What is a biomarker? Research investments and lack of clinical integration necessitate a review of biomarker terminology and validation schema

Mass spectrometry-based proteomic approaches for salivary protein biomarkers discovery and dental caries diagnosis: A critical review

Dental caries is a multifactorial chronic disease resulting from the intricate interplay among acid-generating bacteria, fermentable carbohydrates, and several host factors such as saliva. Saliva comprises several proteins which could be utilized as biomarkers for caries prevention, diagnosis, and prognosis. Mass spectrometry-based salivary proteomics approaches, owing to their sensitivity, provide the opportunity to investigate and unveil crucial cariogenic pathogen activity and host indicators and may demonstrate clinically relevant biomarkers to improve caries diagnosis and management. The present review outlines the published literature of human clinical proteomics investigations on caries and extensively elucidates frequently reported salivary proteins as biomarkers. This review also discusses important aspects while designing an experimental proteomics workflow. The protein-protein interactions and the clinical relevance of salivary proteins as biomarkers for caries, together with uninvestigated domains of the discipline are also discussed critically.

Comprehensive transcriptomic profiling of liver cancer identifies that histone and PTEN are major regulators of SCU‑induced antitumor activity

Worldwide, liver cancer is the most frequent fatal malignancy. Liver cancer prognosis is poor because patients frequently receive advanced-stage diagnoses. The current study aimed to establish the potential pharmacological targets and the biological networks of scutellarein (SCU) in liver cancer, a natural product known to have low toxicity and side effects. To identify the differentially expressed genes between SCU-treated and SCU-untreated HepG2 cells, RNA sequencing (RNA-seq) was carried out. A total of 463 genes were revealed to have differential expression, of which 288 were upregulated and 175 were downregulated in the group that had received SCU treatment compared with a control group. Gene Ontology (GO) enrichment analysis of associated biological process terms revealed they were mostly involved in the regulation of protein heterodimerization activity and nucleosomes. Interaction of protein-protein network analysis using Search Tool for the Retrieval of Interacting Genes/Proteins resulted in two crucial interacting hub targets; namely, histone H1-4 and protein tyrosine phosphatase receptor type C. Additionally, the crucial targets were validated using western blotting. Overall, the present study demonstrated that the use of RNA-seq data, with bioinformatics tools, can provide a valuable resource to identify the pharmacological targets that could have important biological roles in liver cancer.

Computational reactive-diffusive modeling for stratification and prognosis determination of patients with breast cancer receiving Olaparib

Mathematical models based on partial differential equations (PDEs) can be exploited to handle clinical data with space/time dimensions, e.g. tumor growth challenged by neoadjuvant therapy. A model based on simplified assessment of tumor malignancy and pharmacodynamics efficiency was exercised to discover new metrics of patient prognosis in the OLTRE trial. We tested in a 17-patients cohort affected by early-stage triple negative breast cancer (TNBC) treated with 3 weeks of olaparib, the capability of a PDEs-based reactive-diffusive model of tumor growth to efficiently predict the response to olaparib in terms of SUV_max detected at ¹⁸FDG-PET/CT scan, by using specific terms to characterize tumor diffusion and proliferation. Computations were performed with COMSOL Multiphysics. Driving parameters governing the mathematical model were selected with Pearson's correlations. Discrepancies between actual and computed SUV_max values were assessed with Student's t test and Wilcoxon rank sum test. The correlation between post-olaparib true and computed SUV_max was assessed with Pearson's r and Spearman's rho. After defining the proper mathematical assumptions, the nominal drug efficiency (ε_PD) and tumor malignancy (r_c) were computationally evaluated. The former parameter reflected the activity of olaparib on the tumor, while the latter represented the growth rate of metabolic activity as detected by SUV_max. ε_PD was found to be directly dependent on basal tumor-infiltrating lymphocytes (TILs) and Ki67% and was detectable through proper linear regression functions according to TILs values, while r_c was represented by the baseline Ki67-to-TILs ratio. Predicted post-olaparib SUV*_max did not significantly differ from original post-olaparib SUV_max in the overall, gBRCA-mutant and gBRCA-wild-type subpopulations (p > 0.05 in all cases), showing strong positive correlation (r = 0.9 and rho = 0.9, p < 0.0001 both). A model of simplified tumor dynamics was exercised to effectively produce an upfront prediction of efficacy of 3-week neoadjuvant olaparib in terms of SUV_max. Prospective evaluation in independent cohorts and correlation of these outcomes with more recognized efficacy endpoints is now warranted for model confirmation and tailoring of escalated/de-escalated therapeutic strategies for early-TNBC patients.

Proteomic Applications and Considerations: From Research to Patient Care

Despite technological advancements in the field of proteomics, the rate at which serum and plasma biomarkers identified using proteomic approaches are translated into clinical use remains extremely low. In this chapter, we describe recent technological advancements and analytical strategies in proteomic methods. We also describe the progress of proteomic blood-based biomarkers to date and discuss what the future of proteomics might entail with the use of multi-omic approaches and implementing machine learning on large proteomic datasets. Lastly, we provide several key considerations for biomarker studies, ranging from sample type to the use of reference samples, in order to achieve progress from bench to bedside, ultimately improving patient diagnosis, disease, and/or therapeutic monitoring and care.

Histamine and Serotonin Levels in Bone Marrow Stem Cells Niche as Potential Biomarkers of Systemic Mastocytosis and Myeloproliferative Disorders

Bone marrow studies currently provide a lot of valuable information in the diagnostics of hematological diseases including hematopoietic stem cells disorders. Our studies on low-molecular weight organic compounds in bone marrow stem cell niche in various pathogenic conditions, revealed relatively high variability of histamine levels in different groups of hematological diseases. It was also found that serotonin levels were significantly lower than those typically measured in peripheral blood as well as many have the influence on stem cells proliferative potential. This paper presents findings from quantitative and statistical analyses of histamine and serotonin levels. Bone marrow collected from patients undergoing routine diagnostic procedures for hematological diseases and receiving inpatient treatment were analyzed. Histamine and serotonin levels were measured using hydrophilic interaction liquid chromatography (HILIC) coupled with tandem mass spectrometry. Obtained data were analyzed statistically and correlated with the diagnosed groups of hematological diseases and the parameters of complete blood counts. Histamine was found in all tested samples, including those from patients without malignancy, and the reported levels were comparable to the reference values in blood. This observation allows us to assume that bone marrow cells can produce and accumulate histamine. Moreover, the statistical analysis revealed a significant relationship between histamine levels and diagnosed mastocytosis, and between histamine levels and myeloproliferative neoplasms. Different results were obtained for serotonin, and its concentrations in most cases were below the limit of quantification of the method used (< 0.2 ng/mL), which can only be compared to peripheral blood plasma. In a few cases, significantly higher serotonin levels were observed and it concerned diseases associated with an increased number of megakaryocytes in the bone marrow.

