The nucleotide-independent Fe(III)-binding site is located on beta subunit of the mitochondrial F(1)-ATPase

The need for methods to identify disease biomarkers is underscored by the survival-rate of patients diagnosed at early stages of cancer progression. Surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) is a novel approach to biomarker discovery that combines two powerful techniques: chromatography and mass spectrometry. One of the key features of SELDI-TOF MS is its ability to provide a rapid protein expression profile from a variety of biological and clinical samples. It has been used for biomarker identification as well as the study of protein-protein, and protein-DNA interaction. The versatility of SELDI-TOF MS has allowed its use in projects ranging from the identification of potential diagnostic markers for prostate, bladder, breast, and ovarian cancers and Alzheimer's disease, to the study of biomolecular interactions and the characterization of posttranslational modifications. In this minireview we discuss the application of SELDI-TOF MS to protein biomarker discovery and profiling.     

Low abundance protein enrichment for discovery of candidate plasma protein biomarkers for early detection of breast cancer

Molecular biomarkers of early stage breast cancer may improve the sensitivity and specificity of diagnosis. Plasma biomarkers have additional value in that they can be monitored with minimal invasiveness. Plasma biomarker discovery by genome-wide proteomic methods is impeded by the wide dynamic range of protein abundance and the heterogeneity of protein expression in healthy and disease populations which requires the analysis of a large number of samples. We addressed these issues through the development of a novel protocol that couples a combinatorial peptide ligand library protein enrichment strategy with isobaric label-based 2D LC-MS/MS for the identification of candidate biomarkers in high throughput. Plasma was collected from patients with stage I breast cancer or benign breast lesions. Low abundance proteins were enriched using a bead-based combinatorial library of hexapeptides. This resulted in the identification of 397 proteins, 22% of which are novel plasma proteins. Twenty-three differentially expressed plasma proteins were identified, demonstrating the effectiveness of the described protocol and defining a set of candidate biomarkers to be validated in independent samples. This work can be used as the basis for the design of properly powered investigations of plasma protein expression for biomarker discovery in larger cohorts of patients with complex disease.     

Lectin capture strategies combined with mass spectrometry for the discovery of serum glycoprotein biomarkers

The application of mass spectrometry to identify disease biomarkers in clinical fluids like serum using high throughput protein expression profiling continues to evolve as technology development, clinical study design, and bioinformatics improve. Previous protein expression profiling studies have offered needed insight into issues of technical reproducibility, instrument calibration, sample preparation, study design, and supervised bioinformatic data analysis. In this overview, new strategies to increase the utility of protein expression profiling for clinical biomarker assay development are discussed with an emphasis on utilizing differential lectin-based glycoprotein capture and targeted immunoassays. The carbohydrate binding specificities of different lectins offer a biological affinity approach that complements existing mass spectrometer capabilities and retains automated throughput options. Specific examples using serum samples from prostate cancer and hepatocellular carcinoma subjects are provided along with suggested experimental strategies for integration of lectin-based methods into clinical fluid expression profiling strategies. Our example workflow incorporates the necessity of early validation in biomarker discovery using an immunoaffinity-based targeted analytical approach that integrates well with upstream discovery technologies.     

Intact protein profiling in breast cancer biomarker discovery: Protein identification issue and the solutions based on 3D protein separation,bottom‐up and top‐down mass spectrometry

Proteomics profiling of intact proteins based on MALDI‐TOF MS and derived platforms has been used in cancer biomarker discovery studies. This approach suffers from a number of limitations such as low resolution, low sensitivity, and that no knowledge is available on the identity of the respective proteins in the discovery mode. Nevertheless, it remains the most high‐throughput, untargeted mode of clinical proteomics studies to date. Here we compare key protein separation and MS techniques available for protein biomarker identification in this type of studies and define reasons of uncertainty in protein peak identity. As a result of critical data analysis, we consider 3D protein separation and identification workflows as optimal procedures. Subsequently, we present a new protocol based on 3D LC‐MS/MS with top‐down at high resolution that enabled the identification of HNRNP A2/B1 intact peptide as correlating with the estrogen receptor expression in breast cancer tissues. Additional development of this general concept toward next generation, top‐down based protein profiling at high resolution is discussed.     

Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data

MOTIVATION: Novel methods, both molecular and statistical, are urgently needed to take advantage of recent advances in biotechnology and the human genome project for disease diagnosis and prognosis. Mass spectrometry (MS) holds great promise for biomarker identification and genome-wide protein profiling. It has been demonstrated in the literature that biomarkers can be identified to distinguish normal individuals from cancer patients using MS data. Such progress is especially exciting for the detection of early-stage ovarian cancer patients. Although various statistical methods have been utilized to identify biomarkers from MS data, there has been no systematic comparison among these approaches in their relative ability to analyze MS data. RESULTS: We compare the performance of several classes of statistical methods for the classification of cancer based on MS spectra. These methods include: linear discriminant analysis, quadratic discriminant analysis, k-nearest neighbor classifier, bagging and boosting classification trees, support vector machine, and random forest (RF). The methods are applied to ovarian cancer and control serum samples from the National Ovarian Cancer Early Detection Program clinic at Northwestern University Hospital. We found that RF outperforms other methods in the analysis of MS data.     

Optimal selection of epitopes for TXP-immunoaffinity mass spectrometry

Background  

Mass spectrometry (MS) based protein profiling has become one of the key technologies in biomedical research and biomarker discovery. One bottleneck in MS-based protein analysis is sample preparation and an efficient fractionation step to reduce the complexity of the biological samples, which are too complex to be analyzed directly with MS. Sample preparation strategies that reduce the complexity of tryptic digests by using immunoaffinity based methods have shown to lead to a substantial increase in throughput and sensitivity in the proteomic mass spectrometry approach. The limitation of using such immunoaffinity-based approaches is the availability of the appropriate peptide specific capture antibodies. Recent developments in these approaches, where subsets of peptides with short identical terminal sequences can be enriched using antibodies directed against short terminal epitopes, promise a significant gain in efficiency.     

Monitoring mouse prostate development by profiling and imaging mass spectrometry

Mass spectrometry-based tissue profiling and imaging are technologies that allow identification and visualization of protein signals directly on thin sections cut from fresh frozen tissue specimens. These technologies were utilized to evaluate protein expression profiles in the normal mouse prostate during development (1-5 weeks of age), at sexual maturation (6 weeks of age), and in adult prostate (at 10, 15, or 40 weeks of age). The evolution of protein expression during normal prostate development and maturation were subsequently compared with 15-week prostate tumors derived from genetically engineered mice carrying the Large T antigen gene under regulation of the prostate-specific probasin promoter (LPB-Tag mouse model for prostate cancer). This approach identified proteins differentially expressed at specific time points during prostate development. Furthermore expression of some of these proteins, for example probasin and spermine-binding protein, were associated with prostate maturation, and prostate tumor formation resulted in their loss of expression. Cyclophilin A, a protein found in other cancers, was differentially alpha-acetylated on the N terminus, and both isoforms appeared during normal prostate and prostate tumor development. Imaging mass spectrometry localized the protein signals to specific prostatic lobes or regions. Thus, tissue profiling and imaging can be utilized to analyze the ontogeny of protein expression during prostate morphogenesis and tumorigenesis and identify proteins that could potentially serve as biomarkers for prostate cancer.     

Exploring metabolic pathway disruption in the subchronic phencyclidine model of schizophrenia with the Generalized Singular Value Decomposition

Background

One of the major goals in gene and protein expression profiling of cancer is to identify biomarkers and build classification models for prediction of disease prognosis or treatment response. Many traditional statistical methods, based on microarray gene expression data alone and individual genes' discriminatory power, often fail to identify biologically meaningful biomarkers thus resulting in poor prediction performance across data sets. Nonetheless, the variables in multivariable classifiers should synergistically interact to produce more effective classifiers than individual biomarkers.

Results

We developed an integrated approach, namely network-constrained support vector machine (netSVM), for cancer biomarker identification with an improved prediction performance. The netSVM approach is specifically designed for network biomarker identification by integrating gene expression data and protein-protein interaction data. We first evaluated the effectiveness of netSVM using simulation studies, demonstrating its improved performance over state-of-the-art network-based methods and gene-based methods for network biomarker identification. We then applied the netSVM approach to two breast cancer data sets to identify prognostic signatures for prediction of breast cancer metastasis. The experimental results show that: (1) network biomarkers identified by netSVM are highly enriched in biological pathways associated with cancer progression; (2) prediction performance is much improved when tested across different data sets. Specifically, many genes related to apoptosis, cell cycle, and cell proliferation, which are hallmark signatures of breast cancer metastasis, were identified by the netSVM approach. More importantly, several novel hub genes, biologically important with many interactions in PPI network but often showing little change in expression as compared with their downstream genes, were also identified as network biomarkers; the genes were enriched in signaling pathways such as TGF-beta signaling pathway, MAPK signaling pathway, and JAK-STAT signaling pathway. These signaling pathways may provide new insight to the underlying mechanism of breast cancer metastasis.

