SILAC for biomarker discovery

SILAC has been employed in MS-based proteomics for nearly a decade. This method is based on cells in culture metabolically incorporating isotope-coded essential amino acids and allows the quantification of global protein populations to identify characteristic changes. Variations of this technique developed over the years allow the application of SILAC not only to cell culture derived samples but also to tissues and human specimens, making this powerful technique amenable to clinically relevant samples. In this review, we provide an overview of different SILAC-derived methods and their use in the identification and development of biomarkers.     

Quantitative proteomic approaches for biomarker discovery

The rapid advances in proteomic technologies have made possible systematic analysis of hundreds to thousands of proteins in clinical samples with the promise of uncovering novel protein biomarkers for various disease conditions. We will discuss in this review article current MS and protein chip-based quantitative proteomic approaches and their application in biomarker discovery. The emphasis will be placed on new quantification strategies employing stable isotopic labeling coupled with MS/MS, and antibody-based protein chips and nanodevices. The strength and weakness of each technology are briefly highlighted.     

The interface between biomarker discovery and clinical validation: The tar pit of the protein biomarker pipeline

The application of "omics" technologies to biological samples generates hundreds to thousands of biomarker candidates; however, a discouragingly small number make it through the pipeline to clinical use. This is in large part due to the incredible mismatch between the large numbers of biomarker candidates and the paucity of reliable assays and methods for validation studies. We desperately need a pipeline that relieves this bottleneck between biomarker discovery and validation. This paper reviews the requirements for technologies to adequately credential biomarker candidates for costly clinical validation and proposes methods and systems to verify biomarker candidates. Models involving pooling of clinical samples, where appropriate, are discussed. We conclude that current proteomic technologies are on the cusp of significantly affecting translation of molecular diagnostics into the clinic.     

Body fluid proteomics: Prospects for biomarker discovery

Many diseases are caused by perturbations of cellular signaling pathways and related pathway networks as a result of genetic aberrations. These perturbations are manifested by altered cellular protein profiles in the fluids bathing tissue/organs (i.e., the tissue interstitial fluid, TIF). A major challenge of clinical chemistry is to quantitatively map these perturbed protein profiles - the so-called "signatures of disease" - using modern proteomic technologies. This information can be utilized to design protein biomarkers for the early detection of disease, monitoring disease progression and efficacy of drug action. Here, we discuss the use of body fluids in the context of prospective biomarker discovery, and the marked 1000-1500-fold dilution of body fluid proteins, during their passage from TIF to the circulatory system. Further, we discuss proteomics strategies aimed at depleting major serum proteins, especially albumin, in order to focus on low-abundance protein/peptides in plasma. A major limitation of depletion strategies is the removal of low-molecular weight protein/peptides which specifically bind major plasma proteins. We present a prototype model, using albumin, for understanding the multifaceted nature of biomarker research, highlighting the involvement of albumin in Alzheimer's disease. This model underscores the need for a system-level understanding for biomarker research and personalized medicine.     

Proteome analysis of body fluids for amyotrophic lateral sclerosis biomarker discovery

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder of motor neurons leading to death of the patients, mostly within 2–5 years after disease onset. The pathomechanism of motor neuron degeneration is only partially understood and therapeutic strategies based on mechanistic insights are largely ineffective. The discovery of reliable biomarkers of disease diagnosis and progression is the sine qua non of both the revelation of insights into the ALS pathomechanism and the assessment of treatment efficacies. Proteomic approaches are an important pillar in ALS biomarker discovery. Cerebrospinal fluid is the most promising body fluid for differential proteome analyses, followed by blood (serum, plasma), and even urine and saliva. The present study provides an overview about reported peptide/protein biomarker candidates that showed significantly altered levels in certain body fluids of ALS patients. These findings have to be discussed according to proposed pathomechanisms to identify modifiers of disease progression and to pave the way for the development of potential therapeutic strategies. Furthermore, limitations and advantages of proteomic approaches for ALS biomarker discovery in different body fluids and reliable validation of biomarker candidates have been addressed.     

