Proteomic Biomarker Discovery Research Articles

A systematic model of the LC-MS proteomics pipeline

MotivationMass spectrometry is a complex technique used for large-scale protein profiling with clinical and pharmaceutical applications. While individual components in the system have been studied extensively, little work has been done to integrate various modules and evaluate them from a systems point of view.ResultsIn this work, we investigate this problem by putting together the different modules in a typical proteomics work flow, in order to capture and analyze key factors that impact the number of identified peptides and quantified proteins, protein quantification error, differential expression results, and classification performance. The proposed proteomics pipeline model can be used to optimize the work flow as well as to pinpoint critical bottlenecks worth investing time and resources into for improving performance. Using the model-based approach proposed here, one can study systematically the critical problem of proteomic biomarker discovery, by means of simulation using ground-truthed synthetic MS data.

Discovery of serum proteomic biomarkers for prediction of response to infliximab (a monoclonal anti-TNF antibody) treatment in rheumatoid arthritis: An exploratory analysis

Biologics such as TNF antagonists are a new class of drugs that have greatly improved Rheumatoid Arthritis (RA) treatment. However, for unknown reasons, individual patients with RA respond to one of these drugs but not to others even those targeting the same molecule. Methods to predict response are sorely needed because these drugs are currently selected by trial and error, what is very inefficient and prejudicial for the patient and the healthcare system. Here, we have explored the discovery of protein biomarkers in serum from patients treated with infliximab, one of the major anti-TNF drugs. The study was based in a quantitative proteomics approach using 8-plex iTRAQ labeling. It combined depletion of the most abundant serum proteins, two-dimensional LC fractionation, protein identification and relative quantification with a hybrid Orbitrap mass spectrometer. This approach allowed the identification of 315 proteins of which 237 were confidently quantified with two or more peptides. The detection range covered up to 6 orders of magnitude including multiple proteins at the ng/mL level. A new set of putative biomarkers was identified comprising 14 proteins significantly more abundant in the non-responder patients. The differential proteins were enriched in apolipoproteins, components of the complement system and acute phase reactants. These results show the feasibility of this approach and provide a set of candidates for validation as biomarkers for the classification of RA patients before the beginning of treatment, so that anticipated non-responders could be treated with an alternative drug.

Role of proteomics in biomarker discovery and psychiatric disorders: current status, potentials, limitations and future challenges

Proteomic approaches have advanced clinical research towards more reliable, sensitive and specific biological diagnostic markers for diseases. Mood disorders are most difficult to diagnose and very much prevalent in society; hence, their proper diagnosis becomes essential. Despite tremendous research efforts to dissect the neurobiological basis of psychiatric disorders, the diagnosis and evaluation for such diseases is still poor. Biomarker discovery in psychiatry research has been accelerated by proteomic technologies, accepting the challenges in order to meet disease state-related investigations. Proteomics-based research for disease-specific protein signatures is expected to give a new direction in psychiatry research. Therefore, this may become a more powerful tool to predict the development, course and outcome of the disease towards personalized psychiatric ailments. The review discusses the role of proteomics in elucidating mechanisms of psychiatric disorders, current status, prospects, limitations and new possibilities towards a strong diagnostic tool in the clinical laboratory.

Monitoring the progression of metastatic breast cancer on nanoporous silica chips

Breast cancer accounted for 15 per cent of total cancer deaths in female patients in 2010. Although significant progress has been made in treating early-stage breast cancer patients, there is still no effective therapy targeting late-stage metastatic breast cancers except for the conventional chemotherapy interventions. Until effective therapy for later-stage cancers emerges, the identification of biomarkers for the early detection of tumour metastasis continues to hold the key to successful management of breast cancer therapy. Our study concentrated on the low molecular weight (LMW) region of the serum protein and the information it contains for identifying biomarkers that could reflect the ongoing physiological state of all tissues. Owing to technical difficulties in harvesting LMW species, studying these proteins/peptides has been challenging until now. In our study, we have recently developed nanoporous chip-based technologies to separate small proteins/peptides from the large proteins in serum. We used nanoporous silica chips, with a highly periodic nanostructure and uniform pore size distribution, to isolate LMW proteins and peptides from the serum of nude mice with MDA-MB-231 human breast cancer lung metastasis. By matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and biostatistical analysis, we were able to identify protein signatures unique to different stages of cancer development. The approach and results reported in this study possess a significant potential for the discovery of proteomic biomarkers that may significantly enhance personalized medicine targeted at metastatic breast cancer.

