Isotope-differentiated binding energy shift tags (IDBEST™) for improved targeted biomarker discovery and validation

Mass spectrometry has proved to be an important tool for protein biomarker discovery, identification and characterization. However, global proteomic profiling strategies often fail to identify known low-abundance biomarkers as a result of the limited dynamic range of mass spectrometry (two to three orders of magnitude) compared with the large dynamic range of protein concentrations in biologic fluids (11 to 12 orders of magnitude for serum). In addition, the number of peptides generated in such methods vastly overwhelms the resolution capacity of mass spectrometers, requiring extensive sample clean-up (e.g., affinity tag, retentate chromatography and/or high-performance liquid chromatography) before mass spectrometry analysis. Baiting and affinity pre-enrichment strategies, which overcome the dynamic range and sample complexity issues of global proteomic strategies, are very difficult to couple to mass spectrometry. This is due to the fact that it is nearly impossible to sort target peptides from those of the bait since there will be many cases of isobaric peptides. IDBEST? (Target Discovery, Inc.) is a new tagging strategy that enables such pre-enrichment of specific proteins or protein classes as the resulting tagged peptides are distinguishable from those of the bait by a mass defect shift of approximately 0.1 atomic mass units. The special characteristics of these tags allow: resolution of tagged peptides from untagged peptides through incorporation of a mass defect element; high-precision quantitation of up- and downregulation by using stable isotope versions of the same tag; and potential analysis of protein isoforms through more complete peptide coverage from the proteins of interest.     

Global and targeted quantitative proteomics for biomarker discovery

The extraordinary developments made in proteomic technologies in the past decade have enabled investigators to consider designing studies to search for diagnostic and therapeutic biomarkers by scanning complex proteome samples using unbiased methods. The major technology driving these studies is mass spectrometry (MS). The basic premises of most biomarker discovery studies is to use the high data-gathering capabilities of MS to compare biological samples obtained from healthy and disease-afflicted patients and identify proteins that are differentially abundant between the two specimen. To meet the need to compare the abundance of proteins in different samples, a number of quantitative approaches have been developed. In this article, many of these will be described with an emphasis on their advantageous and disadvantageous for the discovery of clinically useful biomarkers.     

Imaging mass spectrometry in biomarker discovery and validation

Biomarker discovery and validation involves the consideration of many issues and challenges in order to be effectively used for translation from bench to bedside. Imaging mass spectrometry (IMS) is a new technology to assess spatial molecular arrangements in tissue sections, going far beyond microscopy in providing hundreds of different molecular images from a single scan without the need of target-specific reagents. The possibility to correlate distribution maps of multiple analytes with histological and clinical features makes it an ideal tool to discover diagnostic and prognostic markers of diseases. Some recently published studies that show the usefulness and advantages of this technology in the field of cancer research are highlighted.     

Proteomics as a tool for clinically relevant biomarker discovery and validation

The excitement associated with clinical applications of proteomics was initially focused on its potential to serve as a vehicle for both biomarker discovery and drug discovery and routine clinical sample analysis. Some approaches were thought to be able to "identify" mass spectral characteristics that distinguished between control and disease samples, and thereafter it was believed that the same tool could be employed to screen samples in a high-throughput clinical setting. However, this has been difficult to achieve, and the early promise is yet to be fully realized. While we see an important place for mass spectrometry in drug and biomarker discovery, we believe that alternative strategies will prove more fruitful for routine analysis. Here we discuss the power and versatility of 2D gels and mass spectrometry in the discovery phase of biomarker work but argue that it is better to rely on immunochemical methods for high-throughput validation and routine assay applications.     

A systematic review on metabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer

Introduction

Metabolomics is an emerging approach for early detection of cancer. Along with the development of metabolomics, high-throughput technologies and statistical learning, the integration of multiple biomarkers has significantly improved clinical diagnosis and management for patients.

Objectives

In this study, we conducted a systematic review to examine recent advancements in the oncometabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer.

Methods

PubMed, Scopus, and Web of Science were searched for relevant studies published before September 2017. We examined the study designs, the metabolomics approaches, and the reporting methodological quality following PRISMA statement.

Results and Conclusion

The included 25 studies primarily focused on the identification rather than the validation of predictive capacity of potential biomarkers. The sample size ranged from 10 to 8760. External validation of the biomarker panels was observed in nine studies. The diagnostic area under the curve ranged from 0.68 to 1.00 (sensitivity: 0.43–1.00, specificity: 0.73–1.00). The effects of patients’ bio-parameters on metabolome alterations in a context-dependent manner have not been thoroughly elucidated. The most reported candidates were glutamic acid and histidine in seven studies, and glutamine and isoleucine in five studies, leading to the predominant enrichment of amino acid-related pathways. Notably, 46 metabolites were estimated in at least two studies. Specific challenges and potential pitfalls to provide better insights into future research directions were thoroughly discussed. Our investigation suggests that metabolomics is a robust approach that will improve the diagnostic assessment of pancreatic cancer. Further studies are warranted to validate their validity in multi-clinical settings.