Development of an ELISA displaying similar reactivity with reduced and oxidized human Thioredoxin-1 (Trx1): The plasma level of Trx1 in early onset psychosis disorders

The plasma level of human thioredoxin-1 (Trx1) has been shown to be increased in various somatic diseases and psychiatric disorders. However, when comparing the reported plasma levels of Trx1, a great inter-study variability, as well as variability in study outcomes of differences between patients and control subjects has been observed, ultimately limiting the possibility to make comparative analyses. Trx1 is a highly redox active protein prone to form various redox forms, e.g. dimers, oligomers or Trx1-protein complexes. We have recently shown that ELISA systems may vary in reactivity to various Trx1 redox forms. The primary aim of the present study was to develop an ELISA system with similar reactivity to various Trx1 redox forms. By evaluating a panel of novel monoclonal antibodies (mAbs), in various paired combinations, three ELISA systems were generated, with observed large variability in reactivity to various Trx1 redox forms. Importantly, an ELISA system (capture mAb MT17R6 and detection mAb MT13X3-biotin), was identified that displayed similar reactivity to oxidized and DTT reduced Trx1. The ELISA system (MT17R6/MT13X3-biotin), was subsequently used to analyze the level of Trx1 in plasma from patients (<18 years) with early onset psychosis disorders (EOP). However, no significant (p > 0.7) difference in plasma Trx1 levels between patients with EOP (n = 23) and healthy age matched controls (HC) (n = 20) were observed. Furthermore, reliable measurement was shown to be dependent on the establishment of platelet poor plasma samples, enabled by rigorous blood sample centrifugation and by efficient blocking of potentially interfering heterophilic antibodies. In conclusion, we report the design and characterization of a Trx1 ELISA system with similar reactivity to various Trx1 redox forms. Importantly, data indicated that generated ELISA systems show large variability in reactivity to various redox forms with ultimate impact on measured levels of Trx1. Overall, results from this study suggests that future studies may be strongly improved by the use of Trx1 ELISA systems with characterized specificity to various redox forms.

Companion Diagnostics: State of the Art and New Regulations

Companion diagnostics (CDx) hail promise of improving the drug development process and precision medicine. However, there are various challenges involved in the clinical development and regulation of CDx, which are considered high-risk in vitro diagnostic medical devices given the role they play in therapeutic decision-making and the complications they may introduce with respect to their sensitivity and specificity. The European Union (E.U.) is currently in the process of bringing into effect in vitro Diagnostic Medical Devices Regulation (IVDR). The new Regulation is introducing a wide range of stringent requirements for scientific validity, analytical and clinical performance, as well as on post-market surveillance activities throughout the lifetime of in vitro diagnostics (IVD). Compliance with General Safety and Performance Requirements (GSPRs) adopts a risk-based approach, which is also the case for the new classification system. This changing regulatory framework has an impact on all stakeholders involved in the IVD Industry, including Authorized Representatives, Distributors, Importers, Notified Bodies, and Reference Laboratories and is expected to have a significant effect on the development of new CDx.

Mass Spectrometry-based Proteomics and Glycoproteomics in COVID-19 Biomarkers Identification: A Mini-review

The first corona-pandemic, coronavirus disease 2019 (COVID-19) caused a huge health crisis and incalculable damage worldwide. Knowledge of how to cure the disease is urgently needed. Emerging immune escaping mutants of the virus suggested that it may be potentially persistent in human society as a regular health threat as the flu virus. Therefore, it is imperative to identify appropriate biomarkers to indicate pathological and physiological states, and more importantly, clinic outcomes. Proteins are the performers of life functions, and their abundance and modification status can directly reflect the immune status. Protein glycosylation serves a great impact in modulating protein function. The use of both unmodified and glycosylated proteins as biomarkers has also been proved feasible in the studies of SARS, Zika virus, influenza, etc. In recent years, mass spectrometry-based glycoproteomics, as well as proteomics approaches, advanced significantly due to the evolution of mass spectrometry. We focus on the current development of the mass spectrometry-based strategy for COVID-19 biomarkers' investigation. Potential application of glycoproteomics approaches and challenges in biomarkers identification are also discussed.

Drug Repurposing: An Emerging Tool for Drug Reuse, Recycling and Discovery

Drug repositioning or repurposing is a revolutionary breakthrough in drug development that focuses on rediscovering new uses for old therapeutic agents. Drug repositioning can be defined more precisely as the process of exploring new indications for an already approved drug while drug repurposing includes overall re-development approaches grounded in the identical chemical structure of the active drug moiety as in the original product. The repositioning approach accelerates the drug development process, curtails the cost and risk inherent to drug development. The strategy focuses on the polypharmacology of drugs to unlocks novel opportunities for logically designing more efficient therapeutic agents for unmet medical disorders. Drug repositioning also expresses certain regulatory challenges that hamper its further utilization. The review outlines the eminent role of drug repositioning in new drug discovery, methods to predict the molecular targets of a drug molecule, advantages that the strategy offers to the pharmaceutical industries, explaining how the industrial collaborations with academics can assist in the discovering more repositioning opportunities. The focus of the review is to highlight the latest applications of drug repositioning in various disorders. The review also includes a comparison of old and new therapeutic uses of repurposed drugs, assessing their novel mechanisms of action and pharmacological effects in the management of various disorders. Various restrictions and challenges that repurposed drugs come across during their development and regulatory phases are also highlighted.

Does Hepatocellular Carcinoma Surveillance Increase Survival in At-Risk Populations? Patient Selection, Biomarkers, and Barriers

Hepatocellular carcinoma (HCC) is a highly morbid and prevalent cancer globally. While high quality evidence for mortality benefit of HCC surveillance is lacking, early detection of HCC is likely beneficial as prognosis is highly correlated with tumor stage. High risk populations, including patients with cirrhosis and subgroups with Hepatitis B, should undergo surveillance with ultrasound ± alpha-fetoprotein (AFP) at 6-month intervals. In addition, emerging data suggest that patients with Hepatitis C cirrhosis who achieve sustained virologic response should continue surveillance. Further research is needed to determine the value of surveillance in patients with nonalcoholic fatty liver disease in the absence of cirrhosis or with advanced fibrosis of other etiologies. Newer biomarkers and models such as Lens culinaris agglutinin-reactive fraction of AFP, des-γ-carboxy prothrombin, and the GALAD score are increasingly utilized in the diagnosis and prognostication of HCC. The role of these biomarkers in surveillance is still under investigation but may potentially offer a more practical alternative to traditional image-based surveillance. Despite recommendations from multiple professional society guidelines, many at-risk patients do not receive HCC surveillance due to barriers at the patient, clinician, and health care system levels. Strategies such as implementing patient navigation services, educating clinicians about surveillance guidelines, and creating automated outreach systems, may improve surveillance rates and ultimately reduce morbidity and mortality from HCC.

