Cancer biomarker discovery: Opportunities and pitfalls in analytical methods

A novel electrochemical impedance immunosensor for the quantification of CYFRA 21-1 in human serum

A sensitive, simple, and reliable immunosensor was constructed to detect the lowest alteration of a fragment of cytokeratin subunit 19 (CYFRA 21-1), a protein lung carcinoma biomarker. The proposed immunosensor was manufactured with a carbon black C45/polythiophene polymer-containing amino terminal groups (C45-PTNH₂) conductive nanocomposite, resulting in an excellent, biocompatible, low-cost, and electrically conductive electrode surface. Anti-CYFRA 21-1 biorecognition molecules were attached to the electrode thanks to the amino terminal groups of the used PTNH₂ polymer with a relatively simple procedure. All electrode surfaces after modifications were characterized by electrochemical, chemical, and microscopic techniques. Electrochemical impedance spectroscopy (EIS) was also utilized for the evaluation of the analytical feature of the immunosensor. The charge transfer resistance of the immunosensor signal was correlated with the CYFRA 21-1 concentration in the concentration range 0.03 to 90 pg/mL. The limit of detection (LOD) and the limit of quantification (LOQ) of the suggested system were 4.7 fg/mL and 14.1 fg/mL, respectively. The proposed biosensor had favorable repeatability and reproducibility, long storage stability, excellent selectivity, and low cost. Furthermore, it was applied to determine CYFRA 21-1 in commercial serum samples, and satisfactory recovery results (98.63-106.18%) were obtained. Thus, this immunosensor can be offered for clinical purposes as a rapid, stable, low-cost, selective, reproducible, and reusable tool.

Metabolic Phenotyping Predicts Gemcitabine and Cisplatin Chemosensitivity in Patients With Cholangiocarcinoma

Gemcitabine and cisplatin serve as appropriate treatments for patients with cholangiocarcinoma (CCA). Our previous study using histoculture drug response assay (HDRA), demonstrated individual response patterns to gemcitabine and cisplatin. The current study aimed to identify predictive biomarkers for gemcitabine and cisplatin sensitivity in tissues and sera from patients with CCA using metabolomics. Metabolic signatures of patients with CCA were correlated with their HDRA response patterns. The tissue metabolic signatures of patients with CCA revealed the inversion of the TCA cycle that is evident with increased levels of citrate and amino acid backbones as TCA cycle intermediates, and glucose which corresponds to cancer stem cell (CSC) properties. The protein expression levels of CSC markers were examined on tissues and showed the significantly inverse association with the responses of patients to cisplatin. Moreover, the elevation of ethanol level was observed in gemcitabine- and cisplatin-sensitive group. In serum, a lower level of glucose but a higher level of methylguanidine was observed in the gemcitabine-responders as non-invasive predictive biomarker for gemcitabine sensitivity. Collectively, our findings indicate that these metabolites may serve as the predictive biomarkers in clinical practice which not only predict the chemotherapy response in patients with CCA but also minimize the adverse effect from chemotherapy.

Urinary Metabolic Signatures Detect Recurrences in Non-Muscle Invasive Bladder Cancer

Patients with non-muscle invasive bladder cancer (NMIBC) undergo lifelong monitoring based on repeated cystoscopy and urinary cytology due to the high recurrence rate of this tumor. Nevertheless, these techniques have some drawbacks, namely, low accuracy in detection of low-grade tumors, omission of pre-neoplastic lesions and carcinomas in situ (CIS), invasiveness, and high costs. This work aims to identify a urinary metabolomic signature of recurrence by proton Nuclear Magnetic Resonance (¹H NMR) spectroscopy for the follow-up of NMIBC patients. To do this, changes in the urinary metabolome before and after transurethral resection (TUR) of tumors are analyzed and a Partial Least Square Discriminant Analysis (PLS-DA) model is developed. The usefulness of this discriminant model for the detection of tumor recurrences is assessed using a cohort of patients undergoing monitoring. The trajectories of the metabolomic profile in the follow-up period provide a negative predictive value of 92.7% in the sample classification. Pathway analyses show taurine, alanine, aspartate, glutamate, and phenylalanine perturbed metabolism associated with NMIBC. These results highlight the potential of ¹H NMR metabolomics to detect bladder cancer (BC) recurrences through a non-invasive approach.

