What do measurements of molecular biomarkers in different body fluids really tell us?

Molecular or biochemical biomarkers of joint metabolism offer promise in helping us understand joint pathology, its detection and treatment. But they have often been studied alone and in only one body fluid. Although the synovial joint is usually the focus of most arthritis pathology, it is often difficult, for a variety of reasons, to obtain synovial fluid that should best reflect changes in biomarkers related to pathology. It is therefore very important to see whether analyses of more readily obtainable sera and urine also reflect changes in synovial fluid. Catterall and colleagues, in a paper in Arthritis Research & Therapy that examines very early biomarker changes following joint injury, provide us with some insights into these important questions. As the study was very small and examined very early changes following joint injury, prior to onset of any recognisable pathology, we look forward to future larger biomarker studies of this kind in patients with clinically defined arthritic changes to which we can relate biomarker data.     

The Use of Biomarker Expression to Characterize Neoplastic Processes

Biomarkers have been used by pathologists to aid the diagnosis of tumors for almost three decades. Their use has resulted in the re-evaluation and reclassification of several types of tumors. Currently, biomarkers are required to differentiate certain specific tumors with similar histologic patterns. Additional uses of biomarkers in the characterization of neoplastic processes are discussed including their use in prognosis, detecting early neoplastic processes, identifying tumor recurrence, measuring the effectiveness of various therapies (surrogate end point biomarkers), and identifying targets for novel therapies including immunotherapy and gene therapy. We propose that these newer uses of biomarkers will be just as important to pathology in the future as the uses of biomarkers in diagnosis have been over the past two decades.     

Biomarkers of neurodegenerative disorders： How good are they？

Biomarkers are very important indicators of normal and abnormal biological processes. Specific changes in pathologies,biochemistries and genetics can give us comprehensive information regarding the nature of any particular disease. A good biomarker should be precise and reliable, distinguishable between normal and interested disease, and differentiable between different diseases. It is believed that biomarkers have great potential in predicting chances for diseases, aiding in early diagnosis, and setting standards for the development of new remedies to treat diseases. New technologies have enabled scientists to identify biomarkers of several different neurodegenerative diseases. The followings, for instance,are only a few of the many new biomarkers that have been recently identified: the phosphorylated tau protein and aggregated β-amyloid peptide for Alzheimer‘s disease (AD), α-synuclein contained Lewy bodies and altered dopamine transporter (DAT) imaging for Parkinson‘s disease (PD), SOD mutations for familial amyotrophic lateral sclerosis (ALS), and CAG repeats resulted from Huntington‘s gene mutations in Huntington‘s disease (HD). This article will focus on the most-recent findings of biomarkers belonging to the four mentioned neurodegenerative diseases.     

The dysregulation of the cell cycle and the diagnosis of Alzheimer's disease

The 'silent epidemic' of Alzheimer's disease is becoming a considerable social and economical problem in the developed countries. Especially so, because we still cannot diagnose the disease early enough, and there is no disease-modifying treatment. At present the only available therapeutic option is the use of cholinesterase inhibitors, which have mainly symptomatic short-term benefit for around one third of the patients. The solution to the problem would be the evidence-based design of early therapies, which could reverse/halt the cellular mechanisms that precede the formation of the typical brain pathology. The development of new therapeutic strategies, however, is hindered by limited knowledge of the pathogenic mechanisms that lead to the development of the sporadic form of the disease. Additionally, by the time the disease can be diagnosed, using the currently available diagnostic protocols, the pathology has spread to large areas of the brain, causing irreversible damage and functional disability. It is imperative therefore that we find early biomarkers for sporadic Alzheimer's disease, which could identify patients before substantial pathology develops.     

Circulating biomarkers of protein oxidation for Alzheimer disease: expectations within limits

Alzheimer disease (AD), the most common dementing disorder, is a multifactorial disease with complex etiology. Among different hypotheses proposed for AD one of the most corroborated is the "oxidative stress hypothesis". Although recent studies extensively demonstrated the specific oxidative modification of selected proteins in the brain of AD patients and how their dysfunction possibly correlates with the pathology, there is still an urgent need to extend these findings to peripheral tissue. So far very few studies showed oxidative damage of proteins in peripheral tissues and current findings need to be replicated. Another limit in AD research is represented by the lack of highly specific diagnostic tools for early diagnosis. For a full screening and early diagnosis, biomarkers easily detectable in biological samples, such as blood, are needed. The search of reliable biomarkers for AD in peripheral blood is a great challenge. A few studies described a set of plasma markers that differentiated AD from controls and were shown to be useful in predicting conversion from mild cognitive impairment, which is considered a prodromal stage, to AD. We review the current state of knowledge on peripheral oxidative biomarkers for AD, including proteomics, which might be useful for early diagnosis and prognosis.     