Prostate cancer serum biomarker discovery through proteomic analysis of alpha‐2 macroglobulin protein complexes

Alpha‐2 macroglobulin (A2M) functions as a universal protease inhibitor in serum and is capable of binding various cytokines and growth factors. In this study, we investigated if immunoaffinity enrichment and proteomic analysis of A2M protein complexes from human serum could improve detection of biologically relevant and novel candidate protein biomarkers in prostate cancer. Serum samples from six patients with androgen‐independent, metastatic prostate cancer and six control patients without malignancy were analyzed by immunoaffinity enrichment of A2M protein complexes and MS identification of associated proteins. Known A2M substrates were reproducibly identified from patient serum in both cohorts, as well as proteins previously undetected in human serum. One example is heat shock protein 90 alpha (HSP90α), which was identified only in the serum of cancer patients in this study. Using an ELISA, the presence of HSP90α in human serum was validated on expanded test cohorts and found to exist in higher median serum concentrations in prostate cancer (n = 18) relative to control (n = 13) patients (median concentrations 50.7 versus 27.6 ng/mL, respectively, p = 0.001). Our results demonstrate the technical feasibility of this approach and support the analysis of A2M protein complexes for proteomic‐based serum biomarker discovery.     

Proteomics is not only a biomarker discovery tool

The relatively young science of proteomics has been extensively used to identify biomarkers. However, a detailed and careful interpretation of proteomics data can also provide a clear picture of integrated biochemical systems, which can lead to a better comprehension of pathological processes. For example, the proteome analysis of human brain tissue from patients with schizophrenia, bipolar disorder, or multiple sclerosis compared with healthy controls has identified differentially expressed proteins that may not only be potential biomarkers but may also provide information that may increase the comprehension of these neurological disorders. Thus, proteomics is not only a biomarker discovery tool but can also identify potential players of relevance for diseases.     

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.     

Immunoproteomics: From biomarker discovery to diagnostic applications

Circulating antibodies reflect a molecular imprint of antigens that are related to autoimmune diseases, cancer or infection. Importantly, serum antibodies are useful clinical markers as they carry diagnostic information from all around the human body. Moreover, the amplification cascade governed by the humoral immune system causes a surplus of circulating antibodies after appearance of the corresponding (low abundance) antigen. In combination with the fact that antibodies are highly stable compared to many other serum proteins, they seem ideal to be implemented in clinical diagnostic assays for the detection of antigen-associated diseases. This review summarises advances in immunoproteomics with respect to technologies for biomarker discovery, with special emphasis on recently developed gel-free MS-based approaches, and looks forward to potential immunoproteomic applications in diagnostic medicine.     

Application of quantitative proteomics technologies to the biomarker discovery pipeline for multiple sclerosis

Multiple sclerosis is an inflammatory-mediated demyelinating disorder most prevalent in young Caucasian adults. The various clinical manifestations of the disease present several challenges in the clinic in terms of diagnosis, monitoring disease progression and response to treatment. Advances in MS-based proteomic technologies have revolutionized the field of biomarker research and paved the way for the identification and validation of disease-specific markers. This review focuses on the novel candidates discovered by the application of quantitative proteomics to relevant disease-affected tissues in both the human context and within the animal model of the disease known as experimental autoimmune encephalomyelitis. The role of targeted MS approaches for biomarker validation studies, such as multiple reaction monitoring will also be discussed.     

Colorectal cancer proteomics, molecular characterization and biomarker discovery

Colorectal cancer (CRC) is a widespread disease, whose major genetic changes and mutations have been well characterized in the sporadic form. Much less is known at the protein and proteome level. Still, CRC has been the subject of multiple proteomic studies due to the urgent necessity of finding clinically relevant markers and to elucidate the molecular mechanisms underlying the progression of the disease. These proteomic approaches have been limited by different technical issues, mainly related with sensitivity and reproducibility. However, recent advances in proteomic techniques and MS systems have rekindled the quest for new biomarkers in CRC and an improved molecular characterization. In this review, we will discuss the application of different proteomic approaches to the identification of differentially expressed proteins in CRC. In particular, we will make a critical assessment about the use of 2-D DIGE, MS and protein microarray technologies, in their different formats, to identify up- or downregulated proteins and/or autoantibodies profiles that could be useful for CRC characterization and diagnosis. Despite a wide list of potential biomarkers, it is clear that more scientific efforts and technical advances are still needed to cover the range of low-abundant proteins, which may play a key role in CRC diagnostics and progression.     

Human kidney glomerulus proteome and biomarker discovery of kidney diseases

The kidney glomerulus is the site of plasma filtration and production of primary urine in the kidney. The structure not only plays a pivotal role in ultrafiltration of plasma into urine but also is the locus of kidney diseases progressing to chronic renal failure. Patients afflicted with these glomerular diseases frequently progress to irreversible loss of renal function and inevitably require replacement therapies. The diagnosis and treatment of glomerular diseases are now based on clinical manifestations, urinary protein excretion level, and renal pathology of needle biopsy specimens. The molecular mechanisms underlying the progression of glomerular diseases are still obscure despite a great number of clinical and experimental studies. Proteomics is a particularly promising approach for the discovery of proteins relevant to physiological and pathophysiological processes, and has been recently employed in nephrology. Although until now most efforts of proteomic analysis have been conducted with urine, the biological fluid that is easily collected without invasive procedures, proteomic analysis of the glomerulus, the tissue most proximal to the disease loci, is the most straightforward approach. In this review, we attempt to outline the current status of clinical proteomics of the glomerulus and provide a perspective of protein biomarker discovery of glomerular diseases.     

