Autoantibody microarrays for biomarker discovery

Identification of autoantigens and the detection of autoantibody reactivity are useful in biomarker discovery and for explaining the role of important biochemical pathways in disease. Despite all of their potential advantages, the main challenge to working with autoantibodies is their sensitivity. Nevertheless, proteomics may hold the key to overcoming this limitation by providing the means to multiplex. Clearly, the ability to detect multiple autoantigens using a platform such as a high-density antigen microarray would improve sensitivity and specificity of detection for autoantibody profiling. The aims of this review are to: briefly describe the current status of antigen-autoantibody microarrays; provide examples of their use in biomarker discoveries; address current limitations; and provide examples and strategies to facilitate their implementation in the clinical setting.     

Biomarker discovery using protein microarray technology platforms: antibody-antigen complex profiling

Protein microarrays represent an important new tool in proteomic systems biology. This review focuses on the contributions of protein microarrays to the discovery of novel disease biomarkers through antibody-based assays. Of particular interest is the use of protein microarrays for immune response profiling, through which a disease-specific antibody repertoire may be defined. The antigens and antibodies revealed by these studies are useful for clinical assay development, with enormous potential to aid in diagnosis, prognosis, disease staging and treatment selection. The discovery and characterization of novel biomarkers specifically tailored to disease type and stage are expected to enable personalized medicine by facilitating preventative medicine, predictive diagnostics and individualized curative therapies.     

Opportunities for pharmacoproteomics in biomarker discovery

Proteomic data are a uniquely valuable resource for drug response prediction and biomarker discovery because most drugs interact directly with proteins in target cells rather than with DNA or RNA. Recent advances in mass spectrometry and associated processing methods have enabled the generation of large-scale proteomic datasets. Here we review the significant opportunities that currently exist to combine large-scale proteomic data with drug-related research, a field termed pharmacoproteomics. We describe successful applications of drug response prediction using molecular data, with an emphasis on oncology. We focus on technical advances in data-independent acquisition mass spectrometry (DIA-MS) that can facilitate the discovery of protein biomarkers for drug responses, alongside the increased availability of big biomedical data. We spotlight new opportunities for machine learning in pharmacoproteomics, driven by the combination of these large datasets and improved high-performance computing. Finally, we explore the value of pre-clinical models for pharmacoproteomic studies and the accompanying challenges of clinical validation. We propose that pharmacoproteomics offers the potential for novel discovery and innovation within the cancer landscape.     

Optimized blood cell profiling method for genomic biomarker discovery using high-density microarray

High-quality biomarkers for disease progression, drug efficacy and toxicity liability are essential for improving the efficiency of drug discovery and development. The identification of drug-activity biomarkers is often limited by access to and the quantity of target tissue. Peripheral blood has increasingly become an attractive alternative to tissue samples from organs as source for biomarker discovery, especially during early clinical studies. However, given the heterogeneous blood cell population, possible artifacts from ex vivo activations, and technical difficulties associated with overall performance of the assay, it is challenging to profile peripheral blood cells directly for biomarker discovery. In the present study, Applied BioSystems’ blood collection system was evaluated for its ability to isolate RNA suitable for use on the Affymetrix microarray platform. Blood was collected in a TEMPUS tube and RNA extracted using an ABI-6100 semi-automated workstation. Using human and rat whole blood samples, it was demonstrated that the RNA isolated using this approach was stable, of high quality and was suitable for Affymetrix microarray applications. The microarray data were statistically analysed and compared with other blood protocols. Minimal haemoglobin interference with RNA labelling efficiency and chip hybridization was found using the TEMPUS tube and extraction method. The RNA quality, stability and ease of handling requirement make the TEMPUS tube protocol an attractive approach for expression profiling of whole blood to support target and biomarker discovery.     

