The knowledge-integrated network biomarkers discovery for major adverse cardiac events

The mass spectrometry (MS) technology in clinical proteomics is very promising for discovery of new biomarkers for diseases management. To overcome the obstacles of data noises in MS analysis, we proposed a new approach of knowledge-integrated biomarker discovery using data from Major Adverse Cardiac Events (MACE) patients. We first built up a cardiovascular-related network based on protein information coming from protein annotations in Uniprot, protein-protein interaction (PPI), and signal transduction database. Distinct from the previous machine learning methods in MS data processing, we then used statistical methods to discover biomarkers in cardiovascular-related network. Through the tradeoff between known protein information and data noises in mass spectrometry data, we finally could firmly identify those high-confident biomarkers. Most importantly, aided by protein-protein interaction network, that is, cardiovascular-related network, we proposed a new type of biomarkers, that is, network biomarkers, composed of a set of proteins and the interactions among them. The candidate network biomarkers can classify the two groups of patients more accurately than current single ones without consideration of biological molecular interaction.     

Upregulation of plasma C9 protein in gastric cancer patients

Gastric cancer is one of the leading causes of cancer‐related deaths worldwide. Current biomarkers used in the clinic do not have sufficient sensitivity for gastric cancer detection. To discover new and better biomarkers, protein profiling on plasma samples from 25 normal, 15 early‐stage and 21 late‐stage cancer was performed using an iTRAQ‐LC‐MS/MS approach. The level of C9 protein was found to be significantly higher in gastric cancer compared with normal subjects. Immunoblotting data revealed a congruent trend with iTRAQ results. The discriminatory power of C9 between normal and cancer states was not due to inter‐patient variations and was independent from gastritis and Helicobacter pylori status of the patients. C9 overexpression could also be detected in a panel of gastric cancer cell lines and their conditioned media compared with normal cells, implying that higher C9 levels in plasma of cancer patients could be attributed to the presence of gastric tumor. A subsequent blind test study on a total of 119 plasma samples showed that the sensitivity of C9 could be as high as 90% at a specificity of 74%. Hence, C9 is a potentially useful biomarker for gastric cancer detection.     

Identification of novel biomarker candidates for immunohistochemical diagnosis to distinguish low‐grade chondrosarcoma from enchondroma

Chondrosarcoma is the third most common primary bone cancer, requiring surgical resection. However, differentiation of low‐grade chondrosarcoma (grade 1) from enchondroma that is benign and only requires regular follow‐up is one of the most frequent diagnostic dilemmas facing orthopedic oncologists in clinical management. Although multiple techniques are applied to make the distinction, immunohistochemistry is an important ancillary technique, especially when a histopathological stain of specimen must be obtained in order to guarantee an accurate confirmation. Currently, no adequate immunohistochemical diagnostic protein biomarkers are available to distinguish low‐grade chondrosarcoma from enchondroma. To discover novel protein biomarker candidates, an LC‐MS/MS approach was applied to directly compare formalin‐fixed, paraffin‐embedded low‐grade chondrosarcoma with enchondroma tissue samples. The proteomics analysis revealed 17 protein biomarker candidates. A principle was developed to prioritize the candidates using category and ranking. An algorithm, prioritization index of biomarker candidates for immunohistochemistry on tissue specimens, was developed to rank the candidates inside each category. Using the proteomics data and bioinformatics results, the prioritization index of biomarker candidates for immunohistochemistry on tissue revealed periostin as a top candidate. Immunohistochemical staining of periostin in 23 low‐grade chondrosarcoma and 31 enchondroma tissue specimens disclosed 87% specificity and 70% sensitivity.     