Proteomic analysis of synovial fluid: current and potential uses to improve clinical outcomes

INTRODUCTION

Synovial fluid (SF) is in close proximity to tissues which are primarily altered during articular disease and has significant potential to better understand the underlying disease pathogeneses of articular pathologies and biomarker discovery. Although development of mass spectrometry-based methods has allowed faster and higher sensitivity techniques, interrogation of the SF proteome has been hindered by its large protein concentration dynamic range, impeding quantification of lower abundant proteins. Areas covered: Recent advances have developed methodologies to reduce the large protein concentration dynamic range of SF and subsequently allow deeper exploration of the SF proteome. This review concentrates on methods to overcome biofluid complexity, mass spectrometry proteomics methodologies, extracellular vesicles proteomics and the application of advances within the field in clinical disease, including osteoarthritis, rheumatoid arthritis, spondyloarthritis and juvenile arthritis. A narrative review was conducted with articles searched using PubMed, 1991-2018. Expert opinion: The SF proteomics field faces various challenges, including the requirement for rigorous and standardised methods of sample collection/storage, the sensitivity and specificity of proteomic assays, techniques to combat the large protein concentration dynamic range and comprehensive data analysis to reduce falsely identified markers. Additionally, there are challenges in developing multi 'omic' integration techniques, with computational integration enhancing analysis.

Diagnostic Biomarkers: Are We Moving from Discovery to Clinical Application?

BACKGROUND

Despite considerable research investment, moving from biomarker discovery to clinical application has presented unique challenges. We aimed to evaluate progress toward clinical application of a sample of molecular- and “omics”-based diagnostic tests over a 10-year period.

METHODS

We used Scopus to locate studies, published before the December 31, 2016, citing 107 original-research articles published in 2006 that assessed the diagnostic value of a molecular- or “omics”-based test. We identified diagnostic studies of the same test and disease and determined whether the article represented progress in the validation of the molecular test. We classified the types of progress: (a) clinical validation (measuring diagnostic accuracy in a series of patients similar to the population in which the test will be used in practice), (b) technical improvement, (c) extended diagnostic application (modification of the diagnostic question attended initially by the test), (d) economic evaluation, or (e) clinical use or implementation.

RESULTS

In the 10-year period analyzed, 4257 articles cited the 107 diagnostic studies; 118 (2.8%) were diagnostic studies of the same test, and of these papers, 25 (21.2%) did not constitute progress toward validation of the test for use in clinical practice (potential research waste). Of the 107 molecular- or “omics”-based tests described in 2006, only 28 (26.2%) appeared to have made progress toward clinical application. Only 4 (9.1%) of 44 proteomics-based tests had made progress toward clinical application.

CONCLUSIONS

Articles evaluating molecular- or “omics”-based diagnostic tests are numerous in biomedical journals. Few tests have made progress toward clinical application in the 10 years following their discovery.

Metabolomics Biomarkers for Detection of Colorectal Neoplasms: A Systematic Review

BACKGROUND

Several approaches have been suggested to be useful in the early detection of colorectal neoplasms. Since metabolites are closely related to the phenotype and are available from different human bio-fluids, metabolomics are candidates for non-invasive early detection of colorectal neoplasms.

OBJECTIVES

We aimed to summarize current knowledge on performance characteristics of metabolomics biomarkers that are potentially applicable in a screening setting for the early detection of colorectal neoplasms.

DESIGN

We conducted a systematic literature search in PubMed and Web of Science and searched for biomarkers for the early detection of colorectal neoplasms in easy-to-collect human bio-fluids. Information on study design and performance characteristics for diagnostic accuracy was extracted.

RESULTS

Finally, we included 41 studies in our analysis investigating biomarkers in different bio-fluids (blood, urine, and feces). Although single metabolites mostly had limited ability to distinguish people with and without colorectal neoplasms, promising results were reported for metabolite panels, especially amino acid panels in blood samples, as well as nucleosides in urine samples in several studies. However, validation of the results is limited.

CONCLUSIONS

Panels of metabolites consisting of amino acids in blood and nucleosides in urinary samples might be useful biomarkers for early detection of advanced colorectal neoplasms. However, to make metabolomic biomarkers clinically applicable, future research in larger studies and external validation of the results is required.

Genome-Scale Signatures of Gene Interaction from Compound Screens Predict Clinical Efficacy of Targeted Cancer Therapies

Identifying reliable drug response biomarkers is a significant challenge in cancer research. We present computational analysis of resistance (CARE), a computational method focused on targeted therapies, to infer genome-wide transcriptomic signatures of drug efficacy from cell line compound screens. CARE outputs genome-scale scores to measure how the drug target gene interacts with other genes to affect the inhibitor efficacy in the compound screens. Such statistical interactions between drug targets and other genes were not considered in previous studies but are critical in identifying predictive biomarkers. When evaluated using transcriptome data from clinical studies, CARE can predict the therapy outcome better than signatures from other computational methods and genomics experiments. Moreover, the CARE signatures for the PLX4720 BRAF inhibitor are associated with an anti-programmed death 1 clinical response, suggesting a common efficacy signature between a targeted therapy and immunotherapy. When searching for genes related to lapatinib resistance, CARE identified PRKD3 as the top candidate. PRKD3 inhibition, by both small interfering RNA and compounds, significantly sensitized breast cancer cells to lapatinib. Thus, CARE should enable large-scale inference of response biomarkers and drug combinations for targeted therapies using compound screen data.

