens of colorectal tumor patients (n = 30) and nonmalignant controls (n = 60).

RESULTS

RP spiking and subsequent quantification of proteolytic fragments with LC-MS showed good reproducibility with CVs always below 26%. The linear discriminant analysis and PCA revealed that a combination of RPs for diagnostic classification is superior to single markers. Classification accuracy reached 88% (79/90) when all five markers were combined.

CONCLUSIONS AND CLINICAL RELEVANCE

Functional protease profiling with RPs might improve the laboratory-based diagnosis, monitoring and prognosis of malignant disease, and has to be evaluated thoroughly in future studies.

A proteomics approach to the identification of plasma biomarkers for latent tuberculosis infection

A proteomic analysis was performed to screen the potential latent tuberculosis infection (LTBI) biomarkers. A training set of spectra was used to generate diagnostic models, and a blind testing set was used to determine the accuracy of the models. Candidate peptides were identified using nano-liquid chromatography-electrospray ionization–tandem mass spectrometry. Based on the training set results, 3 diagnostic models recognized LTBI subjects with good cross-validation accuracy. In the blind testing set, LTBI subjects could be identified with sensitivities and specificities of 85.20% to 88.90% and 85.7% to 100%, respectively. Additionally, 14 potential LTBI biomarkers were identified, and all proteins were identified for the first time through proteomics in the plasma of healthy, latently infected individuals. In all, proteomic pattern analyses can increase the accuracy of LTBI diagnosis, and the data presented here provide novel insights into potential mechanisms involved in LTBI.

Mass-encoded synthetic biomarkers for multiplexed urinary monitoring of disease

Biomarkers are becoming increasingly important in the clinical management of complex diseases, yet our ability to discover new biomarkers remains limited by our dependence on endogenous molecules. Here we describe the development of exogenously administered 'synthetic biomarkers' composed of mass-encoded peptides conjugated to nanoparticles that leverage intrinsic features of human disease and physiology for noninvasive urinary monitoring. These protease-sensitive agents perform three functions in vivo: they target sites of disease, sample dysregulated protease activities and emit mass-encoded reporters into host urine for multiplexed detection by mass spectrometry. Using mouse models of liver fibrosis and cancer, we show that these agents can noninvasively monitor liver fibrosis and resolution without the need for invasive core biopsies and substantially improve early detection of cancer compared with current clinically used blood biomarkers. This approach of engineering synthetic biomarkers for multiplexed urinary monitoring should be broadly amenable to additional pathophysiological processes and point-of-care diagnostics.

Functional protease profiling with reporter peptides in serum specimens of colorectal cancer patients: demonstration of its routine diagnostic applicability

Background

The progression of many solid tumors is characterized by the release of tumor-associated proteases and the detection of tumor specific proteolytic activity in serum specimens is a promising diagnostic tool in oncology. Here we describe a mass spectrometry-based functional proteomic profiling approach that tracks the ex-vivo degradation of a synthetic endoprotease substrate in serum specimens of colorectal tumor patients.

Methods

A reporter peptide (RP) with the amino acid sequence WKPYDAAD was synthesized that has a known cleavage site for the cysteine-endopeptidase cancer procoagulant (EC 3.4.22.26). The RP was added to serum specimens from colorectal cancer patients (n = 30), inflammatory controls (n = 30) and healthy controls (n = 30) and incubated under strictly standardized conditions. The proteolytic fragment of the RP was quantified with liquid chromatography / mass spectrometry (LC/MS).

Results

RP-spiking showed good intra- and inter-day reproducibility with coefficients of variation (CVs) that did not exceed a value of 10%. The calibration curve for the anchor peptide was linear in the concentration range of 0.4 – 50 μmol/L. The median concentration of the RP-fragment in serum specimens from tumor patients (TU: 17.6 μmol/L, SD 9.0) was significantly higher when compared to non-malignant inflammatory controls (IC: 11.1 μmol/L, SD 6.1) and healthy controls (HC: 10.3 μmol/L, SD 3.1). Highest area under receiver operating characteristic (AUROC) values were seen for discrimination of TU versus HC (0.89) followed by TU versus IC (0.77). IC and HC could barely be separated indicated by an AUROC value of 0.57. The proteolytic activity towards the RP was conserved in serum specimens that were kept at room temperature for up to 24 hours prior to the analysis.

Conclusion

The proteolytic cleavage of reporter peptides is a surrogate marker for tumor associated proteolytic activity in serum specimens of cancer patients. A simple, robust and highly reproducible LC/MS method has been developed that allows the quantification of proteolytic fragments in serum specimens. The preanalytical impact of sample handling is minimal as the tumor-associated proteolytic activity towards the reporter peptide is stable for at least up to 24 h. Taken together, the functional protease profiling shows characteristics that are in line with routinely performed diagnostic assays. Further work will focus on the identification of additional reporter peptides for the construction of a multiplex assay to increase diagnostic accuracy of the functional protease profiling.

