How Industry Is Approaching the Search for New Diagnostic Markers and Biomarkers

Gut dysbiosis and neurological modalities: An engineering approach via proteomic analysis of gut-brain axis

The human gut microbiota is a complex and dynamic community of microorganisms, that influence metabolic, neurodevelopmental, and immune pathways. Microbial dysbiosis, characterized by changes in microbial diversity and relative abundances, is implicated in the development of various chronic neurological and neurodegenerative disorders. These disorders are marked by the accumulation of pathological protein aggregates, leading to the progressive loss of neurons and behavioural functions. Dysregulations in protein-protein interaction networks and signalling complexes, critical for normal brain function, are common in neurological disorders but challenging to unravel, particularly at the neuron and synapse-specific levels. To advance therapeutic strategies, a deeper understanding of neuropathogenesis, especially during the progressive disease phase, is needed. Biomarkers play a crucial role in identifying disease pathophysiology and monitoring disease progression. Proteomics, a powerful technology, shows promise in accelerating biomarker discovery and aiding in the development of novel treatments. In this chapter, we provide an in-depth overview of how proteomic techniques, utilizing various biofluid samples from patients with neurological conditions and diverse animal models, have contributed valuable insights into the pathogenesis of numerous neurological disorders. We also discuss the current state of research, potential challenges, and future directions in proteomic approaches to unravel neuro-pathological conditions.

Proteomics in Multiple Sclerosis: The Perspective of the Clinician

Multiple sclerosis (MS) is the inflammatory demyelinating and neurodegenerative disease of the central nervous system (CNS) that affects approximately 2.8 million people worldwide. In the last decade, a new era was heralded in by a new phenotypic classification, a new diagnostic protocol and the first ever therapeutic guideline, making personalized medicine the aim of MS management. However, despite this great evolution, there are still many aspects of the disease that are unknown and need to be further researched. A hallmark of these research are molecular biomarkers that could help in the diagnosis, differential diagnosis, therapy and prognosis of the disease. Proteomics, a rapidly evolving discipline of molecular biology may fulfill this dire need for the discovery of molecular biomarkers. In this review, we aimed to give a comprehensive summary on the utility of proteomics in the field of MS research. We reviewed the published results of the method in case of the pathogenesis of the disease and for biomarkers of diagnosis, differential diagnosis, conversion of disease courses, disease activity, progression and immunological therapy. We found proteomics to be a highly effective emerging tool that has been providing important findings in the research of MS.

Analytical approaches to assess metabolic changes in psoriasis

Psoriasis is one of the most common human skin diseases, although its development is not limited to one tissue, but is associated with autoimmune reactions throughout the body. Overproduction of pro-inflammatory cytokines and growth factors systemically stimulates the proliferation of skin cells, which manifests as excessive exfoliation of the epidermis, and/or arthritis, as well as other comorbidities such as insulin resistance, metabolic syndrome, hypertension, and depression. Thus, there is a great need for a thorough analysis of the pathophysiology of psoriatic patients, including classical methods, such as spectrophotometry, chromatography, or Western blot, and also novel omics approaches such as lipidomics and proteomics. Moreover, the extensive pathophysiology forces increased research examining biological changes in both skin cells, and systemically. A wide range of techniques involved in lipidomic research based on a combination of mass spectrometry and different types of chromatography (RP-LC-QTOF-MS/MS, HILIC-QTOF-MS/MS or RP-LC-QTRAP-MS/MS), have allowed comprehensive assessment of lipid modification in psoriatic skin and provided new insight into the role of lipids and their mechanism of action in psoriasis. Moreover, proteomic analysis using gel-nanoLC-OrbiTrap-MS/MS, as well as MALDI-TOF/TOF techniques facilitates the description of panels of enzymes involved in lipidome modifications, and the response of the endocannabinoid system to metabolic changes. Psoriasis is known to alter the expression of proteins that are involved in the inflammatory and antioxidant response, as well as protein biosynthesis, degradation, as well as cell proliferation and apoptosis. Knowledge of changes in the lipidomic and proteomic profile will not only allow the understanding of psoriasis pathophysiology, but also facilitate proper and early diagnosis and effective pharmacotherapy.

The Significance of Serum S100A9 and TNC Levels as Biomarkers in Colorectal Cancer

Purpose: The aim of this study was to evaluate the diagnostic value of S100A9 and tenascin-c (TNC) levels as colorectal cancer (CRC) biomarkers in several ways, including through screening tests, differentiation tests, combination with existing biomarkers (CEA and CA19-9), and serum level measurements before and after surgery. Materials and Methods: In this case-control study, S100A9 and TNC serum levels were measured in 460 participants: 258 CRC patients, 99 patients with benign colonic disease (BCD) and 103 healthy donors (HD). Results: The serum levels of S100A9 were 22.32 (14.88-29.55) ng/ml, 10.02 (5.83-14.15) ng/ml and 10.05 (7.68-15.34) ng/ml in the CRC, BCD and HD groups, respectively. The serum levels of TNC were 4.30 (2.12-6.04) ng/ml, 1.60 (1.06-2.30) ng/ml and 2.00 (1.37-3.00) ng/ml in the CRC, BCD and HD groups, respectively. Significantly higher levels of both biomarkers (S100A9 and TNC) were found in CRC patients (both p<0.001). Both S100A9 and TNC levels were superior to CEA and CA19-9 levels as CRC diagnostic biomarkers; the combination of S100A9, TNC and CEA levels was an excellent biomarker with 79.8% sensitivity and 89.6% specificity. The serum levels of S100A9 and TNC in CRC patients were significantly lower after surgery than before surgery (p<0.01). Conclusion: S100A9 and TNC levels could serve as diagnostic biomarkers of colorectal cancer.

Methods for the evaluation of biomarkers in patients with kidney and liver diseases: multicentre research programme including ELUCIDATE RCT

Background

Protein biomarkers with associations with the activity and outcomes of diseases are being identified by modern proteomic technologies. They may be simple, accessible, cheap and safe tests that can inform diagnosis, prognosis, treatment selection, monitoring of disease activity and therapy and may substitute for complex, invasive and expensive tests. However, their potential is not yet being realised.

