Liquid Chromatography/Mass Spectrometry (LC/MS)-Based Glycoproteomics Technologies for Cancer Biomarker Discovery

Introduction

Biomarker discovery is a major objective of clinical proteomics; molecular biomarkers allow for detection of early-stage human diseases, especially cancer, and for monitoring their progression and/or regression after treatment. Biomarkers also help to elucidate the pathology of disease and its diagnosis, drug discovery, and toxicology. Glycans are ideal candidates for biomarkers because (1) glycoconjugates are localized on the cell surface and in the secretions such as plasma, (2) their structures are frequently and drastically changed during normal and aberrant cell differentiation, and (3) different cell types express different glycan signatures. Certain serodiagnostic glycoconjugate markers, such as carcinoembryonic antigen (CEA), are currently available; however, comprehensive glycome analysis has yet to be performed, mainly because of the difficulties of isolating and structurally analyzing complex glycans. Large-scale glycoprotein analysis, termed glycoproteomics, has the potential to effectively trace cellular glycoproteins and therefore to search for new serodiagnostic biomarkers.

Conclusions

In this review, we describe current mass spectrometry-based glycoproteomics technologies. Quantitative “shotgun” proteomics analyses of glycopeptides captured from complex biological mixtures such as plasma, coupled with advanced glycome technologies, enhance our knowledge of protein glycosylation and facilitate discovery of new biomarkers for human diseases.     

Chemical methods for glycoprotein discovery

An important frontier in glycoproteomics is the discovery of proteins with post-translational glycan modifications. The first step in glycoprotein identification is the isolation of glycosylated proteins from the remainder of the proteome. New enzymatic and metabolic methods are being used to chemically tag proteins to enable their isolation. Once isolated, glycoproteins can be identified by mass spectrometry. Additional information can be obtained by using either enzymatic or chemoselective reactions to incorporate isotope labels at specific sites of glycosylation. Isotopic labeling facilitates mass spectrometry-based confirmation of glycoprotein identity, identification of glycosylation sites, and quantification of the extent of modification. By combining chemical tagging for isolation and isotope labeling for mass spectrometry analysis, researchers are developing highly effective strategies for glycoproteomics. These techniques are enabling cancer biologists to identify biomarkers whose glycosylation state correlates with disease states, and developmental biologists to characterize stage-specific changes in glycoprotein expression. Next-generation methods will make functional analyses of the glycoproteome possible, including the discovery of glycoprotein interaction partners and the identification of enzymes responsible for synthesis of particular glycan structures.     

Cell surface glycoproteomic analysis of prostate cancer-derived PC-3 cells

Most clinically approved biomarkers of cancer are glycoproteins, and those residing on the cell surface are of particular interest in biotherapeutics. We report a method for selective labeling, affinity enrichment, and identification of cell-surface glycoproteins. PC-3 cells and primary human prostate cancer tissue were treated with peracetylated N-azidoacetylgalactosamine, resulting in metabolic labeling of cell surface glycans with the azidosugar. We used mass spectrometry to identify over 70 cell surface glycoproteins and biochemically validated CD146 and integrin beta-4, both of which are known to promote metastatic behavior. These results establish cell-surface glycoproteomics as an effective technique for discovery of cancer biomarkers.     

Nanotechnologies in Glycoproteomics

Protein glycosylation, as an important post-translational modification, is implicated in a number of ailments. Applying proteomic approaches, including mass spectrometry (MS) analyses that have played a significant role in biomarker detection and early diagnosis of diseases, to the study of glycoproteins or glycopeptides will facilitate a deeper understanding of many physiological functions and biological pathways involved in cancer, inflammatory and degenerative diseases. The abundance of glycopeptides and their ionization potential are relatively lower compared to those of non-glycopeptides; therefore, sample enrichment is necessary for glycopeptides prior to MS analysis. The application of nanotechnology in the past decade has been rapidly penetrating into many diverse scientific research disciplines. Particularly in what we now refer to as the “glycoproteomics area”, nanotechnologies have enabled enhanced sensitivity and specificity of glycopeptide detection in complex biological fluids, which are critical for disease diagnosis and monitoring. In this review, we highlight some recent studies that combine the capabilities of specific nanotechnologies with the comprehensive features of glycoproteomics. In particular, we focus on the ways in which nanotechnology has facilitated the detection of glycopeptides in complex biological samples and enhanced their characterization by MS, in terms of intensity and resolution. These studies reveal an increasingly important role for nanotechnology in helping to overcome certain technical challenges in biomarker discovery, in general, and glycoproteomics research, in particular.     

