Use of SELDI-TOF mass spectrometry for identification of new biomarkers: potential and limitations

Biomarker Reproducibility Challenge: A Review of Non-Nucleotide Biomarker Discovery Protocols from Body Fluids in Breast Cancer Diagnosis

Breast cancer has now become the most commonly diagnosed cancer, accounting for one in eight cancer diagnoses worldwide. Non-invasive diagnostic biomarkers and associated tests are superlative candidates to complement or improve current approaches for screening, early diagnosis, or prognosis of breast cancer. Biomarkers detected from body fluids such as blood (serum/plasma), urine, saliva, nipple aspiration fluid, and tears can detect breast cancer at its early stages in a minimally invasive way. The advancements in high-throughput molecular profiling (omics) technologies have opened an unprecedented opportunity for unbiased biomarker detection. However, the irreproducibility of biomarkers and discrepancies of reported markers have remained a major roadblock to clinical implementation, demanding the investigation of contributing factors and the development of standardised biomarker discovery pipelines. A typical biomarker discovery workflow includes pre-analytical, analytical, and post-analytical phases, from sample collection to model development. Variations introduced during these steps impact the data quality and the reproducibility of the findings. Here, we present a comprehensive review of methodological variations in biomarker discovery studies in breast cancer, with a focus on non-nucleotide biomarkers (i.e., proteins, lipids, and metabolites), highlighting the pre-analytical to post-analytical variables, which may affect the accurate identification of biomarkers from body fluids.

Identification and Characterization of mRNA Biomarkers for Sodium Cyanide Exposure

Biomarkers in exposure assessment are defined as the quantifiable targets that indicate the exposure to hazardous chemicals and their resulting health effect. In this study, we aimed to identify, validate, and characterize the mRNA biomarker that can detect the exposure of sodium cyanide. To identify reliable biomarkers for sodium cyanide exposure, critical criteria were defined for candidate selection: (1) the expression level of mRNA significantly changes in response to sodium thiocyanate treatment in transcriptomics results (fold change > 2.0 or <0.50, adjusted p-value < 0.05); and (2) the mRNA level is significantly modulated by sodium cyanide exposure in both normal human lung cells and rat lung tissue. We identified the following mRNA biomarker candidates: ADCY5, ANGPTL4, CCNG2, CD9, COL1A2, DACT3, GGCX, GRB14, H1F0, HSPA1A, MAF, MAT2A, PPP1R10, and PPP4C. The expression levels of these candidates were commonly downregulated by sodium cyanide exposure both in vitro and in vivo. We functionally characterized the biomarkers and established the impact of sodium cyanide on transcriptomic profiles using in silico approaches. Our results suggest that the biomarkers may contribute to the regulation and degradation of the extracellular matrix, leading to a negative effect on surrounding lung cells.

Proteomics for the Identification of Biomarkers in Testicular Cancer-Review

A large number of biomarkers have been proposed for the diagnosis of testicular cancer, representing putative molecular targets for anticancer treatments. However, no conclusive data have been provided. Proteomics represents a research field recently developed. It evaluates the large-scale analysis of the full protein components of a single cell, of a specific tissue, or of biological fluids. In the last decades, proteomics has been applied in clinical fields, thanks to modern technology and new bioinformatic tools, to identify novel molecular markers of diseases. The aim of this review is to argue the findings of recent studies in the discoveries of putative prognostic and diagnostic markers of testis cancer by proteomic techniques. We present here a panel of proteins identified by proteomics which might be used after validation for early detection and the prognostic evaluation of testicular tumors. In addition, the molecular mechanisms revealed by these proteomic studies might also guide the development of novel treatments in future.

Optimizing High-Resolution Mass Spectrometry for the Identification of Low-Abundance Post-Translational Modifications of Intact Proteins

Intact protein analysis by liquid chromatography-mass spectrometry (LC-MS) is now possible due to the improved capabilities of mass spectrometers yielding greater resolution, mass accuracy, and extended mass ranges. Concurrent measurement of post-translational modifications (PTMs) during LC-MS of intact proteins is advantageous while monitoring critical proteoform status, such as for clinical samples or during production of reference materials. However, difficulties exist for PTM identification when the protein is large or contains multiple modification sites. In this work, analyses of low-abundance proteoforms of proteins of clinical or therapeutic interest, including C-reactive protein, vitamin D-binding protein, transferrin, and immunoglobulin G (NISTmAb), were performed on an Orbitrap Elite mass spectrometer. This work investigated the effect of various instrument parameters including source temperatures, in-source CID, microscan type and quantity, resolution, and automatic gain control on spectral quality. The signal-to-noise ratio was found to be a suitable spectral attribute which facilitated identification of low abundance PTMs. Source temperature and CID voltage were found to require specific optimization for each protein. This study identifies key instrumental parameters requiring optimization for improved detection of a variety of PTMs by LC-MS and establishes a methodological framework to ensure robust proteoform identifications, the first step in their ultimate quantification.

Prognostic prediction by liver tissue proteomic profiling in patients with colorectal liver metastases

AIM

To obtain proteomic profiles in patients with colorectal liver metastases (CRLM) and identify the relationship between profiles and the prognosis of CRLM patients.

