The utility of mass spectrometry-(MS-) based proteomic platforms and their clinical applications have become an emerging field in proteomics in recent years. Owing to its selectivity and sensitivity, MS has become a key technological platform in proteomic research. Using this platform, a large number of potential biomarker candidates for specific diseases have been reported. However, due to lack of validation, none has been approved for use in clinical settings by the Food and Drug Administration (FDA). Successful candidate verification and validation will facilitate the development of potential biomarkers, leading to better strategies for disease diagnostics, prognostics, and treatment. With the recent new developments in mass spectrometers, high sensitivity, high resolution, and high mass accuracy can be achieved. This greatly enhances the capabilities of protein biomarker validation. In this paper, we describe and discuss recent developments and applications of targeted proteomics methods for biomarker validation.
New, more sensitive and specific biomarkers are needed to support other means of clinical diagnosis of neurodegenerative disorders. Proteomics technology is widely used in discovering new biomarkers. There are several difficulties with in-depth analysis of human plasma/serum, including that there is no one proteomic platform that can offer complete identification of differences in proteomic profiles. Another set of problems is associated with heterogeneity of human samples in addition intrinsic variability associated with every step of proteomic investigation. Validation is the very last step of proteomic investigation and it is very often difficult to validate potential biomarker with desired sensitivity and specificity. Even though it may be possible to validate a differentially expressed protein, it may not necessarily prove to be a valid diagnostic biomarker.
In the current study we report results of proteomic analysis of sera from HIV-infected individuals with or without cognitive impairment. Application of SELDI-TOF analysis followed by weak cation exchange chromatography and 1-dimensional electrophoresis led to discovery of gelsolin and prealbumin as differentially expressed proteins which were not detected in this cohort of samples when previously investigated by 2-dimensional electrophoresis with Difference Gel Electrophoresis technology.
Validation using western-blot analysis led us to conclude that relative change of the levels of these proteins in one patient during a timeframe might be more informative, sensitive and specific than application of average level estimated based on an even larger cohort of patients.
The present clinical staging of melanoma stratifies patients into heterogeneous groups, resulting in the application of aggressive therapies to large populations, diluting impact and increasing toxicity. To move to a new era of therapeutic decisions based on highly specific tumor profiling, the discovery and validation of new prognostic and predictive biomarkers in melanoma is critical. Genomic profiling, which is showing promise in other solid tumors, requires fresh tissue from a large number of primary tumors, and thus faces a unique challenge in melanoma. For this and other reasons, proteomics appears to be an ideal choice for the discovery of new melanoma biomarkers. Several approaches to proteomics have been utilized in the search for clinically relevant biomarkers, but to date the results have been relatively limited. This article will review the present work using both tissue and serum proteomics in the search for melanoma biomarkers, highlighting both the relative advantages and disadvantages of each approach. In addition, we review several of the major obstacles that need to be overcome in order to advance the field.
The interest and research into disease-related biomarkers has greatly accelerated over the last 10 years. The potential clinical benefits for disease-specific biomarkers include a more rapid and accurate disease diagnosis, and potential reduction in size and duration of clinical drug trials, which would speed up drug development. The application of biomarkers into the clinical arena of motor neuron disease should both determine if a drug hits its proposed target and whether the drug alters the course of disease. This article will highlight the progress made in discovering suitable biomarker candidates from a variety of sources, including imaging, neurophysiology and proteomics. For biomarkers to have clinical utility, specific criteria must be satisfied. While there has been tremendous effort to discover biomarkers, very few have been translated to the clinic. The bottlenecks in the biomarker pipeline will be highlighted as well as lessons that can be learned from other disciplines, such as oncology.
amyotrophic lateral sclerosis; biomarker; clinical trial; magnetic resonance; motor neuron disease; proteomics imaging
Objective diagnostics of excessive alcohol use are valuable tools in the identification and monitoring of subjects with alcohol use disorders. A number of potential biomarkers of alcohol intake have been proposed, but none have reached widespread clinical usage, often due to limited diagnostic sensitivity and specificity. In order to identify novel potential biomarkers, we performed proteomic biomarker target discovery in plasma samples from non-human primates that chronically self-administer high levels of ethanol. 2-dimensional in-gel electrophoresis (2D-DIGE) was used to quantify plasma proteins from within subject samples collected before exposure to ethanol and after three months of excessive ethanol self-administration. Highly abundant plasma proteins were depleted from plasma samples to increase proteomic coverage. Altered plasma levels of SAA4, RBP, ITIH4, clusterin, and fibronectin, identified by 2D-DIGE analysis, were confirmed in unmanipulated, whole plasma from these animals by immunoblotting. Examination of these target plasma proteins in human subjects with excessive alcohol consumption (and control subjects) revealed increased levels of SAA4 and clusterin and decreased levels of fibronectin compared to controls. These proteins not only serve as targets for further development as biomarker candidates or components of biomarker panels, but also add to the growing understanding of dysregulated immune function and lipoprotein metabolism with chronic, excessive alcohol consumption.