Design and methods

The study consisted of three workstreams to create a framework for research: workstream 1, methodology – to define current practice and explore methodology innovations for biomarkers for monitoring disease; workstream 2, clinical translation – to create a framework of research practice, high-quality samples and related clinical data to evaluate the validity and clinical utility of protein biomarkers; and workstream 3, the ELF to Uncover Cirrhosis as an Indication for Diagnosis and Action for Treatable Event (ELUCIDATE) randomised controlled trial (RCT) – an exemplar RCT of an established test, the ADVIA Centaur® Enhanced Liver Fibrosis (ELF) test (Siemens Healthcare Diagnostics Ltd, Camberley, UK) [consisting of a panel of three markers – (1) serum hyaluronic acid, (2) amino-terminal propeptide of type III procollagen and (3) tissue inhibitor of metalloproteinase 1], for liver cirrhosis to determine its impact on diagnostic timing and the management of cirrhosis and the process of care and improving outcomes.

Results

The methodology workstream evaluated the quality of recommendations for using prostate-specific antigen to monitor patients, systematically reviewed RCTs of monitoring strategies and reviewed the monitoring biomarker literature and how monitoring can have an impact on outcomes. Simulation studies were conducted to evaluate monitoring and improve the merits of health care. The monitoring biomarker literature is modest and robust conclusions are infrequent. We recommend improvements in research practice. Patients strongly endorsed the need for robust and conclusive research in this area. The clinical translation workstream focused on analytical and clinical validity. Cohorts were established for renal cell carcinoma (RCC) and renal transplantation (RT), with samples and patient data from multiple centres, as a rapid-access resource to evaluate the validity of biomarkers. Candidate biomarkers for RCC and RT were identified from the literature and their quality was evaluated and selected biomarkers were prioritised. The duration of follow-up was a limitation but biomarkers were identified that may be taken forward for clinical utility. In the third workstream, the ELUCIDATE trial registered 1303 patients and randomised 878 patients out of a target of 1000. The trial started late and recruited slowly initially but ultimately recruited with good statistical power to answer the key questions. ELF monitoring altered the patient process of care and may show benefits from the early introduction of interventions with further follow-up. The ELUCIDATE trial was an ‘exemplar’ trial that has demonstrated the challenges of evaluating biomarker strategies in ‘end-to-end’ RCTs and will inform future study designs.

Conclusions

The limitations in the programme were principally that, during the collection and curation of the cohorts of patients with RCC and RT, the pace of discovery of new biomarkers in commercial and non-commercial research was slower than anticipated and so conclusive evaluations using the cohorts are few; however, access to the cohorts will be sustained for future new biomarkers. The ELUCIDATE trial was slow to start and recruit to, with a late surge of recruitment, and so final conclusions about the impact of the ELF test on long-term outcomes await further follow-up. The findings from the three workstreams were used to synthesise a strategy and framework for future biomarker evaluations incorporating innovations in study design, health economics and health informatics.

Trial registration

Current Controlled Trials ISRCTN74815110, UKCRN ID 9954 and UKCRN ID 11930.

Funding

This project was funded by the NIHR Programme Grants for Applied Research programme and will be published in full inProgramme Grants for Applied Research; Vol. 6, No. 3. See the NIHR Journals Library website for further project information.

Proteomics in the Diagnosis of Inborn Encephalopathies of Unknown Origin: A Myth or Reality

Synaptopathy underlies a great variety of neurological or neurodevelopmental disorders, including neurodegenerative diseases and the highly complex neuropsychiatric syndromes. Standard diagnostic assays in the majority of synaptopathies are insufficient to make an appropriate and fast diagnosis, which has spurred a search for more accurate diagnostic methods using recent technological advances. As synaptopathy phenotypes strictly depend on genetics and environmental factors, the best way to approach these diseases is the investigation of entire sets of protein characteristics. Thus, proteomics has emerged as a mainstay in the studies on synaptopathies, with mass spectrometry as a technology of choice. This review is an update on the proteomic methods and achievements in the understanding, diagnostics, and novel biomarkers of synaptopathies. The article also provides a critical point of view and future perspectives on the application of neuroproteomics in clinical practice.

RBP4 and THBS2 are serum biomarkers for diagnosis of colorectal cancer

The potential role of serum RBP4 and THBS2 as biomarker in colorectal cancer (CRC) diagnosis has never been studied. We investigated in large sample using quantitative ELISA method to explore whether serum RBP4 and THBS2 can act as biomarkers for CRC diagnosis. The concentration of RBP4 and THBS2 was measured in 402 CRC patients' serum samples and 218 normal controls' serum samples. The results showed that the average RBP4 and THBS2 concentrations in normal controls were significantly higher than in CRC patients (36.5±11.4μg/mL vs 21.8±8.7μg/mL and 20.5±6.1ng/mL vs 14.5±7.3ng/mL, respectively), both p<0.001. RBP4 distinguished CRC patients from normal individuals with the area under the receiver operating characteristic curve (AUC) performing at 0.852, with sensitivity of 74.9% and specificity of 81.7%. While THBS2 distinguished CRC patients performing AUC at 0.794, with sensitivity of 64.9% and specificity of 87.1%. The ability of RBP4 and THBS2 serum concentration distinguishing CRC from normal controls showed better than that of serum CEA (AUC=0.818) or CA19-9 (AUC=0.650) concentration. This is the first study to report RBP4 and THBS2 as diagnosis serum biomarkers for CRC, which might be a good supplement for CEA or CA19-9 for clinical diagnosis.