Making new biomarkers a reality: the case of serum human epididymis protein 4

Background Measurement of human epididymis protein 4 (HE4) in serum has recently been proposed for clinical use in the framework of ovarian cancer (OvCa). We sought to retrace the translational phase and the clinical implementation steps boosting HE4's clinical value and discuss the effects of its introduction on the diagnostic and management pathways. Methods Meta-analyses of running evidence have preliminarily suggested that HE4 may overcome carbohydrate antigen 125 (CA125) in identifying OvCa, showing however several gaps that need to be considered, i.e. definition of biomarker diagnostic performance in the early detection of OvCa, added diagnostic value, biological and lifestyle factors of variation, and optimal interpretative criteria. Investigation of the influencing factors has shown that renal impairment represents a major limitation for HE4's diagnostic power. On the other hand, the demonstration of the substantial equivalence of results obtained by commercially available assays allows recommending harmonized thresholds for diagnostic purpose, even if the study of HE4's biological variation has clarified that the longitudinal interpretation of the biomarker changes according to the reference change value could be more appropriate. Summary We used HE4 as an example for describing the long and bumpy road for making a new biomarker a reality, and the issues that should be checked and the information that should be provided in moving a novel biomarker from its discovery to an effective clinical adoption.

A magneto-microfluidic platform for fluorescence immunosensing using quantum dot nanoparticles

This study reports the online fluorescent detection of carcinoembryonic antigen (CEA) and α-fetoprotein (AFP) biomarker proteins in microfluidic channels using functional nanoparticles. Functional magnetic nanoparticles labeled with two antibodies were predeposited on separated microfluidic channels. Antigens were passed through each microfluidic channel to react with the respective antibodies. Two types of fluorescent nanoparticles labeled with antibodies were then used to detect and confirm antigens in the immunocomplex. Results indicate that online fluorescent detection of proteins can provide advantages for real-time monitoring and diagnostic applications. The running time was less than 20 min for each trial. The detection limits of CEA and AFP were found to be 0.6 and 0.2 pg ml^-1. These detection limits are lower than those of ELISA. The linear ranges of CEA and AFP detection were from 1.8 pg ml^-1 to 1.8 ng ml^-1 and from 0.68 pg ml^-1 to 0.68 ng ml^-1 for two deposition zones in a magnetic sandwich immunoassay. The linear ranges of this method are wider than those of ELISA and those of most other methods. The measurements of CEA and AFP in serum samples from this method differed from ELISA results by 11% and 9.4%, respectively. The detection limit of online detection has achieved the same range as those of previous offline detection. This method has a good potential for automation and multichannel analysis to increase the throughput with some modifications in the future. The proposed method can provide simple, fast, and sensitive online detection for biomarkers.

Glycolysis gene expression profilings screen for prognostic risk signature of hepatocellular carcinoma

Metabolic changes are the markers of cancer and have attracted wide attention in recent years. One of the main metabolic features of tumor cells is the high level of glycolysis, even if there is oxygen. The transformation and preference of metabolic pathways is usually regulated by specific gene expression. The aim of this study is to develop a glycolysis-related risk signature as a biomarker via four common cancer types. Only hepatocellular carcinoma was shown the strong relationship with glycolysis. The mRNA sequencing and chip data of hepatocellular carcinoma, breast invasive carcinoma, renal clear cell carcinoma, colorectal adenocarcinoma were included in the study. Gene set enrichment analysis was performed, profiling three glycolysis-related gene sets, it revealed genes associated with the biological process. Univariate and multivariate Cox proportional regression models were used to screen out prognostic-related gene signature. We identified six mRNAs (DPYSL4, HOMER1, ABCB6, CENPA, CDK1, STMN1) significantly associated with overall survival in the Cox proportional regression model for hepatocellular carcinoma. Based on this gene signature, we were able to divide patients into high-risk and low-risk subgroups. Multivariate Cox regression analysis showed that prognostic power of this six gene signature is independent of clinical variables. Further, we validated this data in our own 55 paired hepatocellular carcinoma and adjacent tissues. The results showed that these proteins were highly expressed in hepatocellular carcinoma tissues compared with adjacent tissue. The survival time of high-risk group was significantly shorter than that of low-risk group, indicating that high-risk group had poor prognosis. We calculated the correlation coefficients between six proteins and found that these six proteins were independent of each other. In conclusions, we developed a glycolysis-related gene signature that could predict survival in hepatocellular carcinoma patients. Our findings provide novel insight to the mechanisms of glycolysis and it is useful for identifying patients with hepatocellular carcinoma with poor prognoses.

Mass spectrometry-based proteomic techniques to identify cerebrospinal fluid biomarkers for diagnosing suspected central nervous system infections. A systematic review

OBJECTIVES

Central nervous system (CNS) infections account for considerable death and disability every year. An urgent research priority is scaling up diagnostic capacity, and introduction of point-of-care tests. We set out to assess current evidence for the application of mass spectrometry (MS) peptide sequencing in identification of diagnostic biomarkers for CNS infections.

METHODS

We performed a systematic review (PROSPEROCRD42018104257) using PRISMA guidelines on use of MS to identify cerebrospinal fluid (CSF) biomarkers for diagnosing CNS infections. We searched PubMed, Embase, Web of Science, and Cochrane for articles published from 1 January 2000 to 1 February 2019, and contacted experts. Inclusion criteria involved primary research except case reports, on the diagnosis of infectious diseases except HIV, applying MS to human CSF samples, and English language.

RESULTS

4,620 papers were identified, of which 11 were included, largely confined to pre-clinical biomarker discovery, and eight (73%) published in the last five years. 6 studies performed further work termed verification or validation. In 2 of these studies, it was possible to extract data on sensitivity and specificity of the biomarkers detected by ELISA, ranging from 89-94% and 58-92% respectively.

CONCLUSIONS

The findings demonstrate feasibility and potential of the methods in a variety of infectious diseases, but emphasise the need for strong interdisciplinary collaborations to ensure appropriate study design and biomarker validation.

Defining Pollinator Health: A Holistic Approach Based on Ecological, Genetic, and Physiological Factors

Evidence for global bee population declines has catalyzed a rapidly evolving area of research that aims to identify the causal factors and to effectively assess the status of pollinator populations. The term pollinator health emerged through efforts to understand causes of bee decline and colony losses, but it lacks a formal definition. In this review, we propose a definition for pollinator health and synthesize the available literature on the application of standardized biomarkers to assess health at the individual, colony, and population levels. We focus on biomarkers in honey bees, a model species, but extrapolate the potential application of these approaches to monitor the health status of wild bee populations. Biomarker-guided health measures can inform beekeeper management decisions, wild bee conservation efforts, and environmental policies. We conclude by addressing challenges to pollinator health from a One Health perspective that emphasizes the interplay between environmental quality and human, animal, and bee health.