MATERIALS & METHODS

Prognosis prediction (favorable or unfavorable according to Fong's score) by a classification and regression tree algorithm of surface-enhanced laser desorption/ionization TOF-MS proteomic profiles from cryopreserved CRLM (patients) and normal liver tissue (controls).

RESULTS

The protein peak 7371 m/z showed the clearest differences between CRLM and control groups (94.1% sensitivity, 100% specificity, p < 0.001). The algorithm that best differentiated favorable and unfavorable groups combined 2970 and 2871 m/z protein peaks (100% sensitivity, 90% specificity).

CONCLUSION

Proteomic profiling in liver samples using classification and regression tree algorithms is a promising technique to differentiate healthy subjects from CRLM patients and to classify the severity of CRLM patients.

Proteomic profiling of mitochondria: what does it tell us about the ageing brain?

Mitochondrial dysfunction is evident in numerous neurodegenerative and age-related disorders. It has also been linked to cellular ageing, however our current understanding of the mitochondrial changes that occur are unclear. Functional studies have made some progress reporting reduced respiration, dynamic structural modifications and loss of membrane potential, though there are conflicts within these findings. Proteomic analyses, together with functional studies, are required in order to profile the mitochondrial changes that occur with age and can contribute to unravelling the complexity of the ageing phenotype. The emergence of improved protein separation techniques, combined with mass spectrometry analyses has allowed the identification of age and cell-type specific mitochondrial changes in energy metabolism, antioxidants, fusion and fission machinery, chaperones, membrane proteins and biosynthesis pathways. Here, we identify and review recent data from the analyses of mitochondria from rodent brains. It is expected that knowledge gained from understanding age-related mitochondrial changes of the brain should lead to improved biomarkers of normal ageing and also age-related disease progression.

Proteomics technology for evaluating preventive and therapeutic effects of acupuncture and moxibustionon in gastric ulcer

Proteomics is a discipline that aims to clarify the mechanism of life at the protein level. In recent years, the application of proteomics technology in studying gastric ulcer and evaluating the preventive and therapetuic effects of acupuncture and moxibustion has attracted great attention. This article reviews recent advances in the study of the mechanism of acupuncture and moxibustion in the prevention and treatment of gastric ulcer from a perspective of proteomics, and discusses the problems encountered in related studies. We believe that proteomics technology will play an important role in further study of the mechanism and regulatory effects of acupuncture and moxibustion in gastric ulcer.

Diagnosis of infection in critical care

Sepsis is the primary cause of death in the intensive care unit. The prevention of sepsis complications requires an early and accurate diagnosis as well as the appropriate mon itoring. A deep knowledge of the immunologic basis of sepsis is essential to better understand the scope of incorporating a new marker into clinical practice. Besides revising this theoretical aspect, the current available tools for bacterial iden tification have been briefly reviewed as well as a variety of new markers showing either well-recognized or potential usefulness for diagnosis and prognosis of infections in crit ically ill patients. Particular conditions such as community acquired pneumonia, pedi atric sepsis, or liver transplantation, among others, have been separately treated, since the optimal approaches and markers might be different in these special cases.

Delineating the synovial fluid proteome: recent advancements and ongoing challenges in biomarker research

There is an urgent need for identifying novel serum biomarkers that can be used to improve diagnosis, predict disease progression or response to therapy, or serve as therapeutic targets for rheumatic diseases. Synovial fluid (SF) is secreted by and remains in direct contact with the synovial membrane, and can reflect the biochemical state of the joint under different physiological and pathological conditions. Therefore, SF is regarded as an excellent source for identifying biomarkers of rheumatologic diseases. The use of high-throughput and/or quantitative proteomics and sophisticated computational software applied to analyze the protein content of SF has been well-adopted as an approach to finding novel arthritis biomarkers. This review will focus on some of the potential pitfalls of biomarker studies using SF, summarize the status of the field of SF proteomics in general, as well as discuss some of the most promising biomarker study approaches using proteomics. A brief status of the biomarker discovery efforts in rheumatoid arthritis, osteoarthritis and juvenile idiopathic arthritis is also provided.

The value of surface enhanced laser desorption/ionization-time of flight mass spectrometry at the diagnosis of non-small cell lung cancer: a systematic review

Numbers of studies used surface enhanced laser desorption/ionization-time of flight mass spectrometry (SELDI-TOF-MS) to find novel serum biomarkers for non-small cell lung cancer (NSCLC). It is arguable whether the SELDI technique has its value of diagnostic accuracy for NSCLC. The purpose of our study is to determine the diagnostic accuracy of SELDI-TOF-MS for diagnosing NSCLC. The Cochrane Central Register of Controlled Trials, MEDLINE, Pub Med, EMBASE, the Chinese Biomedical Literature Database, the China Academic Journals Full-text Database, and the Chinese Scientific Journals Database were searched systematically for potential studies. Reference lists of included studies and review articles were also reviewed. All studies that reported data on patients with a confirmed diagnosis of NSCLC and that compared the measurement of SELDI-TOF-MS with pathology standard were considered for inclusion. 11 studies were included in the systematic review. The ranges of the diagnostic value of SELDI-TOF-MS for NSCLC were as follows: sensitivity (SEN) was 0.70~1.00; specificity (SPE) was 0.68~1.00; positive likelihood ratio (PLR) was 2.23~23.14; negative likelihood ratio (NLR) was 0.04~0.43; and diagnostic odds ratio (DOR) was 5.17~621.0, respectively. SELDI-TOF-MS showed high accuracy in identifying NSCLC, and could be a potential screening tool for diagnosing NSCLC patients.