Response biomarkers in neonatal intervention studies

BackgroundUp to 90% of all drugs used in neonatal intensive care units (NICUs) have not been clinically tested for safety and efficacy. To promote drug development for neonates, the pharmaceutical industry is moving toward rigorous testing, necessitating the need to development, and validating biomarkers in neonates to predict their response. The objective of this review is to evaluate the quality of the response biomarker reporting in neonatal clinical trials.MethodsA validated literature search strategy was applied. Prospective neonatal intervention studies reporting response biomarkers published in 2014 were included. The data were extracted independently and in duplicate using a data-extraction form.ResultsFollowing the full-text review, 167 published prospective neonatal trials were included; 35% (59/167) reported the use of response biomarkers. In these 59 trials, we identified 275 biomarkers used to measure the response (pharmacodynamics and safety) reported as primary or secondary outcomes. Heart rate and oxygen saturation were the most commonly reported. Measurement and instrumentation data were often not provided.ConclusionWe identified a huge variability in the selection, measurement, and reporting of neonatal response biomarkers in prospective intervention studies. Reporting initiatives are needed to reduce research waste and improve the reproducibility of biomarker use in neonatal intervention studies.

Review of biomarkers to assess the effects of switching from cigarettes to modified risk tobacco products

Context: One approach to reducing the harm caused by cigarette smoking, at both individual and population level, is to develop, assess and commercialize modified risk alternatives that adult smokers can switch to. Studies to demonstrate the exposure and risk reduction potential of such products generally involve the measuring of biomarkers, of both exposure and effect, sampled in various biological matrices.Objective: In this review, we detail the pros and cons for using several biomarkers as indicators of effects of changing from conventional cigarettes to modified risk products.Materials and methods: English language publications between 2008 and 2017 were retrieved from PubMed using the same search criteria for each of the 25 assessed biomarkers. Nine exclusion criteria were applied to exclude non-relevant publications.Results: A total of 8876 articles were retrieved (of which 7476 were excluded according to the exclusion criteria). The literature indicates that not all assessed biomarkers return to baseline levels following smoking cessation during the study periods but that nine had potential for use in medium to long-term studies.Discussion and conclusion: In clinical studies, it is important to choose biomarkers that show the biological effect of cessation within the duration of the study.

Origins, Technological Development, and Applications of Peptidomics

Peptidomics is the comprehensive characterization of peptides from biological sources mainly by HPLC and mass spectrometry. Mass spectrometry allows the detection of a multitude of single peptides in complex mixtures. The term first appeared in full papers in the year 2001, after over 100 years of peptide research with a main focus on one or a few specific peptides. Within the last 15 years, this new field has grown to over 1200 publications. Mass spectrometry techniques, in combination with other analytical methods, were developed for the fast and comprehensive analysis of peptides in proteomics and specifically adjusted to implement peptidomics technologies. Although peptidomics is closely linked to proteomics, there are fundamental differences with conventional bottom-up proteomics. The development of peptidomics is described, including the most important implementations for its technological basis. Different strategies are covered which are applied to several important applications, such as neuropeptidomics and discovery of bioactive peptides or biomarkers. This overview includes links to all other chapters in the book as well as recent developments of separation, mass spectrometric, and data processing technologies. Additionally, some new applications in food and plant peptidomics as well as immunopeptidomics are introduced.

Do the complementarities of electrokinetic and chromatographic procedures represent the "Swiss knife" in proteomic investigation? An overview of the literature in the past decade

This report reviews the literature of the past decade dealing with the combination of electrokinetic and chromatographic strategies in the proteomic field. Aim of this article is to highlight how the application of complementary techniques may contribute to substantially improve protein identification. Several studies here considered demonstrate that exploring the combination of these approaches can be a strategy to enrich the extent of proteomic information achieved from a sample. The coupling of "top-down" and "bottom-up" proteomics may result in the generation of a hybrid analytical tool, very efficient not only for large-scale profiling of complex proteomes but also for studying specific subproteomes. The range of applications described, while evidencing a continuous boost in the imagination of researchers for developing new combinations of methods for protein separation, also underlines the adaptability of these techniques to a wide variety of samples. This report points out the general usefulness of combining different procedures for proteomic analysis, an approach that allows researchers to go deeper in the proteome of samples under investigation.

The translation of lipid profiles to nutritional biomarkers in the study of infant metabolism

INTRODUCTION

Links between early life exposures and later health outcomes may, in part, be due to nutritional programming in infancy. This hypothesis is supported by observed long-term benefits associated with breastfeeding, such as better cognitive development in childhood, and lower risks of obesity and high blood pressure in later life. However, the possible underlying mechanisms are expected to be complex and may be difficult to disentangle due to the lack of understanding of the metabolic processes that differentiate breastfed infants compared to those receiving just formula feed.

OBJECTIVE

Our aim was to investigate the relationships between infant feeding and the lipid profiles and to validate specific lipids in separate datasets so that a small set of lipids can be used as nutritional biomarkers.