Ultrasensitive detection of disease biomarkers using an immuno-wall device with enzymatic amplification

We present an ultrasensitive immunoassay system for disease biomarkers utilizing the immuno-wall device and an enzymatic amplification reaction. The immuno-wall device consisted of 40 microchannels, each of which contained an antibody-modified wall-like structure along the longitudinal axis of the microchannel. The wall was fabricated with a water-soluble photopolymer containing streptavidin by photolithography, and biotinylated capture antibodies were immobilized on the sides through streptavidin-biotin interaction. For an assay, introducing the target biomarker and secondary and labeled antibodies produced a sandwich complex anchored on the sides of the wall. A conventional immuno-wall device uses a fluorescence-labeled antibody as a labeling antibody. To achieve an ultrasensitive detection of a trace biomarker, we used an enzyme label and amplified the signal with the enzymatic reaction with a fluorogenic substrate in the microchannel. The highest signal/background ratio was obtained by using alkaline phosphatase-labeled antibody and 9H-(1,3-dichloro-9,9-dimethylacridin-2-one-7-yl) phosphate. To evaluate the device performance, we detected human C-reactive protein (CRP) as a model biomarker. The detection limit (LOD) of CRP in phosphate-buffered saline was 2.5 pg mL^-1 with a sample volume of 0.25 μL. This LOD was approximately 3 orders of magnitude lower than that obtained with fluorescent-dye (DyLight 650)-labeled antibody. In addition, the present device provided a wide detection range of 0.0025-10 ng mL^-1 for CRP. We successfully developed an ultrasensitive immunoassay system with simple operation and only a small sample volume.

Metabolomic-Based Methods in Diagnosis and Monitoring Infection Progression

A robust biomarker screening and validation is crucial for overcoming the current limits in the clinical management of infectious diseases. In this chapter, a general workflow for metabolomics is summarized. Subsequently, an overview of the major contributions of this omics science to the field of biomarkers of infectious diseases is discussed. Different approaches using a variety of analytical platforms can be distinguished to unveil the key metabolites for the diagnosis, prognosis, response to treatment and susceptibility for infectious diseases. To allow the implementation of such biomarkers into the clinics, the performance of large-scale studies employing solid validation criteria becomes essential. Focusing on the etiological agents and after an extensive review of the field, we present a comprehensive revision of the main metabolic biomarkers of viral, bacterial, fungal, and parasitic diseases. Finally, we discussed several articles which show the strongest validation criteria. Following these research avenues, precious clinical resources will be revealed, allowing for reduced misdiagnosis, more efficient therapies, and affordable costs, ultimately leading to a better patient management.

A review of the most promising biomarkers for early diagnosis and prognosis prediction of tongue squamous cell carcinoma

BACKGROUND

There is a great interest in developing biomarkers to enhance early detection and clinical management of tongue squamous cell carcinoma (TSCC). However, the developmental path towards a clinically valid biomarker remains extremely challenging. Ideally, the initial key step in moving a newly discovered biomarker towards clinical implementation is independent replication. Therefore, the focus of this review is on biomarkers that consistently showed clinical relevance in two or more publications.

METHODS

We searched PubMed database for relevant papers across different TSCC sample sources, i.e., body fluids (saliva, serum/plasma) and tissues. No restriction regarding the date of publication was applied except for immunohistochemistry (IHC); only studies published between 2010 and June 2017 were included.

RESULTS

The search strategy identified 1429 abstracts, of which 96 papers, examining 150 biomarkers, were eventually included. Of these papers, 66% were exploratory studies evaluating single or a panel of biomarkers in one publication. Ultimately, based on studies that had undergone validation for their clinical relevance in at least two independent studies, we identified 10 promising candidates, consisting of different types of molecules (IL-6, IL-8, and Prolactin in liquid samples; HIF-1α, SOX2, E-cadherin, vimentin, MALAT1, TP53, and NOTCH1 in tissue biopsies) CONCLUSIONS: Although more exploratory research is needed with newer methods to identify biomarkers for TSCC, rigorous validation of biomarkers that have already shown unbiased assessment in at least two publications should be considered a high priority. Further research on these promising biomarkers or their combination in multi-institutional studies, could provide new possibilities to develop a specific panel for early diagnosis, prognosis, and individualized treatments.

A large-scale and robust dynamic MRM study of colorectal cancer biomarkers

Over the past 20 years, mass spectrometry (MS) has emerged as a dynamic tool for proteomics biomarker discovery. However, published MS biomarker candidates often do not translate to the clinic, failing during attempts at independent replication. The cause can be shortcomings in study design, sample quality, assay quantitation, and/or quality/process control. To address these shortcomings, we developed an MS workflow in accordance with Tier 2 measurement requirements for targeted peptides, defined by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) "fit-for-purpose" approach, using dynamic multiple reaction monitoring (dMRM), which measures specific peptide transitions during predefined retention time (RT) windows. We describe the development of a robust multipex dMRM assay measuring 641 proteotypic peptides from 392 colorectal cancer (CRC) related proteins, and the procedures to track and handle sample processing and instrument variation over a four-month study, during which the assay measured blood samples from 1045 patients with CRC symptoms. After data collection, transitions were filtered by signal quality metrics before entering receiver operating characteristic (ROC) analysis. The results demonstrated CRC signal carried by 127 proteins in the symptomatic population. The workflow might be further developed to build Tier 1 assays for clinical tests identifying symptomatic individuals at elevated risk of CRC.

SIGNIFICANCE

We developed a dMRM MS method with the rigor of a Tier 2 assay as defined by the CPTAC 'fit for purpose approach' [1]. Using quality and process control procedures, the assay was used to quantify 641 proteotypic peptides representing 392 CRC-related proteins in plasma from 1045 CRC-symptomatic patients. To our knowledge, this is the largest MRM method applied to the largest study to date. The results showed that 127 of the proteins carried univariate CRC signal in the symptomatic population. This large number of single biomarkers bodes well for future development of multivariate classifiers to distinguish CRC in the symptomatic population.

Biomarker development for hepatocellular carcinoma early detection: current and future perspectives

Early detection of hepatocellular carcinoma (HCC) leads to improved survival; however, current early detection strategies for HCC surveillance are ineffective. Thus, there has been interest in developing biomarkers to aid in the early detection HCC. In this review, we discuss the five phases of biomarker discovery that are necessary for clinical implementation. We also describe the most promising investigational biomarkers and their phase of discovery. We review several promising technologies for the early detection of HCC, including miRNA, metabolomics and proteomics. Promisingly, there are samples from multiple longitudinal cohorts of patients with cirrhosis in the USA that are being collected in order to validate candidate biomarkers for HCC. A biomarker-based strategy has the potential to become the primary surveillance method for HCC detection.