Biomarkers for early detection of ovarian cancer

Ovarian cancer is the most lethal gynecological malignancy. However, effective screening strategies have not been established and continue to be elusive. A good screening test must adequately address validity, reliability, yield, cost, acceptance and follow-up services. An ideal screening test for ovarian cancer must have a high sensitivity in order to correctly diagnose all women with the disease and a high specificity to avoid false-positive results. The current screening modalities of bimanual examination, CA-125 and transvaginal ultrasonography together allow us to detect only 30-45% of women with early-stage disease. Recent developments in proteomic and genomic research have identified a number of potential biomarkers. Although panels of tumor markers and proteomic-based technologies may improve the positive predictive value, all markers require validation and interfacing with newly developed diagnostic imaging technologies. While a large amount of information on miRNAs has been promising, much remains to be elucidated. This review will examine the current status of biomarkers and technologies of interest in the effort of early detection of ovarian cancer.

Potential role of proteomics in the diagnosis of lymphoma: a meta-analysis

Surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF-MS) has been approved for identifying biomarkers and diagnosing many diseases such as lymphomas. It is arguable whether the SELDI technique has its value of diagnostic accuracy for lymphomas. The purpose of our study is to determine the diagnostic accuracy of SELDI-TOF-MS for diagnosing lymphomas. The Cochrane Central Register of Controlled Trials, MEDLINE, Pub Med, EMBASE, the Chinese Biomedical Literature Database, the China Academic Journals Full-text Database, and the Chinese Scientific Journals Database were searched systematically for potential studies. Reference lists of included studies and review articles were also reviewed. All studies that reported data on patients with a confirmed diagnosis of lymphomas and that compared the measurement of SELDI-TOF-MS with pathology standard were considered for inclusion. Eleven studies were included in the systematic review. The ranges of the diagnostic value of SELDI-TOF-MS for lymphoma were as follows: sensitivity (SEN) was 0.69-0.96; specificity (SPE) was 0.70-1.00; positive likelihood ratio (PLR) was 2.99-96.09; negative likelihood ratio (NLR) was 0.04-0.35; and diagnostic odds ratio (DOR) was 18.13-1250.71, respectively. Further, we analysed serum samples as a subgroup, and the pooled endpoints were as follows: pooled SEN was 0.89 (0.85-0.91); pooled SPE was 0.91 (0.88-0.93); pooled PLR was 12.35 (5.36-28.44); pooled NLR was 0.13 (0.09-0.20); and pooled DOR was 101.04 (39.57-258.04), respectively. SELDI-TOF-MS showed high accuracy in identifying lymphoma and could be a useful screening tool for diagnosing lymphoma patients.

Better cancer biomarker discovery through better study design

BACKGROUND

High-throughput laboratory technologies coupled with sophisticated bioinformatics algorithms have tremendous potential for discovering novel biomarkers, or profiles of biomarkers, that could serve as predictors of disease risk, response to treatment or prognosis. We discuss methodological issues in wedding high-throughput approaches for biomarker discovery with the case-control study designs typically used in biomarker discovery studies, especially focusing on nested case-control designs.

METHODS

We review principles for nested case-control study design in relation to biomarker discovery studies and describe how the efficiency of biomarker discovery can be effected by study design choices. We develop a simulated prostate cancer cohort data set and a series of biomarker discovery case-control studies nested within the cohort to illustrate how study design choices can influence biomarker discovery process.

RESULT

Common elements of nested case-control design, incidence density sampling and matching of controls to cases are not typically factored correctly into biomarker discovery analyses, inducing bias in the discovery process. We illustrate how incidence density sampling and matching of controls to cases reduce the apparent specificity of truly valid biomarkers 'discovered' in a nested case-control study. We also propose and demonstrate a new case-control matching protocol, we call 'antimatching', that improves the efficiency of biomarker discovery studies.

CONCLUSIONS

For a valid, but as yet undiscovered, biomarker(s) disjunctions between correctly designed epidemiologic studies and the practice of biomarker discovery reduce the likelihood that true biomarker(s) will be discovered and increases the false-positive discovery rate.