Functional protease profiling for diagnosis of malignant disease

Clinical proteomic profiling by mass spectrometry (MS) aims at uncovering specific alterations within mass profiles of clinical specimens that are of diagnostic value for the detection and classification of various diseases including cancer. However, despite substantial progress in the field, the clinical proteomic profiling approaches have not matured into routine diagnostic applications so far. Their limitations are mainly related to high-abundance proteins and their complex processing by a multitude of endogenous proteases thus making rigorous standardization difficult. MS is biased towards the detection of low-molecular-weight peptides. Specifically, in serum specimens, the particular fragments of proteolytically degraded proteins are amenable to MS analysis. Proteases are known to be involved in tumour progression and tumour-specific proteases are released into the blood stream presumably as a result of invasive progression and metastasis. Thus, the determination of protease activity in clinical specimens from patients with malignant disease can offer diagnostic and also therapeutic options. The identification of specific substrates for tumour proteases in complex biological samples is challenging, but proteomic screens for proteases/substrate interactions are currently experiencing impressive progress. Such proteomic screens include peptide-based libraries, differential isotope labelling in combination with MS, quantitative degradomic analysis of proteolytically generated neo-N-termini, monitoring the degradation of exogenous reporter peptides with MS, and activity-based protein profiling. In the present article, we summarize and discuss the current status of proteomic techniques to identify tumour-specific protease-substrate interactions for functional protease profiling. Thereby, we focus on the potential diagnostic use of the respective approaches.

The application of atmospheric pressure matrix-assisted laser desorption/ionization to the analysis of long-term cryopreserved serum peptidome

Although most time-of-flight (TOF) mass spectrometers come equipped with vacuum matrix-assisted laser desorption/ionization (MALDI) sources, the atmospheric pressure MALDI (API-MALDI) source is an attractive option because of its ability to be coupled to a wide range of analyzers. This article describes the use of an API-MALDI source coupled to a TOF mass spectrometer for evaluation of the effects of medium- and long-term storage on peptidomic profiles of cryopreserved serum samples from healthy women. Peptides were purified using superparamagnetic beads either from fresh sera or after serum storage at -80°C for 18 months or at -20°C for 8 years. Data were preprocessed using newly developed bioinformatic tools and then were subjected to statistical analysis and class prediction. The analyses showed a dramatic effect of storage on the abundance of several peptides such as fibrinopeptides A and B, complement fractions, bradykinin, and clusterin, indicated by other authors as disease biomarkers. Most of these results were confirmed by shadow clustering analysis, able to classify each sample in the correct group. In addition to demonstrating the suitability of the API-MALDI technique for peptidome profiling studies, our data are of relevance for retrospective studies that involve frozen sera stored for many years in biobanks.

Identification of biomarkers for colorectal cancer through proteomics-based approaches

The early detection of colorectal cancer is one of the great challenges in the battle against this disease. However, owing to its heterogeneous character, single markers are not likely to provide sufficient diagnostic power to be used in colorectal cancer population screens. This review provides an overview of recent studies aimed at the discovery of new diagnostic protein markers through proteomics-based approaches. It indicates that studies that start with the proteomic analysis of tumor tissue or tumor cell lines (near the source) have a high potential to yield novel and colorectal cancer-specific biomarkers. In the next step, the diagnostic accuracy of these candidate markers can be assessed by a targeted ELISA assay using serum from colorectal cancer patients and healthy controls. Instead, direct proteomic analysis of serum yields predominantly secondary markers composed of fragments of abundant serum proteins that may be associated with tumor-associated protease activity, and alternatively, immunoproteomic analysis of the serum antibody repertoire provides a valuable tool to identify the molecular imprint of colorectal cancer-associated antigens directly from patient serum samples. The latter approach also allows a relatively easy translation into targeted assays. Eventually, multimarker assays should be developed to reach a diagnostic accuracy that meets the stringent criteria for colorectal cancer screening at the population level.

Label-free peptide profiling of Orbitrap™ full mass spectra

BACKGROUND

We developed a new version of the open source software package Peptrix that can yet compare large numbers of Orbitrap™ LC-MS data. The peptide profiling results for Peptrix on MS1 spectra were compared with those obtained from a small selection of open source and commercial software packages: msInspect, Sieve™ and Progenesis™. The properties compared in these packages were speed, total number of detected masses, redundancy of masses, reproducibility in numbers and CV of intensity, overlap of masses, and differences in peptide peak intensities. Reproducibility measurements were taken for the different MS1 software applications by measuring in triplicate a complex peptide mixture of immunoglobulin on the Orbitrap™ mass spectrometer. Values of peptide masses detected from the high intensity peaks of the MS1 spectra by peptide profiling were verified with values of the MS2 fragmented and sequenced masses that resulted in protein identifications with a significant score.

FINDINGS

Peptrix finds about the same number of peptide features as the other packages, but peptide masses are in some cases approximately 5 to 10 times less redundant present in the peptide profile matrix. The Peptrix profile matrix displays the largest overlap when comparing the number of masses in a pair between two software applications. The overlap of peptide masses between software packages of low intensity peaks in the spectra is remarkably low with about 50% of the detected masses in the individual packages. Peptrix does not differ from the other packages in detecting 96% of the masses that relate to highly abundant sequenced proteins. MS1 peak intensities vary between the applications in a non linear way as they are not processed using the same method.

CONCLUSIONS

Peptrix is capable of peptide profiling using Orbitrap™ files and finding differential expressed peptides in body fluid and tissue samples. The number of peptide masses detected in Orbitrap™ files can be increased by using more MS1 peptide profiling applications, including Peptrix, since it appears from the comparison of Peptrix with the other applications that all software packages have likely a high false negative rate of low intensity peptide peaks (missing peptides).