Systemic and ocular fluid compounds as potential biomarkers in age-related macular degeneration

Biomarkers can help unravel mechanisms of disease and identify new targets for therapy. They can also be useful in clinical practice for monitoring disease progression, evaluation of treatment efficacy, and risk assessment in multifactorial diseases, such as age-related macular degeneration (AMD). AMD is a highly prevalent progressive retinal disorder for which multiple genetic and environmental risk factors have been described, but the exact etiology is not yet fully understood. Many compounds have been evaluated for their association with AMD. We performed an extensive literature review of all compounds measured in serum, plasma, vitreous, aqueous humor, and urine of AMD patients. Over 3600 articles were screened, resulting in more than 100 different compounds analyzed in AMD studies, involved in neovascularization, immunity, lipid metabolism, extracellular matrix, oxidative stress, diet, hormones, and comorbidities (such as kidney disease). For each compound, we provide a short description of its function and discuss the results of the studies in relation to its usefulness as AMD biomarker. In addition, biomarkers identified by hypothesis-free techniques, including metabolomics, proteomics, and epigenomics, are covered. In summary, compounds belonging to the oxidative stress pathway, the complement system, and lipid metabolism are the most promising biomarker candidates for AMD. We hope that this comprehensive survey of the literature on systemic and ocular fluid compounds as potential biomarkers in AMD will provide a stepping stone for future research and possible implementation in clinical practice.

Mining the fecal proteome: from biomarkers to personalised medicine

INTRODUCTION

Fecal proteomics has gained increased prominence in recent years. It can provide insights into the diagnosis and surveillance of many bowel diseases by both identifying potential biomarkers in stool samples and helping identify disease-related pathways. Fecal proteomics has already shown its potential for the discovery and validation of biomarkers for colorectal cancer screening, and the analysis of fecal microbiota by MALDI-MS for the diagnosis of a range of bowel diseases is gaining clinical acceptance. Areas covered: Based on a comprehensive analysis of the current literature, we introduce the range of sensitive and specific proteomics methods which comprise the current 'Proteomics Toolbox', explain how the integration of fecal proteomics with data processing/bioinformatics has been used for the identification of potential biomarkers for both CRC and other gut-related pathologies and analysis of the fecal microbiome, outline some of the current fecal assays in current clinical practice and introduce the concept of personalised medicine which these technologies will help inform. Expert commentary: Integration of fecal proteomics with other proteomics and genomics strategies as well as bioinformatics is paving the way towards personalised medicine, which will bring with it improved global healthcare.

Biomarker discovery: success as a function of risk mitigation

Protein biomarker discovery is a fascinating enterprise; however, success in terms of products for in vitro diagnostic use is sparse. New developments in mass spectrometry-based quantitative proteomics as discovery technology have opened up new avenues for this endeavor. In addition to choice of technology, sample properties, study design and validation strategy are potent pillars required for project success. The challenge for successful biomarker discovery can be described by a series of risks that need to be mitigated. This article intends to describe the major risks along with possible solutions.

Biomarker development targeting unmet clinical needs

BACKGROUND

The introduction of new biomarkers can lead to inappropriate utilization of tests if they do not fill in existing gaps in clinical care. We aimed to define a strategy and checklist for identifying unmet needs for biomarkers.

METHODS

A multidisciplinary working group used a 4-step process: 1/ scoping literature review; 2/ face-to-face meetings to discuss scope, strategy and checklist items; 3/ iterative process of feedback and consensus to develop the checklist; 4/ testing and refinement of checklist items using case scenarios.

RESULTS

We used clinical pathway mapping to identify clinical management decisions linking biomarker testing to health outcomes and developed a 14-item checklist organized into 4 domains: 1/ identifying and 2/ verifying the unmet need; 3/ validating the intended use; and 4/ assessing the feasibility of the new biomarker to influence clinical practice and health outcome. We present an outcome-focused approach that can be used by multiple stakeholders for any medical test, irrespective of the purpose and role of testing.

CONCLUSIONS

The checklist intends to achieve more efficient biomarker development and translation into practice. We propose the checklist is field tested by stakeholders, and advocate the role of the clinical laboratory professional to foster trans-sector collaboration in this regard.

Diagnostics and Resistance Profiling of Bacterial Pathogens

Worldwide infectious disease is one of the leading causes of death. Despite improvements in technology and healthcare services, morbidity and mortality due to infections have remained unchanged over the past few decades. The high and increasing rate of antibiotic resistance is further aggravating the situation. Growing resistance hampers the use of conventional antibiotics, and substantial higher mortality rates are reported in patients given ineffective empiric therapy mainly due to resistance to the agents used. These infections cause suffering, incapacity, and death and impose an enormous financial burden on both healthcare systems and on society in general. The accelerating development of multidrug resistance is one of the greatest diagnostic and therapeutic challenges to modern medicine. The lack of new antibiotic options underscores the need for optimization of current diagnostics, therapies, and prevention of the spread of multidrug-resistant organisms. The so-called -omics technologies (genomics, transcriptomics, proteomics, and metabolomics) have yielded large-scale datasets that advanced the search for biomarkers of infectious diseases in the last decade. One can imagine that in the future the implementation of biomarker-driven molecular test systems will transform diagnostics of infectious diseases and will significantly accelerate the identification of the bacterial pathogens at the infected host site. Furthermore, molecular tests based on the identification of markers of antibiotic resistance will dramatically change resistance profiling. The replacement of culturing methods by molecular test systems for early diagnosis will provide the basis not only for a prompt and targeted therapy, but also for a much more effective stewardship of antibiotic agents and a reduction of the spread of multidrug resistance as well as the appearance of new antibiotic resistances.

