Mass spectrometry-based targeted quantitative proteomics: achieving sensitive and reproducible detection of proteins

Traditional shotgun proteomics used to detect a mixture of hundreds to thousands of proteins through mass spectrometric analysis, has been the standard approach in research to profile protein content in a biological sample which could lead to the discovery of new (and all) protein candidates with diagnostic, prognostic, and therapeutic values. In practice, this approach requires significant resources and time, and does not necessarily represent the goal of the researcher who would rather study a subset of such discovered proteins (including their variations or posttranslational modifications) under different biological conditions. In this context, targeted proteomics is playing an increasingly important role in the accurate measurement of protein targets in biological samples in the hope of elucidating the molecular mechanism of cellular function via the understanding of intricate protein networks and pathways. One such (targeted) approach, selected reaction monitoring (or multiple reaction monitoring) mass spectrometry (MRM-MS), offers the capability of measuring multiple proteins with higher sensitivity and throughput than shotgun proteomics. Developing and validating MRM-MS-based assays, however, is an extensive and iterative process, requiring a coordinated and collaborative effort by the scientific community through the sharing of publicly accessible data and datasets, bioinformatic tools, standard operating procedures, and well characterized reagents.     

Toward a standardized urine proteome analysis methodology

Urine is an easily accessible bodily fluid particularly suited for the routine clinical analysis of disease biomarkers. Actually, the urinary proteome is more diverse than anticipated a decade ago. Hence, significant analytical and practical issues of urine proteomics such as sample collection and preparation have emerged, in particular for large-scale studies. We have undertaken a systematic study to define standardized and integrated analytical protocols for a biomarker development pipeline, employing two LC-MS analytical platforms, namely accurate mass and time tags and selected reaction monitoring, for the discovery and verification phase, respectively. Urine samples collected from hospital patients were processed using four different protocols, which were evaluated and compared on both analytical platforms. Addition of internal standards at various stages of sample processing allowed the estimation of protein extraction yields and the absolute quantification of selected urinary proteins. Reproducibility of the entire process and dynamic range of quantification were also evaluated. Organic solvent precipitation followed by in-solution digestion provided the best performances and was thus selected as the standard method common to the discovery and verification phases. Finally, we applied this protocol for platforms' cross-validation and obtained excellent consistency between urinary protein concentration estimates by both analytical methods performed in parallel in two laboratories.     

Multiplex targeted proteomic assay for biomarker detection in plasma: a pancreatic cancer biomarker case study

Biomarkers are most frequently proteins that are measured in the blood. Their development largely relies on antibody creation to test the protein candidate performance in blood samples of diseased versus nondiseased patients. The creation of such antibody assays has been a bottleneck in biomarker progress due to the cost, extensive time, and effort required to complete the task. Targeted proteomics is an emerging technology that is playing an increasingly important role to facilitate disease biomarker development. In this study, we applied a SRM-based targeted proteomics platform to directly detect candidate biomarker proteins in plasma to evaluate their clinical utility for pancreatic cancer detection. The characterization of these protein candidates used a clinically well-characterized cohort that included plasma samples from patients with pancreatic cancer, chronic pancreatitis, and healthy age-matched controls. Three of the five candidate proteins, including gelsolin, lumican, and tissue inhibitor of metalloproteinase 1, demonstrated an AUC value greater than 0.75 in distinguishing pancreatic cancer from the controls. In addition, we provide an analysis of the reproducibility, accuracy, and robustness of the SRM-based proteomics platform. This information addresses important technical issues that could aid in the adoption of the targeted proteomics platform for practical clinical utility.     

A tool for biomarker discovery in the urinary proteome: a manually curated human and animal urine protein biomarker database

Urine is an important source of biomarkers. A single proteomics assay can identify hundreds of differentially expressed proteins between disease and control samples; however, the ability to select biomarker candidates with the most promise for further validation study remains difficult. A bioinformatics tool that allows accurate and convenient comparison of all of the existing related studies can markedly aid the development of this area. In this study, we constructed the Urinary Protein Biomarker (UPB) database to collect existing studies of urinary protein biomarkers from published literature. To ensure the quality of data collection, all literature was manually curated. The website (http://122.70.220.102/biomarker) allows users to browse the database by disease categories and search by protein IDs in bulk. Researchers can easily determine whether a biomarker candidate has already been identified by another group for the same disease or for other diseases, which allows for the confidence and disease specificity of their biomarker candidate to be evaluated. Additionally, the pathophysiological processes of the diseases can be studied using our database with the hypothesis that diseases that share biomarkers may have the same pathophysiological processes. Because of the natural relationship between urinary proteins and the urinary system, this database may be especially suitable for studying the pathogenesis of urological diseases. Currently, the database contains 553 and 275 records compiled from 174 and 31 publications of human and animal studies, respectively. We found that biomarkers identified by different proteomic methods had a poor overlap with each other. The differences between sample preparation and separation methods, mass spectrometers, and data analysis algorithms may be influencing factors. Biomarkers identified from animal models also overlapped poorly with those from human samples, but the overlap rate was not lower than that of human proteomics studies. Therefore, it is not clear how well the animal models mimic human diseases.     