Comparative mitochondrial proteomics: perspective in human diseases

Mitochondria are the most complex and the most important organelles of eukaryotic cells, which are involved in many cellular processes, including energy metabolism, apoptosis, and aging. And mitochondria have been identified as the "hot spot" by researchers for exploring relevant associated dysfunctions in many fields. The emergence of comparative proteomics enables us to have a close look at the mitochondrial proteome in a comprehensive and effective manner under various conditions and cellular circumstances. Two-dimensional electrophoresis combined with mass spectrometry is still the most popular techniques to study comparative mitochondrial proteomics. Furthermore, many new techniques, such as ICAT, MudPIT, and SILAC, equip researchers with more flexibilities inselecting proper methods. This article also reviews the recent development of comparative mitochondrial proteomics on diverse human diseases. And the results of mitochondrial proteomics enhance a better understanding of the pathogenesis associated with mitochondria and provide promising therapeutic targets.

Biomarker discovery in serum from patients with carotid atherosclerosis

BACKGROUND

Blood-based biomarkers of atherosclerosis have been used to identify patients at high risk for developing stroke. We hypothesized that patients with carotid artery disease would have a distinctive proteomic signature in blood as compared to a healthy control population without carotid artery disease. In order to discover protein biomarkers associated with increased atherosclerotic risk, we used two different strategies to identify biomarkers from patients with clinically defined atherosclerosis who were undergoing endarterectomy for atherosclerotic carotid artery disease. These patients were compared with healthy matched controls.

METHODS

Serum was obtained from patients undergoing endarterectomy (EA; n = 38) and compared to a group of age-matched healthy controls (n = 40). Serum was fractionated using anion exchange chromatography and three different surface-enhanced laser desorption/ionization (SELDI) chip surfaces and then evaluated with mass spectrometry (MS) and two-dimensional difference gel electrophoresis (2D-DIGE).

RESULTS

A random forest (RF) analysis of the SELDI-MS protein peak data distinguished these two groups with 69.2% sensitivity and 73.2% specificity. Four unique SELDI peaks (4.2, 4.4, 16.7 and 28 kDa, all p< 0.01) showed the greatest influence in the RF model. The EA patients with a history of prior clinical atherosclerotic plaque rupture manifested as either stroke or transient ischemic attack (symptomatic; n = 16) were compared to patients with carotid atherosclerosis but no clinical evidence of plaque rupture (asymptomatic; n = 22). Analysis of the SELDI spectra did not separate these two patient subgroups. A subgroup analysis using 2D-DIGE images obtained from albumin-depleted serum comparing symptomatic (n = 10) to asymptomatic EA patients (n = 10) found 4 proteins that were differentially expressed (p < 0.01) in the symptomatic patients. These proteins were identified as α(1)-antitrypsin, haptoglobin and vitamin D binding protein that were downregulated and α(2)-glycoprotein precursor that was upregulated in the symptomatic EA group.

CONCLUSIONS

SELDI-MS data analysis of fractionated serum suggests that a distinct protein signature exists in patients with carotid atherosclerosis compared to age-matched healthy controls. Identification of 4 proteins in a subset of patients with symptomatic and asymptomatic carotid atherosclerosis suggests that these and other protein biomarkers may assist in identifying high-risk patients with carotid atherosclerosis.

Comparative proteomic profiles of Aspergillus fumigatus and Aspergillus lentulus strains by surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF-MS)

BACKGROUND

Surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF-MS) was applied to analyze the protein profiles in both somatic and metabolic extracts of Aspergillus species. The study was carried out on some Aspergillus species within the Fumigati section (Aspergillus fumigatus wild-types and natural abnormally pigmented mutants, and Aspergillus lentulus). The aim was to validate whether mass spectrometry protein profiles can be used as specific signatures to discriminate different Aspergillus species or even mutants within the same species.

RESULTS

The growth conditions and the SELDI-TOF parameters were determined to generate characteristic protein profiles of somatic and metabolic extracts of Aspergillus fumigatus strains using five different ProteinChips®, eight growth conditions combining two temperatures, two media and two oxygenation conditions. Nine strains were investigated: three wild-types and four natural abnormally pigmented mutant strains of A. fumigatus and two strains of A. lentulus. A total of 242 fungal extracts were prepared. The spectra obtained are protein signatures linked to the physiological states of fungal strains depending on culture conditions. The best resolutions were obtained using the chromatographic surfaces CM10, NP20 and H50 with fractions of fungi grown on modified Sabouraud medium at 37 °C in static condition. Under these conditions, the SELDI-TOF analysis allowed A. fumigatus and A. lentulus strains to be grouped into distinct clusters.

CONCLUSIONS

SELDI-TOF analysis distinguishes A. fumigatus from A. lentulus strains and moreover, permits separate clusters of natural abnormally pigmented A. fumigatus strains to be obtained. In addition, this methodology allowed us to point out fungal components specifically produced by a wild-type strain or natural mutants. It offers attractive potential for further studies of the Aspergillus biology or pathogenesis.

Novel Sample Preparation for Mass Spectral Analysis of Complex Biological Samples

The ability to combine a selective capture strategy with on chip MALDI-TOF analysis allows for rapid, sensitive analysis of a variety of different analytes. In this overview a series of applications of capture enhanced laser desorption ionization time of flight (CELDI-TOF) mass spectrometry are described. The key feature of the assay is an off-chip capture step that utilizes high affinity bacterial binding proteins to capture a selected ligand. This allows large volumes of sample to be used and provides for a concentration step prior to transfer to a gold chip for traditional mass spectral analysis. The approach can also be adapted to utilize specific antibody as the basis of the capture step. The direct and indirect CELDI-TOF assays are rapid, reproducible and can be a valuable proteomic tool for analysis of low abundance molecules present in complex mixtures like blood plasma.