METHOD

We utilized a direct infusion high-resolution mass spectrometry method to analyse the lipid profiles of 3.2 mm dried blood spot samples collected at age 3 months from the Cambridge Baby Growth Study (CBGS-1), which formed the discovery cohort. For validation two sample sets were profiled: Cambridge Baby Growth Study (CBGS-2) and Pregnancy Outcome Prediction Study (POPS). Lipidomic profiles were compared between infant groups who were either exclusively breastfed, exclusively formula-fed or mixed-fed at various levels. Data analysis included supervised Random Forest method with combined classification and regression mode. Selection of lipids was based on an iterative backward elimination procedure without compromising the class error in the classification mode.

CONCLUSION

From this study, we were able to identify and validate three lipids: PC(35:2), SM(36:2) and SM(39:1) that can be used collectively as biomarkers for infant nutrition during early development. These biomarkers can be used to determine whether young infants (3-6 months) are breast-fed or receive formula milk.

Strategies to design clinical studies to identify predictive biomarkers in cancer research

The discovery of reliable biomarkers to predict efficacy and toxicity of anticancer drugs remains one of the key challenges in cancer research. Despite its relevance, no efficient study designs to identify promising candidate biomarkers have been established. This has led to the proliferation of a myriad of exploratory studies using dissimilar strategies, most of which fail to identify any promising targets and are seldom validated. The lack of a proper methodology also determines that many anti-cancer drugs are developed below their potential, due to failure to identify predictive biomarkers. While some drugs will be systematically administered to many patients who will not benefit from them, leading to unnecessary toxicities and costs, others will never reach registration due to our inability to identify the specific patient population in which they are active. Despite these drawbacks, a limited number of outstanding predictive biomarkers have been successfully identified and validated, and have changed the standard practice of oncology. In this manuscript, a multidisciplinary panel reviews how those key biomarkers were identified and, based on those experiences, proposes a methodological framework-the DESIGN guidelines-to standardize the clinical design of biomarker identification studies and to develop future research in this pivotal field.

Recent developments in circulating biomarkers in Parkinson’s disease: the potential use of miRNAs in a clinical setting

Parkinson’s disease (PD) is the second most common neurodegenerative disorder, affecting 5% of the elderly population. PD diagnosis is still based on the identification of neuromotor symptoms although nonmotor manifestations emerge years prior to diagnosis. The discovery of biomarkers at the earliest stages of PD is of extreme interest. miRNAs have been considered potential biomarkers for neurodegenerative diseases, but only a limited number have been found to be PD related. This review focuses on the current findings in the field of circulating miRNAs in PD and the challenges surrounding clinical utility and validation. We briefly describe the more established circulating biomarkers in PD and provide a more thorough review of miRNAs differentially expressed in PD. We highlight their potential for being considered as biomarkers for diagnosis while emphasizing the challenges for adequate validation of the findings and how miRNAs can be envisioned in a clinical setting satisfying regulatory bodies.

Metabolomic profiling identifies potential pathways involved in the interaction of iron homeostasis with glucose metabolism

Objective

Elevated serum ferritin has been linked to type 2 diabetes (T2D) and adverse health outcomes in subjects with the Metabolic Syndrome (MetS). As the mechanisms underlying the negative impact of excess iron have so far remained elusive, we aimed to identify potential links between iron homeostasis and metabolic pathways.

Methods

In a cross-sectional study, data were obtained from 163 patients, allocated to one of three groups: (1) lean, healthy controls (n = 53), (2) MetS without hyperferritinemia (n = 54) and (3) MetS with hyperferritinemia (n = 56). An additional phlebotomy study included 29 patients with biopsy-proven iron overload before and after iron removal. A detailed clinical and biochemical characterization was obtained and metabolomic profiling was performed via a targeted metabolomics approach.

Results

Subjects with MetS and elevated ferritin had higher fasting glucose (p < 0.001), HbA1c (p = 0.035) and 1 h glucose in oral glucose tolerance test (p = 0.002) compared to MetS subjects without iron overload, whereas other clinical and biochemical features of the MetS were not different. The metabolomic study revealed significant differences between MetS with high and low ferritin in the serum concentrations of sarcosine, citrulline and particularly long-chain phosphatidylcholines. Methionine, glutamate, and long-chain phosphatidylcholines were significantly different before and after phlebotomy (p < 0.05 for all metabolites).

Conclusions

Our data suggest that high serum ferritin concentrations are linked to impaired glucose homeostasis in subjects with the MetS. Iron excess is associated to distinct changes in the serum concentrations of phosphatidylcholine subsets. A pathway involving sarcosine and citrulline also may be involved in iron-induced impairment of glucose metabolism.

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This metabolomic study focuses on pathways linking iron status to insulin resistance.

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Metabolomic differences in Metabolic Syndrome with/without iron overload are shown.

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Phlebotomy changes methionine, glutamate and long-chain phosphatidylcholines levels.