Reverse-Phase Protein Array: Technology,Application, Data Processing,and Integration

Reverse-phase protein array (RPPA) is a high-throughput antibody-based targeted proteomics platform that can quantify hundreds of proteins in thousands of samples derived from tissue or cell lysates, serum, plasma, or other body fluids. Protein samples are robotically arrayed as microspots on nitrocellulose-coated glass slides. Each slide is probed with a specific antibody that can detect levels of total protein expression or post-translational modifications, such as phosphorylation as a measure of protein activity. Here we describe workflow protocols and software tools that we have developed and optimized for RPPA in a core facility setting that includes sample preparation, microarray mapping and printing of protein samples, antibody labeling, slide scanning, image analysis, data normalization and quality control, data reporting, statistical analysis, and management of data. Our RPPA platform currently analyzes ∼240 validated antibodies that primarily detect proteins in signaling pathways and cellular processes that are important in cancer biology. This is a robust technology that has proven to be of value for both validation and discovery proteomic research and integration with other omics data sets.     

Proteomic data analysis workflow for discovery of candidate biomarker peaks predictive of clinical outcome for patients with acute myeloid leukemia

Our goal in this paper is to show an analytical workflow for selecting protein biomarker candidates from SELDI-MS data. The clinical question at issue is to enable prediction of the complete remission (CR) duration for acute myeloid leukemia (AML) patients. This would facilitate disease prognosis and make individual therapy possible. SELDI-mass spectrometry proteomics analyses were performed on blast cell samples collected from AML patients pre-chemotherapy. Although the biobank available included approximately 200 samples, only 58 were available for analysis. The presented workflow includes sample selection, experimental optimization, repeatability estimation, data preprocessing, data fusion, and feature selection. Specific difficulties have been the small number of samples and the skew distribution of the CR duration among the patients. Further, we had to deal with both noisy SELDI-MS data and a diverse patient cohort. This has been handled by sample selection and several methods for data preprocessing and feature detection in the analysis workflow. Four conceptually different methods for peak detection and alignment were considered, as well as two diverse methods for feature selection. The peak detection and alignment methods included the recently developed annotated regions of significance (ARS) method, the SELDI-MS software Ciphergen Express which was regarded as the standard method, segment-wise spectral alignment by a genetic algorithm (PAGA) followed by binning, and, finally, binning of raw data. In the feature selection, the "standard" Mann-Whitney t test was compared with a hierarchical orthogonal partial least-squares (O-PLS) analysis approach. The combined information from all these analyses gave a collection of 21 protein peaks. These were regarded as the most potential and robust biomarker candidates since they were picked out as significant features in several of the models. The chosen peaks will now be our first choice for the continuing work on protein identification and biological validation. The identification will be performed by chromatographic purification and MALDI MS/MS. Thus, we have shown that the use of several data handling methods can improve a protein profiling workflow from experimental optimization to a predictive model. The framework of this methodology should be seen as general and could be used with other one-dimensional spectral omics data than SELDI MS including an adequate number of samples.     

Proteomics signature profiling (PSP): a novel contextualization approach for cancer proteomics

