Exosomes in bodily fluids are a highly stable resource of disease biomarkers

Biomarkers are measurable indicators of a biological state. As our understanding of diseases meliorates, it is generally accepted that early diagnosis renders the best chance to cure a disease. In the context of proteomics, the discovery phase of identifying bonafide biomarkers and the ensuing validation phase involving large cohort of patient samples are impeded by the complexity of bodily fluid samples. High abundant proteins found in blood plasma make it difficult for the detection of low abundant proteins that may be potential biomarkers. Extracellular vesicles (EVs) have reignited interest in the field of biomarker discovery. EVs contain a tissue-type signature wherein a rich cargo of proteins and RNA are selectively packaged. In addition, as EVs are membranous structures, the luminal contents are protected from degradation by extracellular proteases and are highly stable in storage conditions. Interestingly, an appealing feature of EV-based biomarker analysis is the significant reduction in the sample complexity compared to whole bodily fluids. With these prescribed attributes, which are the rate-limiting factors of traditional biomarker analysis, there is immense potential for the use of EVs for biomarker detection in clinical settings. This review will discuss the current issues with biomarker analysis and the potential use of EVs as reservoirs of disease biomarkers.     

Quantitation with chemical tagging reagents in biomarker studies

Isobaric tags for relative and absolute quantitation (iTRAQ), Tandem Mass Tags (TMT) and related chemical tag reagents provide analytical platforms for quantitative proteomics applied to clinical samples. In this Viewpoint article, applications for discovery and targeted modes are discussed with an emphasis on study design and technical considerations in biomarker analysis. The evolution and promise of emerging, related strategies are also discussed. It should be noted that iTRAQ and TMT users contributed to the key debates in the biomarker field, to define strategies for biomarker discovery for identification of clinical biomarkers, and continue to inform design of verification and validation assays via implementation of non-isobaric variants for targeted analyses.     

Proteomics of human cerebrospinal fluid - the good, the bad, and the ugly

The development of MALDI ESI in the late 1980s has revolutionized the biological sciences and facilitated the emergence of a new discipline called proteomics. Application of proteomics to human cerebrospinal fluid (CSF) has greatly hastened the advancement of characterizing the CSF proteome as well as revealing novel protein biomarkers that are diagnostic of various neurological diseases. While impressive progressions have been made in this field, it has become increasingly clear that proteomics results generated by various laboratories are highly variable. The underlying issues are vast, including limitations and complications with heterogeneity of patients/testing subjects, experimental design, sample processing, as well as current proteomics technology. Accordingly, this review not only summarizes the current status of characterization of the human CSF proteome and biomarker discovery for major neurodegenerative disorders, i.e., Alzheimer's disease and Parkinson's disease, but also addresses a few essential caveats involved in several steps of CSF proteomics that may contribute to the variable/contradicting results reported by different laboratories. The potential future directions of CSF proteomics are also discussed with this analysis.     

Quantitative proteomics in cardiovascular research: Global and targeted strategies

Extensive technical advances in the past decade have substantially expanded quantitative proteomics in cardiovascular research. This has great promise for elucidating the mechanisms of cardiovascular diseases and the discovery of cardiac biomarkers used for diagnosis and treatment evaluation. Global and targeted proteomics are the two major avenues of quantitative proteomics. While global approaches enable unbiased discovery of altered proteins via relative quantification at the proteome level, targeted techniques provide higher sensitivity and accuracy, and are capable of multiplexed absolute quantification in numerous clinical/biological samples. While promising, technical challenges need to be overcome to enable full utilization of these techniques in cardiovascular medicine. Here, we discuss recent advances in quantitative proteomics and summarize applications in cardiovascular research with an emphasis on biomarker discovery and elucidating molecular mechanisms of disease. We propose the integration of global and targeted strategies as a high-throughput pipeline for cardiovascular proteomics. Targeted approaches enable rapid, extensive validation of biomarker candidates discovered by global proteomics. These approaches provide a promising alternative to immunoassays and other low-throughput means currently used for limited validation.     

Is label-free LC-MS/MS ready for biomarker discovery?