Recent progress in quantitative glycoproteomics

Protein glycosylation is acknowledged as one of the major posttranslational modifications that elicit significant effects on protein folding, conformation, distribution, stability, and activity. The changes in glycoprotein abundance, glycosylation degree, and glycan structure are associated with a variety of diseases. Therefore, the quantitative study of glycoproteomics has become a new and popular research topic, and is quickly emerging as an important technique for biomarker discovery. Mass spectrometry-based protein quantification technologies provide a powerful tool for the systematic and quantitative assessment of the quantitative differences in the protein profiles of different samples. Combined with various glycoprotein/glycopeptide enrichment strategies and other glycoprotein analysis methods, these techniques have been further developed for application in quantitative glycoproteomics. A comprehensive quantitative analysis of the glycoproteome in a complex biological sample remains challenging because of the enormous complexity of biological samples, intrinsic characteristics of glycoproteins, and lack of universal quantitative technology. In this review, recently developed technologies in quantitative glycoproteome, especially those focused on two of the most common types of glycosylation (N-linked and O-linked glycoproteome), were summarized. The strengths and weaknesses of the various approaches were also discussed.     

Proteomics approaches to urologic diseases

Biomarkers are greatly needed for several urologic diseases, such as interstitial cystitis, the symptomatic and clinical progression of benign prostate hyperplasia, as well as the specific detection of urologic cancers, including prostate and bladder cancer. This review aims to: briefly describe the need for biomarkers in the field and biomarkers that are currently available for clinicians; address the limitations and roadblocks to effective biomarker discovery; and provide examples and strategies for implementing biomarkers in clinical practice and/or drug discovery.     

Glycoscience aids in biomarker discovery

The glycome consists of all glycans (or carbohydrates) within a biological system, and modulates a wide range of important biological activities, from protein folding to cellular communications. The mining of the glycome for disease markers represents a new paradigm for biomarker discovery; however, this effort is severely complicated by the vast complexity and structural diversity of glycans. This review summarizes recent developments in analytical technology and methodology as applied to the fields of glycomics and glycoproteomics. Mass spectrometric strategies for glycan compositional profiling are described, as are potential refinements which allow structure-specific profiling. Analytical methods that can discern protein glycosylation at a specific site of modification are also discussed in detail. Biomarker discovery applications are shown at each level of analysis, highlighting the key role that glycoscience can play in helping scientists understand disease biology.     

Strategies for biomarker discovery in fibrotic disease

The discovery and development of biomarkers for fibrotic diseases have potential utility in clinical decision-making as well as in pharmaceutical research and development. This review describes strategies for identifying diagnostic, prognostic and theranostic biomarkers. A range of technologies and platforms for biomarker discovery are highlighted, including several with specific relevance for fibrosis. Some challenges specific to fibrotic diseases are outlined including; benchmarking biomarkers against imperfect clinical measures of fibrosis, the complexity resulting from diverse aetiologies and target organs, and the availability of samples (including biopsy) from well-characterised patients with fibrotic disease. To overcome these challenges collaboration amongst clinical specialities as well as between academia and industry is essential. This article is part of a Special Issue entitled: Fibrosis: Translation of basic research to human disease.     