The need for prognosticators in rheumatoid arthritis. Biological and clinical markers: where are we now?

Rheumatoid arthritis is a heterogeneous disease with respect to clinical manifestations, serologic abnormalities, joint damage and functional impairment. Predicting outcome in a reliable way to allow for strategic therapeutic decision-making as well as for prediction of the response to the various therapeutic modalities available today, especially biological agents, would provide means for optimization of care. In the present article, the current information on biological and clinical markers related to disease activity and joint damage as well as for predictive purposes is reviewed. It will be shown that the relationship of many biomarkers with disease characteristics is confounded by factors unrelated to the disease, and that only few biomarkers exist with some predictive value. Moreover, clinical markers appear of equal value as biomarkers for this purpose, although they likewise have limited capacity in these regards. The analysis suggests the search for better markers to predict outcomes and therapeutic responsiveness in rheumatoid arthritis needs to be intensified.     

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.     

Application of Artificial Intelligence/Machine Vision & Learning for the Development of a Live Single-cell Phenotypic Biomarker Test to Predict Prostate Cancer Tumor Aggressiveness

To assess the usefulness and applications of machine vision (MV) and machine learning (ML) techniques that have been used to develop a single cell-based phenotypic (live and fixed biomarkers) platform that correlates with tumor biological aggressiveness and risk stratification, 100 fresh prostate samples were acquired, and areas of prostate cancer were determined by post-surgery pathology reports logged by an independent pathologist. The prostate samples were dissociated into single-cell suspensions in the presence of an extracellular matrix formulation. These samples were analyzed via live-cell microscopy. Dynamic and fixed phenotypic biomarkers per cell were quantified using objective MV software and ML algorithms. The predictive nature of the ML algorithms was developed in two stages. First, random forest (RF) algorithms were developed using 70% of the samples. The developed algorithms were then tested for their predictive performance using the blinded test dataset that contained 30% of the samples in the second stage. Based on the ROC (receiver operating characteristic) curve analysis, thresholds were set to maximize both sensitivity and specificity. We determined the sensitivity and specificity of the assay by comparing the algorithm-generated predictions with adverse pathologic features in the radical prostatectomy (RP) specimens. Using MV and ML algorithms, the biomarkers predictive of adverse pathology at RP were ranked and a prostate cancer patient risk stratification test was developed that distinguishes patients based on surgical adverse pathology features. The ability to identify and track large numbers of individual cells over the length of the microscopy experimental monitoring cycles, in an automated way, created a large biomarker dataset of primary biomarkers. This biomarker dataset was then interrogated with ML algorithms used to correlate with post-surgical adverse pathology findings. Algorithms were generated that predicted adverse pathology with >0.85 sensitivity and specificity and an AUC (area under the curve) of >0.85. Phenotypic biomarkers provide cellular and molecular details that are informative for predicting post-surgical adverse pathologies when considering tumor biopsy samples. Artificial intelligence ML-based approaches for cancer risk stratification are emerging as important and powerful tools to compliment current measures of risk stratification. These techniques have capabilities to address tumor heterogeneity and the molecular complexity of prostate cancer. Specifically, the phenotypic test is a novel example of leveraging biomarkers and advances in MV and ML for developing a powerful prognostic and risk-stratification tool for prostate cancer patients.     

多发性硬化生物标记物的研究进展

多发性硬化(MS)是主要以中枢神经系统(CNS)白质脱髓鞘病变为主要特点的自身免疫性疾病。生物标记物是中枢神经系统自身免疫性疾病病因、诊断和预后的重要参考因素,因为他们可能反映遗传以及环境变化所引起的某些免疫反应的存在,性质和强度。因此生物标记有助于MS的早期诊断和鉴别诊断,指导治疗方案,推断MS疾病活动性,以及判断疗效。本文概述了多发性硬化领域当前的生物标记物研究状况及其相关的临床实践,并通过对三种具有较大潜力的生物标记物与病理的特异性、灵敏度、可靠性和临床实用工具进行分析,以确定最优化的治疗以防止致残,同时还可以对疾病修饰药物的有效性进行测试。     

An immunoaffinity liquid chromatography-tandem mass spectrometry assay for detection of endogenous aggrecan fragments in biological fluids: Use as a biomarker for aggrecanase activity and cartilage degradation