Multiplexed MRM‐based quantitation of candidate cancer biomarker proteins in undepleted and non‐enriched human plasma

An emerging approach for multiplexed targeted proteomics involves bottom‐up LC‐MRM‐MS, with stable isotope‐labeled internal standard peptides, to accurately quantitate panels of putative disease biomarkers in biofluids. In this paper, we used this approach to quantitate 27 candidate cancer‐biomarker proteins in human plasma that had not been treated by immunoaffinity depletion or enrichment techniques. These proteins have been reported as biomarkers for a variety of human cancers, from laryngeal to ovarian, with breast cancer having the highest correlation. We implemented measures to minimize the analytical variability, improve the quantitative accuracy, and increase the feasibility and applicability of this MRM‐based method. We have demonstrated excellent retention time reproducibility (median interday CV: 0.08%) and signal stability (median interday CV: 4.5% for the analytical platform and 6.1% for the bottom‐up workflow) for the 27 biomarker proteins (represented by 57 interference‐free peptides). The linear dynamic range for the MRM assays spanned four orders‐of‐magnitude, with 25 assays covering a 10³–10⁴ range in protein concentration. The lowest abundance quantifiable protein in our biomarker panel was insulin‐like growth factor 1 (calculated concentration: 127 ng/mL). Overall, the analytical performance of this assay demonstrates high robustness and sensitivity, and provides the necessary throughput and multiplexing capabilities required to verify and validate cancer‐associated protein biomarker panels in human plasma, prior to clinical use.     

Improving the default data analysis workflow for large autoimmune biomarker discovery studies with ProtoArrays

Contemporary protein microarrays such as the ProtoArray® are used for autoimmune antibody screening studies to discover biomarker panels. For ProtoArray data analysis, the software Prospector and a default workflow are suggested by the manufacturer. While analyzing a large data set of a discovery study for diagnostic biomarkers of the Parkinson's disease (ParkCHIP), we have revealed the need for distinct improvements of the suggested workflow concerning raw data acquisition, normalization and preselection method availability, batch effects, feature selection, and feature validation. In this work, appropriate improvements of the default workflow are proposed. It is shown that completely automatic data acquisition as a batch, a re‐implementation of Prospector's pre‐selection method, multivariate or hybrid feature selection, and validation of the selected protein panel using an independent test set define in combination an improved workflow for large studies.     

Why have so few proteomic biomarkers “survived” validation? (Sample size and independent validation considerations)

Proteomic biomarker discovery has led to the identification of numerous potential candidates for disease diagnosis, prognosis, and prediction of response to therapy. However, very few of these identified candidate biomarkers reach clinical validation and go on to be routinely used in clinical practice. One particular issue with biomarker discovery is the identification of significantly changing proteins in the initial discovery experiment that do not validate when subsequently tested on separate patient sample cohorts. Here, we seek to highlight some of the statistical challenges surrounding the analysis of LC‐MS proteomic data for biomarker candidate discovery. We show that common statistical algorithms run on data with low sample sizes can overfit and yield misleading misclassification rates and AUC values. A common solution to this problem is to prefilter variables (via, e.g. ANOVA and or use of correction methods such as Bonferonni or false discovery rate) to give a smaller dataset and reduce the size of the apparent statistical challenge. However, we show that this exacerbates the problem yielding even higher performance metrics while reducing the predictive accuracy of the biomarker panel. To illustrate some of these limitations, we have run simulation analyses with known biomarkers. For our chosen algorithm (random forests), we show that the above problems are substantially reduced if a sufficient number of samples are analyzed and the data are not prefiltered. Our view is that LC‐MS proteomic biomarker discovery data should be analyzed without prefiltering and that increasing the sample size in biomarker discovery experiments should be a very high priority.     

LC‐MS/MS‐based serum proteomics for identification of candidate biomarkers for hepatocellular carcinoma

Associating changes in protein levels with the onset of cancer has been widely investigated to identify clinically relevant diagnostic biomarkers. In the present study, we analyzed sera from 205 patients recruited in the United States and Egypt for biomarker discovery using label‐free proteomic analysis by LC‐MS/MS. We performed untargeted proteomic analysis of sera to identify candidate proteins with statistically significant differences between hepatocellular carcinoma (HCC) and patients with liver cirrhosis. We further evaluated the significance of 101 proteins in sera from the same 205 patients through targeted quantitation by MRM on a triple quadrupole mass spectrometer. This led to the identification of 21 candidate protein biomarkers that were significantly altered in both the United States and Egyptian cohorts. Among the 21 candidates, ten were previously reported as HCC‐associated proteins (eight exhibiting consistent trends with our observation), whereas 11 are new candidates discovered by this study. Pathway analysis based on the significant proteins reveals upregulation of the complement and coagulation cascades pathway and downregulation of the antigen processing and presentation pathway in HCC cases versus patients with liver cirrhosis. The results of this study demonstrate the power of combining untargeted and targeted quantitation methods for a comprehensive serum proteomic analysis, to evaluate changes in protein levels and discover novel diagnostic biomarkers. All MS data have been deposited in the ProteomeXchange with identifier PXD001171 ( http://proteomecentral.proteomexchange.org/dataset/PXD001171 ).     