Optimized blood cell profiling method for genomic biomarker discovery using high-density microarray

High-quality biomarkers for disease progression, drug efficacy and toxicity liability are essential for improving the efficiency of drug discovery and development. The identification of drug-activity biomarkers is often limited by access to and the quantity of target tissue. Peripheral blood has increasingly become an attractive alternative to tissue samples from organs as source for biomarker discovery, especially during early clinical studies. However, given the heterogeneous blood cell population, possible artifacts from ex vivo activations, and technical difficulties associated with overall performance of the assay, it is challenging to profile peripheral blood cells directly for biomarker discovery. In the present study, Applied BioSystems' blood collection system was evaluated for its ability to isolate RNA suitable for use on the Affymetrix microarray platform. Blood was collected in a TEMPUS tube and RNA extracted using an ABI-6100 semi-automated workstation. Using human and rat whole blood samples, it was demonstrated that the RNA isolated using this approach was stable, of high quality and was suitable for Affymetrix microarray applications. The microarray data were statistically analysed and compared with other blood protocols. Minimal haemoglobin interference with RNA labelling efficiency and chip hybridization was found using the TEMPUS tube and extraction method. The RNA quality, stability and ease of handling requirement make the TEMPUS tube protocol an attractive approach for expression profiling of whole blood to support target and biomarker discovery.     

Protein biomarker discovery for head and neck cancer

Squamous cell carcinoma of the head and neck (HNSCC) is the sixth most common cancer worldwide. Despite improvements in diagnosis and treatment, the five-year-survival rate of advanced HNSCC has only moderately increased, which is largely due to the high proportion of patients that present with advanced disease stage and the frequent development of relapse and second primary tumors. Protein biomarkers allowing early detection of primary HNSCC or relapse may aid to improve clinical outcome. Screening for precursor changes in the mucosal linings preceding the development of invasive tumors and for accurate prediction of risk of malignant transformation, may be propitious opportunities, which are as yet difficult. This review summarizes recent results in HNSCC proteomics for biomarker research. Despite the wide diversity of experimental designs, a few common markers have been detected. Although some of these potential biomarkers are very promising, they still have to be further clinically validated. Finally, treatment of advanced cancers of several sites within the head and neck has shifted significantly during the last decade, and also, targeted drugs have entered the clinic. This has major consequences for the research questions in HNSCC research and accordingly for the future direction of proteome research in HNSCC biomarker discovery.     

蛋白质芯片在肿瘤血清标识物发现中的应用

蛋白质芯片是一种新型的高通量蛋白质组学技术,由于其具有高通量、微型化、可平行快速分析等优点,因此在肿瘤血清标识物发现研究方面具有广泛的应用前景。本文综述了蛋白质芯片的基本原理、类型及其在肿瘤血清标记物发现研究中的应用,将蛋白质芯片技术与传统的肿瘤标志物发现技术进行了比较,并对蛋白质芯片技术在肿瘤标识物发现研究上的进一步应用进行了展望。     

Microarray technology GEM microarrays and drug discovery

Incyte Genomics' GEM™ Gene Expression Microarray is a proven genomics tool used by a large number of pharmaceutical companies to speed up the drug discovery and development process. The development and integration of this technology, together with Incyte's sequence databases and clone resources, have resulted in GEM microarrays that span approximately 60,000 human genes as well as approximately 60,000 plant, rat, mouse, yeast, and bacterial genes. The technology underlying the use of these arrays and their application to the drug discovery process is highlighted. Journal of Industrial Microbiology & Biotechnology (2002) 28, 180–185 DOI: 10.1038/sj/jim/7000136 Received 16 November 2000/ Accepted in revised form 01 March 2001     

Cancer biomarker discovery and translation: proteomics and beyond

Introduction: Cancer is often diagnosed at late stages when the chance of cure is relatively low and although research initiatives in oncology discover many potential cancer biomarkers, few transition to clinical applications. This review addresses the current landscape of cancer biomarker discovery and translation with a focus on proteomics and beyond.

Areas covered: The review examines proteomic and genomic techniques for cancer biomarker detection and outlines advantages and challenges of integrating multiple omics approaches to achieve optimal sensitivity and address tumor heterogeneity. This discussion is based on a systematic literature review and direct participation in translational studies.

Expert commentary: Identifying aggressive cancers early on requires improved sensitivity and implementation of biomarkers representative of tumor heterogeneity. During the last decade of genomic and proteomic research, significant advancements have been made in next generation sequencing and mass spectrometry techniques. This in turn has led to a dramatic increase in identification of potential genomic and proteomic cancer biomarkers. However, limited successes have been shown with translation of these discoveries into clinical practice. We believe that the integration of these omics approaches is the most promising molecular tool for comprehensive cancer evaluation, early detection and transition to Precision Medicine in oncology.     