Traditional proteomics analysis is plagued by the use of arbitrary thresholds resulting in large loss of information. We propose here a novel method in proteomics that utilizes all detected proteins. We demonstrate its efficacy in a proteomics screen of 5 and 7 liver cancer patients in the moderate and late stage, respectively. Utilizing biological complexes as a cluster vector, and augmenting it with submodules obtained from partitioning an integrated and cleaned protein-protein interaction network, we calculate a Proteomics Signature Profile (PSP) for each patient based on the hit rates of their reported proteins, in the absence of fold change thresholds, against the cluster vector. Using this, we demonstrated that moderate- and late-stage patients segregate with high confidence. We also discovered a moderate-stage patient who displayed a proteomics profile similar to other poor-stage patients. We identified significant clusters using a modified version of the SNet approach. Comparing our results against the Proteomics Expansion Pipeline (PEP) on which the same patient data was analyzed, we found good correlation. Building on this finding, we report significantly more clusters (176 clusters here compared to 70 in PEP), demonstrating the sensitivity of this approach. Gene Ontology (GO) terms analysis also reveals that the significant clusters are functionally congruent with the liver cancer phenotype. PSP is a powerful and sensitive method for analyzing proteomics profiles even when sample sizes are small. It does not rely on the ratio scores but, rather, whether a protein is detected or not. Although consistency of individual proteins between patients is low, we found the reported proteins tend to hit clusters in a meaningful and informative manner. By extracting this information in the form of a Proteomics Signature Profile, we confirm that this information is conserved and can be used for (1) clustering of patient samples, (2) identification of significant clusters based on real biological complexes, and (3) overcoming consistency and coverage issues prevalent in proteomics data sets.     

Experimental evaluation of protein identification by an LC/MALDI/on-target digestion approach

Tryptic digestion of proteins continues to be a workhorse of proteomics. Traditional tryptic digestion requires several hours to generate an adequate protein digest. A number of enhanced accelerated digestion protocols have been developed in recent years. Nonetheless, a need still exists for new digestion strategies that meet the demands of proteomics for high-throughput and rapid detection and identification of proteins. We performed an evaluation of direct tryptic digestion of proteins on a MALDI target plate and the potential for integrating RP HPLC separation of protein with on-target tryptic digestion in order to achieve a rapid and effective identification of proteins in complex biological samples. To this end, we used a Tempo HPLC/MALDI target plate deposition hybrid instrument (ABI). The technique was evaluated using a number of soluble and membrane proteins and an MRC5 cell lysate. We demonstrated that direct deposition of proteins on a MALDI target plate after reverse-phase HPLC separation and subsequent tryptic digestion of the proteins on the target followed by MALDI TOF/TOF analysis provided substantial data (intact protein mass, peptide mass and peptide fragment mass) that allowed a rapid and unambiguous identification of proteins. The rapid protein separation and direct deposition of fractions on a MALDI target plate provided by the RP HPLC combined with off-line interfacing with the MALDI MS is a unique platform for rapid protein identification with improved sequence coverage. This simple and robust approach significantly reduces the sample handling and potential loss in large-scale proteomics experiments. This approach allows combination of peptide mass fingerprinting (PMF), MS/MS peptide fragment fingerprinting (PPF) and whole protein MS for both protein identification and structural analysis of proteins.     

On the development of plasma protein biomarkers

The development of plasma biomarkers has proven to be more challenging than initially anticipated. Many studies have reported lists of candidate proteins rather than validated candidate markers with an assigned performance to a specific clinical objective. Biomarker research necessitates a clear rational framework with requirements on a multitude of levels. On the technological front, the platform needs to be effective to detect low abundant plasma proteins and be able to measure them in a high throughput manner over a large amount of samples reproducibly. At a conceptual level, the choice of the technological platform and available samples should be part of an overall clinical study design that depends on a joint effort between basic and clinical research. Solutions to these needs are likely to facilitate more feasible studies. Targeted proteomic workflows based on SRM mass spectrometry show the potential of fast verification of biomarker candidates in plasma and thereby closing the gap between discovery and validation in the biomarker development pipeline. Biological samples need to be carefully chosen based on well-established guidelines either for candidate discovery in the form of disease models with optimal fidelity to human disease or for candidate evaluation as well-designed and annotated clinical cohort groups. Most importantly, they should be representative of the target population and directly address the investigated clinical question. A conceptual structure of a biomarker study can be provided in the form of several sequential phases, each having clear objectives and predefined goals. Furthermore, guidelines for reporting the outcome of biomarker studies are critical to adequately assess the quality of the research, interpretation and generalization of the results. By being attentive to and applying these considerations, biomarker research should become more efficient and lead to directly translatable biomarker candidates into clinical evaluation.     