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Phosphatidylcholines are involved in the interaction of iron and glucose homeostasis.

MALDI imaging mass spectrometry - From bench to bedside

Today, pathologists face many challenges in defining the precise morphomolecular diagnosis and in guiding clinicians to the optimal patients' treatment. To achieve this goal, increasingly, classical histomorphological methods have to be supplemented by high throughput molecular assays. Since MALDI imaging mass spectrometry (IMS) enables the assessment of spatial molecular arrangements in tissue sections, it goes far beyond microscopy in providing hundreds of different molecular images from a single scan without the need of target-specific reagents. Thus, this technology has the potential to uncover new markers for diagnostic purposes or markers that correlate with disease severity as well as prognosis and therapeutic response. Additionally, in the future MALDI IMS based classifiers measured with this technology in real time in the diagnostic setting might be applicable in the routine diagnostic setting. In this review, recently published studies that show the usefulness, advantages, and applicability of MALDI IMS in different fields of pathology (diagnosis, prognosis and treatment response) are highlighted. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

Building a 'Repository of Science': The importance of integrating biobanks within molecular pathology programmes

Repositories containing high quality human biospecimens linked with robust and relevant clinical and pathological information are required for the discovery and validation of biomarkers for disease diagnosis, progression and response to treatment. Current molecular based discovery projects using either low or high throughput technologies rely heavily on ready access to such sample collections. It is imperative that modern biobanks align with molecular diagnostic pathology practices not only to provide the type of samples needed for discovery projects but also to ensure requirements for ongoing sample collections and the future needs of researchers are adequately addressed. Biobanks within comprehensive molecular pathology programmes are perfectly positioned to offer more than just tumour derived biospecimens; for example, they have the ability to facilitate researchers gaining access to sample metadata such as digitised scans of tissue samples annotated prior to macrodissection for molecular diagnostics or pseudoanonymised clinical outcome data or research results retrieved from other users utilising the same or overlapping cohorts of samples. Furthermore, biobanks can work with molecular diagnostic laboratories to develop standardised methodologies for the acquisition and storage of samples required for new approaches to research such as 'liquid biopsies' which will ultimately feed into the test validations required in large prospective clinical studies in order to implement liquid biopsy approaches for routine clinical practice. We draw on our experience in Northern Ireland to discuss how this harmonised approach of biobanks working synergistically with molecular pathology programmes is a key for the future success of precision medicine.

Omics-Based Strategies in Precision Medicine: Toward a Paradigm Shift in Inborn Errors of Metabolism Investigations

The rise of technologies that simultaneously measure thousands of data points represents the heart of systems biology. These technologies have had a huge impact on the discovery of next-generation diagnostics, biomarkers, and drugs in the precision medicine era. Systems biology aims to achieve systemic exploration of complex interactions in biological systems. Driven by high-throughput omics technologies and the computational surge, it enables multi-scale and insightful overviews of cells, organisms, and populations. Precision medicine capitalizes on these conceptual and technological advancements and stands on two main pillars: data generation and data modeling. High-throughput omics technologies allow the retrieval of comprehensive and holistic biological information, whereas computational capabilities enable high-dimensional data modeling and, therefore, accessible and user-friendly visualization. Furthermore, bioinformatics has enabled comprehensive multi-omics and clinical data integration for insightful interpretation. Despite their promise, the translation of these technologies into clinically actionable tools has been slow. In this review, we present state-of-the-art multi-omics data analysis strategies in a clinical context. The challenges of omics-based biomarker translation are discussed. Perspectives regarding the use of multi-omics approaches for inborn errors of metabolism (IEM) are presented by introducing a new paradigm shift in addressing IEM investigations in the post-genomic era.

A robust biomarker of differential correlations improves the diagnosis of cytologically indeterminate thyroid cancers

The fine-needle aspiration of thyroid nodules and subsequent cytological analysis is unable to determine the diagnosis in 15 to 30% of thyroid cancer cases; patients with indeterminate cytological results undergo diagnostic surgery which is potentially unnecessary. Current gene expression biomarkers based on well-determined cytology are complex and their accuracy is inconsistent across public datasets. In the present study, we identified a robust biomarker using the differences in gene expression values specifically from cytologically indeterminate thyroid tumors and a powerful multivariate search tool coupled with a nearest centroid classifier. The biomarker is based on differences in the expression of the following genes: CCND1, CLDN16, CPE, LRP1B, MAGI3, MAPK6, MATN2, MPPED2, PFKFB2, PTPRE, PYGL, SEMA3D, SERGEF, SLC4A4 and TIMP1. This 15-gene biomarker exhibited superior accuracy independently of the cytology in six datasets, including The Cancer Genome Atlas (TCGA) thyroid dataset. In addition, this biomarker exhibited differences in the correlation coefficients between benign and malignant samples that indicate its discriminatory power, and these 15 genes have been previously related to cancer in the literature. Thus, this 15-gene biomarker provides advantages in clinical practice for the effective diagnosis of thyroid cancer.

iTRAQ-based quantitative proteomic analysis for identification of biomarkers associated with emodin against severe acute pancreatitis in rats

Emodin has potent actions against SAP injury by inhibiting the HTRA1/TGF-β1 signaling pathway and subsequent inflammatory responses.