Use of synthetic peptides for the detection and quantification of autoantibodies

Summary and conclusions The rapid progress made over the last 10 years in the identification of individual autoantigens and in the localization of the epitopes involved, has resulted in a parallel reduction in the complexity of the antigen required for the detection of autoantibodies. The ability to use synthetic peptides as antigens is a remarkable culmination of this process considering that many antigenic particles contain multiple proteins (eg. Sm consist of 8 or more individual proteins).Despite the fact that patients with SLE have a polyclonal hypergammaglobulinemia, excellent correlations between ELISAs utilizing the P2 or SmB/B synthetic peptides, ELISAs utilizing r proteins and immunoblotting were obtained [28, 38, 50]. However, false positive/non-specific binding to a P2-BSA-glutaraldehyde conjugate has been observed with serum from old MRL/lpr mice (unpublished observations). In addition, some of the results obtained in human autoimmune diseases suggest that non-specific binding may be problematic in some instances. It is difficult, at present, to know whether the higher frequencies of detection of autoantibodies to certain synthetic peptide antigens reflect increased sensitivity or decreased specificity.Synthetic peptide antigens have beeen used to detect autoantibodies in both organ specific and multisystem autoimmune diseases. In only a small number of cases have these reagents been rigorously tested for sensitivity and specificity. Despite this, synthetic peptides have been shown to be valuable for detection and quantification of autoantibodies in certain clinical situations. Undoubtedly, further progress in epitope mapping of autoantigens coupled with technological advances in protein synthesis and improved prediction of protein structure will lead to a large number of synthetic peptide antigens for research and clinical applications. It is unlikely that short synthetic peptides will substitute for native proteins in all instances since some autoantibodies show a striking preference for conformational epitopes.Abbreviations r recombinant - SLE systemic lupus erythematosus     

Proteomics in biomarker discovery and drug development

Proteomics is a research field aiming to characterize molecular and cellular dynamics in protein expression and function on a global level. The introduction of proteomics has been greatly broadening our view and accelerating our path in various medical researches. The most significant advantage of proteomics is its ability to examine a whole proteome or sub-proteome in a single experiment so that the protein alterations corresponding to a pathological or biochemical condition at a given time can be considered in an integrated way. Proteomic technology has been extensively used to tackle a wide variety of medical subjects including biomarker discovery and drug development. By complement with other new technique advances in genomics and bioinformatics, proteomics has a great potential to make considerable contribution to biomarker identification and to revolutionize drug development process. This article provides a brief overview of the proteomic technologies and their application in biomarker discovery and drug development.     

Fast quantification of immunohistochemistry tissue microarrays in lung carcinoma

Tissue microarrays (TMAs) are an effective tool for high-throughput molecular analysis of tissues to help identify new diagnostic and prognostic markers and targets in human cancers. We have developed a fully automated method for rapid, continuous and quantitative analysis of TMAs based on immunohistochemistry. The method deals with complex and varying tissue architectures, segments tumour cells from normal cells, conducts cell compartmentalisation, identifies nuclei and cytoplasm and produces three different continuous measurements of marker expression levels within tumour cell nuclei, tumour cell cytoplasm and total tumour cell protein expression. We have demonstrated this method using three independent protein markers (BAK, BAX and a novel biomarker, named KS) over 7 TMAs, involving 2 BAK stained TMAs with 229 tumour tissue cores, 2 BAX stained TMAs with 229 tumour tissue cores and 3 KS stained TMAs with 373 tumour cores of lung carcinomas. We validated the automated method, showing that the automated scoring is significantly correlated with the pathologist-based scoring.     

Advanced proteomic technologies for cancer biomarker discovery

Proteomic technologies have experienced major improvements in recent years. Such advances have facilitated the discovery of potential tumor markers with improved sensitivities and specificities for the diagnosis, prognosis and treatment monitoring of cancer patients. This review will focus on four state-of-the-art proteomic technologies, namely 2D difference gel electrophoresis, MALDI imaging mass spectrometry, electron transfer dissociation mass spectrometry and reverse-phase protein array. The major advancements these techniques have brought about and examples of their applications in cancer biomarker discovery will be presented in this review, so that readers can appreciate the immense progress in proteomic technologies from 1997 to 2008. Finally, a summary will be presented that discusses current hurdles faced by proteomic researchers, such as the wide dynamic range of protein abundance, standardization of protocols and validation of cancer biomarkers, and a 5-year view of potential solutions to such problems will be provided.     

