CE-MS-based proteomics in biomarker discovery and clinical application

CE-MS is applied in clinical proteomics for both the identification of biomarkers of disease and assessment of biomarkers in clinical diagnosis. The analysis is reproducible, fast, and requires only small sample volumes. However, successful CE-MS analysis depends on several critical steps that can be consolidated as follows: (i) proper sample preparation and fractionation, (ii) application of suitable capillary coating and appropriate CE-MS interfaces, to ensure the reproducibility and stability of the analysis, and (iii) an optimized clinical and statistical study design to increase the chances for obtaining clinically relevant results. In this review, we cover all these aspects, and present several examples of the application of CE-MS in clinical proteomics.     

Cathepsin B: Multiple roles in cancer

Proteases, including intracellular proteases, play roles at many different stages of malignant progression. Our focus here is cathepsin B, a lysosomal cysteine cathepsin. High levels of cathepsin B are found in a wide variety of human cancers, levels that often induce secretion and association of cathepsin B with the tumor cell membrane. In experimental models, such as transgenic models of murine pancreatic and mammary carcinomas, causal roles for cathepsin B have been demonstrated in initiation, growth/tumor cell proliferation, angiogenesis, invasion, and metastasis. Tumor growth in transgenic models is promoted by cathepsin B in tumor-associated cells, for example, tumor-associated macrophages, as well as in tumor cells. In transgenic models, the absence of cathepsin B has been associated with enhanced apoptosis, yet cathepsin B also has been shown to contribute to apoptosis. Cathepsin B is part of a proteolytic pathway identified in xenograft models of human glioma; targeting only cathepsin B in these tumors is less effective than targeting cathepsin B in combination with other proteases or protease receptors. Understanding the mechanisms responsible for increased expression of cathepsin B in tumors and association of cathepsin B with tumor cell membranes is needed to determine whether targeting cathepsin B could be of therapeutic benefit.     

Glycoproteomic strategies: From discovery to clinical application of cancer carbohydrate biomarkers

Carbohydrate antigens are the most frequently and traditionally used biomarkers for cancer, such as CA19–9, CA125, DUPAN-II, AFP-L3, and many others. The diagnostic potential of them was simply based on the cancer-specific alterations of glycan structures on particular glycoproteins in serum/plasma. In spite of the facts that glycosylation disorders are feasible for cancer biomarkers and glycomic analysis technologies to explore them have been rapidly developed, it remains difficult to sensitively screen glycan structure changes on cancer-associated glycoproteins from clinical specimens. Moreover, a lot of additional issues should be appropriately addressed for the clinical application of newly identified glycosylation biomarkers, including analytical throughput, quantitative confirmation of structural changes, and biological explanation for the alterations. In the last decade, significant improvement of mass spectrometric techniques is being made in the aspects of both hardware spec and preanalytical purification procedures for glycoprotein analysis. Here we review potential approaches to perform comprehensive analysis of glycoproteomic biomarker screening from serum/plasma and to realize high-throughput validation of site-specific oligosaccharide variations. The power and problems of mass spectrometric applications on the clinical use of carbohydrate biomarkers are also discussed in this review.     

Proteomic approach toward personalized sarcoma treatment: Lessons from prognostic biomarker discovery in gastrointestinal stromal tumor

Sarcomas range from curable tumors to those causing death via metastasis and recurrence. Thus, there is an urgent need for biomarker identification in order to assess the degree of malignancy, predict prognosis, and evaluate possible therapies. Various proteomic approaches and different clinical materials have been used to this end, and candidate biomarkers have been reported for the different types of sarcomas. However, the sample size used in these biomarker studies was generally insufficient, and thus far, no biomarker has been proved useful in clinics. Given that sarcomas are rare, biomarker validation in this setting is more challenging than in other malignancies. In gastrointestinal stromal tumor, adjuvant therapy has proven to be effective. However, only 40% patients experience metastasis after curative surgery alone, and the rest of the patients may not need adjuvant therapy. Using a proteomic approach, we identified pfetin (potassium channel tetramerization domain containing 12, KCTD 12) as a novel prognostic biomarker for sarcoma, and immunohistochemically confirmed its clinical usefulness by a multiinstitutional validation study. Here, we describe our experience and discuss the critical points in the discovery of this biomarker.     