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.     

Customising an antibody leukocyte capture microarray for systemic lupus erythematosus: beyond biomarker discovery

Systemic lupus erythematosus (SLE) is a complex autoimmune disease that has heterogeneous clinical manifestation with diverse patterns of organ involvement, autoantibody profiles and varying degrees of severity of disease. Research and clinical experience indicate that different subtypes of SLE patients will likely benefit from more tailored treatment regimes, but we currently lack a fast and objective test with high enough sensitivity to enable us to perform such sub-grouping for clinical use. In this article, we review how proteomic technologies could be used as such an objective test. In particular, we extensively review many leukocyte surface markers that are known to have an association with the pathogenesis of SLE, and we discuss how these markers can be used in the further development of a novel SLE-specific antibody leukocyte capture microarray. In addition, we review some bioinformatics challenges and current methods for using the data generated by these cell-capture microarrays in clinical use. In a broader context, we hope our experience in developing a disease specific cell-capture microarray for clinical application can be a guide to other proteomic practitioners who intend to extend their technologies to develop clinical diagnostic and prognostic tests for complex diseases.     

A genetic programming method for protein motif discovery and protein classification

Proteins can be grouped into families according to some features such as hydrophobicity, composition or structure, aiming to establish common biological functions. This paper presents MAHATMA—memetic algorithm-based highly adapted tool for motif ascertainment—a system that was conceived to discover features (particular sequences of amino acids, or motifs) that occur very often in proteins of a given family but rarely occur in proteins of other families. These features can be used for the classification of unknown proteins, that is, to predict their function by analyzing their primary structure. Experiments were done with a set of enzymes extracted from the Protein Data Bank. The heuristic method used was based on genetic programming using operators specially tailored for the target problem. The final performance was measured using sensitivity, specificity and hit rate. The best results obtained for the enzyme dataset suggest that the proposed evolutionary computation method is effective in finding predictive features (motifs) for protein classification.     

Cysteine cathepsins and their potential in clinical therapy and biomarker discovery

Since their discovery, cysteine cathepsins were generally considered to be involved mainly in the nonspecific bulk protein degradation that takes place within the lysosomes. However, it has become clear that their proteolytical activity can also influence various specific pathological processes such as cancer, arthritis, and atherosclerosis. Furthermore, their localization was found not to be limited strictly to the lysosomes. In the light of those findings, it is not surprising that cysteine cathepsins are currently considered as highly relevant clinical targets. Moreover, recent development of proteomic-based methods for identification of novel physiological substrates of proteases provides a major opportunity also in the field of cysteine cathepsins. In this review, we will therefore present cysteine cathepsin roles in disease progression and discuss their potential relevance as prognostic and diagnostic biomarkers.     

Prostate cancer biomarker discovery using high performance mass spectral serum profiling

Prostate-specific antigen (PSA) is the most widely used serum biomarker for early detection of prostate cancer (PCA). Nevertheless, PSA level can be falsely elevated due to prostatic enlargement, inflammation or infection, which limits the PSA test specificity. The objective of this study is to use a machine learning approach for the analysis of mass spectrometry data to discover more reliable biomarkers that distinguish PCA from benign specimens. Serum samples from 179 prostate cancer patients and 74 benign patients were analyzed. These samples were processed using ProXPRESSION™ Biomarker Enrichment Kits (PerkinElmer). Mass spectra were acquired using a prOTOF™ 2000 matrix-assisted laser desorption/ionization orthogonal time-of-flight (MALDI-O-TOF) mass spectrometer. In this study, we search for potential biomarkers using our feature selection method, the Extended Markov Blanket (EMB). From the new marker selection algorithm, a panel of 26 peaks achieved an accuracy of 80.7%, a sensitivity of 83.5%, a specificity of 74.4%, a positive predictive value (PPV) of 87.9%, and a negative predictive value (NPV) of 68.2%. On the other hand, when PSA alone was used (with a cutoff of 4.0 ng/ml), a sensitivity of 66.7%, a specificity of 53.6%, a PPV of 73.5%, and a NPV of 45.4% were obtained.