Proteomic Identification of Target Proteins of Thiodigalactoside in White Adipose Tissue from Diet-Induced Obese Rats

Previously, galectin-1 (GAL1) was found to be up-regulated in obesity-prone subjects, suggesting that use of a GAL1 inhibitor could be a novel therapeutic approach for treatment of obesity. We evaluated thiodigalactoside (TDG) as a potent inhibitor of GAL1 and identified target proteins of TDG by performing comparative proteome analysis of white adipose tissue (WAT) from control and TDG-treated rats fed a high fat diet (HFD) using two dimensional gel electrophoresis (2-DE) combined with MALDI-TOF-MS. Thirty-two spots from a total of 356 matched spots showed differential expression between control and TDG-treated rats, as identified by peptide mass fingerprinting. These proteins were categorized into groups such as carbohydrate metabolism, tricarboxylic acid (TCA) cycle, signal transduction, cytoskeletal, and mitochondrial proteins based on functional analysis using Protein Annotation Through Evolutionary Relationship (PANTHER) and Database for Annotation, Visualization, Integrated Discovery (DAVID) classification. One of the most striking findings of this study was significant changes in Carbonic anhydrase 3 (CA3), Voltage-dependent anion channel 1 (VDAC1), phosphatidylethanolamine-binding protein 1 (PEBP1), annexin A2 (ANXA2) and lactate dehydrogenase A chain (LDHA) protein levels between WAT from control and TDG-treated groups. In addition, we confirmed increased expression of thermogenic proteins as well as reduced expression of lipogenic proteins in response to TDG treatment. These results suggest that TDG may effectively prevent obesity, and TDG-responsive proteins can be used as novel target proteins for obesity treatment.

Changes to serum sample tube and processing methodology does not cause Intra-Individual [corrected] variation in automated whole serum N-glycan profiling in health and disease

Introduction

Serum N-glycans have been identified as putative biomarkers for numerous diseases. The impact of different serum sample tubes and processing methods on N-glycan analysis has received relatively little attention. This study aimed to determine the effect of different sample tubes and processing methods on the whole serum N-glycan profile in both health and disease. A secondary objective was to describe a robot automated N-glycan release, labeling and cleanup process for use in a biomarker discovery system.

Methods

25 patients with active and quiescent inflammatory bowel disease and controls had three different serum sample tubes taken at the same draw. Two different processing methods were used for three types of tube (with and without gel-separation medium). Samples were randomised and processed in a blinded fashion. Whole serum N-glycan release, 2-aminobenzamide labeling and cleanup was automated using a Hamilton Microlab STARlet Liquid Handling robot. Samples were analysed using a hydrophilic interaction liquid chromatography/ethylene bridged hybrid(BEH) column on an ultra-high performance liquid chromatography instrument. Data were analysed quantitatively by pairwise correlation and hierarchical clustering using the area under each chromatogram peak. Qualitatively, a blinded assessor attempted to match chromatograms to each individual.

Results

There was small intra-individual variation in serum N-glycan profiles from samples collected using different sample processing methods. Intra-individual correlation coefficients were between 0.99 and 1. Unsupervised hierarchical clustering and principal coordinate analyses accurately matched samples from the same individual. Qualitative analysis demonstrated good chromatogram overlay and a blinded assessor was able to accurately match individuals based on chromatogram profile, regardless of disease status.

Conclusions

The three different serum sample tubes processed using the described methods cause minimal inter-individual variation in serum whole N-glycan profile when processed using an automated workstream. This has important implications for N-glycan biomarker discovery studies using different serum processing standard operating procedures.

Determination of variation parameters as a crucial step in designing TMT-based clinical proteomics experiments

In quantitative shotgun proteomic analyses by liquid chromatography and mass spectrometry, a rigid study design is necessary in order to obtain statistically relevant results. Hypothesis testing, sample size calculation and power estimation are fundamental concepts that require consideration upon designing an experiment. For this reason, the reproducibility and variability of the proteomic platform needs to be assessed. In this study, we evaluate the technical (sample preparation), labeling (isobaric labels), and total (biological + technical + labeling + experimental) variability and reproducibility of a workflow that employs a shotgun LC-MS/MS approach in combination with TMT peptide labeling for the quantification of peripheral blood mononuclear cell (PBMC) proteome. We illustrate that the variability induced by TMT labeling is small when compared to the technical variation. The latter is also responsible for a substantial part of the total variation. Prior knowledge about the experimental variability allows for a correct design, a prerequisite for the detection of biologically significant disease-specific differential proteins in clinical proteomics experiments.

Biomarkers in electrophysiology: role in arrhythmias and resynchronization therapy

Circulating biomarkers related to inflammation, neurohormones, myocardial stress, and necrosis have been associated with commonly encountered arrhythmic disorders such as atrial fibrillation (AF) and more malignant processes including ventricular arrhythmias (VA) and sudden cardiac death (SCD). Both direct and indirect biomarkers implicated in the heart failure cascade have potential prognostic value in patients undergoing cardiac resynchronization therapy (CRT). This review will focus on the role of biomarkers in AF, history of SCD, and CRT with an emphasis to improve clinical risk assessment for arrhythmias and patient selection for device therapy. Notably, information obtained from biomarkers may supplement traditional diagnostic and imaging techniques, thus providing an additional benefit in the management of patients.

Antibody arrays in biomarker discovery

All of life is regulated by complex and organized chemical reactions that help dictate when to grow, to move, to reproduce, and to die. When these processes go awry, or are interrupted by pathological agents, diseases such as cancer, autoimmunity, or infections can result. Cytokines, chemokines, growth factors, adipokines, and other chemical moieties make up a vast subset of these chemical reactions that are altered in disease states, and monitoring changes in these molecules could provide for the identification of disease biomarkers. From the first identification of carcinoembryonic antigen, to the discovery of prostate-specific antigen, to numerous others described within, biomarkers of disease are detectable in a plethora of sample types. The growing number of biomarkers for infection, autoimmunity, and cancer allow for increasingly early detection, to identification of novel drug targets, to prognostic indicators of disease outcome. However, more and more studies are finding that a single cytokine or growth factor is insufficient as a true disease biomarker and that a more global perspective is needed to understand true disease biology. Such a broad view requires a multiplexed platform for chemical detection, and antibody arrays meet and exceed this need by performing this detection in a high-throughput fashion. Herein, we will discuss how antibody arrays have evolved, and how they have helped direct new drug target design, helped identify therapeutic disease markers, and helped in earlier disease detection. From asthma to renal disease, and neurological dysfunction to immunologic disorders, antibody arrays afford a bright future for new biomarkers discovery.