CE-MS in biomarker discovery, validation, and clinical application

CE coupled MS (CE-MS) has become an increasingly employed technology in proteome analysis with focus on the identification of biomarker peptides in clinical proteomics. In this review, we will cover technical aspects of CE-MS coupling and highlight the improvements made in the last few years. We examine CE-MS from an application point of view, and evaluate its merits and vices for biomarker discovery and clinical applications. We discuss the principal theoretical and practical obstacles encountered when employing CE-MS (and most other proteomic technologies) for the analysis of body fluids for biomarker discovery. We will present several examples of a successful application of CE-MS for biomarker discovery, implications for disease diagnosis, prognosis, and therapy evaluation, and will discuss current challenges and possible future improvements.     

Application of immunoproteomics to leptospirosis: towards clinical diagnostics and vaccine discovery

Each of the currently available methods for serodiagnosis of leptospirosis, including the microscopic agglutination test (MAT), has its own drawback(s) when used in clinical practice. A new diagnostic test is therefore required for an earlier and more accurate diagnosis of leptospirosis. We applied immunoproteomics to define potential immunogens from five serovars of Leptospira reference strains. A leptospiral whole cell lysate from each serovar was used as the antigen to react with IgG and IgM in the sera from four patients with a positive MAT. Sera from four non-leptospirosis patients with a negative MAT were pooled and used as the negative control. 2-D Western blot analysis showed that the degree of immunoreactivity corresponded with the MAT titers. No immunoreactive spots were detected when the pooled control sera were used. A total of 24 protein spots immunoreacted with IgM and/or IgG from patients with leptospirosis. These immunoreactive proteins were identified by MALDI-TOF MS and were classified into five groups, including flagellar proteins, chaperones/heat shock proteins, transport proteins, metabolic enzymes, and hypothetical proteins. More immunoreactive spots were detected with anti-human IgG in the sera of all patients and with all the serovars of leptospires used. Some of the identified proteins immunoreacted only with IgG, whereas the others were detectable with both IgM and IgG. Among the immunoreactive proteins identified, FlaB proteins (flagellin and flagellar core protein) have been shown to have a potential role in clinical diagnostics and vaccine development. These data underscore the significant impact of immunoproteomics in clinical applications.     

Multiplex assays for biomarker research and clinical application: translational science coming of age

Over the last decade, translational science has come into the focus of academic medicine, and significant intellectual and financial efforts have been made to initiate a multitude of bench-to-bedside projects. The quest for suitable biomarkers that will significantly change clinical practice has become one of the biggest challenges in translational medicine. Quantitative measurement of proteins is a critical step in biomarker discovery. Assessing a large number of potential protein biomarkers in a statistically significant number of samples and controls still constitutes a major technical hurdle. Multiplexed analysis offers significant advantages regarding time, reagent cost, sample requirements and the amount of data that can be generated. The two contemporary approaches in multiplexed and quantitative biomarker validation, antibody-based immunoassays and MS-based multiple (or selected) reaction monitoring, are based on different assay principles and instrument requirements. Both approaches have their own advantages and disadvantages and therefore have complementary roles in the multi-staged biomarker verification and validation process. In this review, we discuss quantitative immunoassay and multiple reaction monitoring/selected reaction monitoring assay principles and development. We also discuss choosing an appropriate platform, judging the performance of assays, obtaining reliable, quantitative results for translational research and clinical applications in the biomarker field.     

Using data-independent,high-resolution mass spectrometry in protein biomarker research: Perspectives and clinical applications

In medicine, there is an urgent need for protein biomarkers in a range of applications that includes diagnostics, disease stratification, and therapeutic decisions. One of the main technologies to address this need is MS, used for protein biomarker discovery and, increasingly, also for protein biomarker validation. Currently, data-dependent analysis (also referred to as shotgun proteomics) and targeted MS, exemplified by SRM, are the most frequently used mass spectrometric methods. Recently developed data-independent acquisition techniques combine the strength of shotgun and targeted proteomics, while avoiding some of the limitations of the respective methods. They provide high-throughput, accurate quantification, and reproducible measurements within a single experimental setup. Here, we describe and review data-independent acquisition strategies and their recent use in clinically oriented studies. In addition, we also provide a detailed guide for the implementation of SWATH-MS (where SWATH is sequential window acquisition of all theoretical mass spectra)—one of the data-independent strategies that have gained wide application of late.     