Inflammation, Stress Response, and Redox Dysregulation Biomarkers: Clinical Outcomes and Pharmacological Implications for Psychosis

In recent years, several studies claiming the finding of a specific biomarker for the identification of the "high-risk state" to develop psychosis, first psychotic episode, as well as the prediction of the individual response to antipsychotics have been published. Together with genetic reports, numerous publications in this field have been focused on inflammation and stress response blood biomarkers, as well as on indicators of redox dysregulation. In this review, we focus on human studies found in PubMed from January 1^st 2010 to January 31^st 2017, describing the clinical use of these biomarkers to detect the "premorbid" psychotic state and early phases of the disease. Their pharmacological implications in predicting and monitoring the individual response to antipsychotic medication is also discussed.

Fibrinogen alpha C chain 5.9 kDa fragment (FIC5.9), a biomarker for various pathological conditions, is produced in post-blood collection by fibrinolysis and coagulation factors

BACKGROUND

Fibrinogen alpha C chain 5.9 kDa fragment (FIC5.9) is a new serum biomarker for chronic hepatitis that was discovered by proteomics analysis. Previous studies have shown that FIC5.9 is derived from the C-terminal region of fibrinogen alpha chain and the serum levels of FIC5.9 decrease in chronic hepatitis. It also have been reported that FIC5.9 cannot be detected in the blood stream of the systemic circulation and it is released from fibrinogen during blood clotting in collecting tube. However, the mechanism of FIC5.9 releasing from fibrinogen is unclear.

METHODS

We formulated a hypothesis that FIC5.9 is released by enzymes that are activated by post-blood collection and may be coagulation and fibrinolysis factors. In this study, we analyzed the mechanisms of FIC5.9 releasing from fibrinogen in healthy blood.

RESULTS

Our analysis showed that thrombin acts as an initiator for FIC5.9 releasing, and that mainly plasmin cleaves N-terminal end of FIC5.9 and neutrophil elastase cleave C-terminal end of FIC5.9.

CONCLUSION

FIC5.9 reflects minute changes in coagulation and fibrinolysis factors and may be associated with pathological conditions.

Identification and functional characterization of microRNAs reveal a potential role in gastric cancer progression

PURPOSE

To investigate the potential candidate microRNA (miRNA) biomarkers for the clinical diagnosis, classification, and prognosis of gastric cancer (GC).

METHODS

We use bioinformatics overlapping subclasses analysis to find the tumor grade and lymphatic metastasis-related GC specific miRNAs from the Cancer Genome Atlas (TCGA) database. Then, we further investigated these GC specific miRNAs distributions in different GC clinical features and their correlations overall survival on the basis of GC patients' information and their related RNA sequencing profile from TCGA. Finally, we randomly selected some of key miRNAs use qRT-PCR to confirm the reliability and validity.

RESULTS

22 GC specific key miRNAs were identified (Fold-change >2, P < 0.05), 11 of them were discriminatively expressed with tumor size, grade, TNM stage and lymphatic metastasis (P < 0.05). In addition, nine miRNAs (miR-196b-5p, miR-135b-5p, miR-183-5p, miR-182-5p, miR-133a-3p, miR-486-5p, miR-144-5p, miR-129-5p and miR-145-5p) were found to be significantly associated with overall survival (log-rank P < 0.05). Finally, four key miRNAs (miR-183-5p, miR-486-5p, miR-30c-2-3p and miR-133a-3p) were randomly selected to validation and their expression levels in 53 newly diagnosed GC patients by qRT-PCR. Results showed that the fold-changes between TCGA and qRT-PCR were 100 % in agreement. We also found miR-183-5p and miR-486-5p were significantly correlated with tumor TNM stage (P < 0.05), and miR-30c-2-3p and miR-133a-3p were associated with tumor differentiation degree and lymph-node metastasis (P < 0.05). These verified miRNAs clinically relevant, and the bioinformatics analysis results were almost the same.

CONCLUSION

These key miRNAs may functions as potential candidate biomarkers for the clinical diagnosis, classification and prognosis for GC.

New and emerging biomarkers in cardiovascular disease

Cardiovascular disease (CVD) is the most common cause of death and disability worldwide. Therefore, great importance has been placed on the discovery of novel risk factors and metabolic pathways relevant in the prevention and management of CVD. Such research is ongoing and may continue to lead to better risk stratification of individuals and/or the development of new intervention targets and treatment options. This review highlights emerging biomarkers related to lipid metabolism, glycemia, inflammation, and cardiac damage, some of which show promising associations with CVD risk and provide further understanding of the underlying pathophysiology. However, their measurement methodology and assays will require validation and standardization, and it will take time to accumulate evidence of their role in CVD in various population settings in order to fully assess their clinical utility. Several of the novel biomarkers represent intriguing, potentially game-changing targets for therapy.

Blood-borne biomarkers of mortality risk: systematic review of cohort studies

BACKGROUND

Lifespan and the proportion of older people in the population are increasing, with far reaching consequences for the social, political and economic landscape. Unless accompanied by an increase in health span, increases in age-related diseases will increase the burden on health care resources. Intervention studies to enhance healthy ageing need appropriate outcome measures, such as blood-borne biomarkers, which are easily obtainable, cost-effective, and widely accepted. To date there have been no systematic reviews of blood-borne biomarkers of mortality.

AIM

To conduct a systematic review to identify available blood-borne biomarkers of mortality that can be used to predict healthy ageing post-retirement.

METHODS

Four databases (Medline, Embase, Scopus, Web of Science) were searched. We included prospective cohort studies with a minimum of two years follow up and data available for participants with a mean age of 50 to 75 years at baseline.

RESULTS

From a total of 11,555 studies identified in initial searches, 23 fulfilled the inclusion criteria. Fifty-one blood borne biomarkers potentially predictive of mortality risk were identified. In total, 20 biomarkers were associated with mortality risk. Meta-analyses of mortality risk showed significant associations with C-reactive protein (Hazard ratios for all-cause mortality 1.42, p<0.001; Cancer-mortality 1.62, p<0.009; CVD-mortality 1.31, p = 0.033), N Terminal-pro brain natriuretic peptide (Hazard ratios for all-cause mortality 1.43, p<0.001; CHD-mortality 1.58, p<0.001; CVD-mortality 1.67, p<0.001) and white blood cell count (Hazard ratios for all-cause mortality 1.36, p = 0.001). There was also evidence that brain natriuretic peptide, cholesterol fractions, erythrocyte sedimentation rate, fibrinogen, granulocytes, homocysteine, intercellular adhesion molecule-1, neutrophils, osteoprotegerin, procollagen type III aminoterminal peptide, serum uric acid, soluble urokinase plasminogen activator receptor, tissue inhibitor of metalloproteinases 1 and tumour necrosis factor receptor II may predict mortality risk. There was equivocal evidence for the utility of 14 biomarkers and no association with mortality risk for CD40 ligand, cortisol, dehydroepiandrosterone, ferritin, haemoglobin, interleukin-12, monocyte chemoattractant protein 1, matrix metalloproteinase 9, myelopereoxidase, P-selectin, receptor activator of nuclear factor KappaB ligand, sex hormone binding globulin, testosterone, transferrin, and thyroid stimulating hormone and thyroxine.