Recent advances in atherosclerosis-based proteomics: new biomarkers and a future perspective

Vascular proteomics is providing two main types of data: proteins that actively participate in vascular pathophysiological processes and novel protein candidates that can potentially serve as useful clinical biomarkers. Although both types of proteins can be identified by similar proteomic strategies and methods, it is important to clearly distinguish biomarkers from mediators of disease. A particular protein, or group of proteins, may participate in a pathogenic process but not serve as an effective biomarker. Alternatively, a useful biomarker may not mediate pathogenic pathways associated with disease (i.e., C-reactive protein). To date, there are no clear successful examples in which discovery proteomics has led to a novel useful clinical biomarker in cardiovascular diseases. Nevertheless, new sources of biomarkers are being explored (i.e., secretomes, circulating cells, exosomes and microparticles), an increasing number of novel proteins involved in atherogenesis are constantly described, and new technologies and analytical strategies (i.e., quantitative proteomics) are being developed to access low abundant proteins. Therefore, this presages a new era of discovery and a further step in the practical application to diagnosis, prognosis and early action by medical treatment of cardiovascular diseases.     

A case for protein-level and site-level specificity in glycoproteomic studies of disease

Abnormal glycosylation of proteins is known to be either resultant or causative of a variety of diseases. This makes glycoproteins appealing targets as potential biomarkers and focal points of molecular studies on the development and progression of human ailment. To date, a majority of efforts in disease glycoproteomics have tended to center on either determining the concentration of a given glycoprotein, or on profiling the total population of glycans released from a mixture of glycoproteins. While these approaches have demonstrated some diagnostic potential, they are inherently insensitive to the fine molecular detail which distinguishes unique and possibly disease relevant glycoforms of specific proteins. As a consequence, such analyses can be of limited sensitivity, specificity, and accuracy because they do not comprehensively consider the glycosylation status of any particular glycoprotein, or of any particular glycosylation site. Therefore, significant opportunities exist to improve glycoproteomic inquiry into disease by engaging in these studies at the level of individual glycoproteins and their exact loci of glycosylation. In this concise review, the rationale for glycoprotein and glycosylation site specificity is developed in the context of human disease glycoproteomics with an emphasis on N-glycosylation. Recent examples highlighting disease-related perturbations in glycosylation will be presented, including those involving alterations in the overall glycosylation of a specific protein, alterations in the occupancy of a given glycosylation site, and alterations in the compositional heterogeneity of glycans occurring at a given glycosylation site. Each will be discussed with particular emphasis on how protein-specific and site-specific approaches can contribute to improved discrimination between glycoproteomes and glycoproteins associated with healthy and unhealthy states.     

A generic research paradigm for identification and validation of early molecular diagnostics and new therapeutics in common disorders

Genetically complex disorders continue to confound investigators because of their many underlying factors, both genetic and environmental. In order to tease apart the heritable from the non-heritable contributions to disease, clinicians are relying on researchers in the rapidly expanding fields of high-throughput genomics to identify surrogate clinical endpoints, called biomarkers, that provide a measure of the probability that an individual will succumb to the disease in question. The goals of current biomedical research into complex disorders are to identify and utilize these biomarkers, not only for early detection, but also for personalized treatment with knowledge-guided therapeutics. As the identification of these biomarkers is basically a problem of discovery, we discuss new insights into biomarker detection utilizing the most current genomic technologies available. Additionally, we present here a generic paradigm for the validation of such molecular diagnostics as well as new treatment modalities for complex and increasingly common diseases. Lastly, we delve into the ways genomic biomarkers might be implemented in a clinical setting to allow the subsequent application of targeted therapeutics, which can help the ever expanding groups of individuals experiencing these insidious diseases.     

Proteomics and its applications for biomarker discovery in human saliva

Human saliva is a biological fluid with enormous diagnostic potential. Because saliva can be non-invasively collected, it provides an attractive alternative for blood, serum or plasma. It has been postulated that the blood concentrations of many components are reflected in saliva. Saliva harbors a wide array of proteins, which can be informative for the detection of diseases. Profiling the proteins in saliva over the course of disease progression could reveal potential biomarkers indicative of different stages of diseases, which may be useful in medical diagnostics. With advanced instrumentation and developed refined analytical techniques, proteomics is widely envisioned as a useful and powerful approach for salivary proteomic biomarker discovery. As proteomic technologies continue to mature, salivary proteomics have great potential for biomarker research and clinical applications. The progress and current status of salivary proteomics and its application in the biomarker discovery of oral and systematic diseases will be reviewed. The scientific and clinical challenges underlying this approach will also be discussed.     