Batch effects correction improves the sensitivity of significance tests in spectral counting-based comparative discovery proteomics

Shotgun proteomics has become the standard proteomics technique for the large-scale measurement of protein abundances in biological samples. Despite quantitative proteomics has been usually performed using label-based approaches, label-free quantitation offers advantages related to the avoidance of labeling steps, no limitation in the number of samples to be compared, and the gain in protein detection sensitivity. However, since samples are analyzed separately, experimental design becomes critical. The exploration of spectral counting quantitation based on LC-MS presented here gathers experimental evidence of the influence of batch effects on comparative proteomics. The batch effects shown with spiking experiments clearly interfere with the biological signal. In order to minimize the interferences from batch effects, a statistical correction is proposed and implemented. Our results show that batch effects can be attenuated statistically when proper experimental design is used. Furthermore, the batch effect correction implemented leads to a substantial increase in the sensitivity of statistical tests. Finally, the applicability of our batch effects correction is shown on two different biomarker discovery projects involving cancer secretomes. We think that our findings will allow designing and executing better comparative proteomics projects and will help to avoid reaching false conclusions in the field of proteomics biomarker discovery.

Label-free mass spectrometry-based proteomics for biomarker discovery and validation

For most diseases, better biomarkers are urgently needed to enable (early) detection, diagnosis, prognosis, stratification for therapy and response monitoring. Proteomics delineates gene products that carry out the majority of cellular functions, and thereby may not only yield insight into altered signaling pathways in disease, but also yield novel biomarkers. In recent years, great progress has been made in mass spectrometry-based analysis of clinical tissues and biofluids, with identification and quantification of thousands of proteins now becoming increasingly routine. However, biomarker validation and clinical translation has turned out to be challenging. In this review, we summarize current mass spectrometry-based proteomics strategies for biomarker discovery and verification using selected reaction monitoring, with a focus on progress and recent applications in clinical material using label-free approaches.

Bio-Inspired Metaheuristic Optimization Algorithms for Biomarker Identification in Mass Spectrometry Analysis

Mass spectrometry is an emerging technique that is continuously gaining momentum among bioinformatics researchers who intend to study biological or chemical properties of complex structures such as protein sequences. This advancement also embarks in the discovery of proteomic biomarkers through accessible body fluids such as serum, saliva, and urine. Recently, literature reveals that sophisticated computational techniques mimetic survival and natural processes adapted from biological life for reasoning voluminous mass spectrometry data yields promising results. Such advanced approaches can provide efficient ways to mine mass spectrometry data in order to extract parsimonious features that represent vital information, specifically in discovering disease-related protein patterns in complex proteins sequences. This article intends to provide a systematic survey on bio-inspired approaches for feature subset selection via mass spectrometry data for biomarker analysis.

Urinary Proteome Analysis at 5-Year Followup of Patients With Nonoperated Ureteropelvic Junction Obstruction Suggests Ongoing Kidney Remodeling

Severe ureteropelvic junction obstruction is treated surgically. However, for milder cases most clinical teams adopt a watchful waiting approach and only operate in the presence of significant decline of renal function combined with severe hydronephrosis. Little is known about the long-term consequences of ureteropelvic junction obstruction. Using capillary electrophoresis coupled with mass spectrometry, we analyzed the urinary proteome of 42 patients with ureteropelvic junction obstruction 5 years postoperatively or 5 years following spontaneous resolution. At 5-year followup urinary proteomes were similar between patients with early surgical correction of ureteropelvic junction obstruction and age matched controls. In contrast, urinary proteomes differed significantly between conservatively followed patients and controls. Analyses of the proteomic differences suggested ongoing renal or ureteral remodeling in the conservatively followed patients that was not visible clinically. Long-term followup studies are warranted in patients with ureteropelvic junction obstruction, especially those followed conservatively, to determine whether the observed changes in the urinary proteomes become clinically relevant at a later stage.