Vascular proteomics and the discovery process of clinical biomarkers: The case of TWEAK

In the last years, big efforts are devoted to the search of novel biomarkers. Proteomic approaches in healthy and pathological samples may help us to discern differential protein expression patterns. These identified proteins include potential culprits in pathological pathways and/or clinical biomarkers to identify individuals at risk. However, extensively validation must be carried out before their implementation into the clinical practice. Biomarkers need to discriminate between health and disease, detect preclinical disease stages, have impact on survival prediction, and add predictive value beyond traditional risk factors and global risk algorithms. Now, we summarize the data of soluble tumor necrosis factor-like weak inducer of apoptosis (sTWEAK), a new cardiovascular biomarker identified by proteomic analysis. Decreased sTWEAK concentrations have been shown in patients with carotid atherosclerosis, coronary artery disease, congestive heart failure, peripheral artery disease, or chronic kidney disease (CKD). sTWEAK predicted adverse outcomes in patients with heart failure, myocardial infarction, and CKD. Finally, different drug regimens were able to modify sTWEAK plasma levels in patients with CKD. Although sTWEAK seems so far to fulfill the requisites in the development of a new biomarker, more large-scale studies are warranted to consolidate its usefulness.

Quest for new genomic and proteomic biomarkers in neurology

The possibility of identifying novel biomarkers for neurodegenerative diseases has been greatly enhanced with recent advances in genomics and proteomics. Novel technologies have the potential to hasten the development of new biomarkers useful as predictors of disease etiology and outcome, as well as responsiveness to therapy. Disease-modifying new therapies are very much needed in modern approaches to treatment of neurodegenerative diseases. Current progress in the field encounters a degree of skepticism about the reliability of genomic and proteomic data and its relevance for clinical applications. Standard operating procedures covering sample collection, methodology and statistical analysis need to be fully developed and strictly adhered to in order to assure reproducible and clinically relevant results. Previous studies involving patients with neurodegenerative diseases show promise in using genomic and proteomic approaches for development of new biomarkers. Confirmation of any novel biomarker in multiple independent patient cohorts and correlation of the improvement in biomarker endpoint with clinical improvement in longitudinal patient studies remains crucial for future successful application. We propose that a combination of approaches in biomarker discovery may in the end lead to identification of promising candidates at DNA, RNA, protein and small molecule level.

Lipoproteomics: using mass spectrometry-based proteomics to explore the assembly, structure, and function of lipoproteins

Lipoproteins are centrally important in lipid transport, fuel metabolism, and cardiovascular disease. The prototypic lipoprotein has an outer shell of amphipathic lipids and proteins that solubilizes a hydrophobic lipid core. Lipoprotein-associated proteins have classically been viewed as structural elements and factors important in lipid metabolism. Recent mass spectrometric analyses reveal that the protein cargo of lipoproteins is much more diverse than previously appreciated, raising the possibility that lipoproteins play previously unsuspected roles in host defense mechanisms and inflammation. They further suggest that lipoprotein-associated proteins can identify humans at increased risk of cardiovascular disease. Here, we summarize recent developments in lipoproteomics, the proteomic analysis of lipoproteins. We also discuss the promises and challenges this powerful analytical strategy offers for expanding our understanding of the biology and structures of lipoproteins.

New pre-analytical approach for the deep proteome analysis of sera from pancreatitis and pancreas cancer patients

The presence of high-abundance proteins and the wide dynamic range of protein-distribution complicates the proteome analysis of crude serum. The aim was to establish a new preanalytical protocol for analysis of the deep serum-proteome for biomarker discovery.

METHODS

We investigated the stability and functionality of ProteoMiner™ and tested the new protocol by SELDI-TOF-MS profiling with serum samples obtained from patients with different pancreatic diseases.

RESULTS

We developed a high-throughput protocol and proved the convenience of ProteoMiner™ in the 96-well format to provide insights into the deep serum proteome and facilitate the detection of novel serum biomarkers. Serum samples spiked with defined amounts of insulin, processed with ProteoMiner™ and analyzed by SELDI-TOF-MS revealed that the concentration of the spiked insulin was not altered by ProteoMiner™ treatment.

CONCLUSION

ProteoMiner™ technology is robust preanalytical step and can be used in a high-throughput format for analysis of low-abundant proteins in serum.

Application of proteomics to the study of inflammatory bowel disease

Inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn's disease (CD), is a chronic inflammatory disorder of the gastrointestinal tract. The initiation and development of IBD involve environmental and genetic factors, such as microorganisms. Complicated pathogenesis, diverse risk factors and atypical clinical features lead to a difficult diagnosis of IBD. The emergence of proteomics has given new impetus to IBD research. In this article, we will review the application of proteomics to the diagnosis of IBD and prediction of IBD-associated tumors.