OMICS-rooted studies of milk proteins,oligosaccharides and lipids

Milk has co-evolved with mammals and mankind to nourish their offspring and is a biological fluid of unique complexity and richness. It contains all necessary nutrients for the growth and development of the newborn. Structure and function of biomolecules in milk such as the macronutrients (glyco-) proteins, lipids, and oligosaccharides are central topics in nutritional research. Omics disciplines such as proteomics, glycomics, glycoproteomics, and lipidomics enable comprehensive analysis of these biomolecule components in food science and industry. Mass spectrometry has largely expanded our knowledge on these milk bioactives as it enables identification, quantification and characterization of milk proteins, carbohydrates, and lipids. In this article, we describe the biological importance of milk macronutrients and review the application of proteomics, glycomics, glycoproteomics, and lipidomics to the analysis of milk. Proteomics is a central platform among the Omics tools that have more recently been adapted and applied to nutrition and health research in order to deliver biomarkers for health and comfort as well as to discover beneficial food bioactives.     

Human saliva proteome analysis and disease biomarker discovery

Human saliva is an attractive body fluid for disease diagnosis and prognosis because saliva testing is simple, safe, low-cost and noninvasive. Comprehensive analysis and identification of the proteomic content in human whole and ductal saliva will not only contribute to the understanding of oral health and disease pathogenesis, but also form a foundation for the discovery of saliva protein biomarkers for human disease detection. In this article, we have summarized the proteomic technologies for comprehensive identification of proteins in human whole and ductal saliva. We have also discussed potential quantitative proteomic approaches to the discovery of saliva protein biomarkers for human oral and systemic diseases. With the fast development of mass spectrometry and proteomic technologies, we are enthusiastic that saliva protein biomarkers will be developed for clinical diagnosis and prognosis of human diseases in the future.     

Proteomics of human cerebrospinal fluid: discovery and verification of biomarker candidates in neurodegenerative diseases using quantitative proteomics

There is an urgent need for novel biomarkers that can be used to improve the diagnosis, predict the disease progression, improve our understanding of the pathology or serve as therapeutic targets for neurodegenerative diseases. Cerebrospinal fluid (CSF) is in direct contact with the CNS and reflects the biochemical state of the CNS under different physiological and pathological settings. Because of this, CSF is regarded as an excellent source for identifying biomarkers for neurological diseases and other diseases affecting the CNS. Quantitative proteomics and sophisticated computational software applied to analyze the protein content of CSF has been fronted as an attractive approach to find novel biomarkers for neurological diseases. This review will focus on some of the potential pitfalls in biomarker studies using CSF, summarize the status of the field of CSF proteomics in general, and discuss some of the most promising proteomics biomarker study approaches. A brief status of the biomarker discovery efforts in multiple sclerosis, Alzheimer's disease, and Parkinson's disease is also given.     

Introduction to proteomics: tools for the new biology

Towards revolutionary biomarkers, a considerable amount of research funds and time have been dedicated to proteomics. Although the discovery of novel biomarkers at the dawn of proteomics was a promising development, only a few identified biomarkers seemed to be beneficial for cancer patients. We may need to approach this issue differently, instead of only extending the conventional approaches that have been used historically. The study of biomarkers is essentially a study of diseases and the biochemistry relating to peptide, protein and post-translational modifications is only a tool. A problem-oriented approach should be needed in biomarker development. Clinician participation in the study of biomarkers will lead to realistic, practical and interesting biomarker candidates, which justify the time and expense involved in validation studies. Although discussion in this article is focused on cancer biomarkers, it can generally be applied to biomarker studies for other diseases.     