Alcohol abuse; alcoholism; biomarker; diagnostic; plasma; proteomic
Biomarkers are of increasing importance for personalized medicine, with applications including diagnosis, prognosis, and selection of targeted therapies. Their use is extremely diverse, ranging from pharmacodynamics to treatment monitoring. Following a concise review of terminology, we provide examples and current applications of three broad categories of biomarkers—DNA biomarkers, DNA tumor biomarkers, and other general biomarkers. We outline clinical trial phases for identifying and validating diagnostic and prognostic biomarkers. Predictive biomarkers, more generally termed companion diagnostic tests predict treatment response in terms of efficacy and/or safety. We consider suitability of clinical trial designs for predictive biomarkers, including a detailed discussion of validation study designs, with emphasis on interpretation of study results. We specifically discuss the interpretability of treatment effects if a large set of DNA biomarker profiles is available and the number of therapies is identical to the number of different profiles.
INTRODUCTION: There has been an exponential increase in the number of ‘potential’ protein biomarkers discovered; thus requiring the need for better quantification strategies to confirm or refute their ultimate utility. Also required is increased throughput which means reduced sample preparation and/or accelerated chromatography which increases the chance of interferences that could confound robust quantification. The purpose of this study is to explore a range of new MS analysis methodologies that enable higher selectivity quantification. The different techniques rely on different properties of the molecule for specificity so their utility will depend to a large degree on the target molecules. But an exploration to determine some general guidelines will be helpful when choosing the best strategy. In this study, we compare the quantification of tryptic peptides in complex biological matrices using various strategies including combinations of sample preparation and mass spectrometric methodologies on different mass spectrometric platforms. EXPERIMENTAL METHODS: The intact or digested BNP was spiked into the crashed plasma to create calibration curves. An AB SCIEX QTRAP® 5500 system equipped with Turbo V™ source was used. Multiple reaction monitoring (MRM) transitions and MRM3 experiments for intact and digested BNP were developed and used to measure the calibration curves. For the differential mobility separations, a QTRAP 5500 system equipped with SelexION™ Technology was used. RESULTS: Three quantitative methodologies were used with the QTRAP® 5500 System: MRM provides selectivity based on the fragmentation of the peptide and monitoring of a specific product ion. When matrix interference is a problem with MRM, further selectivity can be performed using MRM3, which provides a second level of selectivity based on monitoring a secondary product ion. Alternatively, the differential mobility separation (DMS) system which provides selectivity based on the mobility of the various chemicals in the sample can also be used. Intact BNP provided good fragmentation for MS/MS and MS3, thus best sensitivity was obtained using MRM3 or MRM. However, for large peptides which do not fragment well, SIM (single ion monitoring) using DMS may be an alternative methodology for quantification.
Proteomics allows characterization of protein structure and function, protein-protein interactions, and peptide modifications. It has given us insight into the perturbations of signaling pathways within tumor cells and has improved the discovery of new therapeutic targets and possible indicators of response to and duration of therapy. The discovery, verification, and validation of novel biomarkers are critical in streamlining clinical development of targeted compounds, and directing rational treatments for patients whose tumors are dependent upon select signaling pathways. Studies are now underway in many diseases to examine the immune or inflammatory proteome, vascular proteome, cancer or disease proteome, and other subsets of the specific pathology microenvironment. Successful assay verification and biological validation of such biomarkers will speed development of potential agents to targetable dominant pathways and lead to selection of individuals most likely to benefit. Reconsideration of analytical and clinical trials methods for acquisition, examination, and translation of proteomics data must occur before we march further into future of drug development.