Proteomic analysis of eccrine sweat: implications for the discovery of schizophrenia biomarker proteins

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) and multiple reaction monitoring mass spectrometry (MRM-MS) proteomics analyses were performed on eccrine sweat of healthy controls, and the results were compared with those from individuals diagnosed with schizophrenia (SZ). This is the first large scale study of the sweat proteome. First, we performed LC-MS/MS on pooled SZ samples and pooled control samples for global proteomics analysis. Results revealed a high abundance of diverse proteins and peptides in eccrine sweat. Most of the proteins identified from sweat samples were found to be different than the most abundant proteins from serum, which indicates that eccrine sweat is not simply a plasma transudate and may thereby be a source of unique disease-associated biomolecules. A second independent set of patient and control sweat samples were analyzed by LC-MS/MS and spectral counting to determine qualitative protein differential abundances between the control and disease groups. Differential abundances of selected proteins, initially determined by spectral counting, were verified by MRM-MS analyses. Seventeen proteins showed a differential abundance of approximately 2-fold or greater between the SZ pooled sample and the control pooled sample. This study demonstrates the utility of LC-MS/MS and MRM-MS as a viable strategy for the discovery and verification of potential sweat protein disease biomarkers.     

High Throughput Sequential ELISA for Validation of Biomarkers of Acute Graft-Versus-Host Disease

Unbiased discovery proteomics strategies have the potential to identify large numbers of novel biomarkers that can improve diagnostic and prognostic testing in a clinical setting and may help guide therapeutic interventions. When large numbers of candidate proteins are identified, it may be difficult to validate candidate biomarkers in a timely and efficient fashion from patient plasma samples that are event-driven, of finite volume and irreplaceable, such as at the onset of acute graft-versus-host disease (GVHD), a potentially life-threatening complication of allogeneic hematopoietic stem cell transplantation (HSCT).Here we describe the process of performing commercially available ELISAs for six validated GVHD proteins: IL-2Rα⁵, TNFR1⁶, HGF⁷, IL-8⁸, elafin², and REG3α³ (also known as PAP1) in a sequential fashion to minimize freeze-thaw cycles, thawed plasma time and plasma usage. For this procedure we perform the ELISAs in sequential order as determined by sample dilution factor as established in our laboratory using manufacturer ELISA kits and protocols with minor adjustments to facilitate optimal sequential ELISA performance. The resulting plasma biomarker concentrations can then be compiled and analyzed for significant findings within a patient cohort. While these biomarkers are currently for research purposes only, their incorporation into clinical care is currently being investigated in clinical trials.This technique can be applied to perform ELISAs for multiple proteins/cytokines of interest on the same sample(s) provided the samples do not need to be mixed with other reagents. If ELISA kits do not come with pre-coated plates, 96-well half-well plates or 384-well plates can be used to further minimize use of samples/reagents.     

Systematic quantification of peptides/proteins in urine using selected reaction monitoring

The evaluation of biomarkers in bodily fluids necessitates the development of robust methods to quantify proteins in a complex background, using large sets of samples. The ability to multiplex numerous analytes in a single assay expedites the process. Liquid chromatography-mass spectrometry (LC-MS) analyses performed in selected reaction monitoring (SRM) in conjunction with stable isotope dilution MS present an effective way to detect and quantify biomarker candidates in bodily fluids. The strategy presented involves an initial qualification of predefined sets of proteins in urine. The technique was applied to detect and quantify peptides in urine samples as surrogates for a few endogenous proteins. Multiplexed assays were developed to analyze proteins associated with bladder cancer; a few exogenous proteins were added as internal standards. The sample preparation and the analytical protocols were optimized to ensure reproducibility, analytical precision, and quantification limits in the low nanogram per milliliter range. Analyses were performed using known amounts of isotopically labeled peptides. Systematic replication of the measurements indicated intra-assay and inter-assay variability, with CVs in the range of 10%. The differences measured for two targeted proteins were correlated with their level of expression in the corresponding tumors using immunohistochemistry.     

Protein alterations associated with pancreatic cancer and chronic pancreatitis found in human plasma using global quantitative proteomics profiling