The role of quantitative mass spectrometry in the discovery of pancreatic cancer biomarkers for translational science

In the post-genomic era, it has become evident that genetic changes alone are not sufficient to understand most disease processes including pancreatic cancer. Genome sequencing has revealed a complex set of genetic alterations in pancreatic cancer such as point mutations, chromosomal losses, gene amplifications and telomere shortening that drive cancerous growth through specific signaling pathways. Proteome-based approaches are important complements to genomic data and provide crucial information of the target driver molecules and their post-translational modifications. By applying quantitative mass spectrometry, this is an alternative way to identify biomarkers for early diagnosis and personalized medicine. We review the current quantitative mass spectrometric technologies and analyses that have been developed and applied in the last decade in the context of pancreatic cancer. Examples of candidate biomarkers that have been identified from these pancreas studies include among others, asporin, CD9, CXC chemokine ligand 7, fibronectin 1, galectin-1, gelsolin, intercellular adhesion molecule 1, insulin-like growth factor binding protein 2, metalloproteinase inhibitor 1, stromal cell derived factor 4, and transforming growth factor beta-induced protein. Many of these proteins are involved in various steps in pancreatic tumor progression including cell proliferation, adhesion, migration, invasion, metastasis, immune response and angiogenesis. These new protein candidates may provide essential information for the development of protein diagnostics and targeted therapies. We further argue that new strategies must be advanced and established for the integration of proteomic, transcriptomic and genomic data, in order to enhance biomarker translation. Large scale studies with meta data processing will pave the way for novel and unexpected correlations within pancreatic cancer, that will benefit the patient, with targeted treatment.

Recent technical strategies to identify diagnostic biomarkers for ovarian cancer

Ovarian cancer is the fifth leading cause of cancer deaths among North American women. Regrettably, there is currently no reliable circulating biomarker that can detect ovarian cancer in its early stages. The CA125 biomarker is very useful for treatment response monitoring, but its sensitivity is very low for early detection. Thus, there is an urgent need for the identification of new circulating biomarkers/panel of biomarkers that could be used to diagnose ovarian cancer before it becomes clinically detectable and advanced. Unfortunately, the strategies used in the past years to identify such biomarkers have not led to any outstanding candidate. This review summarizes the different approaches used in the last decade and suggests which strategies should be adopted in the near future in order to lead to the successful identification of new ovarian cancer diagnostic biomarkers.

Role and challenges of proteomics in pharma and biotech: technical, scientific and commercial perspective

Contemporary proteomics, currently in its exponential growth phase, is a bewildering array of tools. Proteomic methods are the result of a convergence of rapidly improving mass spectrometry technologies, protein chemistry and separation sciences, genomics and bioinformatics. Strides in improving proteomics technologies to map and measure proteomes and subproteomes are being made. However, no single proteomic platform appears ideally suited to address all research needs or accomplish ambitious goals satisfactorily. However, proteomics is in a unique position to contribute to protein discovery and to public health in terms of better biomarkers, diagnostics and treatment of disease. While the potential is great, many challenges and issues remain to be solved. Fundamental issues, such as biological variability, pre-analytic factors and analytical reproducibility, remain to be resolved. Neither an all-genetic approach nor an all-proteomic approach will solve biological complexity. Proteomics will be the foundation for constructing and extracting useful knowledge to pharma and biotech depicted in the following path: data --> structured data --> information --> information architecture --> knowledge --> useful knowledge.

Developing liquid chromatography ion mobility mass spectometry techniques

When a packet of ions in a buffer gas is exposed to a weak electric field, the ions will separate according to differences in their mobilities through the gas. This separation forms the basis of the analytical method known as ion mobility spectroscopy and is highly efficient, in that it can be carried out in a very short time frame (micro- to milliseconds). Recently, efforts have been made to couple the approach with liquid-phase separations and mass spectrometry in order to create a high-throughput and high-coverage approach for analyzing complex mixtures. This article reviews recent work to develop this approach for proteomics analyses. The instrumentation is described briefly. Several multidimensional data sets obtained upon analyzing complex mixtures are shown in order to illustrate the approach as well as provide a view of the limitations and required future work.

Peptides in body fluids and tissues as markers of disease

The general awareness of the importance of peptides in physiology and pathophysiology has increased strongly over the last few years. With worldwide progress in the analysis of whole genomes, the knowledge base in gene sequence and expression data useful for protein and peptide analysis has drastically increased. The medical need for relevant biomarkers is enormous. This is particularly true for the many types of cancer, but other diseases such as Type 2 diabetes also lack useful and adequate diagnostic markers with high specificity and sensitivity. Despite advances in imaging technologies for early detection of diseases, proteomic and peptidomic multiplex techniques have evolved in recent years. This review focuses on the application of peptidomics technologies to peptides in health and disease. Peptidomics technologies provide new opportunities for the detection of low-molecular-weight proteome biomarkers (peptides) by mass spectrometry. Improvements in peptidomics research are based on separation of peptides and/or proteins by their physicochemical properties in combination with mass spectrometric detection, identification and sophisticated bioinformatics tools for data analysis. Therefore, peptidomics technologies offer an opportunity to discover novel biomarkers for diagnosis and management of disease (e.g., prognosis, treatment decision and monitoring response to therapy).

How proteomics reveals potential biomarkers in brain diseases

The brain is complex, and so are the proteomics studies of brain tissue and its diseases, including Alzheimer's Disease, Parkinson's Disease and schizophrenia. In this review, general considerations and strategies of proteomics technologies, the advantages and challenges as well as the special needs for brain tissue are described and summarized. In addition, the results of the first studies are presented including a quality evaluation of the candidate proteins for these diseases. A paragraph is dedicated to the efforts of standardization in this field.