proteomics; biomarkers; clinical trial; drug development; cancer; targeted therapy
Due to insufficient biomarker validation and poor performances in diagnostic assays, the candidate biomarker verification process has to be improved. Multi-analyte immunoassays are the tool of choice for the identification and detailed validation of protein biomarkers in serum. The process of identification and validation of serum biomarkers, as well as their implementation in diagnostic routine requires an application of independent immunoassay platforms with the possibility of high-throughput. This review will focus on three main multi-analyte immunoassay platforms: planar microarrays, multiplex bead systems and, array-based surface plasmon resonance (SPR) chips. Recent developments of each platform will be discussed for application in clinical proteomics, principles, detection methods, and performance strength. The requirements for specific surface functionalization of assay platforms are continuously increasing. The reasons for this increase is the demand for highly sensitive assays, as well as the reduction of non-specific adsorption from complex samples, and with it high signal-to-noise-ratios. To achieve this, different support materials were adapted to the immobilized biomarker/ligand, allowing a high binding capacity and immobilization efficiency. In the case of immunoassays, the immobilized ligands are proteins, antibodies or peptides, which exhibit a diversity of chemical properties (acidic/alkaline; hydrophobic/hydrophilic; secondary or tertiary structure/linear). Consequently it is more challenging to develop immobilization strategies necessary to ensure a homogenous covered surface and reliable assay in comparison to DNA immobilization. New developments concerning material support for each platform are discussed especially with regard to increase the immobilization efficiency and reducing the non-specific adsorption from complex samples like serum and cell lysates.
clinical proteomics and diagnostic; multi-analyte immunoassays; serum screening; antibody-antigen interaction
Purpose of review
The desire for biomarkers for diagnosis and prognosis of diseases has never been greater. With the availability of genome data and an increased availability of proteome data, the discovery of biomarkers has become increasingly feasible. This article reviews some recent applications of the many evolving “omic” technologies to organ transplantation.
With the advancement of many high throughput “omic” techniques such as genomics, metabolomics, antibiomics, peptidomics and proteomics, efforts have been made to understand potential mechanisms of specific graft injuries and develop novel biomarkers for acute rejection, chronic rejection, and operational tolerance.
The translation of potential biomarkers from the lab bench to the clinical bedside is not an easy task and will require the concerted effort of the immunologists, molecular biologists, transplantation specialists, geneticists, and experts in bioinformatics. Rigorous prospective validation studies will be needed using large sets of independent patient samples. The appropriate and timely exploitation of evolving “omic” technologies will lay the cornerstone for a new age of translational research for organ transplant monitoring.
genomics; proteomics; organ transplant; biomarker; translational medicine
Proteomics refers to the study of the entire set of proteins in a given cell or tissue. With the extensive development of protein separation, mass spectrometry, and bioinformatics technologies, clinical proteomics has shown its potential as a powerful approach for biomarker discovery, particularly in the area of oncology. More than 130 exploratory studies have defined candidate markers in serum, gastrointestinal (GI) fluids, or cancer tissue. In this article, we introduce the commonly adopted proteomic technologies and describe results of a comprehensive review of studies that have applied these technologies to GI oncology, with a particular emphasis on developments in the last 3 years. We discuss reasons why the more than 130 studies to date have had little discernible clinical impact, and we outline steps that may allow proteomics to realize its promise for early detection of disease, monitoring of disease recurrence, and identification of targets for individualized therapy.
Clinical proteomics; Gastrointestinal oncology; Mass spectrometry; Biomarker discovery
Proteomics technologies have revolutionized cell biology and biochemistry by providing powerful new tools to characterize complex proteomes, multiprotein complexes and post-translational modifications. Although proteomics technologies could address important problems in clinical and translational cancer research, attempts to use proteomics approaches to discover cancer biomarkers in biofluids and tissues have been largely unsuccessful and have given rise to considerable skepticism. The National Cancer Institute has taken a leading role in facilitating the translation of proteomics from research to clinical application, through its Clinical Proteomic Technologies for Cancer. This article highlights the building of a more reliable and efficient protein biomarker development pipeline that incorporates three steps: discovery, verification and qualification. In addition, we discuss the merits of multiple reaction monitoring mass spectrometry, a multiplex targeted proteomics platform, which has emerged as a potentially promising, high-throughput protein biomarker measurements technology for preclinical ‘verification’.
biomarker; multiple reaction monitoring mass spectrometry; proteomics; verification
The purpose of this manuscript is to provide, based on an extensive analysis of a proteomic data set, suggestions for proper statistical analysis for the discovery of sets of clinically relevant biomarkers. As tractable example we define the measurable proteomic differences between apparently healthy adult males and females. We choose urine as body-fluid of interest and CE-MS, a thoroughly validated platform technology, allowing for routine analysis of a large number of samples. The second urine of the morning was collected from apparently healthy male and female volunteers (aged 21-40) in the course of the routine medical check-up before recruitment at the Hannover Medical School.