Pancreatic cancer is a lethal disease that is difficult to diagnose at early stages when curable treatments are effective. Biomarkers that can improve current pancreatic cancer detection would have great value in improving patient management and survival rate. A large scale quantitative proteomics study was performed to search for the plasma protein alterations associated with pancreatic cancer. The enormous complexity of the plasma proteome and the vast dynamic range of protein concentration therein present major challenges for quantitative global profiling of plasma. To address these challenges, multidimensional fractionation at both protein and peptide levels was applied to enhance the depth of proteomics analysis. Employing stringent criteria, more than 1300 proteins total were identified in plasma across 8-orders of magnitude in protein concentration. Differential proteins associated with pancreatic cancer were identified, and their relationship with the proteome of pancreatic tissue and pancreatic juice from our previous studies was discussed. A subgroup of differentially expressed proteins was selected for biomarker testing using an independent cohort of plasma and serum samples from well-diagnosed patients with pancreatic cancer, chronic pancreatitis, and nonpancreatic disease controls. Using ELISA methodology, the performance of each of these protein candidates was benchmarked against CA19-9, the current gold standard for a pancreatic cancer blood test. A composite marker of TIMP1 and ICAM1 demonstrate significantly better performance than CA19-9 in distinguishing pancreatic cancer from the nonpancreatic disease controls and chronic pancreatitis controls. In addition, protein AZGP1 was identified as a biomarker candidate for chronic pancreatitis. The discovery and technical challenges associated with plasma-based quantitative proteomics are discussed and may benefit the development of plasma proteomics technology in general. The protein candidates identified in this study provide a biomarker candidate pool for future investigations.     

Strategy for SRM-based Verification of Biomarker Candidates Discovered by iTRAQ Method in Limited Breast Cancer Tissue Samples

Since LC-MS-based quantitative proteomics has become increasingly applied to a wide range of biological applications over the past decade, numerous studies have performed relative and/or absolute abundance determinations across large sets of proteins. In this study, we discovered prognostic biomarker candidates from limited breast cancer tissue samples using discovery-through-verification strategy combining iTRAQ method followed by selected reaction monitoring/multiple reaction monitoring analysis (SRM/MRM). We identified and quantified 5122 proteins with high confidence in 18 patient tissue samples (pooled high-risk (n = 9) or low-risk (n = 9)). A total of 2480 proteins (48.4%) of them were annotated as membrane proteins, 16.1% were plasma membrane and 6.6% were extracellular space proteins by Gene Ontology analysis. Forty-nine proteins with >2-fold differences in two groups were chosen for further analysis and verified in 16 individual tissue samples (high-risk (n = 9) or low-risk (n = 7)) using SRM/MRM. Twenty-three proteins were differentially expressed among two groups of which MFAP4 and GP2 were further confirmed by Western blotting in 17 tissue samples (high-risk (n = 9) or low-risk (n = 8)) and Immunohistochemistry (IHC) in 24 tissue samples (high-risk (n = 12) or low-risk (n = 12)). These results indicate that the combination of iTRAQ and SRM/MRM proteomics will be a powerful tool for identification and verification of candidate protein biomarkers.     

Simultaneous Extraction of Viral and Bacterial Nucleic Acids for Molecular Diagnostic Applications

Molecular detection of microbial pathogens in clinical samples requires the application of efficient sample lysis protocols and subsequent extraction and isolation of their nucleic acids. Here, we describe a simple and time-efficient method for simultaneous extraction of genomic DNA from gram-positive and -negative bacteria, as well as RNA from viral agents present in a sample. This method compared well with existing bacterial- and viral-specialized extraction protocols, worked reliably on clinical samples, and was not pathogen specific. This method may be used to extract DNA and RNA concurrently from viral and bacterial pathogens present in a sample and effectively detect coinfections in routine clinical diagnostics.     

Effects of preanalytical variables on peptide and protein measurements in human serum and plasma: implications for clinical proteomics

There is a wealth of knowledge in the field of in vitro diagnostics with regard to preanalytical variables and their impact on the determination of peptide and protein analytes in human serum and plasma. This information is applicable to clinical proteomics investigations, which utilize the same sample types. Studies have demonstrated that the majority of variations and errors in in vitro diagnostics seem to occur in the preanalytical phase prior to specimen analysis. Preanalytical processes include study design, compliance of the subjects investigated, compliance of the technical staff in adherence to protocols, choice of specimens utilized and sample collection and processing. These variables can have a dramatic impact on the determination of analytes and can affect result outcomes, reproducibility and the validity of investigations. By drawing analogies to in vitro diagnostics practices, specific variables that are likely to impact the results of proteomics studies can be identified. Recognition of such variables is the first step towards their understanding and, eventually, controlling their impact. In this article, we will review preanalytical variables, provide examples for their effects on the determination of distinct peptides and proteins and discuss potential implications for clinical proteomics investigations.     

Serum protein profiling by solid phase extraction and mass spectrometry: A future diagnostics tool?