Optimizing Size Exclusion Chromatography for Extracellular Vesicle Enrichment and Proteomic Analysis from Clinically Relevant Samples

The field of extracellular vesicle (EV) research has rapidly expanded in recent years, with particular interest in their potential as circulating biomarkers. Proteomic analysis of EVs from clinical samples is complicated by the low abundance of EV proteins relative to highly abundant circulating proteins such as albumin and apolipoproteins. To overcome this, size exclusion chromatography (SEC) has been proposed as a method to enrich EVs whilst depleting protein contaminants; however, the optimal SEC parameters for EV proteomics have not been thoroughly investigated. Here, quantitative evaluation and optimization of SEC are reported for separating EVs from contaminating proteins. Using a synthetic model system followed by cell line‐derived EVs, it is found that a 10 mL Sepharose 4B column in PBS produces optimal resolution of EVs from background protein. By spiking‐in cancer cell‐derived EVs to healthy plasma, it is shown that some cancer EV‐associated proteins are detectable by nano‐LC‐MS/MS when as little as 1% of the total plasma EV number are derived from a cancer cell line. These results suggest that an optimized SEC and nanoLC‐MS/MS workflow may be sufficiently sensitive for disease EV protein biomarker discovery from patient‐derived clinical samples.     

Biomarker discovery for kidney diseases by mass spectrometry

By the development of soft ionization such as matrix-assisted laser desorption/ionization (MALDI) and electrospray ionization (ESI), mass spectrometry (MS) has become an indispensable technique to analyze proteins. The combination of protein separation and identification such as two-dimensional gel electrophoresis and MS, surface-enhanced laser desorption/ionization-MS, liquid chromatography/MS, and capillary electrophoresis/MS has been successfully applied for proteome analysis of urine and plasma to discover biomarkers of kidney diseases. Some urinary proteins and their proteolytic fragments have been identified as biomarker candidates for kidney diseases. This article reviews recent advances in the application of proteomics using MS to discover biomarkers for kidney diseases.     

Epitope mapping and targeted quantitation of the cardiac biomarker troponin by SID‐MRM mass spectrometry

The absolute quantitation of the targeted protein using MS provides a promising method to evaluate/verify biomarkers used in clinical diagnostics. In this study, a cardiac biomarker, troponin I (TnI), was used as a model protein for method development. The epitope peptide of TnI was characterized by epitope excision followed with LC/MS/MS method and acted as the surrogate peptide for the targeted protein quantitation. The MRM‐based MS assay using a stable internal standard that improved the selectivity, specificity, and sensitivity of the protein quantitation. Also, plasma albumin depletion and affinity enrichment of TnI by anti‐TnI mAb‐coated microparticles reduced the sample complexity, enhanced the dynamic range, and further improved the detecting sensitivity of the targeted protein in the biological matrix. Therefore, quantitation of TnI, a low abundant protein in human plasma, has demonstrated the applicability of the targeted protein quantitation strategy through its epitope peptide determined by epitope mapping method.     

Multiplexed LC‐MS/MS analysis of horse plasma proteins to study doping in sport

The development of protein biomarkers for the indirect detection of doping in horse is a potential solution to doping threats such as gene and protein doping. A method for biomarker candidate discovery in horse plasma is presented using targeted analysis of proteotypic peptides from horse proteins. These peptides were first identified in a novel list of the abundant proteins in horse plasma. To monitor these peptides, an LC‐MS/MS method using multiple reaction monitoring was developed to study the quantity of 49 proteins in horse plasma in a single run. The method was optimised and validated, and then applied to a population of race‐horses to study protein variance within a population. The method was finally applied to longitudinal time courses of horse plasma collected after administration of an anabolic steroid to demonstrate utility for hypothesis‐driven discovery of doping biomarker candidates.     