The application of mass-spectrometry-based protein biomarker discovery to theragnostics

Over the last decade rapid developments in mass spectrometry have allowed the identification of multiple proteins in complex biological samples. This proteomic approach has been applied to biomarker discovery in the context of clinical pharmacology (the combination of biomarker and drug now being termed 'theragnostics'). In this review we provide a roadmap for early protein biomarker discovery studies, focusing on some key questions that regularly confront researchers.

Discovery and verification of amyotrophic lateral sclerosis biomarkers by proteomics

Recent studies using mass spectrometry have discovered candidate biomarkers for amyotrophic lateral sclerosis (ALS). However, those studies utilized small numbers of ALS and control subjects. Additional studies using larger subject cohorts are required to verify these candidate biomarkers. Cerebrospinal fluid (CSF) samples from 100 patients with ALS, 100 disease control, and 41 healthy control subjects were examined by mass spectrometry. Sixty-one mass spectral peaks exhibited altered levels between ALS and controls. Mass peaks for cystatin C and transthyretin were reduced in ALS, whereas mass peaks for posttranslational modified transthyretin and C-reactive protein (CRP) were increased. CRP levels were 5.84 +/- 1.01 ng/ml for controls and 11.24 +/- 1.52 ng/ml for ALS subjects, as determined by enzyme-linked immunoassay. This study verified prior mass spectrometry results for cystatin C and transthyretin in ALS. CRP levels were increased in the CSF of ALS patients, and cystatin C level correlated with survival in patients with limb-onset disease. Our biomarker panel predicted ALS with an overall accuracy of 82%.

Proteomics and islet research

The complementary disciplines of genomics and proteomics offer better insights into the molecular mechanisms of diseases. While genomics hunts for defining our static genetic substrate, proteomics explores the structure and function of proteins expressed by a cell or tissue type under specified conditions. In the past decade, proteomics has been revolutionized by the application of techniques such as two-dimensional gel electrophoresis (2DGE), mass spectrometry (MS), and protein arrays. These techniques have tremendous potential for biomarker development, target validation, diagnosis, prognosis, and optimization of treatment in medical care, especially in the field of islet and diabetes research. This chapter will highlight the contributions of proteomic technologies toward the dissection of complex network of signaling molecules regulating islet function, the identification of potential biomarkers, and the understanding of mechanisms involved in the pathogenesis of diabetes.

Association of serum amyloid A protein and peptide fragments with prognosis in renal cancer

BACKGROUND

In renal cell carcinoma (RCC), the discovery of biomarkers for clinical use is a priority. This study aimed to identify and validate diagnostic and prognostic serum markers using proteomic profiling.

METHODS

Pre-operative sera from 119 patients with clear cell RCC and 69 healthy controls was analysed by surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry with stringent in-house quality control and analysis routines. Following identification of one prognostic peak as a fragment of serum amyloid A (SAA), total serum SAA and CRP were also determined by immunoassay for further validation.

RESULTS

Several peptides were identified as having independent prognostic but not diagnostic significance on multivariable analysis. One was subsequently identified as a 1525 Da fragment of SAA (hazard ratio (HR)=0.26, 95% CI 0.08-0.85, P=0.026). This was weakly negatively correlated with total SAA, which was also of independent prognostic significance (HR=2.46, 95% CI 1.17-5.15, P=0.017). Both potentially strengthened prognostic models based solely on pre-operative variables.

CONCLUSIONS

This is the first description of the prognostic value of this peptide in RCC and demonstrates proof of principle of the approach. The subsequent examination of SAA protein considerably extends previous studies, being the first study to focus solely on pre-operative samples and describing potential clinical utility in pre-operative prognostic models.

Tsetse flies, trypanosomes, humans and animals: what is proteomics revealing about their crosstalks?

Human and animal African trypanosomoses, or sleeping sickness and Nagana, are neglected vector-borne parasitic diseases caused by protozoa belonging to the Trypanosoma genus. Advances in proteomics offer new tools to better understand host-vector-parasite crosstalks occurring during the complex parasitic developmental cycle, and to determine the outcome of both transmission and infection. In this review, we summarize proteomics studies performed on African trypanosomes and on the interactions with their vector and mammalian hosts. We discuss the contributions and pitfalls of using diverse proteomics tools, and argue about the interest of pathogenoproteomics, both to generate advances in basic research on the best knowledge and understanding of host-vector-pathogen interactions, and to lead to the concrete development of new tools to improve diagnosis and treatment management of trypanosomoses in the near future.