We found that the Wilcoxon-test is best suited for the definition of potential biomarkers. Adjustment for multiple testing is necessary. Sample size estimation can be performed based on a small number of observations via resampling from pilot data. Machine learning algorithms appear ideally suited to generate classifiers. Assessment of any results in an independent test-set is essential.
Valid proteomic biomarkers for diagnosis and prognosis only can be defined by applying proper statistical data mining procedures. In particular, a justification of the sample size should be part of the study design.
Oncoproteomics is the study of proteins and their interactions in a cancer cell by proteomic technologies. Proteomic research first came to the fore with the introduction of two-dimensional gel electrophoresis. At the turn of the century, proteomics has been increasingly applied to cancer research with the wide-spread introduction of mass spectrometry and proteinchip. There is an intense interest in applying proteomics to foster an improved understanding of cancer pathogenesis, develop new tumor biomarkers for diagnosis, and early detection using proteomic portrait of samples. Oncoproteomics has the potential to revolutionize clinical practice, including cancer diagnosis and screening based on proteomic platforms as a complement to histopathology, individualized selection of therapeutic combinations that target the entire cancer-specific protein network, real-time assessment of therapeutic efficacy and toxicity, and rational modulation of therapy based on changes in the cancer protein network associated with prognosis and drug resistance. Besides, oncoproteomics is also applied to the discovery of new therapeutic targets and to the study of drug effects. In pace with the successful completion of the Human Genome Project, the wave of proteomics has raised the curtain on the postgenome era. The study of oncoproteomics provides mankind with a better understanding of neoplasia. In this article, the discovery of cancer biomarkers in recent years is reviewed. The challenges ahead and perspectives of oncoproteomics for biomarkers development are also addressed. With a wealth of information that can be applied to a broad spectrum of biomarker research projects, this review serves as a reference for biomarker researchers, scientists working in proteomics and bioinformatics, oncologists, pharmaceutical scientists, biochemists, biologists, and chemists.
Since the emergence of the so-called omics technology, thousands of putative biomarkers have been identified and published, which have dramatically increased the opportunities for developing more effective therapeutics. These opportunities can have profound benefits for patients and for the economics of healthcare. However, the transfer of biomarkers from discovery to clinical practice is still a process filled with lots of pitfalls and limitations, mostly limited by structural and scientific factors. To become a clinically approved test, a potential biomarker should be confirmed and validated using hundreds of specimens and should be reproducible, specific and sensitive. Besides the lack of quality in biomarker validation, a number of other key issues can be identified and should be addressed. Therefore, the aim of this article is to discuss a series of interpretative and practical issues that need to be understood and resolved before potential biomarkers become a clinically approved test or are already on the diagnostic market. Some of these issues are shortly discussed here.
Predictive medicine; Targeted prevention; Validation strategies; Regulatory overview; Biomarker perspectives; Tailored therapy
In this article we review the ‘state of the art’ with regards to biomarkers for prediction, diagnosis and prognosis in acute lung injury (ALI). We begin by defining biomarkers and the goals of biomarker research in ALI including their ability to define more homogenous populations for recruitment into trials of novel therapies as well as to identify important biological pathways in the pathogenesis of ALI. Progress along four general routes is then examined. First the results of wide-ranging existing protein biomarkers are reported. Secondly, we describe newer biomarkers awaiting or with strong potential for validation. Thirdly, we report progress in the fields of genomics and proteomics. Finally given the complexity and number of potential biomarkers, we examine the results of combining clinical predictors with protein and other biomarkers to produce better prognostic and diagnostic indices.
biomarkers; clinical predictors; ALI; ARDS
Disease biomarkers are used widely in medicine. But very few biomarkers are useful for cancer diagnosis and monitoring. Over the past 15 years, major investments have been made to discover and validate cancer biomarkers. Despite such investments, no new major cancer biomarkers have been approved for clinical use for at least 25 years. In the last decade, many reports have described new cancer biomarkers that promised to revolutionize the diagnosis of cancer and the management of cancer patients. However, many initially promising biomarkers have not been validated for clinical use. In this commentary, a plethora of parameters before sample analysis, during sample analysis, and after sample analysis that can complicate biomarker discovery and validation and lead to “false discovery” are discussed. Several examples of biomarker discoveries that were published in high-profile journals are also presented, as well as why they were not validated and the lessons learned from these false discoveries, so that similar mistakes can be avoided in the future.