Analysis of the Human Prostate-Specific Proteome Defined by Transcriptomics and Antibody-Based Profiling Identifies TMEM79 and ACOXL as Two Putative, Diagnostic Markers in Prostate Cancer

To better understand prostate function and disease, it is important to define and explore the molecular constituents that signify the prostate gland. The aim of this study was to define the prostate specific transcriptome and proteome, in comparison to 26 other human tissues. Deep sequencing of mRNA (RNA-seq) and immunohistochemistry-based protein profiling were combined to identify prostate specific gene expression patterns and to explore tissue biomarkers for potential clinical use in prostate cancer diagnostics. We identified 203 genes with elevated expression in the prostate, 22 of which showed more than five-fold higher expression levels compared to all other tissue types. In addition to previously well-known proteins we identified two poorly characterized proteins, TMEM79 and ACOXL, with potential to differentiate between benign and cancerous prostatic glands in tissue biopsies. In conclusion, we have applied a genome-wide analysis to identify the prostate specific proteome using transcriptomics and antibody-based protein profiling to identify genes with elevated expression in the prostate. Our data provides a starting point for further functional studies to explore the molecular repertoire of normal and diseased prostate including potential prostate cancer markers such as TMEM79 and ACOXL.

Evaluating the effects of preanalytical variables on the stability of the human plasma proteome

High quality clinical biospecimens are vital for biomarker discovery, verification, and validation. Variations in blood processing and handling can affect protein abundances and assay reliability. Using an untargeted LC-MS approach, we systematically measured the impact of preanalytical variables on the plasma proteome. Time prior to processing was the only variable that affected the plasma protein levels. LC-MS quantification showed that preprocessing times <6h had minimal effects on the immunodepleted plasma proteome, but by 4 days significant changes were apparent. Elevated levels of many proteins were observed, suggesting that in addition to proteolytic degradation during the preanalytical phase, changes in protein structure are also important considerations for protocols using antibody depletion. As to processing variables, a comparison of single- vs double-spun plasma showed minimal differences. After processing, the impact ⩽3 freeze-thaw cycles was negligible regardless of whether freshly collected samples were processed in short succession or the cycles occurred during 14-17 years of frozen storage (-80 °C). Thus, clinical workflows that necessitate modest delays in blood processing times or employ different centrifugation steps can yield valuable samples for biomarker discovery and verification studies.

[Injectable hospital preparation of valine labeled with the carbon 13 and nitrogen 15 (5 mg/mL) for a clinical trial on the brain tumor metabolism: Pharmaceutical control of active pharmaceutical ingredient and stability study of the finished product]

INTRODUCTION

The L-Valine labeled (L-[U-(13)C,(15)N] Val) is a stable isotopic tracer administered by parenteral route within the framework of a new clinical research program concerning the brain tumor metabolism. To meet regulatory requirements and have ready to use solution with an expiration date, a pharmaceutical control of active pharmaceutical ingredient followed by stability study of hospital preparation were realised.

MATERIALS AND METHODS

After the pharmaceutical control of the L-[U-(13)C,(15)N] Val, the hospital preparation was prepared according to the good manufacturing preparation. Prepared bottles were stored at 5°C±3°C and 25°C±2°C for six months. The stability of the preparation was determined by physico-chemical controls (pH, osmolality, sub-visible particles, L-[U-(13)C,(15)N] Val concentration, sodium concentration, isotopic enrichment) and microbiological (bacterial endotoxin and sterility).

RESULTS

Concentrations of L-[U-(13)C, (15)N] Val and sodium does not significantly decrease during the stability study. In parallel, no change in pH and osmolality were highlighted. Isotopic enrichment higher than 99.9% reflected the stability of labeling of L-valine molecule. The sub-visible particles, the bacterial endotoxin and sterility were in accordance with the European Pharmacopoeia attesting limpidity, apyrogenicity and sterility of this injectable preparation.

DISCUSSION AND CONCLUSION

The stability of this hospital preparation of L-[U-(13)C, (15)N] Val has been demonstrated for six months at 5°C±3°C and 25°C±2°C, ensuring a parenteral administration as part of the clinical trial.

Quantitative chemical proteomics for investigating the biomarkers of dioscin against liver fibrosis caused by CCl4 in rats

2D-DIGE technology was used for screening the biomarkers and drug-targets of dioscin against liver fibrosis in rats caused by CCl₄.