Multispot, label-free biodetection at a phantom plastic-water interface

Recognizing and quantifying specific biomolecules in aqueous samples are constantly needed in research and diagnostic laboratories. As the typical detection procedures are rather lengthy and involve the use of labeled secondary antibodies or other agents to provide a signal, efforts have been made over the last 10 y to develop alternative label-free methods that enable direct detection. We propose and demonstrate an extremely simple, low-cost, label-free biodetector based on measuring the intensity of light reflected by the interface between a fluid sample and an amorphous fluoropolymer substrate having a refractive index very close to that of water and hosting various antibodies immobilized in spots. Under these index-matching conditions, the amount of light reflected by the interface allows straightforward quantification of the amount of antigen binding to each spot. Using antibodies targeting heterologous immunoglobulins and antigens commonly used as markers for diagnoses of hepatitis B and HIV, we demonstrate the limit of detection of a few picograms per square millimeter of surface-bound molecules. We also show that direct and real-time access to the amount of binding molecules allows the precise extrapolation of adhesion rates, from which the concentrations of antigens in solution can be estimated down to fractions of nanograms per milliliter.

The A, B, Cs of G-protein-coupled receptor pharmacology in assay development for HTS

G-protein-coupled receptors represent one of the most important areas of research in the pharmaceutical industry, being one of the largest druggable gene families. Recognising this fact, manufacturers have developed a huge variety of homogeneous assay technologies that facilitate the quantification of receptor ligand binding events and their downstream signalling cascades. However, while early emphasis was placed on the most sensitive, high-throughput and cost-effective screening technologies to enable identification of the most lead matter for further development, in recent years emphasis has shifted to a focus on maximising the identification of compounds that are new and developing assays that are more biologically/pharmacologically relevant. Therefore, this review provides an overview of the binding and functional techniques available for high-throughput screening, with particular attention on how assay application and configuration can be maximised to ensure their successful identification of relevant chemical matter and thereby optimising project success.

Glycocapture-based proteomics for secretome analysis

Protein glycosylation represents the most abundant extracellular posttranslational modification in multicellular organisms. These glycoproteins unequivocally comprise the major biomolecules involved in extracellular processes, such as growth factors, signaling proteins for cellular communication, enzymes, and proteases for on- and off-site processing. It is now known that altered protein glycosylation is a hallmark event in many different pathologies. Glycoproteins are found mostly in the so-called secretome, which comprises classically and nonclassically secreted proteins and protein fragments that are released from the cell surface through ectodomain shedding. Due to biological complexity and technical difficulty, comparably few studies have taken an in-depth investigation of cellular secretomes using system-wide approaches. The cellular secretomes are considered to be a valuable source of therapeutic targets and novel biomarkers. It is not surprising that many existing biomarkers, including biomarkers for breast, ovarian, prostate, and colorectal cancers are glycoproteins. Focused analysis of secreted glycoproteins could thus provide valuable information for early disease diagnosis, and surveillance. Furthermore, since most secreted proteins are glycosylated and glycosylation predominantly targets secreted proteins, the glycan/sugar moiety itself can be used as a chemical "handle" for the targeted analysis of cellular secretomes, thereby reducing sample complexity and allowing detection of low abundance proteins in proteomic workflows. This review will focus on various glycoprotein enrichment strategies that facilitate proteomics-based technologies for the quantitative analysis of cell secretomes and cell surface proteomes.

The application of quantification techniques in proteomics for biomedical research

The systematic analysis of biological processes requires an understanding of the quantitative expression patterns of proteins, their interacting partners and their subcellular localization. This information was formerly difficult to accrue as the relative quantification of proteins relied on antibody-based methods and other approaches with low throughput. The advent of soft ionization techniques in mass spectrometry plus advances in separation technologies has aligned protein systems biology with messenger RNA, DNA, and microarray technologies to provide data on systems as opposed to singular protein entities. Another aspect of quantitative proteomics that increases its importance for the coming few years is the significant technical developments underway both for high pressure liquid chromatography and mass spectrum devices. Hence, robustness, reproducibility and mass accuracy are still improving with every new generation of instruments. Nonetheless, the methods employed require validation and comparison to design fit for purpose experiments in advanced protein analyses. This review considers the newly developed systematic protein investigation methods and their value from the standpoint that relative or absolute protein quantification is required de rigueur in biomedical research.

Validation processes of protein biomarkers in serum--a cross platform comparison

Due to insufficient biomarker validation and poor performances in diagnostic assays, the candidate biomarker verification process has to be improved. Multi-analyte immunoassays are the tool of choice for the identification and detailed validation of protein biomarkers in serum. The process of identification and validation of serum biomarkers, as well as their implementation in diagnostic routine requires an application of independent immunoassay platforms with the possibility of high-throughput. This review will focus on three main multi-analyte immunoassay platforms: planar microarrays, multiplex bead systems and, array-based surface plasmon resonance (SPR) chips. Recent developments of each platform will be discussed for application in clinical proteomics, principles, detection methods, and performance strength. The requirements for specific surface functionalization of assay platforms are continuously increasing. The reasons for this increase is the demand for highly sensitive assays, as well as the reduction of non-specific adsorption from complex samples, and with it high signal-to-noise-ratios. To achieve this, different support materials were adapted to the immobilized biomarker/ligand, allowing a high binding capacity and immobilization efficiency. In the case of immunoassays, the immobilized ligands are proteins, antibodies or peptides, which exhibit a diversity of chemical properties (acidic/alkaline; hydrophobic/hydrophilic; secondary or tertiary structure/linear). Consequently it is more challenging to develop immobilization strategies necessary to ensure a homogenous covered surface and reliable assay in comparison to DNA immobilization. New developments concerning material support for each platform are discussed especially with regard to increase the immobilization efficiency and reducing the non-specific adsorption from complex samples like serum and cell lysates.