Evaluation of hepcidin isoforms in hemodialysis patients by a proteomic approach based on SELDI-TOF MS

The hepatic iron regulator hormone hepcidin consists, in its mature form, of 25 amino acids, but two other isoforms, hepcidin-20 and hepcidin-22, have been reported, whose biological meaning remains poorly understood. We evaluated hepcidin isoforms in sera from 57 control and 54 chronic haemodialysis patients using a quantitative proteomic approach based on SELDI-TOF-MS. Patients had elevated serum levels of both hepcidin-25 and hepcidin-20 as compared to controls (geometric means: 7.52 versus 4.69 nM, and 4.06 versus 1.76 nM, resp., P < .05 for both). The clearance effects of a single dialysis session by different dialysis techniques and membranes were also investigated, showing an average reduction by 51.3% +/- 29.2% for hepcidin-25 and 34.2% +/- 28.4% for hepcidin-20 but only minor differences among the different dialysis modalities. Measurement of hepcidin isoforms through MS-based techniques can be a useful tool for better understanding of their biological role in hemodialysis patients and other clinical conditions.

Urine proteomics biomarkers in renal transplantation: an overview

A major goal of clinical proteomics was to identify biomarkers that can aid in the diagnosis and prognosis of different conditions. These biomarkers will not only assist the clinician in the diagnosis of a disease but they will also give directions as to which therapy may be more appropriate for each patient, thus contributing to the development of personalized medicine. This review discusses the current concepts in urine proteomics aimed at identifying predictive biomarkers that could detect the presence of acute rejection or chronic allograft dysfunction early on and for instance be used to personalize immunosuppressive therapies for kidney transplant patients.

An update on clinical proteomics in Alzheimer's research

Alzheimer's disease (AD) is a pathologically complex and aetiologically multifactorial dementing disorder affecting millions of people worldwide. The pathological brain changes are assumed to occur decades prior to the onset of clinical symptoms. The diagnosis of early AD remains problematic and is mainly based on clinical and neuropsychological findings after the onset of symptoms. Currently available drugs are able to delay the symptom progression of the disease but not to attenuate the progression of pathological brain changes. Many studies exploring AD proteomes have been conducted as the middle of 1990s as a consequence of recent advances in the development of both gel-based and gel-free proteomics approaches. It is hoped that proteomics can contribute to improving the understanding, diagnosis, and follow-up of the progression of AD. In this review, we summarise the present status of proteome alterations, with emphasis on quantitative approaches, in AD brain, CSF and blood, and their relevance to dementia research.

Challenges for biomarker discovery in body fluids using SELDI-TOF-MS

Protein profiling using SELDI-TOF-MS has gained over the past few years an increasing interest in the field of biomarker discovery. The technology presents great potential if some parameters, such as sample handling, SELDI settings, and data analysis, are strictly controlled. Practical considerations to set up a robust and sensitive strategy for biomarker discovery are presented. This paper also reviews biological fluids generally available including a description of their peculiar properties and the preanalytical challenges inherent to sample collection and storage. Finally, some new insights for biomarker identification and validation challenges are provided.

Enhancing mass spectrometry based serum profiling by a combination of free flow electrophoresis and ClinProt

Pre-fractionation of serum specimen is a precondition for a reliable and sensitive proteomic analysis, but systemic variation of various techniques impairs the pre-analytical workflow and at least assay reproducibility. In the present study we introduce and evaluate a two-dimensional fractionation approach combining liquid-based isoelectric focusing (IEF) and affinity chromatography (ClinProt magnetic beads). In the first dimension serum specimen were fractionated according to the isoelectric point of protein components by free-flow electrophoresis (IEF-FFE). Subsequently, fractions were separated in a second dimension by ClinProt and protein profiling was done by MALDI-TOF analysis. Our investigation show that in contrast to ClinProt approach alone the combination of both techniques enhances detectable protein mass signals by a factor of fifteen coincided with high reproducibility (CV < 12% between different experiments). Therefore, we conclude that our combined approach improves the efficiency of MS based serum proteome analysis significantly.

The isolation and identification of a secreted biomarker associated with cell stress in serum-free CHO cell culture

One approach to improving mammalian culture productivity has been to reduce cell stress and cell death in the bioreactor, thus enhancing productivity through a longer phase of viability. Here we describe the isolation and identification of a biomarker for stress and viability loss in CHO culture. Using SELDI-TOF mass spectrometry to profile the protein component of supernatant culture media we have identified a peak at 7.7 kDa that was associated with loss of viability toward the end of the culture and simulated stress from both toxic metabolite accumulation and nutrient depletion. The relative intensity (signal/noise ratio) of the peak increased rapidly at the onset of dropping viability toward the end of the growth phase. Also, the peak height was seen to increase significantly when cells were grown under conditions emulating ammonia accumulation and glutamine deprivation. The species has been identified as a fragment of Galectin-1 (Gal-1) via MS/MS fingerprinting. We propose that this peak could be utilized as a marker for early onset of stress in cell culture. This work demonstrates the efficacy of SELDI technology to identify biomarkers in mammalian cell culture and highlights its value as a tool for the monitoring and improvement of culture processes.