Proteomic Changes in Cerebrospinal Fluid of Presymptomatic and Affected Persons Carrying Familial Alzheimer Disease Mutations

To identify cerebrospinal fluid (CSF) protein changes in persons who will develop familial Alzheimer disease (FAD) due to PSEN1 and APP mutations, using unbiased proteomics. We compared proteomic profiles of CSF from individuals with FAD who were mutation carriers (MCs) and related noncarriers (NCs). Abundant proteins were depleted and samples were analyzed using liquid chromatography-electrospray ionization-mass spectrometry on a high-resolution time-of-flight instrument. Tryptic peptides were identified by tandem mass spectrometry. Proteins differing in concentration between the MCs and NCs were identified. A tertiary dementia referral center and a proteomic biomarker discovery laboratory. Fourteen FAD MCs (mean age, 34.2 years; 10 are asymptomatic, 12 have presenilin-1 [PSEN1 ] gene mutations, and 2 have amyloid precursor protein [APP ] gene mutations) and 5 related NCs (mean age, 37.6 years). Fifty-six proteins were identified, represented by multiple tryptic peptides showing significant differences between MCs and NCs (46 upregulated and 10 downregulated); 40 of these proteins differed when the analysis was restricted to asymptomatic individuals. Fourteen proteins have been reported in prior proteomic studies in late-onset AD, including amyloid precursor protein, transferrin, α(1)β-glycoprotein, complement components, afamin precursor, spondin 1, plasminogen, hemopexin, and neuronal pentraxin receptor. Many other proteins were unique to our study, including calsyntenin 3, AMPA (α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid) 4 glutamate receptor, CD99 antigen, di- N-acetyl-chitobiase, and secreted phosphoprotein 1. We found much overlap in CSF protein changes between individuals with presymptomatic and symptomatic FAD and those with late-onset AD. Our results are consistent with inflammation and synaptic loss early in FAD and suggest new presymptomatic biomarkers of potential usefulness in drug development.

Editorial introductions

Editorial introductions

Statistical Methods for Proteomic Biomarker Discovery based on Feature Extraction or Functional Modeling Approaches.

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.

Proteomic Analysis of Human Saliva From Lung Cancer Patients Using Two-Dimensional Difference Gel Electrophoresis and Mass Spectrometry

Lung cancer is often asymptomatic or causes only nonspecific symptoms in its early stages. Early detection represents one of the most promising approaches to reduce the growing lung cancer burden. Human saliva is an attractive diagnostic fluid because its collection is less invasive than that of tissue or blood. Profiling of proteins in saliva over the course of disease progression could reveal potential biomarkers indicative of oral or systematic diseases, which may be used extensively in future medical diagnostics. There were 72 subjects enrolled in this study for saliva sample collection according to the approved protocol. Two-dimensional difference gel electrophoresis combined with MS was the platform for salivary proteome separation, quantification, and identification from two pooled samples. Candidate proteomic biomarkers were verified and prevalidated by using immunoassay methods. There were 16 candidate protein biomarkers discovered by two-dimensional difference gel electrophoresis and MS. Three proteins were further verified in the discovery sample set, prevalidation sample set, and lung cancer cell lines. The discriminatory power of these candidate biomarkers in lung cancer patients and healthy control subjects can reach 88.5% sensitivity and 92.3% specificity with AUC = 0.90. This preliminary data report demonstrates that proteomic biomarkers are present in human saliva when people develop lung cancer. The discriminatory power of these candidate biomarkers indicate that a simple saliva test might be established for lung cancer clinical screening and detection.

Molecular pathology of prostate cancer

This chapter includes discussion of the molecular pathology of tissue, blood, urine, and expressed prostatic secretions. Because we are unable to reliably image the disease in vivo, a 12 core method that oversamples the peripheral zone is widely used. This generates large numbers of cores that need to be carefully processed and sampled. In spite of the large number of tissue cores, the amount of tumor available for study is often quite limited. This is a particular challenge for research, as new biomarker assays will need to preserve tissue architecture intact for histopathology. Methods of processing and reporting pathology are discussed. With the exception of ductal variants, recognized subtypes of prostate cancer are largely confined to research applications, and most prostate cancers are acinar. Biomarker discovery in urine and expressed prostatic secretions would be useful since these are readily obtained and are proximate fluids. The well-known challenges of biomarker discovery in blood and urine are referenced and discussed. Mediators of carcinogenesis can serve as biomarkers as exemplified by mutations in PTEN and TMPRSS2:ERG fusion. The use of proteomics in biomarker discovery with an emphasis on imaging mass spectroscopy of tissues is discussed. Small RNAs are of great interest, however, their usefulness as biomarkers in clinical decision making remains the subject of ongoing research. The chapter concludes with an overview of blood biomarkers such as circulating nucleic acids and tumor cells and bound/free isoforms of prostate specific antigen (PSA).