Blood biomarker discovery in drug-free schizophrenia: the contribution of proteomics and multiplex immunoassays

Introduction: Recent evidence supports an association between systemic abnormalities and the pathology of psychotic disorders which has led to the search for peripheral blood-based biomarkers.

Areas covered: Here, we summarize blood biomarker findings in schizophrenia from the literature identified by two methods currently driving biomarker discovery in the human proteome; mass spectrometry and multiplex immunoassay. From a total of 14 studies in the serum or plasma of drug-free schizophrenia patients; 47 proteins were found to be significantly altered twice or more, in the same direction. Pathway analysis was performed on these proteins, and the resulting pathways discussed in relation to schizophrenia pathology. Future directions are also discussed, with particular emphasis on the potential for high-throughput validation techniques such as data-independent analysis for confirmation of biomarker candidates.

Expert commentary: We present promising findings that point to a convergence of pathophysiological mechanisms in schizophrenia that involve the acute-phase response, glucocorticoid receptor signalling, coagulation, and lipid and glucose metabolism.     

Proteomics in prostate cancer biomarker discovery

Despite advances in molecular medicine, genomics, proteomics and translational research, prostate cancer remains the second most common cause of cancer-related mortality for men in the Western world. Clearly, early detection, targeted treatment and post-treatment monitoring are vital tools to combat this disease. Tumor markers can be useful for diagnosis and early detection of cancer, assessment of prognosis, prediction of therapeutic effect and treatment monitoring. Such tumor markers include prostate-specific antigen (prostate), cancer antigen (CA)15.3 (breast), CA125 (ovarian), CA19.9 (gastrointestinal) and serum α-fetoprotein (testicular cancer). However, all of these biomarkers lack sensitivity and specificity and, therefore, there is a large drive towards proteomic biomarker discovery. Current research efforts are directed towards discovering biosignatures from biological samples using novel proteomic technologies that provide high-throughput, in-depth analysis and quantification of the proteome. Several of these studies have revealed promising biomarkers for use in diagnosis, assessment of prognosis, and targeting treatment of prostate cancer. This review focuses on prostate cancer proteomic biomarker discovery and its future potential.     

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.     

Methods of epitope mapping

Concluding remarks It is important to remember that the merits of the different approaches to epitope mapping should be judged against the purpose of the study. A peptide specifically recognized by nearly all sera containing a certain autoimmune specificity [20, 21, 26], would most likely be selected for the detection of the anti-linear/continuous epitope fraction among those autoantibodies and could be highly useful for diagnostic purposes. This is true even if a majority of the antibodies were directed against conformational/discontinuous protein epitopes. If the purpose is to study the induction or maintenance of the autoimmune response, one would also have to look at and account for the conformation-dependent autoantibodies. There is also the possibility that some pathogenic autoantibodies could constitute a small fraction requiring the complete nucleic acid-protein complex as an antigen. In that case, the pathogenic epitope would not be identified nor mapped using the techniques discussed in this review.     

Proteomic biomarker discovery for the monogenic disease cystic fibrosis

Proteomics was initially viewed as a promising new scientific discipline to study complex disorders such as polygenic, infectious and environment-related diseases. However, the first attempts to understand a monogenic disease such as cystic fibrosis (CF) by proteomics-based approaches have proved quite rewarding. In CF, the impairment of a unique protein, the CF transmembrane conductance regulator, does not completely explain the complex and variable CF clinical phenotype. The great advances in our knowledge about the molecular and cellular consequences of such impairment have not been sufficient to be translated into effective treatments, and CF patients are still dying due to chronic progressive lung dysfunction. The progression of proteomics application in CF will certainly unravel new proteins that could be useful as biomarkers either to elucidate CF basic mechanisms and to better monitor the disease progression, or to promote the development of novel therapeutic strategies against CF. This review will summarize the recent technological advances in proteomics and the first results of its application to address the most important issues in the CF field.