Metabonomics and toxicology

Being an emerging field of "omics" research, metabonomics has been increasingly used in toxicological studies mostly because this technology has the ability to provide more detailed information to elucidate mechanism of toxicity. As an interdisciplinary field of science, metabonomics combines analytical chemistry, bioinformatics, statistics, and biochemistry. When applied to toxicology, metabonomics also includes aspects of patho-biochemistry, systems biology, and molecular diagnostics. During a toxicological study, the metabolic changes over time and dose after chemical treatment can be monitored. Therefore, the most important use of this emerging technology is the identification of signatures of toxicity-patterns of metabolic changes predictive of a hazard manifestation. This chapter summarizes the current state of metabonomics technology and its applications in various areas of toxicological studies.

Identification of potential serum proteomic biomarkers for clear cell renal cell carcinoma

Objective

To investigate discriminating protein patterns and serum biomarkers between clear cell renal cell carcinoma (ccRCC) patients and healthy controls, as well as between paired pre- and post-operative ccRCC patients.

Methods

We used magnetic bead-based separation followed by matrix-assisted laser desorption ionization (MALDI) time-of-flight (TOF) mass spectrometry (MS) to identify patients with ccRCC. A total of 162 serum samples were analyzed in this study, among which there were 58 serum samples from ccRCC patients, 40 from additional paired pre- and post-operative ccRCC patients (n = 20), and 64 from healthy volunteers as healthy controls. ClinProTools software identified several distinct markers between ccRCC patients and healthy controls, as well as between pre- and post-operative patients.

Results

Patients with ccRCC could be identified with a mean sensitivity of 88.38% and a mean specificity of 91.67%. Of 67 m/z peaks that differed among the ccRCC, healthy controls, pre- and post-operative ccRCC patients, 24 were significantly different (P<0.05). Three candidate peaks, which were upregulated in ccRCC group and showed a tendency to return to healthy control values after surgery, were identified as peptide regions of RNA-binding protein 6 (RBP6), tubulin beta chain (TUBB), and zinc finger protein 3 (ZFP3) with the m/z values of 1466.98, 1618.22, and 5905.23, respectively.

Conclusion

MB-MALDI-TOF-MS method could generate serum peptidome profiles of ccRCC, and provide a new approach to identify potential biomarkers for diagnosis as well as prognosis of this malignancy.

Metabonomics of human colorectal cancer: new approaches for early diagnosis and biomarker discovery

Colorectal cancer (CRC) is one of the most common cancers in the world, having both high prevalence and mortality. It is usually diagnosed at advanced stages due to the limitations of current screening methods used in the clinic. There is an urgent need to develop new biomarkers and modalities to detect, diagnose, and monitor the disease. Metabonomics, an approach that involves the comprehensive profiling of the full complement of endogenous metabolites in a biological system, has demonstrated its great potential for use in the early diagnosis and personalized treatment of various cancers including CRC. By applying advanced analytical techniques and bioinformatics tools, the metabolome is mined for biomarkers that are associated with carcinogenesis and prognosis. This review provides an overview of the metabonomics workflow and studies, with a focus on recent advances and findings in biomarker discovery for the early diagnosis and prognosis of CRC.

Colorectal cancer biomarker discovery and validation using LC-MS/MS-based proteomics in blood: truth or dare?

Globally, colorectal cancer (CRC) is the third most common malignant neoplasm. However, highly sensitive, specific, noninvasive tests that allow CRC diagnosis at an early stage are still needed. As circulatory blood reflects the physiological status of an individual and/or the disease status for several disorders, efforts have been undertaken to identify candidate diagnostic CRC markers in plasma and serum. In this review, the challenges, bottlenecks and promising properties of mass spectrometry (MS)-based proteomics in blood are discussed. More specifically, important aspects in clinical design, sample retrieval, sample preparation, and MS analysis are presented. The recent developments in targeted MS approaches in plasma or serum are highlighted as well.

Cancer biomarker discovery: current status and future perspectives

PURPOSE

Cancer is a multigene disease which arises as a result of mutational and epigenetic changes coupled with activation of complex signaling networks. The use of biomarkers for early cancer detection, staging and individualization of therapy might improve patient care. A few fundamental issues such as tumor heterogeneity, a highly dynamic nature of the intrinsic and extrinsic determinants of radio- and chemoresistance, along with the plasticity and diversity of cancer stem cells (CSC) make biomarker development a challenging task. In this review we outline the preclinical strategies of cancer biomarker discovery including genomic, proteomic, metabolomic and microRNomic profiling, comparative genome hybridization (CGH), single nucleotide polymorphism (SNP) analysis, high throughput screening (HTS) and next generation sequencing (NGS). Other promising approaches such as assessment of circulating tumor cells (CTC), analysis of CSC-specific markers and cell-free circulating tumor DNA (ctDNA) are also discussed.

CONCLUSIONS

The emergence of powerful proteomic and genomic technologies in conjunction with advanced bioinformatic tools allows the simultaneous analysis of thousands of biological molecules. These techniques yield the discovery of new tumor signatures, which are sensitive and specific enough for early cancer detection, for monitoring disease progression and for proper treatment selection, paving the way to individualized cancer treatment.