CONCLUSIONS

Twenty biomarkers should be prioritised as potential predictors of mortality in future studies. More studies using standardised protocols and reporting methods, and which focus on mortality rather than risk of disease or health status as an outcome, are needed.

Serum metabolomics analysis reveals impaired lipid metabolism in rats after oral exposure to benzo(a)pyrene

A metabolomics study was conducted to unveil the metabolic profiling of rats exposed to benzo(a)pyrene, and twelve differentiated metabolites were identified.

MicroRNA signatures as biomarkers and therapeutic target for CNS embryonal tumors: the pros and the cons

Embryonal tumors of the central nervous system represent a heterogeneous group of childhood cancers with an unknown pathogenesis; diagnosis, on the basis of histological appearance alone, is controversial and patients’ response to therapy is difficult to predict. They encompass medulloblastoma, atypical teratoid/rhabdoid tumors and a group of primitive neuroectodermal tumors. All are aggressive tumors with the tendency to disseminate throughout the central nervous system. The large amount of genomic and molecular data generated over the last 5–10 years encourages optimism that new molecular targets will soon improve outcomes. Recent neurobiological studies have uncovered the key role of microRNAs (miRNAs) in embryonal tumors biology and their potential use as biomarkers is increasingly being recognized and investigated. However the successful use of microRNAs as reliable biomarkers for the detection and management of pediatric brain tumors represents a substantial challenge. This review debates the importance of miRNAs in the biology of central nervous systemembryonal tumors focusing on medulloblastoma and atypical teratoid/rhabdoid tumors and highlights the advantages as well as the limitations of their prospective application as biomarkers and candidates for molecular therapeutic targets.

Toward an integrated pipeline for protein biomarker development

Protein biomarker development is a multidisciplinary task involving basic, translational and clinical research. Integration of multidisciplinary efforts in a single pipeline is challenging, but crucial to facilitate rational discovery of protein biomarkers and alleviate existing disappointments in the field. In this review, we discuss in detail individual phases of biomarker development pipeline, such as biomarker candidate identification, verification and validation. We focus on mass spectrometry as a principal technique for protein identification and quantification, and discuss complementary -omics approaches for selection of biomarker candidates. Proteomic samples, protein-based clinical laboratory tests and limitations of biomarker development are reviewed in detail, and critical assessment of all phases of biomarker development pipeline is provided. This article is part of a Special Issue entitled: Medical Proteomics.

Microfluidic platforms for biomarker analysis

Biomarkers have been described as characteristics, most often molecular, that provide information about biological states, whether normal, pathological, or therapeutically modified. They hold great potential to assist diagnosis and prognosis, monitor disease, and assess therapeutic effectiveness. While a few biomarkers are routinely utilised clinically, these only reflect a very small percentage of all biomarkers discovered. Numerous factors contribute to the slow uptake of these new biomarkers, with challenges faced throughout the biomarker development pipeline. Microfluidics offers two important opportunities to the field of biomarkers: firstly, it can address some of these developmental obstacles, and secondly, it can provide the precise and complex platform required to bridge the gap between biomarker research and the biomarker-based analytical device market. Indeed, adoption of microfluidics has provided a new avenue for advancement, promoting clinical utilisation of both biomarkers and their analytical platforms. This review will discuss biomarkers and outline microfluidic platforms developed for biomarker analysis.

Functional molecular units for guiding biomarker panel design

The field of biomarker research has experienced a major boost in recent years, and the number of publications on biomarker studies evaluating given, but also proposing novel biomarker candidates is increasing rapidly for numerous clinically relevant disease areas. However, individual markers often lack sensitivity and specificity in the clinical context, resting essentially on the intra-individual phenotype variability hampering sensitivity, or on assessing more general processes downstream of the causative molecular events characterizing a disease term, in consequence impairing disease specificity. The trend to circumvent these shortcomings goes towards utilizing multimarker panels, thus combining the strength of individual markers to further enhance performance regarding both sensitivity and specificity. A way of identifying the optimal composition of individual markers in a panel approach is to pick each marker as representative for a specific pathophysiological (mechanistic) process relevant for the disease under investigation, hence resulting in a multimarker panel for covering the set of pathophysiological processes underlying the frequently multifactorial composition of a clinical phenotype.Here we outline a procedure of identifying such sets of disease-specific pathophysiological processes (units) delineated on the basis of disease-associated molecular feature lists derived from literature mining as well as aggregated, publicly available Omics profiling experiments. With such molecular units in hand, providing an improved reflection of a specific clinical phenotype, biomarker candidates can then be assigned to or novel candidates are to be selected from these units, subsequently resulting in a multimarker panel promising improved accuracy in disease diagnosis as well as prognosis.

Proteomic biomarkers of endometrial cancer risk in women with polycystic ovary syndrome: a systematic review and biomarker database integration

There is a need for research studies into the molecular mechanisms underpinning the link between polycystic ovary syndrome (PCOS) and endometrial cancer (EC) to facilitate screening and to encourage the development of novel strategies to prevent disease progression. The objective of this review was to identify proteomic biomarkers of EC risk in women with PCOS. All eligible published studies on proteomic biomarkers for EC identified through the literature were evaluated. Proteomic biomarkers for EC were then integrated with an updated previously published database of all proteomic biomarkers identified so far in PCOS women. Nine protein biomarkers were similarly either under or over expressed in women with EC and PCOS in various tissues. These include transgelin, pyruvate kinase M1/M2, gelsolin-like capping protein (macrophage capping protein), glutathione S-transferase P, leucine aminopeptidase (cytosol aminopeptidase), peptidyl-prolyl cis-transisomerase, cyclophilin A, complement component C4A and manganese-superoxide dismutase. If validated, these biomarkers may provide a useful framework on which the knowledge base in this area could be developed and will facilitate future mathematical modelling to enhance screening and prevention of EC in women with PCOS who have been shown to be at increased risk.