The degradation of articular cartilage by aggrecanases (ADAMTS-4 and ADAMTS-5) plays a significant role in the pathology of osteoarthritis (OA). To monitor aggrecanase activity in OA, we have developed a sensitive, accurate, and versatile assay for detection of two specific cleavage sites on aggrecan. The assay uses an immunoaffinity-based liquid chromatography-tandem mass spectrometry (LC-MS/MS) method to detect cleavage at the ³⁷⁴ARGS site and the ¹⁸²⁰AGEG site. The dynamic range of the assay is more than three orders of magnitude, with interassay precision less than 15%. It has been successfully applied to various biological fluids and species, including rat, bovine, dog, and human. The assay has been analytically qualified for use in human urine and synovial fluid (SF). The limits of detection (LODs) for ARGS in urine and SF are 2.5 and 10 pg/ml, respectively, whereas the LOD for AGEG is 20 pg/ml in SF. Analysis of these biomarkers from OA subjects and normal healthy volunteers revealed a significant elevation of both markers in OA. Similarly, in a rat model of cartilage degradation, both ARGS and AGEG were elevated, demonstrating the utility of these biomarkers for translational research. These data suggest that the ARGS and AGEG biomarkers developed have potential as measures of aggrecanase activity in OA and may contribute to our understanding of OA pathology.     

Early biomarkers of joint damage in rheumatoid and psoriatic arthritis

Joint destruction, as evidenced by radiographic findings, is a significant problem for patients suffering from rheumatoid arthritis and psoriatic arthritis. Inherently irreversible and frequently progressive, the process of joint damage begins at and even before the clinical onset of disease. However, rheumatoid and psoriatic arthropathies are heterogeneous in nature and not all patients progress to joint damage. It is therefore important to identify patients susceptible to joint destruction in order to initiate more aggressive treatment as soon as possible and thereby potentially prevent irreversible joint damage. At the same time, the high cost and potential side effects associated with aggressive treatment mean it is also important not to over treat patients and especially those who, even if left untreated, would not progress to joint destruction. It is therefore clear that a protein biomarker signature that could predict joint damage at an early stage would support more informed clinical decisions on the most appropriate treatment regimens for individual patients. Although many candidate biomarkers for rheumatoid and psoriatic arthritis have been reported in the literature, relatively few have reached clinical use and as a consequence the number of prognostic biomarkers used in rheumatology has remained relatively static for several years. It has become evident that a significant challenge in the transition of biomarker candidates to clinical diagnostic assays lies in the development of suitably robust biomarker assays, especially multiplexed assays, and their clinical validation in appropriate patient sample cohorts. Recent developments in mass spectrometry-based targeted quantitative protein measurements have transformed our ability to rapidly develop multiplexed protein biomarker assays. These advances are likely to have a significant impact on the validation of biomarkers in the future. In this review, we have comprehensively compiled a list of candidate biomarkers in rheumatoid and psoriatic arthritis, evaluated the evidence for their potential as biomarkers of bone (joint) damage, and outlined how mass spectrometry-based targeted and multiplexed measurement of candidate biomarker proteins is likely to accelerate their clinical validation and the development of clinical diagnostic tests.     

Development of a classification and ranking method for the identification of possible biomarkers in two-dimensional gel-electrophoresis based on principal component analysis and variable selection procedures

The identification of biomarkers is one of the leading research areas in proteomics. When biomarkers have to be searched for in spot volume datasets produced by 2D gel-electrophoresis, problems may arise related to the large number of spots present in each map and the small number of samples available in each class (control/pathological). In such cases multivariate methods are usually exploited together with variable selection procedures, to provide a set of possible biomarkers: they are however usually aimed to the selection of the smallest set of variables (spots) providing the best performances in prediction. This approach seems not to be suitable for the identification of potential biomarkers since in this case all the possible candidate biomarkers have to be identified to provide a general picture of the "pathological state": in this case exhaustivity has to be preferred to provide a complete understanding of the mechanisms underlying the pathology. We propose here a ranking and classification method, "Ranking-PCA", based on Principal Component Analysis and variable selection in forward search: the method selects one variable at a time as the one providing the best separation of the two classes investigated in the space given by the relevant PCs. The method was applied to an artificial dataset and a real case-study: Ranking-PCA exhaustively identified the potential biomarkers and provided reliable and robust results.     

Tissue microarrays enabling high-throughput molecular pathology

The tissue microarray has enabled high-throughput pathology. Rather than the laborious review of individual slides and issues of assay reproducibility across large series of specimens, tissue microarrays allow the review of a single stain on a single slide containing tens to hundreds of samples. This is a paradigm shift in pathology, away from histomorphology and toward molecular characterization by immunohistochemistry. This platform allows large retrospective clinical studies of biomarkers for correlation with outcome and can equally well be applied toward high-throughput analysis of cell lines and xenografts. Tissue microarrays encourage novel approaches to assaying tissue with retained histomorphology and have enabled image analysis in pathology. The reduction of tissue to an analyte for high-throughput analysis has highlighted the importance of a high quality tissue and the impact of tissue handling and processing in the quality of data that can be obtained from analysis of tissue.     