Label-free LC-MS methods are attractive for high-throughput quantitative proteomics, as the sample processing is straightforward and can be scaled to a large number of samples. Label-free methods therefore facilitate biomarker discovery in studies involving dozens of clinical samples. However, despite the increased popularity of label-free workflows, there is a hesitance in the research community to use it in clinical proteomics studies. Therefore, we here discuss pros and cons of label-free LC-MS/MS for biomarker discovery, and delineate the main prerequisites for its successful employment. Furthermore, we cite studies where label-free LC-MS/MS was successfully used to identify novel biomarkers, and foresee an increased acceptance of label-free techniques by the proteomics community in the near future.     

Discovery of diagnostic serum biomarkers of gastric cancer using proteomics

Gastric cancer has significant morbidity and mortality worldwide and locally. Good prognosis relies on an early diagnosis. However, this remains a challenge due to the lack of specific and sensitive serum biomarkers for early detection. Hence, there is a constant search for these biomarkers for screening purposes. Proteomic profiling enables a new approach to the discovery of biomarkers in disease. This review presents recent attempts in search of gastric cancer serum biomarker using proteomics. Different methodologies and different types of samples were employed by different groups of researchers. Major difficulties were encountered in the discovery processes, including interference from abundant proteins and continuous changing serum proteomes from different individuals.     

Network views for personalized medicine

Clinical Proteomics has traveled a long way pinpointing potential biomarkers for a variety of diseases. However, the absence of clinical implementation of proteomics findings has led to a frank evaluation and reconsideration of applied practices in biomarker discovery, recruitment of technological tools for biomarker verification and generation of new guidelines for data reporting. Nevertheless, considering the need for vast clinical resources for biomarker validation, the frequent lack of clear definitions of contexts of use, in combination to the biomarker “high offer,” progress toward biomarker implementation will even more require the adoption of an extensive open-minded approach: disease-focused networks are needed to ensure rapid exchange of information, initiation of appropriate studies, parallel validation of multiple biomarkers and sharing of valuable clinical resources. This viewpoint article targets to reflect on these issues and advocates the added value of multidisciplinary networks in biomarker development using bladder cancer as a paradigm.     

Quantitative proteomics for identification of cancer biomarkers

Quantitative proteomics can be used for the identification of cancer biomarkers that could be used for early detection, serve as therapeutic targets, or monitor response to treatment. Several quantitative proteomics tools are currently available to study differential expression of proteins in samples ranging from cancer cell lines to tissues to body fluids. 2-DE, which was classically used for proteomic profiling, has been coupled to fluorescence labeling for differential proteomics. Isotope labeling methods such as stable isotope labeling with amino acids in cell culture (SILAC), isotope-coded affinity tagging (ICAT), isobaric tags for relative and absolute quantitation (iTRAQ), and (18) O labeling have all been used in quantitative approaches for identification of cancer biomarkers. In addition, heavy isotope labeled peptides can be used to obtain absolute quantitative data. Most recently, label-free methods for quantitative proteomics, which have the potential of replacing isotope-labeling strategies, are becoming popular. Other emerging technologies such as protein microarrays have the potential for providing additional opportunities for biomarker identification. This review highlights commonly used methods for quantitative proteomic analysis and their advantages and limitations for cancer biomarker analysis.     

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.     

Opportunities and challenges of proteomics in pediatric patients: Circulating biomarkers after hematopoietic stem cell transplantation as a successful example

Biomarkers have the potential to improve diagnosis and prognosis, facilitate-targeted treatment, and reduce health care costs. Thus, there is great hope that biomarkers will be integrated in all clinical decisions in the near future. A decade ago, the biomarker field was launched with great enthusiasm because MS revealed that blood contains a rich library of candidate biomarkers. However, biomarker research has not yet delivered on its promise due to several limitations: (i) improper sample handling and tracking as well as limited sample availability in the pediatric population, (ii) omission of appropriate controls in original study designs, (iii) lability and low abundance of interesting biomarkers in blood, and (iv) the inability to mechanistically tie biomarker presence to disease biology. These limitations as well as successful strategies to overcome them are discussed in this review. Several advances in biomarker discovery and validation have been made in hematopoietic stem cell transplantation, the current most effective tumor immunotherapy, and these could serve as examples for other conditions. This review provides fresh optimism that biomarkers clinically relevant in pediatrics are closer to being realized based on: (i) a uniform protocol for low-volume blood collection and preservation, (ii) inclusion of well-controlled independent cohorts, (iii) novel technologies and instrumentation with low analytical sensitivity, and (iv) integrated animal models for exploring potential biomarkers and targeted therapies.     