     

Protein biomarker discovery and validation: the long and uncertain path to clinical utility

Better biomarkers are urgently needed to improve diagnosis, guide molecularly targeted therapy and monitor activity and therapeutic response across a wide spectrum of disease. Proteomics methods based on mass spectrometry hold special promise for the discovery of novel biomarkers that might form the foundation for new clinical blood tests, but to date their contribution to the diagnostic armamentarium has been disappointing. This is due in part to the lack of a coherent pipeline connecting marker discovery with well-established methods for validation. Advances in methods and technology now enable construction of a comprehensive biomarker pipeline from six essential process components: candidate discovery, qualification, verification, research assay optimization, biomarker validation and commercialization. Better understanding of the overall process of biomarker discovery and validation and of the challenges and strategies inherent in each phase should improve experimental study design, in turn increasing the efficiency of biomarker development and facilitating the delivery and deployment of novel clinical tests.     

Opportunities for pharmacoproteomics in biomarker discovery

Proteomic data are a uniquely valuable resource for drug response prediction and biomarker discovery because most drugs interact directly with proteins in target cells rather than with DNA or RNA. Recent advances in mass spectrometry and associated processing methods have enabled the generation of large-scale proteomic datasets. Here we review the significant opportunities that currently exist to combine large-scale proteomic data with drug-related research, a field termed pharmacoproteomics. We describe successful applications of drug response prediction using molecular data, with an emphasis on oncology. We focus on technical advances in data-independent acquisition mass spectrometry (DIA-MS) that can facilitate the discovery of protein biomarkers for drug responses, alongside the increased availability of big biomedical data. We spotlight new opportunities for machine learning in pharmacoproteomics, driven by the combination of these large datasets and improved high-performance computing. Finally, we explore the value of pre-clinical models for pharmacoproteomic studies and the accompanying challenges of clinical validation. We propose that pharmacoproteomics offers the potential for novel discovery and innovation within the cancer landscape.     

Proteomics in asthma and COPD phenotypes and endotypes for biomarker discovery and improved understanding of disease entities

The application of proteomics to respiratory diseases, such as asthma and COPD, has been limited compared to other fields, like cancer. Both asthma and COPD are recognised to be multi-factorial and complex diseases, both consisting of clusters of multiple disease phenotypes. The complexity of these diseases combined with the inaccessibility and invasiveness of disease relevant samples have provided a hurdle to the progress of respiratory proteomics. Advances in proteomic instrumentation and methodology have led to the possibility to identify proteomes in much smaller quantities of biological material. This review focuses on the efforts in respiratory proteomics in relation to asthma and COPD, and the importance of identifying subgroups of disease entities to establish appropriate biomarkers, and to enhance the understanding of underlying mechanisms in each subgroup. Careful phenotype characterisation of patient subpopulations is required to make improvement in the field of heterogeneous diseases such as asthma and COPD, and the clusters of phenotypes are likely to encompass subgroups of disease with distinct molecular mechanisms; endotypes. The utilisation of modern advanced proteomics in endotypes of asthma and COPD will likely contribute to the increased understanding of disease mechanisms, establishment of biomarkers for these endotypes and improved patient care.     

A sequence-specific exopeptidase activity test (SSEAT) for "functional" biomarker discovery

One form of functional proteomics entails profiling of genuine activities, as opposed to surrogates of activity or active "states," in a complex biological matrix: for example, tracking enzyme-catalyzed changes, in real time, ranging from simple modifications to complex anabolic or catabolic reactions. Here we present a test to compare defined exoprotease activities within individual proteomes of two or more groups of biological samples. It tracks degradation of artificial substrates, under strictly controlled conditions, using semiautomated MALDI-TOF mass spectrometric analysis of the resulting patterns. Each fragment is quantitated by comparison with double labeled, non-degradable internal standards (all-d-amino acid peptides) spiked into the samples at the same time as the substrates to reflect adsorptive and processing-related losses. The full array of metabolites is then quantitated (coefficients of variation of 6.3-14.3% over five replicates) and subjected to multivariate statistical analysis. Using this approach, we tested serum samples of 48 metastatic thyroid cancer patients and 48 healthy controls, with selected peptide substrates taken from earlier standard peptidomics screens (i.e. the "discovery" phase), and obtained class predictions with 94% sensitivity and 90% specificity without prior feature selection (24 features). The test all but eliminates reproducibility problems related to sample collection, storage, and handling as well as to possible variability in endogenous peptide precursor levels because of hemostatic alterations in cancer patients.     