Early diagnosis of complex diseases by molecular biomarkers, network biomarkers, and dynamical network biomarkers

Many studies have been carried out for early diagnosis of complex diseases by finding accurate and robust biomarkers specific to respective diseases. In particular, recent rapid advance of high-throughput technologies provides unprecedented rich information to characterize various disease genotypes and phenotypes in a global and also dynamical manner, which significantly accelerates the study of biomarkers from both theoretical and clinical perspectives. Traditionally, molecular biomarkers that distinguish disease samples from normal samples are widely adopted in clinical practices due to their ease of data measurement. However, many of them suffer from low coverage and high false-positive rates or high false-negative rates, which seriously limit their further clinical applications. To overcome those difficulties, network biomarkers (or module biomarkers) attract much attention and also achieve better performance because a network (or subnetwork) is considered to be a more robust form to characterize diseases than individual molecules. But, both molecular biomarkers and network biomarkers mainly distinguish disease samples from normal samples, and they generally cannot ensure to identify predisease samples due to their static nature, thereby lacking ability to early diagnosis. Based on nonlinear dynamical theory and complex network theory, a new concept of dynamical network biomarkers (DNBs, or a dynamical network of biomarkers) has been developed, which is different from traditional static approaches, and the DNB is able to distinguish a predisease state from normal and disease states by even a small number of samples, and therefore has great potential to achieve "real" early diagnosis of complex diseases. In this paper, we comprehensively review the recent advances and developments on molecular biomarkers, network biomarkers, and DNBs in particular, focusing on the biomarkers for early diagnosis of complex diseases considering a small number of samples and high-throughput data (or big data). Detailed comparisons of various types of biomarkers as well as their applications are also discussed.

Biomarkers of hypochromia: the contemporary assessment of iron status and erythropoiesis

Iron status is the result of the balance between the rate of erythropoiesis and the amount of the iron stores. Direct consequence of an imbalance between the erythroid marrow iron requirements and the actual supply is a reduction of red cell hemoglobin content, which causes hypochromic mature red cells and reticulocytes. The diagnosis of iron deficiency is particularly challenging in patients with acute or chronic inflammatory conditions because most of the biochemical markers for iron metabolism (serum ferritin and transferrin ) are affected by acute phase reaction. For these reasons, interest has been generated in the use of erythrocyte and reticulocyte parameters, available on the modern hematology analyzers. Reported during blood analysis routinely performed on the instrument, these parameters can assist in early detection of clinical conditions (iron deficiency, absolute, or functional; ineffective erythropoiesis, including iron restricted or thalassemia), without additional cost. Technological progress has meant that in recent years modern analyzers report new parameters that provide further information from the traditional count. Nevertheless these new parameters are exclusive of each manufacturer, and they are patented. This is an update of these new laboratory test biomarkers of hypochromia reported by different manufactures, their meaning, and clinical utility on daily practice.

Metabolic biomarkers for predicting cardiovascular disease

Cardiac and peripheral vascular biomarkers are increasingly becoming targets of both research and clinical practice. As of 2008, cardiovascular-related medical care accounts for greater than 20% of all the economic costs of illness in the United States. In the age of burgeoning financial pressures on the entire health care system, never has it been more important to try to understand who is at risk for cardiovascular disease in order to prevent new events. In this paper, we will discuss the cost of cardiovascular disease to society, clarify the definition of and need for biomarkers, offer an example of a current biomarker, namely high-sensitivity C-reactive protein, and finally examine the approval process for utilizing these in clinical practice.

Proteomic biomarkers of preterm birth risk in women with polycystic ovary syndrome (PCOS): a systematic review and biomarker database integration

Background

Preterm Birth (PTB) is a major cause of neonatal mortality and morbidity. Women with Polycystic Ovary Syndrome (PCOS) are at high risk of PTB. There is a need for research studies to investigate the mechanisms linking PCOS and PTB, to facilitate screening, and develop novel preventative strategies.

Objective

To list all the proteomic biomarkers of PTB and integrate this list with the PCOS biomarker database to identify commonly expressed biomarkers of the two conditions.

Search Strategy

A systematic review of PTB biomarkers and update of PCOS biomarker database. All eligible published studies on proteomic biomarkers for PTB and PCOS identified through various databases were evaluated.

Selection Criteria

For the identification of the relevant studies, the following search terms were used: “proteomics”, “proteomic”, “preterm birth”, “preterm labour”, “proteomic biomarker” and “polycystic ovary syndrome”. This search was restricted to humans only

Data Collection and Analysis

A database on proteomic biomarkers for PTB was created while an already existing PCOS biomarker database was updated. The two databases were integrated and biomarkers that were co-expressed in both women with PCOS and PTB were identified and investigated.

Results

A panel of six proteomic biomarkers was similarly differentially expressed in women with PTB and women with PCOS compared to their respective controls (normal age-matched women in the case of PCOS studies and women with term pregnancy in the case of PTB studies). These biomarkers include Pyruvate kinase M1/M2, Vimentin, Fructose bisphosphonate aldolase A, Heat shock protein beta-1, Peroxiredoxin-1 and Transferrin.

Conclusions

These proteomic biomarkers (Pyruvate kinase M1/M2, Vimentin, Fructose bisphosphonate aldolase A, Heat shock protein beta-1, Peroxiredoxin-1 and Transferrin) can be potentially used to better understand the pathophysiological mechanisms linking PCOS and PTB. This would help to identify subgroups of women with PCOS at risk of PTB and hence the potential of developing preventative strategies.

Rational basis for the combination of PCA3 and TMPRSS2:ERG gene fusion for prostate cancer diagnosis

BACKGROUND

The prostate cancer gene 3 (PCA3) and TMPRSS2:ERG gene fusion are promising prostate cancer (PCa) specific biomarkers. Our aim was to simultaneously quantify the expression levels of PCA3 and TMPRSS2:ERG in a panel of benign prostatic hyperplasia (BPH), normal prostate adjacent to PCa (NP) and PCa tissue samples, to provide a rational basis for the understanding of the false-positive and false-negative results of the urine assays.

METHODS

The tissue samples were carefully histopathologically characterized to obtain homogeneous groups. The mRNA was isolated, transcribed into cDNA and the relative expressions of PCA3 and TMPRSS2:ERG were measured using a quantitative real-time polymerase chain reaction. The expression levels of PCA3 and TMPRSS2:ERG were compared between the different groups.

RESULTS

We included 48 BPH, 32 NP, and 48 PCa. The PCA3 expression levels progressively increased from BPH to NP (3 times) and finally to PCa (30 times). There were one false-positive sample and seven false-negative samples. The TMPRSS2:ERG gene fusion was found in 8.3% of the BPH, 15.6% of the NP, and 50% of the PCa samples. The use of TMPRSS2:ERG in the PCA3 negative cases allowed diagnosis of four of the seven false-negative samples and added one false-positive, but we had to define a cut-off value to avoid eight false-positive results.

CONCLUSIONS

Considering tissue expression of the markers, most of the false-negative results of the PCA3 test were corrected by TMPRSS2:ERG (57%) and the combination of both had a higher sensitivity for PCa diagnosis. Some of the control samples did express TMPRSS2:ERG and a cut-off value had to be defined to avoid false-positive results.