Microvesicles/exosomes as potential novel biomarkers of metabolic diseases

Biomarkers are of tremendous importance for the prediction, diagnosis, and observation of the therapeutic success of common complex multifactorial metabolic diseases, such as type II diabetes and obesity. However, the predictive power of the traditional biomarkers used (eg, plasma metabolites and cytokines, body parameters) is apparently not sufficient for reliable monitoring of stage-dependent pathogenesis starting with the healthy state via its initiation and development to the established disease and further progression to late clinical outcomes. Moreover, the elucidation of putative considerable differences in the underlying pathogenetic pathways (eg, related to cellular/tissue origin, epigenetic and environmental effects) within the patient population and, consequently, the differentiation between individual options for disease prevention and therapy - hallmarks of personalized medicine - plays only a minor role in the traditional biomarker concept of metabolic diseases. In contrast, multidimensional and interdependent patterns of genetic, epigenetic, and phenotypic markers presumably will add a novel quality to predictive values, provided they can be followed routinely along the complete individual disease pathway with sufficient precision. These requirements may be fulfilled by small membrane vesicles, which are so-called exosomes and microvesicles (EMVs) that are released via two distinct molecular mechanisms from a wide variety of tissue and blood cells into the circulation in response to normal and stress/pathogenic conditions and are equipped with a multitude of transmembrane, soluble and glycosylphosphatidylinositol-anchored proteins, mRNAs, and microRNAs. Based on the currently available data, EMVs seem to reflect the diverse functional and dysfunctional states of the releasing cells and tissues along the complete individual pathogenetic pathways underlying metabolic diseases. A critical step in further validation of EMVs as biomarkers will rely on the identification of unequivocal correlations between critical disease states and specific EMV signatures, which in future may be determined in rapid and convenient fashion using nanoparticle-driven biosensors.

Proteomics technologies for biomarker discovery in multiple sclerosis

Multiple sclerosis is a disabling inflammatory and neurodegenerative disorder that predominantly affects young adults. There is a great need for biomarkers, which could elucidate pathology as well as provide prognosis of disease progression and therapy response in multiple sclerosis. Rapidly evolving, technology driven applications such as mass spectrometry based proteomics are currently being developed for this purpose. In this review, we will outline the current status of the field and detail a number of the bottlenecks as well as future prospects of this type of biomarker research.

Biobank resources for future patient care: developments, principles and concepts

The aim of the overview is to give a perspective of global biobank development is given in a view of positioning biobanking as a key resource for healthcare to identify new potential markers that can be used in patient diagnosis and complement the targeted personalized drug treatment. The fast progression of biobanks around the world is becoming an important resource for society where the patient benefit is in the focus, with a high degree of personal integrity and ethical standard. Biobanks are providing patient benefits by large scale screening studies, generating large database repositories. It is envisioned by all participating stakeholders that the biobank initiatives will become the future gateway to discover new frontiers within life science and patient care. There is a great importance of biobank establishment globally, as biobanks has been identified as a key area for development in order to speed up the discovery and development of new drugs and protein biomarker diagnostics. One of the major objectives in Europe is to establish concerted actions, where biobank networks are being developed in order to combine and have the opportunity to share and build new science and understanding from complex disease biology. These networks are currently building bridges to facilitate the establishments of best practice and standardizations.

Mass spectrometry of peptides and proteins from human blood

It is difficult to convey the accelerating rate and growing importance of mass spectrometry applications to human blood proteins and peptides. Mass spectrometry can rapidly detect and identify the ionizable peptides from the proteins in a simple mixture and reveal many of their post-translational modifications. However, blood is a complex mixture that may contain many proteins first expressed in cells and tissues. The complete analysis of blood proteins is a daunting task that will rely on a wide range of disciplines from physics, chemistry, biochemistry, genetics, electromagnetic instrumentation, mathematics and computation. Therefore the comprehensive discovery and analysis of blood proteins will rank among the great technical challenges and require the cumulative sum of many of mankind's scientific achievements together. A variety of methods have been used to fractionate, analyze and identify proteins from blood, each yielding a small piece of the whole and throwing the great size of the task into sharp relief. The approaches attempted to date clearly indicate that enumerating the proteins and peptides of blood can be accomplished. There is no doubt that the mass spectrometry of blood will be crucial to the discovery and analysis of proteins, enzyme activities, and post-translational processes that underlay the mechanisms of disease. At present both discovery and quantification of proteins from blood are commonly reaching sensitivities of ∼1 ng/mL.

Unmasking low-abundance peptides from human blood plasma and serum samples by a simple and robust two-step precipitation/immunoaffinity enrichment method

Proteomic analysis of human plasma and serum for identifying and validating disease-specific marker proteins and peptides has one major drawback besides its unique advantage as a readily available sample source for diagnostic assays. This disadvantage is represented by the predominance of several high- and middle-abundant proteins, which clearly hamper identification and quantification approaches of potential and validated protein and peptide biomarkers, which are often of very low abundance. During the last decades, a significant number of depletion and enrichment techniques evolved to address these two issues. We present here a cost-effective and easy-to-use strategy for protein depletion comprising a thermal precipitation protocol followed by a two-step liquid/liquid precipitation as well as using an immunoaffinity chromatography method for the specific enrichment and isolation of the low-abundance polypeptide N-terminal pro-B-type natriuretic peptide and its precursor proBNP clinically used as biomarkers for the detection of severe human heart failure and related diseases. The applicability of this approach is shown by SDS -CGE, SDS-PAGE, electrochemiluminescence immunoassay and nano-LC ESI-MS/MS. Our thermal precipitation protocol followed by a two-step liquid/liquid precipitation could also serve as a potential depletion technique for the characterization of other low-abundance peptides and proteins.

Use of multiple reaction monitoring for multiplex analysis of colorectal cancer-associated proteins in human feces

Colorectal cancer (CRC) is the second most common cause of cancer-related deaths worldwide with an annual incidence of almost a million cases and an annual mortality around 500,000. The fecal occult blood test is currently the first line method for CRC screening, but has unacceptably low sensitivity and specificity. Improved screening tests are therefore urgently required for early-stage CRC screening when therapy is most likely to be effective. We describe a discovery-based proteomics hypothesis using orthogonal multi-dimensional fractionation (1-D SDS-PAGE, RP-HPLC, size exclusion chromatography) to mine deep into the fecal proteome for the initial discovery process, which generated a library containing 108 human fecal proteins with the associated peptide and MS/MS data. These data were then used to develop and optimize a multiplex multiple reaction monitoring assay for 40 non-redundant human proteins present in the feces. To show proof of principal for clinical analysis, multiplex screening of these 40 proteins was carried out on fecal samples from eight CRC patient and seven normal volunteers. We identified 24 proteins consistently found in all samples and nine proteins found only in the CRC patients, showing the potential of this approach for the analysis of potential CRC biomarkers. Absolute quantitation using C-terminal isotopically labeled synthetic peptides corresponding to hemoglobin and carcinoembryonic antigen 5 was also performed.