Conclusions

We have developed a network-based approach for cancer biomarker identification, netSVM, resulting in an improved prediction performance with network biomarkers. We have applied the netSVM approach to breast cancer gene expression data to predict metastasis in patients. Network biomarkers identified by netSVM reveal potential signaling pathways associated with breast cancer metastasis, and help improve the prediction performance across independent data sets.     

A Network Biology Approach Identifies Molecular Cross-Talk between Normal Prostate Epithelial and Prostate Carcinoma Cells

The advent of functional genomics has enabled the genome-wide characterization of the molecular state of cells and tissues, virtually at every level of biological organization. The difficulty in organizing and mining this unprecedented amount of information has stimulated the development of computational methods designed to infer the underlying structure of regulatory networks from observational data. These important developments had a profound impact in biological sciences since they triggered the development of a novel data-driven investigative approach. In cancer research, this strategy has been particularly successful. It has contributed to the identification of novel biomarkers, to a better characterization of disease heterogeneity and to a more in depth understanding of cancer pathophysiology. However, so far these approaches have not explicitly addressed the challenge of identifying networks representing the interaction of different cell types in a complex tissue. Since these interactions represent an essential part of the biology of both diseased and healthy tissues, it is of paramount importance that this challenge is addressed. Here we report the definition of a network reverse engineering strategy designed to infer directional signals linking adjacent cell types within a complex tissue. The application of this inference strategy to prostate cancer genome-wide expression profiling data validated the approach and revealed that normal epithelial cells exert an anti-tumour activity on prostate carcinoma cells. Moreover, by using a Bayesian hierarchical model integrating genetics and gene expression data and combining this with survival analysis, we show that the expression of putative cell communication genes related to focal adhesion and secretion is affected by epistatic gene copy number variation and it is predictive of patient survival. Ultimately, this study represents a generalizable approach to the challenge of deciphering cell communication networks in a wide spectrum of biological systems.     

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.     

Proteomic profiling of the cancer cell secretome: informing clinical research

Introduction: Cancer represents one of the major causes of human deaths. Identification of proteins as biomarkers for early detection of cancer and therapeutic targets for cancer treatment are important issues in precision medicine. Secretome of cancer cells represents the collection of proteins secreted or shed from cancer cells. Proteomic profiling of the cancer cell secretome has been proven to be a convenient and efficient way to discover cancer biomarker and/or therapeutic targets.

Areas covered: There have been numerous reviews describing the history and application of secretome analysis in cancer biomarker/therapeutic target research. The present review focuses on the technological advancement for profiling low-molecular-mass proteins in secretome, the latest information regarding the new candidate biomarkers and molecular mechanisms discovered on the basis of cancer cell secretome analysis, as well as the previously discovered candidate biomarkers that enter into clinical trials.

Expert commentary: Current technologies for protein sample preparation/separation and MS-based protein identification have allowed in-depth analysis of cancer cell secretome. Future efforts should focus on the comprehensiveness of cancer cell secretome, meta-analysis of different secretome datasets and integrated analysis via combining other omics datasets, as well as the incorporation of MS-based biomarker verification pipeline into both preclinical studies and clinical trials.     

Prevalidation of potential protein biomarkers in toxicology using iTRAQ reagent technology