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.
Biomarker verification; Multiple reaction monitoring; Targeted proteomics
Owing to its availability, ease of collection, and correlation with pathophysiology of diseases, urine is an attractive source for clinical proteomics. However, many proteomic studies have had only limited clinical impact, due to factors such as modest numbers of subjects, absence of disease controls, small numbers of defined biomarkers, and diversity of analytical platforms. Therefore, it is difficult to merge biomarkers from different studies into a broadly applicable human urinary proteome database. Ideally, the methodology for defining the biomarkers should combine a reasonable analysis time with high resolution, thereby enabling the profiling of adequate samples and recognition of sufficient features to yield robust diagnostic panels. Capillary electrophoresis coupled to mass spectrometry (CE-MS), which was used to analyze urine samples from healthy subjects and patients with various diseases, is a suitable approach for this task. The database of these datasets compiled from the urinary peptides enabled the diagnosis, classification, and monitoring of a wide range of diseases. CE-MS exhibits excellent performance for biomarker discovery and allows subsequent biomarker sequencing independent of the separation platform. This approach may elucidate the pathogenesis of many diseases, and better define especially renal and urological disorders at the molecular level.
Capillary electrophoresis; database; mass spectrometry; proteomics; urine
Many putative disease blood biomarkers discovered in genomic and proteomic studies await validation in large clinically annotated cohorts of patient samples. ELISA assays require large quantities of precious blood samples and are not high-throughput. The reverse phase protein microarray platform has been developed for the high-throughput quantification of protein levels in small amounts of clinical samples.
In the present study we present the development of reverse-phase protein microarrays (RPPMs) for the measurement of clusterin, a mid-abundant blood biomarker. An experimental protocol was optimized for the printing of serum and plasma on RPPMs using epoxy coated microscope slides and a non-denaturing printing buffer. Using fluorescent-tagged secondary antibodies, we achieved the reproducible detection of clusterin in spotted serum and plasma and reached a limit of detection of 780 ng/mL. Validation studies using both spiked clusterin and clinical samples showed excellent correlations with ELISA measurements of clusterin.
Serum and plasma spotted in the reverse phase array format allow for reliable and reproducible high-throughput validation of a mid-abundant blood biomarker such as clusterin.
A lack of sensitive and specific biomarkers is a major reason for the high rate of Primary hepatocellular carcinoma (HCC)-related mortality. The aim of this study was to investigate potential proteomic biomarkers specific for HCC.
81 patients with hepatitis B-related HCC and 33 healthy controls were randomly divided into a training set (33 HCC, 33 controls) and a testing set (48 HCC, 33 controls). Serum proteomic profiles were measured using Surface-enhanced laser desorption/ionization-time-of-flight mass spectroscopy (SELDI-TOF-MS).) A classification tree was established by Biomarker Pattern Software (BPS). Candidate SELDI peaks were isolated by tricine-SDS-PAGE, identified by HPLC-MS/MS and validated by immunohistochemistry (IHC) in liver tissues.
A total of 6 proteomic peaks (3157.33 m/z, 4177.02 m/z, 4284.79 m/z, 4300.80 m/z, 7789.87 m/z, and 7984.14 m/z) were chosen by BPS to establish a classification tree with the highest discriminatory power in the training set. The sensitivity and specificity of this classification tree were 95.92%, and 100% respectively in the testing set. A candidate marker of about 7984 m/z was isolated and identified as neutrophil-activating peptide 2 (NAP-2). IHC staining showed that NAP-2 signals were positive in HCC tissues but negative in adjacent tissues.
The NAP-2 may be a specific proteomic biomarker of hepatitis B-related HCC.
While large numbers of proteomic biomarkers have been described, they are generally not implemented in medical practice. We have investigated the reasons for this shortcoming, focusing on hurdles downstream of biomarker verification, and describe major obstacles and possible solutions to ease valid biomarker implementation. Some of the problems lie in suboptimal biomarker discovery and validation, especially lack of validated platforms with well-described performance characteristics to support biomarker qualification. These issues have been acknowledged and are being addressed, raising the hope that valid biomarkers may start accumulating in the foreseeable future. However, successful biomarker discovery and qualification alone does not suffice for successful implementation. Additional challenges include, among others, limited access to appropriate specimens and insufficient funding, the need to validate new biomarker utility in interventional trials, and large communication gaps between the parties involved in implementation. To address this problem, we propose an implementation roadmap. The implementation effort needs to involve a wide variety of stakeholders (clinicians, statisticians, health economists, and representatives of patient groups, health insurance, pharmaceutical companies, biobanks, and regulatory agencies). Knowledgeable panels with adequate representation of all these stakeholders may facilitate biomarker evaluation and guide implementation for the specific context of use. This approach may avoid unwarranted delays or failure to implement potentially useful biomarkers, and may expedite meaningful contributions of the biomarker community to healthcare.