Molecular classification of liver cirrhosis in a rat model by proteomics and bioinformatics

Liver cirrhosis is a worldwide health problem. Reliable, noninvasive methods for early detection of liver cirrhosis are not available. Using a three-step approach, we classified sera from rats with liver cirrhosis following different treatment insults. The approach consisted of: (i) protein profiling using surface-enhanced laser desorption/ionization (SELDI) technology; (ii) selection of a statistically significant serum biomarker set using machine learning algorithms; and (iii) identification of selected serum biomarkers by peptide sequencing. We generated serum protein profiles from three groups of rats: (i) normal (n=8), (ii) thioacetamide-induced liver cirrhosis (n=22), and (iii) bile duct ligation-induced liver fibrosis (n=5) using a weak cation exchanger surface. Profiling data were further analyzed by a recursive support vector machine algorithm to select a panel of statistically significant biomarkers for class prediction. Sensitivity and specificity of classification using the selected protein marker set were higher than 92%. A consistently down-regulated 3495 Da protein in cirrhosis samples was one of the selected significant biomarkers. This 3495 Da protein was purified on-chip and trypsin digested. Further structural characterization of this biomarkers candidate was done by using cross-platform matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) peptide mass fingerprinting (PMF) and matrix-assisted laser desorption/ionization time of flight/time of flight (MALDI-TOF/TOF) tandem mass spectrometry (MS/MS). Combined data from PMF and MS/MS spectra of two tryptic peptides suggested that this 3495 Da protein shared homology to a histidine-rich glycoprotein. These results demonstrated a novel approach to discovery of new biomarkers for early detection of liver cirrhosis and classification of liver diseases.     

Pareto Optimization Identifies Diverse Set of Phosphorylation Signatures Predicting Response to Treatment with Dasatinib

Multivariate biomarkers that can predict the effectiveness of targeted therapy in individual patients are highly desired. Previous biomarker discovery studies have largely focused on the identification of single biomarker signatures, aimed at maximizing prediction accuracy. Here, we present a different approach that identifies multiple biomarkers by simultaneously optimizing their predictive power, number of features, and proximity to the drug target in a protein-protein interaction network. To this end, we incorporated NSGA-II, a fast and elitist multi-objective optimization algorithm that is based on the principle of Pareto optimality, into the biomarker discovery workflow. The method was applied to quantitative phosphoproteome data of 19 non-small cell lung cancer (NSCLC) cell lines from a previous biomarker study. The algorithm successfully identified a total of 77 candidate biomarker signatures predicting response to treatment with dasatinib. Through filtering and similarity clustering, this set was trimmed to four final biomarker signatures, which then were validated on an independent set of breast cancer cell lines. All four candidates reached the same good prediction accuracy (83%) as the originally published biomarker. Although the newly discovered signatures were diverse in their composition and in their size, the central protein of the originally published signature — integrin β4 (ITGB4) — was also present in all four Pareto signatures, confirming its pivotal role in predicting dasatinib response in NSCLC cell lines. In summary, the method presented here allows for a robust and simultaneous identification of multiple multivariate biomarkers that are optimized for prediction performance, size, and relevance.     

Quantitative proteomics suggests decrease in the secretogranin‐1 cerebrospinal fluid levels during the disease course of multiple sclerosis

Multiple sclerosis (MS) is a chronic inflammatory disease of the CNS with unknown cause. Proteins with different abundance in the cerebrospinal fluid (CSF) from relapsing‐remitting MS (RRMS) patients and neurological controls could give novel insight to the MS pathogenesis and be used to improve diagnosis, predict prognosis and disease course, and guide in therapy decisions. We combined iTRAQ labeling and Orbitrap mass spectrometry to discover proteins with different CSF abundance between six RRMS patients and 18 neurological disease controls. From 777 quantified proteins seven were selected as biomarker candidates, namely chitinase‐3‐like protein 1, secretogranin‐1 (Sg1), cerebellin‐1, neuroserpin, cell surface glycoprotein MUC18, testican‐2 and glutamate receptor 4. An independent sample set of 13 early‐MS patients, 13 RRMS patients and 13 neurological controls was used in a multiple reaction monitoring verification study. We found the intracellular calcium binding protein Sg1 to be increased in early‐MS patients compared to RRMS and neurological controls. Sg1 should be included in further studies to elucidate its role in the early phases of MS pathogenesis and its potential as a biomarker for this disease.     