Today, toxicoproteomics still relies mainly on 2-DE followed by MS for detection and identification of proteins, which might characterize a certain state of disease, indicate toxicity or even predict carcinogenicity. We utilized the classical 2-DE/MS approach for the evaluation of early protein biomarkers which are predictive for chemically induced hepatocarcinogenesis in rats. We were able to identify statistically significantly deregulated proteins in N-nitrosomorpholine exposed rat liver tissue. Based on literature data, biological relevance in the early molecular process of hepatocarcinogenicity could be suggested for most of these potential biomarkers. However, in order to ensure reliable results and to create the prerequisites necessary for integration in routine toxicology studies in the future, these protein expression patterns need to be prevalidated using independent technology platforms. In the current study, we evaluated the usefulness of iTRAQ reagent technology (Applied Biosystems, Framingham, USA), a recently introduced MS-based protein quantitation method, for verification of the 2-DE/MS biomarkers. In summary, the regulation of 26 2-DE/MS derived protein biomarkers could be verified. Proteins like HSP 90-beta, annexin A5, ketohexokinase, N-hydroxyarylamine sulfotransferase, ornithine aminotransferase, and adenosine kinase showed highly comparable fold changes using both proteomic quantitation strategies. In addition, iTRAQ analysis delivered further potential biomarkers with biological relevance to the processes of hepatocarcinogenicity: e.g. placental form of glutathione S-transferase (GST-P), carbonic anhydrase, and aflatoxin B1 aldehyde reductase. Our results show both the usefulness of iTRAQ reagent technology for biomarker prevalidation as well as for identification of further potential marker proteins, which are indicative for liver hepatocarcinogenicity.     

Discovery of urinary biomarkers

A myriad of proteins and peptides can be identified in normal human urine. These are derived from a variety of sources including glomerular filtration of blood plasma, cell sloughing, apoptosis, proteolytic cleavage of cell surface glycosylphosphatidylinositol-linked proteins, and secretion of exosomes by epithelial cells. Mass spectrometry-based approaches to urinary protein and peptide profiling can, in principle, reveal changes in excretion rates of specific proteins/peptides that can have predictive value in the clinical arena, e.g. in the early diagnosis of disease, in classification of disease with regard to likely therapeutic responses, in assessment of prognosis, and in monitoring response to therapy. These approaches have potential value, not only in diseases of the kidney and urinary tract but also in systemic diseases that are associated with circulating small protein and peptide markers that can pass the glomerular filter. Most large scale biomarker discovery studies reported thus far have used one of two approaches to identify proteins and peptides whose excretion in urine changes in specific disease states: 1) two-dimensional electrophoresis with mass spectrometric and/or immunochemical identification of proteins and 2) top-down mass spectrometric methods (SELDI-TOF-MS and capillary electrophoresis-MS). These studies have been chiefly in the areas of nephrology, urology, and oncology. We review these applications, focusing on two areas of progress, viz. in bladder cancer and in acute rejection of renal transplants. Progress has been limited so far. However, with the advent of powerful LC-MS/MS methods along with methods for quantifying LC-MS/MS output, there is hope for an accelerated discovery and validation of disease biomarkers in urine.     

MALDI mass spectrometry in prostate cancer biomarker discovery

Matrix-assisted laser desorption/ionisation (MALDI) mass spectrometry (MS) is a highly versatile and sensitive analytical technique, which is known for its soft ionisation of biomolecules such as peptides and proteins. Generally, MALDI MS analysis requires little sample preparation, and in some cases like MS profiling it can be automated through the use of robotic liquid-handling systems. For more than a decade now, MALDI MS has been extensively utilised in the search for biomarkers that could aid clinicians in diagnosis, prognosis, and treatment decision making. This review examines the various MALDI-based MS techniques like MS imaging, MS profiling and proteomics in-depth analysis where MALDI MS follows fractionation and separation methods such as gel electrophoresis, and how these have contributed to prostate cancer biomarker research. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.     

Advances in proteomic prostate cancer biomarker discovery

Prostate cancer is the most common non-cutaneous cancer in men in the United States. For reasons largely unknown, the incidence of prostate cancer has increased in the last two decades, in spite or perhaps because of a concomitant increase in serum prostate-specific antigen (PSA) screening. While PSA is acknowledged not to be an ideal biomarker for prostate cancer detection, it is however widely used by physicians due to lack of an alternative. Thus, the identification of a biomarker(s) that can complement or replace PSA represents a major goal for prostate cancer research. Screening complex biological specimens such as blood, urine, and tissue to identify protein biomarkers has become increasingly popular over the last decade thanks to advances in proteomic discovery methods. The completion of human genome sequence together with new development in mass spectrometry instrumentation and bioinformatics has been a major driving force in biomarker discovery research. Here we review the current state of proteomic applications as applied to various sample sources including blood, urine, tissue, and “secretome” for the purpose of prostate cancer biomarker discovery. Additionally, we review recent developments in validation of putative markers, efforts at systems biology approach, and current challenges of proteomics in biomarker discovery.     