Cell-specific aptamer probes for membrane protein elucidation in cancer cells

Disease biomarkers play critical roles in the management of various pathological conditions of diseases. This involves diagnosing diseases, predicting disease progression and monitoring the efficacy of treatment modalities. While efforts to identify specific disease biomarkers using a variety of technologies has increased the number of biomarkers or augmented information about them, the effective use of disease-specific biomarkers is still scarce. Here, we report that a high expression of protein tyrosine kinase 7 (PTK7), a transmembrane receptor protein tyrosine kinase-like molecule, was discovered in a series of leukemia cell lines using whole cell aptamer selection. With the implementation of a two-step strategy (aptamer selection and biomarker discovery), combined with mass spectrometry, PTK7 was ultimately identified as a potential biomarker for T-cell acute lymphoblastic leukemia (T-ALL). Specifically, the aptamers for T-ALL cells were selected using the cell-SELEX process, without any prior knowledge of the cell biomarker population, conjugated with magnetic beads and then used to capture and purify their binding targets on the leukemia cell surface. This demonstrates that a panel of molecular aptamers can be easily generated for a specific type of diseased cells. It further demonstrates that this two-step strategy, that is, first selecting cancer cell-specific aptamers and then identifying their binding target proteins, has major clinical implications in that the technique promises to substantially improve the overall effectiveness of biomarker discovery. Specifically, our strategy will enable efficient discovery of new malignancy-related biomarkers, facilitate the development of diagnostic tools and therapeutic approaches to cancer, and markedly improve our understanding of cancer biology.     

Human saliva proteome analysis and disease biomarker discovery

Human saliva is an attractive body fluid for disease diagnosis and prognosis because saliva testing is simple, safe, low-cost and noninvasive. Comprehensive analysis and identification of the proteomic content in human whole and ductal saliva will not only contribute to the understanding of oral health and disease pathogenesis, but also form a foundation for the discovery of saliva protein biomarkers for human disease detection. In this article, we have summarized the proteomic technologies for comprehensive identification of proteins in human whole and ductal saliva. We have also discussed potential quantitative proteomic approaches to the discovery of saliva protein biomarkers for human oral and systemic diseases. With the fast development of mass spectrometry and proteomic technologies, we are enthusiastic that saliva protein biomarkers will be developed for clinical diagnosis and prognosis of human diseases in the future.     

Proteome profiling for the identification of lung cancer signatures

Comprehensive and in-depth discovery of the disease proteome is an important issue in recent proteomics developments. Previous studies have shown a number of biomarkers discovered in various diseases, including lung cancer. Some of them are potentially useful in lung cancer diagnostics and prognostics. However, few of them can act as organ-specific biomarkers to extensively compare multiple cancer models. This article evaluates a recently published study employing comparative proteomics on multiple genetically engineered mouse models and sheds light on the usefulness and application of the discovered marker panel for human lung cancer diagnostics.     

The knowledge-integrated network biomarkers discovery for major adverse cardiac events

The mass spectrometry (MS) technology in clinical proteomics is very promising for discovery of new biomarkers for diseases management. To overcome the obstacles of data noises in MS analysis, we proposed a new approach of knowledge-integrated biomarker discovery using data from Major Adverse Cardiac Events (MACE) patients. We first built up a cardiovascular-related network based on protein information coming from protein annotations in Uniprot, protein-protein interaction (PPI), and signal transduction database. Distinct from the previous machine learning methods in MS data processing, we then used statistical methods to discover biomarkers in cardiovascular-related network. Through the tradeoff between known protein information and data noises in mass spectrometry data, we finally could firmly identify those high-confident biomarkers. Most importantly, aided by protein-protein interaction network, that is, cardiovascular-related network, we proposed a new type of biomarkers, that is, network biomarkers, composed of a set of proteins and the interactions among them. The candidate network biomarkers can classify the two groups of patients more accurately than current single ones without consideration of biological molecular interaction.