A hierarchical Bayes model for biomarker subset effects in clinical trials

Some baseline patient factors, such as biomarkers, are useful in predicting patients’ responses to a new therapy. Identification of such factors is important in enhancing treatment outcomes, avoiding potentially toxic therapy that is destined to fail and improving the cost-effectiveness of treatment. Many of the biomarkers, such as gene expression, are measured on a continuous scale. A threshold of the biomarker is often needed to define a sensitive subset for making easy clinical decisions. A novel hierarchical Bayesian method is developed to make statistical inference simultaneously on the threshold and the treatment effect restricted on the sensitive subset defined by the biomarker threshold. In the proposed method, the threshold parameter is treated as a random variable that takes values with a certain probability distribution. The observed data are used to estimate parameters in the prior distribution for the threshold, so that the posterior is less dependent on the prior assumption. The proposed Bayesian method is evaluated through simulation studies. Compared to the existing approaches such as the profile likelihood method, which makes inferences about the threshold parameter using the bootstrap, the proposed method provides better finite sample properties in terms of the coverage probability of a 95% credible interval. The proposed method is also applied to a clinical trial of prostate cancer with the serum prostatic acid phosphatase (AP) biomarker.     

Comparison of proteomic biomarker panels in urine and serum for ovarian cancer diagnosis

Purpose : The purposes of this study were to confirm previously found candidate epithelial ovarian cancer biomarkers in urine and to compare a paired serum biomarker panel and a urine biomarker panel from the same study cohort with regard to the receiver operating characteristic curve (ROC) area under the ROC curve (AUC) values. Experimental design : Four significant urine biomarkers were confirmed among 130 pelvic mass patients in the present study. The four biomarkers form a potential urine biomarker panel. From the same study cohort, the potential urine biomarker panel was compared to a serum biomarker panel, consisting of seven proteins/peptides, OvaRI. Results : Multivariate analysis of the urine panel demonstrated a significant differentiation (p<0.0001) between epithelial ovarian cancer patients and patients with benign ovarian pelvic masses. The ROC AUC of the urine panel was 0.84 and the ROC AUC of OvaRI was 0.83. Combining the urine panel with OvaRI demonstrated a significant contribution from both, for urine peaks, OR=2.12 and for OvaRI, OR=1.39; the ROC AUC of this model was 0.88. Conclusions and clinical relevance : We demonstrated that both urine and serum can be used individually or in combination to potentially aid in ovarian cancer diagnostics. Urine proteomic profiling could provide biomarkers for the non‐invasive test required in clinical practice.     

Using lectins in biomarker research: Addressing the limitations of sensitivity and availability

Carbohydrates have fundamental roles throughout biology, yet they have not been as well studied as proteins and nucleic acids, in part due to limitations in the experimental tools. Improved methods for studying glycans could spur significant advances in the understanding and application of glycobiology. The use of affinity reagents, such as lectins and glycan-binding antibodies, is a valuable complement to methods involving mass spectrometry and chromatography. This article addresses two limitations that have prevented the broader experimental use of glycan-binding proteins: sensitivity and availability. The sensitivity limitation stems from the poor affinity that many glycan-binding proteins have as isolated analytical reagents. To address this problem, I propose making use of multivalent interactions between lectins and glycans, mimicking those frequently found in the biological setting. Recent experiments show that a practical technique for producing lectin multimers can significantly improve detection sensitivity. The second limitation, availability, is the difficulty of finding and obtaining glycan-binding proteins that recognize less common or arbitrarily defined glycan structures. To address this problem, I propose translating the wealth of existing glycan array data into a quantitative, searchable database of the specificities of glycan-binding proteins. Such a resource would allow us to more easily identify proteins with defined specificities and perform detailed comparisons between reagents. Solutions to these two limitations could lead to the more effective use of, and a broader range of, glycan-binding reagents.     