The impact of cerebrospinal fluid biomarkers on the diagnosis of Alzheimer's disease

The cerebrospinal fluid (CSF) biomarkers β-amyloid(1-42) (Aβ(1-42)), total tau protein (T-tau), and tau phosphorylated at threonine 181 (P-tau(181P)) are gradually finding their way into routine clinical practice as an affirmative diagnostic tool for Alzheimer's disease (AD). These biomarkers have also been implemented in the revised diagnostic criteria for AD. The combination of the CSF biomarkers Aβ(1-42), T-tau, and P-tau(181P) leads to high (around 80%) levels of sensitivity, specificity, and diagnostic accuracy for discrimination between AD and controls (including psychiatric disorders like depression) and can be applied for diagnosing AD in the predementia phases of the disease (mild cognitive impairment). The added value of CSF biomarkers could lie within those cases in which the clinical diagnostic work-up is not able to discriminate between AD and non-AD dementias. However, their discriminatory power for the differential diagnosis of dementia is suboptimal. Other CSF biomarkers, especially those that are reflective of the pathology of non-AD dementia etiologies, could improve the accuracy of differential dementia diagnosis. CSF biomarkers will be of help to establish a correct and early AD diagnosis, even in the preclinical stages of the disease, which will be of importance once disease-modifying drugs for AD become available. Variation in biomarker measurements still jeopardize the introduction of CSF biomarkers into routine clinical practice and clinical trials, but several national and international standardization initiatives are ongoing.     

Biomarkers of myeloperoxidase-derived hypochlorous acid

Hypochlorous acid is the major strong oxidant generated by neutrophils. The heme enzyme myeloperoxidase catalyzes the production of hypochlorous acid from hydrogen peroxide and chloride. Although myeloperoxidase has been implicated in the tissue damage that occurs in numerous diseases that involve inflammatory cells, it has proven difficult to categorically demonstrate that it plays a crucial role in any pathology. This situation should soon be rectified with the advent of sensitive biomarkers for hypochlorous acid. In this review, we outline the advantages and limitations of chlorinated tyrosines, chlorohydrins, 5-chlorocytosine, protein carbonyls, antibodies that recognize HOCl-treated proteins, and glutathione sulfonamide as potential biomarkers of hypochlorous acid. Levels of 3-chlorotyrosine and 3,5-dichlorotyrosine are increased in proteins after exposure to low concentrations of hypochlorous acid and we conclude that their analysis by gas chromatography and mass spectrometry is currently the best method available for probing the involvement of oxidation by myeloperoxidase in the pathology of particular diseases. The appropriate use of other biomarkers should provide complementary information.Keywords-Free radicals, Myeloperoxidase, Neutrophil oxidant, Hypochlorous acid, Chlorotyrosine, Chlorohydrin, Oxidant biomarker     

Clinical application of urinary proteomics/peptidomics

The technology platforms for proteome analysis have advanced considerably over the last few years. Driven by these advancements in technology, the number of studies on the analysis of the proteome/peptidome, with the aim of defining clinically relevant biomarkers, has substantially risen. Urine has become an increasingly relevant target for clinically oriented proteome analysis; the first clinical trials based on urinary proteomics have been initiated, and studies including several hundred patients have been published. In this article, we summarize the relevant technical aspects in biomarkers discovery and the course from biomarker discovery or ‘potential’ biomarkers to those that have been validated and are clinically important. We discuss experimental design based on the statistics calculated to produce a clinically important end point. We present several examples of proteomic studies that have defined urinary biomarkers for clinical applications, focusing on capillary electrophoresis coupled to mass spectrometry as a technology. Finally, current challenges and considerations for future studies will be discussed.     

Clinical application of urinary proteomics/peptidomics

The technology platforms for proteome analysis have advanced considerably over the last few years. Driven by these advancements in technology, the number of studies on the analysis of the proteome/peptidome, with the aim of defining clinically relevant biomarkers, has substantially risen. Urine has become an increasingly relevant target for clinically oriented proteome analysis; the first clinical trials based on urinary proteomics have been initiated, and studies including several hundred patients have been published. In this article, we summarize the relevant technical aspects in biomarkers discovery and the course from biomarker discovery or 'potential' biomarkers to those that have been validated and are clinically important. We discuss experimental design based on the statistics calculated to produce a clinically important end point. We present several examples of proteomic studies that have defined urinary biomarkers for clinical applications, focusing on capillary electrophoresis coupled to mass spectrometry as a technology. Finally, current challenges and considerations for future studies will be discussed.