Proteome profiling reveals gender differences in the composition of human serum

Proteome analysis using human serum is a technological advancement that will enable the discovery of novel biomarkers and biomarker patterns of various human diseases. Although proteome analysis using serum has potential in disease prevention, early diagnosis and treatment of diseases, and evaluation of pharmacotherapies, this technology is still in its infancy. Thus, we sought to develop an advanced method of conducting proteome analysis on human serum. In this study, we report the development of the semi‐comprehensive protein analytical technique, which involves the systematic use of iTRAQ labeling, HPLC, nano‐LC and MS. We compared the composition of the serum proteome in males and females using this technique and detected gender‐based differences in serum protein composition. This technology will enable the generation of databases that may ultimately lead to the discovery of specific biomarkers or biomarker patterns of various diseases.     

Sampling and analytical strategies for biomarker discovery using mass spectrometry

There is an often unspoken truth behind the course of scientific investigation that involves not what is necessarily academically worthy of study, but rather what is scientifically worthy in the eyes of funding agencies. The perception of worthy research is, as cost is driven in the simplest sense in economics, often driven by demand. Presently, the demand for novel diagnostic and therapeutic protein biomarkers that possess high sensitivity and specificity is placing major impact on the field of proteomics. The focal discovery technology that is being relied on is mass spectrometry (MS), whereas the challenge of biomarker discovery often lies not in the application of MS but in the underlying proteome sampling and bioinformatic processing strategies. Although biomarker discovery research has been historically technology-driven, it is clear from the meager success in generating validated biomarkers that increasing attention must be placed at the pre-analytic stage, such as sample retrieval and preparation. As diseases vary, so do the combinations of sampling and sample analyses necessary to discover novel biomarkers. In this review, we highlight different strategies used toward biomarker discovery and discuss them in terms of their reliance on technology and methodology.     

Biomarker Studies in Multiple Sclerosis: From Proteins to Noncoding RNAs

Multiple sclerosis (MS) is a neuroimmunological disorder characterized by central nervous system demyelination, axonal injury and loss. Considering the complexity of its aetiopathogenesis, early diagnosis of MS and individualized management are challenging in clinical practice. As the pathophysiologic and pharmacological indicators, studies on biomarkers in MS are useful for early prediction and diagnosis, monitoring of disease activity and predicting treatment response. In this review, we will summarize recent development of biomarker studies in MS from protein molecules to noncoding RNAs.     

Rapid discovery and identification of a tissue-specific tumor biomarker from 39 human cancer cell lines using the SELDI ProteinChip platform

Useful biomarkers are needed for early detection of cancers. To demonstrate the potential diagnostic usefulness of a new proteomic technology, we performed Expression Difference Mapping analysis on 39 cancer cell lines from 9 different tissues using ProteinChip technology. A protein biomarker candidate of 12kDa was found in colon cancer cells. We then optimized the purification conditions for this biomarker by utilizing Retentate Chromatography mass spectrometry (RC-MS). The optimized purification conditions developed "on-chip" were directly transferred to conventional chromatography to purify the biomarker, which was identified as prothymosin-alpha by ProteinChip time-of-flight mass spectrometry (TOF MS) and ProteinChip-Tandem MS systems. The relative expression level of prothymosin-alpha between colon cancer cells and normal colon mucosal cells was evaluated on the same ProteinChip platform. Prothymosin-alpha expression in colon cancer cells was clearly higher than in normal colon cells. These results indicate that prothymosin-alpha could be a potential biomarker for colon cancer, and that the ProteinChip platform could perform the whole process of biomarker discovery from screening to evaluation of the identified marker.