Secretory protein enrichment and analysis: an optimized approach applied on cancer cell lines using 2D LC-MS/MS

Reliable methods for profiling secretory proteins are highly desirable for the identification of biomarkers of disease progression. Secreted proteins are often masked by high amounts of protein supplements in the culture medium. We have developed an efficient method for the enrichment and analysis of the secretome of different cancer cell lines, free of essential contaminants. The method is based on the optimization of cell incubation conditions in protein-free medium. Secreted proteins are concentrated and fractionated using a reversed-phase tC2 Sorbent, followed by peptide mass fingerprinting for protein identification. An average of 88 proteins were identified in each cancer cell line, of which more than 76% are known to be secreted, possess a signal peptide or a transmembrane domain. Given the importance of secreted proteins as a source for early detection and diagnosis of disease, this approach may help to discover novel candidate biomarkers with potential clinical significance.     

The discovery of protein biomarkers in pre-eclampsia: the promising role of mass spectrometry

Abstract

Context: Pre-eclampsia (PE) is a common hypertensive disorder of pregnancy that substantially affects maternal and neonatal morbidity and mortality worldwide. The aetiology of the disease remains poorly understood with lack of reliable diagnostic tests. PE is a multisystem disorder so it is very unlikely that a single or a small group of biomarkers will accurately predict the disease. Mass spectrometry (MS) is indispensable analytical tool in protein analysis studies. MS-based proteomics have the ability to detect the entire protein complement to provide a useful window into a range of biological processes and allow the identification of differentially expressed proteins between samples.

Objective: The aim of this review is to summarise, discuss and evaluate the current predominant MS-based approaches applied for protein biomarker discovery. The paper also seeks to evaluate the current potential PE biomarkers described in the literature and identify issues that can guide future research.

Conclusion: MS-based proteomics studies are promising alternatives to classical hypothesis-driven approaches to discover novel biomarkers and provide new insights into the underlying phathophysiological mechanisms of PE. This should aid in the early diagnosis of PE and the understanding of the aetiology of the disease.     

ellipsoidFN: a tool for identifying a heterogeneous set of cancer biomarkers based on gene expressions

Computationally identifying effective biomarkers for cancers from gene expression profiles is an important and challenging task. The challenge lies in the complicated pathogenesis of cancers that often involve the dysfunction of many genes and regulatory interactions. Thus, sophisticated classification model is in pressing need. In this study, we proposed an efficient approach, called ellipsoidFN (ellipsoid Feature Net), to model the disease complexity by ellipsoids and seek a set of heterogeneous biomarkers. Our approach achieves a non-linear classification scheme for the mixed samples by the ellipsoid concept, and at the same time uses a linear programming framework to efficiently select biomarkers from high-dimensional space. ellipsoidFN reduces the redundancy and improves the complementariness between the identified biomarkers, thus significantly enhancing the distinctiveness between cancers and normal samples, and even between cancer types. Numerical evaluation on real prostate cancer, breast cancer and leukemia gene expression datasets suggested that ellipsoidFN outperforms the state-of-the-art biomarker identification methods, and it can serve as a useful tool for cancer biomarker identification in the future. The Matlab code of ellipsoidFN is freely available from http://doc.aporc.org/wiki/EllipsoidFN.     

A computational platform for MALDI-TOF mass spectrometry data: Application to serum and plasma samples

BackgroundMass spectrometry (MS) is becoming the gold standard for biomarker discovery. Several MS-based bioinformatics methods have been proposed for this application, but the divergence of the findings by different research groups on the same MS data suggests that the definition of a reliable method has not been achieved yet. In this work, we propose an integrated software platform, MASCAP, intended for comparative biomarker detection from MALDI-TOF MS data.ResultsMASCAP integrates denoising and feature extraction algorithms, which have already shown to provide consistent peaks across mass spectra; furthermore, it relies on statistical analysis and graphical tools to compare the results between groups. The effectiveness in mass spectrum processing is demonstrated using MALDI-TOF data, as well as SELDI-TOF data. The usefulness in detecting potential protein biomarkers is shown comparing MALDI-TOF mass spectra collected from serum and plasma samples belonging to the same clinical population.ConclusionsThe analysis approach implemented in MASCAP may simplify biomarker detection, by assisting the recognition of proteomic expression signatures of the disease. A MATLAB implementation of the software and the data used for its validation are available at http://www.unich.it/proteomica/bioinf.