Fiber evanescent wave spectroscopy using the mid-infrared provides useful fingerprints for metabolic profiling in humans

Fiber evanescent wave spectroscopy (FEWS) explores the mid-infrared domain, providing information on functional chemical groups represented in the sample. Our goal is to evaluate whether spectral fingerprints obtained by FEWS might orientate clinical diagnosis. Serum samples from normal volunteers and from four groups of patients with metabolic abnormalities are analyzed by FEWS. These groups consist of iron overloaded genetic hemochromatosis (GH), iron depleted GH, cirrhosis, and dysmetabolic hepatosiderosis (DYSH). A partial least squares (PLS) logistic method is used in a training group to create a classification algorithm, thereafter applied to a test group. Patients with cirrhosis or DYSH, two groups exhibiting important metabolic disturbances, are clearly discriminated from control groups with AUROC values of 0.94+/-0.05 and 0.90+/-0.06, and sensibility/specificity of 8684% and 8787%, respectively. When pooling all groups, the PLS method contributes to discriminate controls, cirrhotic, and dysmetabolic patients. Our data demonstrate that metabolic profiling using infrared FEWS is a possible way to investigate metabolic alterations in patients.

Proteomics methods and applications for the practicing clinician

OBJECTIVE

To describe clinical proteomics from discovery techniques and their limitations, to applications in allergy, asthma, and immunology, and finally to how proteomics can be integrated into clinical practice.

DATA SOURCES

Despite many inherent challenges, proteomics-based methods have become a powerful and popular means of profiling clinical samples for the purpose of biomarker discovery. Although several strategies exist, clinical proteomics for the purpose of biomarker discovery generally focuses on 1 of 3 basic workflows: (1) 2-dimensional gel electrophoresis to quantitate relative protein levels followed by mass spectrometry (MS) to identify proteins of interest, (2) non-gel-based methods that rely on liquid chromatography MS (LCMS) for both quantitation and identification of proteins, and (3) protein profiling methods that do not directly result in the identification of proteins but rather generate "fingerprints" that are compared among individuals or samples.

STUDY SELECTION

Regardless of the strategy being pursued, a few general experimental steps are followed that will be expounded on in the text. These proteomics techniques have been applied to discover new biomarkers in biofluids and tissues from individuals with a variety of conditions, including allergy, asthma, atopic dermatitis, inflammatory diseases, chronic obstructive pulmonary disease, and other lung diseases.

RESULTS

After biomarker discovery, LCMS-based proteomics offers several advantages over traditional antibody-based clinical assays, including greater specificity, cost- and time-effectiveness, and the potential to multiplex up to hundreds of peptides in a single assay.

CONCLUSION

With many guidelines now in place and model studies on which to design future experiments, there is reason to be optimistic that candidate protein biomarkers will be discovered using proteomics and translated into clinical assays.

Proteomics-based prediction of clinical response in acute myeloid leukemia

OBJECTIVE

Response to chemotherapy is achieved in 60% to 70% of patients with acute myeloid leukemia. The ability to predict responders may help in stratifying patients and exploring different therapeutic approaches for nonresponders. Proteomics methods were used to search for predictive factors or combinations of factors.

MATERIALS AND METHODS

Peripheral blood plasma samples from 41 patients with confirmed acute myeloid leukemia with intermediate or poor cytogenetics were obtained prior to induction therapy for proteomic analysis. For each plasma sample, four fractions eluted from a strong anion column were applied to 3 different ProteinChip array surfaces and 12 surface-enhanced laser desorption/ionization spectra were generated. Peaks that correlated with response were identified, and decision trees incorporating these peaks along with various clinical and laboratory findings were constructed to predict response.

RESULTS

Multiple decision trees were constructed. One peak, when combined with age, provided strong positive prediction of responders with 83% accuracy. A second tree, which combined one peak with both cytogenetics and the percent of monocytes in peripheral blood, detected responders with 95% accuracy. A third peak was adequate to predict responders in the intermediate cytogenetic group with 86% accuracy.

CONCLUSIONS

Proteomic analysis should be further explored to define factors important in predicting clinical response in patients with acute myeloid leukemia.

When one chip is not enough: augmenting the validity of SELDI-TOF proteomic profiles of clinical specimens

To improve recovery, selectivity and reproducibility of SELDI-TOF analyses, we found it necessary to modify manufacturer's recommended protocols on sample and chip preparation. To yield reproducible denaturing conditions we verified concentrations of denaturing, reducing and lipid-solubilizing agents. We improved sorption of molecules of interest and reproducibility of analyses by introducing the preconditioning step and alkaline/acidic elutions for normal phase chips. The ratio that reproducibly decomposed the specimen was urea 9 mol l(-1) + DTT 10 mmol l(-1) + CHAPS 20 g l(-1). For sample denaturation, 100 microl of the fresh mixture was added to 100 microl of the specimen. Our modification of a chip processing increased recovery of the NP20 chip by up to 400% as assessed by total ion current. We obtained the range of mass accuracy of 0.02-0.04% and response precision between 30-40% of m/z+. We observed about 50% peak overlap. To obtain approximately 92% of possible peaks three chip selectivities, IMAC, H50 and normal phase with alkaline wash should be used. The selectivity of the SELDI chips is affected by unspecific interactions of a sample with a chip backbone. The system is compatible with matrix-based biological materials and does not suffer from urea interference and sensitivity to covalently bound alkaline ions. The technique is reasonably suitable for semiquantitative screening in the mammalian low-molecular weight cellular, tissue and plasma proteome.