Plasma prefractionation methods for proteomic analysis and perspectives in clinical applications

Plasma is a rich source of biomarkers with clinical relevance. However, the wide dynamic range of protein concentration hinders the detection of low abundance proteins. Plasma prefractionation methods serve as indispensable tools to reduce plasma complexity, allowing the opportunity to explore tissue‐derived proteins which leak into the circulation. This review summarizes common approaches in plasma prefractionation methods for proteomic analysis and then discusses some considerations in plasma prefractionation for clinical applications, reviewing some examples of its use in clinical situations.     

Towards a comprehensive proteome of normal and malignant human colon tissue by 2-D-LC-ESI-MS and 2-DE proteomics and identification of S100A12 as potential cancer biomarker

The aim of this study was to characterize the proteome of normal and malignant colonic tissue. We previously studied the colon proteome using 2‐DE and MALDI‐MS and identified 734 proteins (Roeßler, M., Rollinger, W., Palme S., Hagmann, M.‐L., et al.., Clin. Cancer Res. 2005, 11, 6550–6557). Here we report the identification of additional colon proteins from the same set of tissue samples using a complementary nano‐flow 2‐D‐LC‐ESI‐MS. In total, 484 proteins were identified in colon. Of these, 252 had also been identified by the 2‐DE/MALDI‐MS approach, whereas 232 proteins were unique to the 2‐D‐LC‐ESI‐MS analysis. Comparing protein expression in neoplastic and normal colon tissue indicated elevated expression of several proteins in colorectal cancer, among them the well established tumor marker carcinoembryonic antigen, as well as calnexin, 40S ribosomal protein S15a, serpin H1, and S100A12. Overexpression of these proteins was confirmed by immunoblotting. Serum levels of S100A12 were determined by ELISA and were found to be strongly elevated in colorectal cancer patients compared to healthy individuals. We conclude, that 2‐D‐LC‐ESI‐MS is a powerful approach to identify and compare protein profiles of tissue samples, that it is complementary to 2‐DE/MALDI‐MS approaches and has the potential to identify novel biomarkers.     

Biomarker discovery by proteomics‐based approaches for early detection and personalized medicine in colorectal cancer

About one million people per year develop colorectal cancer (CRC) and approximately half of them die. The extent of the disease (i.e. local invasion at the time of diagnosis) is a key prognostic factor. The 5‐year survival rate is almost 90% in the case of delimited CRC and 10% in the case of metastasized CRC. Hence, one of the great challenges in the battle against CRC is to improve early diagnosis strategies. Large‐scale proteomic approaches are widely used in cancer research to search for novel biomarkers. Such biomarkers can help in improving the accuracy of the diagnosis and in the optimization of personalized therapy. Herein, we provide an overview of studies published in the last 5 years on CRC that led to the identification of protein biomarkers suitable for clinical application by using proteomic approaches. We discussed these findings according to biomarker application, including also the role of protein phosphorylation and cancer stem cells in biomarker discovery. Our review provides a cross section of scientific approaches and can furnish suggestions for future experimental strategies to be used as reference by scientists, clinicians and researchers interested in proteomics for biomarker discovery.     

Geoinformation systems and fields of their potential application in modern information society

This paper discusses the characteristic features of geoinformation systems (GISs) that make them different from other types of information systems. The components, functions, and application areas of GISs are considered, and GIS software solutions are described in detail. Application of GISs in different fields is demonstrated with particular examples.     

Group-membership recognition methods based on confidence boundaries and their testing in clinical oncology

Confidence intervals are constructed for an unknown correlation coefficient and mathematical expectation using 3s ₁ and 2s ₁ rules. The intervals make it possible to solve the problem of statistical analysis of the dependence between different random variables. The results obtained are used to analyze the correlation between the content of homocysteine and various clinical indices. Translated from Kibernetika i Sistemnyi Analiz, No. 4, pp. 19–30, July–August 2009.