Discovery of Candidate Serum Proteomic and Metabolomic Biomarkers in Ankylosing Spondylitis

Ankylosing Spondylitis (AS) is a common inflammatory rheumatic disease with a predilection for the axial skeleton, affecting 0.2% of the population. Current diagnostic criteria rely on a composite of clinical and radiological changes, with a mean time to diagnosis of 5 to 10 years. In this study we employed nano liquid-chromatography mass spectrometry analysis to detect and quantify proteins and small compounds including endogenous peptides and metabolites in serum from 18 AS patients and nine healthy individuals. We identified a total of 316 proteins in serum, of which 22 showed significant up- or down-regulation (p < 0.05) in AS patients. Receiver operating characteristic analysis of combined levels of serum amyloid P component and inter-α-trypsin inhibitor heavy chain 1 revealed high diagnostic value for Ankylosing Spondylitis (area under the curve = 0.98). We also depleted individual sera of proteins to analyze endogenous peptides and metabolic compounds. We detected more than 7000 molecular features in patients and healthy individuals. Quantitative MS analysis revealed compound profiles that correlate with the clinical assessment of disease activity. One molecular feature identified as a Vitamin D3 metabolite—(23S,25R)-25-hydroxyvitamin D3 26,23-peroxylactone—was down-regulated in AS. The ratio of this vitamin D metabolite versus vitamin D binding protein serum levels was also altered in AS as compared with controls. These changes may contribute to pathological skeletal changes in AS. Our study is the first example of an integration of proteomic and metabolomic techniques to find new biomarker candidates for the diagnosis of Ankylosing Spondylitis.

From “Clinical Proteomics” to “Clinical Chemistry Proteomics”: considerations using quantitative mass-spectrometry as a model approach

Clinical Proteomics biomarker discovery programs lead to the selection of putative new biomarkers of human pathologies. Following an initial discovery phase, validation of these candidates in larger populations is a major task that recently started relying upon the use of mass spectrometry approaches, especially in cases where classical immune-detection methods were lacking. Thanks to highly sensitive spectrometers, adapted measurement methods like selective reaction monitoring (SRM) and various pre-fractionation methods, the quantitative detection of protein/peptide biomarkers in low concentrations is now feasible from complex biological fluids. This possibility leads to the use of similar methodologies in clinical biology laboratories, within a new proteomic field that we shall name "Clinical Chemistry Proteomics" (CCP). Such evolution of Clinical Proteomics adds important constraints with regards to the in vitro diagnostic (IVD) application. As measured values of analytes will be used to diagnose, follow-up and adapt patient treatment on a routine basis; medical utility, robustness, reference materials and clinical feasibility are among the new issues of CCP to consider.

P1.49 Mass spectrometry based clinical proteomics for biomarker discovery in Duchenne muscular dystrophy

P1.49 Mass spectrometry based clinical proteomics for biomarker discovery in Duchenne muscular dystrophy

A Systematic Analysis of Eluted Fraction of Plasma Post Immunoaffinity Depletion: Implications in Biomarker Discovery

Plasma is the most easily accessible source for biomarker discovery in clinical proteomics. However, identifying potential biomarkers from plasma is a challenge given the large dynamic range of proteins. The potential biomarkers in plasma are generally present at very low abundance levels and hence identification of these low abundance proteins necessitates the depletion of highly abundant proteins. Sample pre-fractionation using immuno-depletion of high abundance proteins using multi-affinity removal system (MARS) has been a popular method to deplete multiple high abundance proteins. However, depletion of these abundant proteins can result in concomitant removal of low abundant proteins. Although there are some reports suggesting the removal of non-targeted proteins, the predominant view is that number of such proteins is small. In this study, we identified proteins that are removed along with the targeted high abundant proteins. Three plasma samples were depleted using each of the three MARS (Hu-6, Hu-14 and Proteoprep 20) cartridges. The affinity bound fractions were subjected to gelC-MS using an LTQ-Orbitrap instrument. Using four database search algorithms including MassWiz (developed in house), we selected the peptides identified at <1% FDR. Peptides identified by at least two algorithms were selected for protein identification. After this rigorous bioinformatics analysis, we identified 101 proteins with high confidence. Thus, we believe that for biomarker discovery and proper quantitation of proteins, it might be better to study both bound and depleted fractions from any MARS depleted plasma sample.