Accurate inclusion mass screening: a bridge from unbiased discovery to targeted assay development for biomarker verification

Verification of candidate biomarker proteins in blood is typically done using multiple reaction monitoring (MRM) of peptides by LC-MS/MS on triple quadrupole MS systems. MRM assay development for each protein requires significant time and cost, much of which is likely to be of little value if the candidate biomarker is below the detection limit in blood or a false positive in the original discovery data. Here we present a new technology, accurate inclusion mass screening (AIMS), designed to provide a bridge from unbiased discovery to MS-based targeted assay development. Masses on the software inclusion list are monitored in each scan on the Orbitrap MS system, and MS/MS spectra for sequence confirmation are acquired only when a peptide from the list is detected with both the correct accurate mass and charge state. The AIMS experiment confirms that a given peptide (and thus the protein from which it is derived) is present in the plasma. Throughput of the method is sufficient to qualify up to a hundred proteins/week. The sensitivity of AIMS is similar to MRM on a triple quadrupole MS system using optimized sample preparation methods (low tens of ng/ml in plasma), and MS/MS data from the AIMS experiments on the Orbitrap can be directly used to configure MRM assays. The method was shown to be at least 4-fold more efficient at detecting peptides of interest than undirected LC-MS/MS experiments using the same instrumentation, and relative quantitation information can be obtained by AIMS in case versus control experiments. Detection by AIMS ensures that a quantitative MRM-based assay can be configured for that protein. The method has the potential to qualify large number of biomarker candidates based on their detection in plasma prior to committing to the time- and resource-intensive steps of establishing a quantitative assay.     

Metagenomic biomarker discovery and explanation

This study describes and validates a new method for metagenomic biomarker discovery by way of class comparison, tests of biological consistency and effect size estimation. This addresses the challenge of finding organisms, genes, or pathways that consistently explain the differences between two or more microbial communities, which is a central problem to the study of metagenomics. We extensively validate our method on several microbiomes and a convenient online interface for the method is provided at .     

Nutrition proteomics and biomarker discovery

Baukje de Roos is a principal investigator at the University of Aberdeen, Rowett Institute of Nutrition and Health. She investigates mechanisms through which dietary fats and fatty acids, and also polyphenols, affect parameters involved in the development of heart disease in vivo. This is achieved not only by measuring their effect on conventional risk markers for heart disease but also by assessing their effect on new markers that are being developed through proteomic and mass spectrometry methods. She obtained her PhD in Human Nutrition at Wageningen University, The Netherlands, in January 2000, after which she was appointed as a post-doctoral research fellow at the Department of Vascular Biochemistry, Glasgow Royal Infirmary, in collaboration with GlaxoSmithKline. In June 2001 she joined the Rowett Research Institute in Aberdeen. She is currently working for the University of Aberdeen, where her research is funded by the Scottish Government Rural and Environment Research and Analysis Directorate (RERAD). She is an active member of the European Nutrigenomics Organisation (NuGO), an EU-funded Network of Excellence, which merges the nutrigenomics activities of its 23 partners across Europe.     

Protein biomarker discovery for head and neck cancer

Squamous cell carcinoma of the head and neck (HNSCC) is the sixth most common cancer worldwide. Despite improvements in diagnosis and treatment, the five-year-survival rate of advanced HNSCC has only moderately increased, which is largely due to the high proportion of patients that present with advanced disease stage and the frequent development of relapse and second primary tumors. Protein biomarkers allowing early detection of primary HNSCC or relapse may aid to improve clinical outcome. Screening for precursor changes in the mucosal linings preceding the development of invasive tumors and for accurate prediction of risk of malignant transformation, may be propitious opportunities, which are as yet difficult. This review summarizes recent results in HNSCC proteomics for biomarker research. Despite the wide diversity of experimental designs, a few common markers have been detected. Although some of these potential biomarkers are very promising, they still have to be further clinically validated. Finally, treatment of advanced cancers of several sites within the head and neck has shifted significantly during the last decade, and also, targeted drugs have entered the clinic. This has major consequences for the research questions in HNSCC research and accordingly for the future direction of proteome research in HNSCC biomarker discovery.     

The ProteinChip Biomarker System from Ciphergen Biosystems: a novel proteomics platform for rapid biomarker discovery and validation

Protein biochips are emerging in two distinct formats. The first involves high-density immobilized arrays of recognition molecules (e.g. antibodies), where target binding is monitored indirectly (e.g. via fluorescence). This technology is in its infancy, being limited by the availability of suitable binding molecules that can cope effectively with protein diversity. The second format involves the capture of proteins by biochemical or intermolecular interaction, coupled with direct detection by MS. This technology is available as the ProteinChip Biomarker System. ProteinChip technology uses surface-enhanced laser desorption/ionization processes to analyse proteins directly from biological samples. Chromatographic surfaces are placed on to ProteinChip Arrays and used to capture subclasses of proteins, dependent on their physical properties. Time-of-flight MS then assigns native molecular masses to the captured proteins. Reproducible protein profiles can be generated from crude biological fluids (e.g. cell lysates, urine or serum). The technology is being applied to a wide range of disciplines, from plant sciences to cancer research, and will be reviewed here.     

Innovative proteomic approaches for cancer biomarker discovery

Substantial technological advances in proteomics and related computational science have been made in the past few years. These advances overcome in part the complexity and heterogeneity of the human proteome, permitting quantitative analysis and identification of protein changes associated with tumor development. Here, we discuss some of these advances that are uncovering new cancer biomarkers that have potential to detect cancer at early and curable stages and address remaining challenges.