The implications of biomarker evidence for systematic reviews

BACKGROUND

In Evidence-Based Medicine, clinical practice guidelines and systematic reviews are crucial devices for medical practitioners in making clinical decision. Clinical practice guidelines are systematically developed statements to support health care decisions for specific circumstances whereas systematic reviews are summaries of evidence on clearly formulated clinical questions. Biomarkers are biological measurements (primarily molecular) that are used to diagnose, predict treatment outcomes and prognosticate disease and are increasingly used in randomized controlled trials (RCT).

METHODS

We search PubMed for systematic reviews, RCTs, case reports and non-systematic reviews with and without mentions of biomarkers between years 1990-2011. We compared the frequency and growth rate of biomarkers and non-biomarkers publications. We also compared the growth of the proportion of biomarker-based RCTs with the growth of the proportion of biomarker-based systematic reviews.

RESULTS

With 147,774 systematic reviews indexed in PubMed from 1990 to 2011 (accessed on 18/10/2012), only 4,431 (3%) are dedicated to biomarkers. The annual growth rate of biomarkers publications is consistently higher than non-biomarkers publications, showing the growth in biomarkers research. From 20 years of systematic review publications indexed in PubMed, we identified a bias in systematic reviews against the inclusion of biomarker-based RCTs.

CONCLUSIONS

With the realisation of genome-based personalised medicine, biomarkers are becoming important for clinical decision making. The bias against the inclusion of biomarkers in systematic reviews leads to medical practitioners deprive of important information they require to address clinical questions. Sparse or weak evidence and lack of genetic training for systematic reviewers may contribute to this trend.

Design and application of a data-independent precursor and product ion repository

The functional design and application of a data-independent LC-MS precursor and product ion repository for protein identification, quantification, and validation is conceptually described. The ion repository was constructed from the sequence search results of a broad range of discovery experiments investigating various tissue types of two closely related mammalian species. The relative high degree of similarity in protein complement, ion detection, and peptide and protein identification allows for the analysis of normalized precursor and product ion intensity values, as well as standardized retention times, creating a multidimensional/orthogonal queryable, qualitative, and quantitative space. Peptide ion map selection for identification and quantification is primarily based on replication and limited variation. The information is stored in a relational database and is used to create peptide- and protein-specific fragment ion maps that can be queried in a targeted fashion against the raw or time aligned ion detections. These queries can be conducted either individually or as groups, where the latter affords pathway and molecular machinery analysis of the protein complement. The presented results also suggest that peptide ionization and fragmentation efficiencies are highly conserved between experiments and practically independent of the analyzed biological sample when using similar instrumentation. Moreover, the data illustrate only minor variation in ionization efficiency with amino acid sequence substitutions occurring between species. Finally, the data and the presented results illustrate how LC-MS performance metrics can be extracted and utilized to ensure optimal performance of the employed analytical workflows.

Microarray-based sketches of the HERV transcriptome landscape

Human endogenous retroviruses (HERVs) are spread throughout the genome and their long terminal repeats (LTRs) constitute a wide collection of putative regulatory sequences. Phylogenetic similarities and the profusion of integration sites, two inherent characteristics of transposable elements, make it difficult to study individual locus expression in a large-scale approach, and historically apart from some placental and testis-regulated elements, it was generally accepted that HERVs are silent due to epigenetic control. Herein, we have introduced a generic method aiming to optimally characterize individual loci associated with 25-mer probes by minimizing cross-hybridization risks. We therefore set up a microarray dedicated to a collection of 5,573 HERVs that can reasonably be assigned to a unique genomic position. We obtained a first view of the HERV transcriptome by using a composite panel of 40 normal and 39 tumor samples. The experiment showed that almost one third of the HERV repertoire is indeed transcribed. The HERV transcriptome follows tropism rules, is sensitive to the state of differentiation and, unexpectedly, seems not to correlate with the age of the HERV families. The probeset definition within the U3 and U5 regions was used to assign a function to some LTRs (i.e. promoter or polyA) and revealed that (i) autonomous active LTRs are broadly subjected to operational determinism (ii) the cellular gene density is substantially higher in the surrounding environment of active LTRs compared to silent LTRs and (iii) the configuration of neighboring cellular genes differs between active and silent LTRs, showing an approximately 8 kb zone upstream of promoter LTRs characterized by a drastic reduction in sense cellular genes. These gathered observations are discussed in terms of virus/host adaptive strategies, and together with the methods and tools developed for this purpose, this work paves the way for further HERV transcriptome projects.

Development of an ELISA to detect the secreted prostate cancer biomarker AGR2 in voided urine

BACKGROUND

Comparative transcriptomics between sorted cells identified AGR2 as one of the highest up-regulated genes in cancer. Overexpression in primary tumors was verified by tissue microarray analysis. AGR2 encodes a 19-kDa secreted protein that might be found in urine.

METHODS

Monoclonal antibodies were generated against AGR2. One antibody pair, P1G4 (IgG1) to capture and P3A5 (IgG2a) to detect, showed good performance characteristics in a sandwich ELISA. This assay could detect AGR2 at sub ng/ml quantities.

RESULTS

AGR2 was detected in tissue digestion media of tumor specimens and culture media of AGR2-secreting prostate cancer cell lines. Additional testings involved frozen section immunohistochemistry, immunoprecipitation, and Western blot analysis. Voided urine samples were collected from pre-operative cancer patients, and urinary protein was desalted and concentrated by filtration. The amount of AGR2 detected was scored as pg/100 µg total protein, and then converted to pg/ml urine. The developed ELISA detected AGR2 protein, ranging from 3.6 to 181 pg/ml, in an initial cohort of samples. AGR2 was not detected in the urine of non-cancer and a bladder cancer patient.

CONCLUSIONS

For prostate cancer, an AGR2 urine test could be used for diagnosis. The data, although derived from a small number of samples assayed, showed that developing such a test for clinical application is viable because AGR2 is specific to cancer cells, and apparently secreted into urine.

Accounting for control mislabeling in case-control biomarker studies

In biomarker discovery studies, uncertainty associated with case and control labels is often overlooked. By omitting to take into account label uncertainty, model parameters and the predictive risk can become biased, sometimes severely. The most common situation is when the control set contains an unknown number of undiagnosed, or future, cases. This has a marked impact in situations where the model needs to be well-calibrated, e.g., when the prediction performance of a biomarker panel is evaluated. Failing to account for class label uncertainty may lead to underestimation of classification performance and bias in parameter estimates. This can further impact on meta-analysis for combining evidence from multiple studies. Using a simulation study, we outline how conventional statistical models can be modified to address class label uncertainty leading to well-calibrated prediction performance estimates and reduced bias in meta-analysis. We focus on the problem of mislabeled control subjects in case-control studies, i.e., when some of the control subjects are undiagnosed cases, although the procedures we report are generic. The uncertainty in control status is a particular situation common in biomarker discovery studies in the context of genomic and molecular epidemiology, where control subjects are commonly sampled from the general population with an established expected disease incidence rate.