Proteomic detection of cancer in asbestosis patients using SELDI-TOF discovered serum protein biomarkers

OBJECTIVES

To identify biomarkers for cancer in asbestosis patients.

METHODS

SELDI-TOF and CART were used to identify serum biomarker profiles in 35 asbestosis patients who subsequently developed cancer and 35 did not develop cancer.

RESULTS

Three polypeptide peaks (5707.01, 6598.10, and 20,780.70 Da) could predict the development of cancer with 87% sensitivity and 70% specificity. The first two peaks were identified as KIF18A and KIF5A, respectively, and are part of the Kinesin Superfamily of proteins.

CONCLUSIONS

We identified two Kinesin proteins that can be potentially used as blood biomarkers to identify asbestosis patients at risk of developing lung cancer.

Usage of cancer associated autoantibodies in the detection of disease

Efforts toward deciphering the complexity of the tumor specific proteome by profiling immune responses generated against tumor associated antigens (TAAs) holds great promise for predicting the presence of cancer long before the development of clinical symptoms. The immune system is capable of sensing aberrant expression of certain cellular components involved in tumorigenesis and the resultant autoantibody response provides insights to the targets that are responsible for eliciting immunogenicity to these cellular components. Analysis of the cancer-specific humoral immune response has led to panels of biomarkers that are specific and sensitive biomarkers of disease. Using multianalyte-based in vitro analytical discovery platforms which can be easily adapted into clinical diagnostic screening tests, body fluids such as serum, plasma saliva, or urine can be interrogated to detect autoantibodies against natural or recombinant antigens, which may possess etiologic significance to cancer. Non-invasive screening tests exhibiting high specificity and sensitivity to detect early stage cancer in the heterogeneous population of cancer patients potentially have the greatest impact in decreasing mortality rates. Overall, this review summarizes different experimental approaches in the development of diagnostic screening tests for the early detection of cancer and their implementation in the development of clinical multianalyte biomarker assays.

Plasma proteome analysis in diet-induced obesity-prone and obesity-resistant rats

One of the major issues in the field of obesity is why some humans become obese and others resist development of obesity when exposed to high-calorie diets. Despite the same genetic background, namely obesity-prone (OP) and -resistant (OR) rats, differing responses have been demonstrated in a high fat diet-induced rodent model. The aim of the present study was to discover novel obesity-related biomarkers for susceptibility and/or resistance to obesity by proteomic analysis of OP and OR rat plasma. After feeding of high fat diet, OP rats gained approximately 25% more body weight than OR rats and were used for proteomic analysis using 2-DE combined with MALDI-TOF-MS. We categorized identified proteins into three groups by analysis of both average spot density in each group and individual spot density of six rats as a function of body weight. Consequently, category (1) included inter-α-inhibitor H4 heavy chain and fetuin B precursor, which can be used as novel plasma biomarkers for risk of obesity. Nine proteins of category (2) and (3) can also be plausible plasma markers in the study of obesity. This proteomic study is an important advancement over the previous steps needed for identification of OP and OR rats.

The discovery and validation of colorectal cancer biomarkers

Colorectal cancer is currently the third most common malignancy in the world. Patients have excellent prognosis following surgical resection if their tumour is still localized at diagnosis. By contrast, once the tumour has started to metastasize, prognosis is much poorer. Accurate early detection can therefore significantly reduce the mortality from this disease. However, current tests either lack the required sensitivity and selectivity or are costly and invasive. Improved biomarkers, or panels of biomarkers, are therefore urgently required. We have addressed current screening strategies and potential protein biomarkers that have been proposed. The role of both discovery and hypothesis-driven proteomics approaches for biomarker discovery and validation is discussed. Using such approaches we show how multiple reaction monitoring (MRM) can be successfully developed and used for quantitative multiplexed analysis of potential faecal biomarkers.

Prediction of the clinical outcome in invasive candidiasis patients based on molecular fingerprints of five anti-Candida antibodies in serum

Better prognostic predictors for invasive candidiasis (IC) are needed to tailor and individualize therapeutic decision-making and minimize its high morbidity and mortality. We investigated whether molecular profiling of IgG-antibody response to the whole soluble Candida proteome could reveal a prognostic signature that may serve to devise a clinical-outcome prediction model for IC and contribute to known IC prognostic factors. By serological proteome analysis and data-mining procedures, serum 31-IgG antibody-reactivity patterns were examined in 45 IC patients randomly split into training and test sets. Within the training cohort, unsupervised two-way hierarchical clustering and principal-component analyses segregated IC patients into two antibody-reactivity subgroups with distinct prognoses that were unbiased by traditional IC prognostic factors and other patients-related variables. Supervised discriminant analysis with leave-one-out cross-validation identified a five-IgG antibody-reactivity signature as the most simplified and accurate IC clinical-outcome predictor, from which an IC prognosis score (ICPS) was derived. Its robustness was confirmed in the test set. Multivariate logistic-regression and receiver-operating-characteristic curve analyses demonstrated that the ICPS was able to accurately discriminate IC patients at high risk for death from those at low risk and outperformed conventional IC prognostic factors. Further validation of the five-IgG antibody-reactivity signature on a multiplexed immunoassay supported the serological proteome analysis results. The five IgG antibodies incorporated in the ICPS made biologic sense and were associated either with good-prognosis and protective patterns (those to Met6p, Hsp90p, and Pgk1p, putative Candida virulence factors and antiapoptotic mediators) or with poor-prognosis and risk patterns (those to Ssb1p and Gap1p/Tdh3p, potential Candida proapoptotic mediators). We conclude that the ICPS, with additional refinement in future larger prospective cohorts, could be applicable to reliably predict patient clinical-outcome for individualized therapy of IC. Our data further provide insights into molecular mechanisms that may influence clinical outcome in IC and uncover potential targets for vaccine design and immunotherapy against IC.