Discovery and Validation of Serum Protein Changes in Type 1 Diabetes Patients Using High Throughput Two Dimensional Liquid Chromatography-Mass Spectrometry and Immunoassays

Type 1 diabetes (T1D) is expected to cause significant changes in the serum proteome; however, few studies have systematically assessed the proteomic profile change associated with the disease. In this study, a semiquantitative spectral counting-based two dimensional liquid chromatography mass spectrometry platform was used to analyze serum samples from T1D patients and controls. In this discovery phase, significant differences were found for 21 serum proteins implicated in inflammation, oxidation, metabolic regulation, and autoimmunity. To assess the validity of these findings, six candidate proteins including adiponectin, insulin-like growth factor binding protein 2, serum amyloid protein A, C-reactive protein, myeloperoxidase, and transforming growth factor beta induced were selected for subsequent immune assays for 1139 T1D patients and 848 controls. A series of statistical analyses using cases and controls matched for age, sex, and genetic risk confirmed that T1D patients have significantly higher serum levels for four of the six proteins: adiponectin (odds ratio (OR) = 1.95, p = 10(-27)), insulin-like growth factor binding protein 2 (OR = 2.02, p < 10(-20)), C-reactive protein (OR = 1.13, p = 0.007), serum amyloid protein A (OR = 1.51, p < 10(-16)); whereas the serum levels were significantly lower in patients than controls for the two other proteins: transforming growth factor beta induced (OR = 0.74, p < 10(-5)) and myeloperoxidase (OR = 0.51, p < 10(-41)). Compared with subjects in the bottom quartile, subjects in the top quartile for adiponectin (OR = 6.29, p < 10(-37)), insulin-like growth factor binding protein 2 (OR = 7.95, p < 10(-46)), C-reactive protein (OR = 1.38, p = 0.025), serum amyloid protein A (OR = 3.36, p < 10(-16)) had the highest risk of T1D, whereas subjects in the top quartile of transforming growth factor beta induced (OR = 0.41, p < 10(-11)) and myeloperoxidase (OR = 0.10, p < 10(-43)) had the lowest risk of T1D. These findings provided valuable information on the proteomic changes in the sera of T1D patients.

Dynamic Range Compression: A Solution for Proteomic Biomarker Discovery?

Dynamic Range Compression: A Solution for Proteomic Biomarker Discovery?

Clustering with position-specific constraints on variance: Applying redescending M-estimators to label-free LC-MS data analysis

BackgroundClustering is a widely applicable pattern recognition method for discovering groups of similar observations in data. While there are a large variety of clustering algorithms, very few of these can enforce constraints on the variation of attributes for data points included in a given cluster. In particular, a clustering algorithm that can limit variation within a cluster according to that cluster's position (centroid location) can produce effective and optimal results in many important applications ranging from clustering of silicon pixels or calorimeter cells in high-energy physics to label-free liquid chromatography based mass spectrometry (LC-MS) data analysis in proteomics and metabolomics.ResultsWe present MEDEA (M-Estimator with DEterministic Annealing), an M-estimator based, new unsupervised algorithm that is designed to enforce position-specific constraints on variance during the clustering process. The utility of MEDEA is demonstrated by applying it to the problem of "peak matching"--identifying the common LC-MS peaks across multiple samples--in proteomic biomarker discovery. Using real-life datasets, we show that MEDEA not only outperforms current state-of-the-art model-based clustering methods, but also results in an implementation that is significantly more efficient, and hence applicable to much larger LC-MS data sets.ConclusionsMEDEA is an effective and efficient solution to the problem of peak matching in label-free LC-MS data. The program implementing the MEDEA algorithm, including datasets, clustering results, and supplementary information is available from the author website at http://www.hephy.at/user/fru/medea/.