Biomarker; biomarker implementation; clinical proteomics; clinical studies; expert panel; proteomics
In recent years, developments in molecular biotechnology have led to the increased promise of detecting and validating biomarkers, or molecular markers that relate to various biological or medical outcomes. Proteomics, the direct study of proteins in biological samples, plays an important role in the biomarker discovery process. These technologies produce complex, high dimensional functional and image data that present many analytical challenges that must be addressed properly for effective comparative proteomics studies that can yield potential biomarkers. Specific challenges include experimental design, preprocessing, feature extraction, and statistical analysis accounting for the inherent multiple testing issues. This paper reviews various computational aspects of comparative proteomic studies, and summarizes contributions I along with numerous collaborators have made. First, there is an overview of comparative proteomics technologies, followed by a discussion of important experimental design and preprocessing issues that must be considered before statistical analysis can be done. Next, the two key approaches to analyzing proteomics data, feature extraction and functional modeling, are described. Feature extraction involves detection and quantification of discrete features like peaks or spots that theoretically correspond to different proteins in the sample. After an overview of the feature extraction approach, specific methods for mass spectrometry (Cromwell) and 2D gel electrophoresis (Pinnacle) are described. The functional modeling approach involves modeling the proteomic data in their entirety as functions or images. A general discussion of the approach is followed by the presentation of a specific method that can be applied, wavelet-based functional mixed models, and its extensions. All methods are illustrated by application to two example proteomic data sets, one from mass spectrometry and one from 2D gel electrophoresis. While the specific methods presented are applied to two specific proteomic technologies, MALDI-TOF and 2D gel electrophoresis, these methods and the other principles discussed in the paper apply much more broadly to other expression proteomics technologies.
Bayesian Methods; Biomarkers; Classification; False Discovery Rate; Functional Data Analysis; Functional Mixed Models; MALDI-TOF; Mass Spectrometry; Multiple Testing; Nonparametric Regression; Proteomics; Reproducibility; Robust Regression; Wavelets; 2D Gel Electrophoresis
Accurate diagnosis and proper monitoring of cancer patients remain a key obstacle for successful cancer treatment and prevention. Therein comes the need for biomarker discovery, which is crucial to the current oncological and other clinical practices having the potential to impact the diagnosis and prognosis. In fact, most of the biomarkers have been discovered utilizing the proteomics-based approaches. Although high-throughput mass spectrometry-based proteomic approaches like SILAC, 2D-DIGE, and iTRAQ are filling up the pitfalls of the conventional techniques, still serum proteomics importunately poses hurdle in overcoming a wide range of protein concentrations, and also the availability of patient tissue samples is a limitation for the biomarker discovery. Thus, researchers have looked for alternatives, and profiling of candidate biomarkers through tissue culture of tumor cell lines comes up as a promising option. It is a rich source of tumor cell-derived proteins, thereby, representing a wide array of potential biomarkers. Interestingly, most of the clinical biomarkers in use today (CA 125, CA 15.3, CA 19.9, and PSA) were discovered through tissue culture-based system and tissue extracts. This paper tries to emphasize the tissue culture-based discovery of candidate biomarkers through various mass spectrometry-based proteomic approaches.
“clinical NEUroPROteomics of neurodegenerative diseases” (cNEUPRO) is a Specific Targeted Research Project (STREP) within the sixth framework program of the European Commission dedicated to the search for novel biomarker candidates for Alzheimer's disease and other neurodegenerative diseases. The ultimate goal of cNEUPRO is to identify one or more valid biomarker(s) in blood and CSF applicable to support the early and differential diagnosis of dementia disorders. The consortium covers all steps required for the discovery of novel biomarker candidates such as acquisition of high quality CSF and blood samples from relevant patient groups and controls, analysis of body fluids by various methods, and finally assay development and assay validation. Here we report the standardized procedures for diagnosis and preanalytical sample-handling within the project, as well as the status of the ongoing research activities and some first results.