Combining bioinformatics and MS-based proteomics: clinical implications

Clinical proteomics research aims at i) discovery of protein biomarkers for screening, diagnosis and prognosis of disease, ii) discovery of protein therapeutic targets for improvement of disease prevention, treatment and follow-up, and iii) development of mass spectrometry (MS)-based assays that could be implemented in clinical chemistry, microbiology or hematology laboratories. MS has been increasingly applied in clinical proteomics studies for the identification and quantification of proteins. Bioinformatics plays a key role in the exploitation of MS data in several aspects such as the generation and curation of protein sequence databases, the development of appropriate software for MS data treatment and integration with other omics data and the establishment of adequate standard files for data sharing. In this article, we discuss the main MS approaches and bioinformatics solutions that are currently applied to accomplish the objectives of clinical proteomic research.

Screening and functional analysis of a differential protein profile of human breast cancer

To improve the understanding of the enriched functions of proteins and to identify potential biomarkers in human breast cancer, the present study constructed a differentially expressed protein profile by screening immunohistochemistry maps of human breast cancer proteins. A total of 1,688 proteins were found to be differentially expressed in human breast cancer, including 773 upregulated and 915 downregulated proteins. Of these proteins, secreted and membrane proteins were screened and clustered, and more enriched biological functions and pathways were presented in the upregulated protein profiles. Furthermore, altered serum levels of peroxiredoxin (PRDX)2, PRDX6, cathepsin (CTS)B and CTSD were detected by ELISA assay. The present study provides a novel global mapping of potential breast cancer biomarkers that could be used as background to identify the altered pathways in human breast cancer, as well as potential cancer targets.

High-Throughput Analysis of Plasma Hybrid Markers for Early Detection of Cancers

Biomarkers for the early detection of cancer in the general population have to perform with high sensitivity and specificity in order to prevent the costs associated with over-diagnosis. There are only a few current tissue or blood markers that are recommended for generalized cancer screening. Despite the recognition that combinations of multiple biomarkers will likely improve their utility, biomarker panels are usually limited to a single class of molecules. Tissues and body fluids including plasma and serum contain not only proteins, DNA and microRNAs that are differentially expressed in cancers but further cancer specific information might be gleaned by comparing different classes of biomolecules. For example, the level of a certain microRNA might be related to the level of a particular protein in a cancer specific manner. Proteins might have cancer-specific post-translational modifications (e.g., phosphorylation or glycosylation) or lead to the generation of autoantibodies. Most currently approved biomarkers are glycoproteins. Autoantibodies can be produced as a host's early surveillance response to cancer-specific proteins in pre-symptomatic and pre-diagnostic stages of cancer. Thus, measurement of the level of a protein, the level of its glycosylation or phosphorylation and whether autoantibodies are produced to it can yield multi-dimensional information on each protein. We consider specific proteins that show consistent cancer-specific changes in two or three of these measurements to be "hybrid markers". We hypothesize these markers will suffer less variation between different individuals since one component can act to "standardize" the other measurement. As a proof of principle, a 180 plasma sample set consisting of 120 cases (60 colon cancers and 60 adenomas) and 60 controls were analyzed using our high-density antibody array for changes in their protein, IgG-complex and sialyl-Lewis A (SLeA) modified proteins. At p < 0.05, expression changes in 1,070 proteins, 49 IgG-complexes (11 present in the protein list) and 488 Lewis X-modified proteins (57 on the protein list) were observed. The biomarkers significant on both lists are potential hybrid markers. Thus, plasma hybrid markers have the potential to create a new class of early detection markers of cancers.

VIZR--an automated chemometric technique for metabolic profiling

A chemometric technique, visual interpretation of z-score ratios (VIZR), written in the open source code R, has been developed to identify metabolic differences between individual biosamples and a control group. To demonstrate the capabilities of VIZR, 49 urine samples were collected from healthy volunteers: 41 samples were collected randomly following a normal dietary routine and 7 test samples were collected after dietary supplementation with either ibuprofen or alcoholic beverages. An eighth test sample was prepared by 50% dilution of a control sample. Sample analysis was conducted by (1)H nuclear magnetic resonance (NMR) spectroscopy and the collected data were subjected to VIZR analysis, which successfully discriminated each of the 8 test samples from the 41 control samples. In addition, VIZR analysis revealed the NMR spectral regions responsible for the disparity between the individual test samples and the control group. The self-normalizing nature of the VIZR calculation provides a robust analysis independent of dilution effects, which is especially important in urine analyses. Potential applications of VIZR include high-throughput data analysis for toxicological profiling, disease diagnosis, and biomarker identification in any type of biosample for which a control dataset can be established. Although demonstrated herein for the statistical analysis of (1)H NMR data, the VIZR program is platform independent and could be applied to digitized metabolic datasets acquired using other techniques including hyphenated mass spectrometry measurements.