Clinical proteomics in neurodegenerative diseases

Investigation of the human specimens is an essential element for understanding the pathogenesis of neurodegenerative disorders, such as Alzheimer's disease, Parkinson's disease, and multiple sclerosis. The studies hold promise for identifying biomarkers for diagnosis and prognosis, elucidating disease mechanisms, and accelerating the development of new strategies for therapeutic intervention. Here, we review proteomics studies of human brain samples in light of recent advances of mass spectrometry, focusing on the general strategies for experimental design and analysis (e.g., sample pooling and replication, selection of proteomics platforms, and false discovery rate in data processing), because quantitative analysis of clinical samples is confounded by a number of variables, including genetic differences, antemortem and postmortem factors, and experimental errors. Diverse proteomics platforms are also discussed with respect to sensitivity, throughput, and accuracy. Regarding the enormous complexity of the human brain and the limitation of current proteomics technologies, it may be more practical to analyze a subset of proteome in a functional context, in order to facilitate the identification of important disease-related proteins in the substantial noise reflecting biological and technical variances.     

Proteomic analysis of serum, plasma, and lymph for the identification of biomarkers

Probably no topic has generated more excitement in the world of proteomics than the search for biomarkers. This excitement has been generated by two realities: the constant need for better biomarkers that can be used for disease diagnosis and prognosis, and the recent developments in proteomic technologies that are capable of scanning the individual proteins within varying complex clinical samples. Ideally a biomarker would be assayable from a noninvasively collected sample, therefore, much of the focus in proteomics has been on the analysis of biofluids such as serum, plasma, urine, cerebrospinal fluid, lymph, etc. While the discovery of biomarkers has been elusive, there have been many advances made in the understanding of the proteome content of various biofluids, and in the technologies used for their analysis, that continues to point the research community toward new methods for achieving the ultimate goal of identifying novel disease-specific biomarkers. In this review, we will describe and discuss many of the proteomic approaches taken in an attempt to find novel biomarkers in serum, plasma, and lymph.     

Urine proteomics in kidney and urogenital diseases: Moving towards clinical applications

To date, multiple biomarker discovery studies in urine have been conducted. Nevertheless, the rate of progression of these biomarkers to qualification and even more clinical application is extremely low. The scope of this article is to provide an overview of main clinically relevant proteomic findings from urine focusing on kidney diseases, bladder and prostate cancers. In addition, approaches for promoting the use of urine in clinical proteomics including potential means to facilitate the validation of existing promising findings (biomarker candidates identified from previous studies) and to increase the chances for success for the identification of new biomarkers are discussed.     

Ensembles of protein termini and specific proteolytic signatures as candidate biomarkers of disease

Early accurate diagnosis and personalized treatment are essential in order to treat complex or fatal diseases such as cancer and autoimmune, cardiovascular and neurodegenerative diseases. To realize this vision, new diagnostic and prognostic biomarkers are urgently required. MS-based proteomics is the most promising approach for protein biomarker identification, but suffers in clinical translation of biomarker candidates that show only quantitative differences from normal tissue. Indeed, success in translating proteomic data to biomarkers in the clinic has been disappointing. Here, we propose that protein termini provide a new opportunity for biomarker discovery due to qualitative differences in intact and new protein termini between diseased and normal tissues. Altered proteolysis occurs in most pathologies. Disease- and process-specific protein modifications, including proteolytic processing and subsequent modification of the terminal amino acids, frequently lead to altered protein activity that plays key roles in the disease process. Thus, mapping of ensembles of characteristic protein termini provides a proteolytic signature of high information content that shows both quantitative and most importantly qualitative differences in different diseases and stage of disease. These unique protein biomarkers have the added benefit of being mechanistically informative by revealing the activity state of the bioactive protein. Moreover, proteome-wide isolation of protein termini leads to generalized sample simplification, thereby enabling up to three orders of magnitude lower LODs compared to traditional shotgun proteomic approaches. We introduce the potential of protein termini for biomarker discovery, briefly review methods enabling large-scale studies of protein termini, and discuss how these may be integrated into a termini-oriented biomarker discovery pipeline from discovery to clinical application.     