Serum protein profiling by MS is a promising method for early detection of disease. Important characteristics for serum protein profiling are preanalytical factors, analytical reproducibility and high throughput. Problems related to preanalytical factors can be overcome by using standardized and rigorous sample collection and sample handling protocols. The sensitivity of the MS analysis relies on the quality of the sample; consequently, the blood sample preparation step is crucial to obtain pure and concentrated samples and enrichment of the proteins and peptides of interest. This review focuses on the serum sample preparation step prior to protein profiling by MALDI MS analysis, with particular focus on various SPE methods. The application of SPE techniques with different chromatographic properties such as RP, ion exchange, or affinity binding to isolate specific subsets of molecules (subproteomes) is advantageous for increasing resolution and sensitivity in the subsequent MS analysis. In addition, several of the SPE sample preparation methods are simple and scalable and have proven easy to automate for higher reproducibility and throughput, which is important in a clinical proteomics setting.     

Liquid Chromatography/Mass Spectrometry (LC/MS)-Based Glycoproteomics Technologies for Cancer Biomarker Discovery

Introduction

Biomarker discovery is a major objective of clinical proteomics; molecular biomarkers allow for detection of early-stage human diseases, especially cancer, and for monitoring their progression and/or regression after treatment. Biomarkers also help to elucidate the pathology of disease and its diagnosis, drug discovery, and toxicology. Glycans are ideal candidates for biomarkers because (1) glycoconjugates are localized on the cell surface and in the secretions such as plasma, (2) their structures are frequently and drastically changed during normal and aberrant cell differentiation, and (3) different cell types express different glycan signatures. Certain serodiagnostic glycoconjugate markers, such as carcinoembryonic antigen (CEA), are currently available; however, comprehensive glycome analysis has yet to be performed, mainly because of the difficulties of isolating and structurally analyzing complex glycans. Large-scale glycoprotein analysis, termed glycoproteomics, has the potential to effectively trace cellular glycoproteins and therefore to search for new serodiagnostic biomarkers.

Conclusions

In this review, we describe current mass spectrometry-based glycoproteomics technologies. Quantitative “shotgun” proteomics analyses of glycopeptides captured from complex biological mixtures such as plasma, coupled with advanced glycome technologies, enhance our knowledge of protein glycosylation and facilitate discovery of new biomarkers for human diseases.     

Integrated pipeline for mass spectrometry-based discovery and confirmation of biomarkers demonstrated in a mouse model of breast cancer

Despite their potential to impact diagnosis and treatment of cancer, few protein biomarkers are in clinical use. Biomarker discovery is plagued with difficulties ranging from technological (inability to globally interrogate proteomes) to biological (genetic and environmental differences among patients and their tumors). We urgently need paradigms for biomarker discovery. To minimize biological variation and facilitate testing of proteomic approaches, we employed a mouse model of breast cancer. Specifically, we performed LC-MS/MS of tumor and normal mammary tissue from a conditional HER2/Neu-driven mouse model of breast cancer, identifying 6758 peptides representing >700 proteins. We developed a novel statistical approach (SASPECT) for prioritizing proteins differentially represented in LC-MS/MS datasets and identified proteins over- or under-represented in tumors. Using a combination of antibody-based approaches and multiple reaction monitoring-mass spectrometry (MRM-MS), we confirmed the overproduction of multiple proteins at the tissue level, identified fibulin-2 as a plasma biomarker, and extensively characterized osteopontin as a plasma biomarker capable of early disease detection in the mouse. Our results show that a staged pipeline employing shotgun-based comparative proteomics for biomarker discovery and multiple reaction monitoring for confirmation of biomarker candidates is capable of finding novel tissue and plasma biomarkers in a mouse model of breast cancer. Furthermore, the approach can be extended to find biomarkers relevant to human disease.     

High-performance proteomics as a tool in biomarker discovery

Biomarkers allowing early detection of disease or therapy control have a huge influence in curing a disease. A wide variety of methods were applied to find new biomarkers. In contrast to methods focused on DNA or mRNA techniques, approaches considering proteins as potential biomarker candidates have the advantage that proteins are more diverse than DNA or RNA and are more reflective of a biological system. Here, we present an approach for the identification of new biomarkers relying on our experience from the past 10 years of proteomics, outlining a concept of "high-performance proteomics" This approach is based on quantitative proteome analysis using a sufficient number of clinical samples and statistical validation of proteomics data by independent methods, such as Western blot analysis or immunohistochemistry.