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.     

Identifying novel biomarkers through data mining—A realistic scenario?

In this article we discuss the requirements to use data mining of published proteomics datasets to assist proteomics-based biomarker discovery, the use of external data integration to solve the issue of inadequate small sample sizes and finally, we try to estimate the probability that new biomarkers will be identified through data mining alone.     

Potential biomarkers for oral squamous cell carcinoma: Proteomics discovery and clinical validation

Oral squamous cell carcinoma (OSCC) is the worldwide concerned cancer. In spite of the advances in treatment, the 5-year survival rate has only increased subtly during the past two decades, which is largely due to the advanced stages of disease at diagnosis and the frequent development of relapse and second primary tumors. Therefore, the identification of underlying OSCC protein biomarker during cancer initiation and progression could aid the diagnosis and treatment of OSCC. In this review, recent researches on proteomics analysis of tissue, saliva, and serum for early detection and evaluation aggressiveness and occurrence of OSCC were summarized. The emphasis is placed on early detection by tissues, saliva, and serum of patients with histologically defined OSCC patients. Although lots of researches for searching OSCC protein biomarker have done, few common protein biomarkers have been detected. Low-redundant protein in tissues, saliva, and serum from OSCC may more accurately reflected the progression of OSCC, so novel approach for the depth research strategy and the sample choice for proteomics are of importance in OSCC biomarker discovery.     

Biomarker discovery in glomerular diseases using urinary proteomics

Renal biopsy remains the gold standard test for definitive diagnosis of glomerular diseases. This invasive procedure; however, has a potential risk for serious complications and is contraindicated in some patients. It is therefore essential to search for noninvasive biomarkers for the diagnosis and prognosis of glomerular diseases. The urine is the most appropriate sample for biomarker discovery in glomerular diseases. Urinary proteomics has thus gained a wide acceptance and has been extensively applied to this area. This review focuses mainly on applications of proteomic technologies to urinary proteome profiling for biomarker discovery in various glomerular diseases, including diabetic nephropathy, IgA nephropathy, membranous nephropathy, focal segmental glomerulosclerosis, primary membranoproliferative glomerulonephritis, lupus nephritis, antiglomerular basement membrane disease, minimal change disease, and pediatric nephrotic syndrome. Recent findings from these studies are summarized and discussed. These data clearly underline the great promise of urinary proteomics in biomarker discovery for glomerular diseases.     

Implementation of Proteomics in Clinical Trials

The application of protein (or peptide) biomarkers in clinical studies is a dynamic, ever‐growing field. The introduction of clinical proteomics/peptidomics, such as mass spectrometry–based assays and multiplexed antibody–based protein arrays, has reshaped the landscape of biomarker identification and validation, allowing the discovery of novel biomarkers at an unprecedented rate and reliability. To reflect the current status with respect to implementation of protein/peptide biomarkers, an investigation of the most recent (last 6 years) clinical studies from clinicaltrials.gov is presented. Forty‐two clinical trials involving the direct use of protein or peptide biomarkers in patient stratification and/or disease diagnosis and prognosis are highlighted. Most of the clinical trials that include proteomics/protein assays are aiming toward implementation of non‐invasive diagnostic tools for early detection, while many of the clinical trials are targeting to correlate the protein abundance with the risk of a disease event. Less in number are the studies in which the protein biomarkers are applied to stratify the patients for intervention. All the above areas of application are considered of great importance for improving disease management, in an era where implementation toward precision medicine is the desired outcome of proteomics biomarker research.