Biomarkers In Clinical Studies Research Articles

Quantitative clinical glycomics strategies: A guide for selecting the best analysis approach.

Glycans introduce complexity to the proteins to which they are attached. These modifications vary during the progression of many diseases; thus, they serve as potential biomarkers for disease diagnosis and prognosis. The immense structural diversity of glycans makes glycosylation analysis and quantitation difficult. Fortunately, recent advances in analytical techniques provide the opportunity to quantify even low-abundant glycopeptides and glycans derived from complex biological mixtures, allowing for the identification of glycosylation differences between healthy samples and those derived from disease states. Understanding the strengths and weaknesses of different quantitative glycomics analysis methods is important for selecting the best strategy to analyze glycosylation changes in any given set of clinical samples. To provide guidance towards selecting the proper approach, we discuss four widely used quantitative glycomics analysis platforms, including fluorescence-based analysis of released N-linked glycans and three different varieties of MS-based analysis: liquid chromatography (LC)-mass spectrometry (MS) analysis of glycopeptides, matrix-assisted laser desorption ionization-time of flight MS, and LC-ESI-MS analysis of released N-linked glycans. These methods' strengths and weaknesses are compared, particularly associated with the figures of merit that are important for clinical biomarker studies, including: the initial sample requirements, the methods' throughput, sample preparation time, the number of species identified, the methods' utility for isomer separation and structural characterization, method-related challenges associated with quantitation, repeatability, the expertise required, and the cost for each analysis. This review, therefore, provides unique guidance to researchers who endeavor to undertake a clinical glycomics analysis by offering insights on the available analysis technologies.

Preclinical and clinical biomarker studies of CT1812: A novel approach to Alzheimer's disease modification.

IntroductionAmyloid beta (Aβ) oligomers are one of the most toxic structural forms of the Aβ protein and are hypothesized to cause synaptotoxicity and memory failure as they build up in Alzheimer's disease (AD) patients’ brain tissue. We previously demonstrated that antagonists of the sigma‐2 receptor complex effectively block Aβ oligomer toxicity. CT1812 is an orally bioavailable, brain penetrant small molecule antagonist of the sigma‐2 receptor complex that appears safe and well tolerated in healthy elderly volunteers. We tested CT1812's effect on Aβ oligomer pathobiology in preclinical AD models and evaluated CT1812's impact on cerebrospinal fluid (CSF) protein biomarkers in mild to moderate AD patients in a clinical trial (ClinicalTrials.gov NCT02907567).MethodsExperiments were performed to measure the impact of CT1812 versus vehicle on Aβ oligomer binding to synapses in vitro, to human AD patient post mortem brain tissue ex vivo, and in living APPSwe/PS1dE9 transgenic mice in vivo. Additional experiments were performed to measure the impact of CT1812 versus vehicle on Aβ oligomer‐induced deficits in membrane trafficking rate, synapse number, and protein expression in mature hippocampal/cortical neurons in vitro. The impact of CT1812 on cognitive function was measured in transgenic Thy1 huAPPSwe/Lnd+ and wild‐type littermates. A multicenter, double‐blind, placebo‐controlled parallel group trial was performed to evaluate the safety, tolerability, and impact on protein biomarker expression of CT1812 or placebo given once daily for 28 days to AD patients (Mini‐Mental State Examination 18–26). CSF protein expression was measured by liquid chromatography with tandem mass spectrometry or enzyme‐linked immunosorbent assay in samples drawn prior to dosing (Day 0) and at end of dosing (Day 28) and compared within each patient and between pooled treated versus placebo‐treated dosing groups.ResultsCT1812 significantly and dose‐dependently displaced Aβ oligomers bound to synaptic receptors in three independent preclinical models of AD, facilitated oligomer clearance into the CSF, increased synaptic number and protein expression in neurons, and improved cognitive performance in transgenic mice. CT1812 significantly increased CSF concentrations of Aβ oligomers in AD patient CSF, reduced concentrations of synaptic proteins and phosphorylated tau fragments, and reversed expression of many AD‐related proteins dysregulated in CSF.DiscussionThese preclinical studies demonstrate the novel disease‐modifying mechanism of action of CT1812 against AD and Aβ oligomers. The clinical results are consistent with preclinical data and provide evidence of target engagement and impact on fundamental disease‐related signaling pathways in AD patients, supporting further development of CT1812.

Development of acoustically isolated extracellular plasma vesicles for biomarker discovery in allogeneic hematopoietic stem cell transplantation

BackgroundInfection and graft-versus-host disease (GvHD) are the major causes for mortality and morbidity of allogeneic hematopoietic stem cell transplantation (allo-HSCT). Plasma-derived extracellular vesicles (EVs) contain disease-related proteins, DNAs and RNAs, and have recently been suggested as potential biomarker candidates for transplantation complications. However, EV isolation from small plasma volumes in clinical biomarker studies using conventional methods is challenging. We therefore investigated if EVs isolated by novel automated acoustic trapping could be developed as potential biomarkers for allo-HSCT complications by performing a clinical proof-of-principle study.ResultsPlasma samples were collected from twenty consecutive patients with high-risk/relapsed hematologic malignancies undergoing allo-HSCT before transplantation and post-transplant up to 12 weeks. EVs were isolated from small plasma sample volumes (150 μl) by an automated, acoustofluidic-based particle trapping device, which utilizes a local λ/2 ultrasonic standing wave in a borosilicate glass capillary to capture plasma EVs among pre-seeded polystyrene microbeads through sound scatter interactions. We found that EVs could be reliably isolated from all plasma samples (n = 173) and that EV numbers increased more than 2-fold in the majority of patients after transplantation. Also, sufficient quantities of RNA for downstream microRNA (miRNA) analysis were obtained from all samples and EV miRNA profiles were found to differ from whole plasma profiles. As a proof of principle, expression of platelet-specific miR-142-3p in EVs was shown to correlate with platelet count kinetics after transplantation as expected. Importantly, we identified plasma EV miRNAs that were consistently positively correlated with infection and GvHD, respectively, as well as miRNAs that were consistently negatively correlated with these complications.ConclusionsThis study demonstrates that acoustic enrichment of EVs in a clinical biomarker study setting is feasible and that downstream analysis of acoustically-enriched EVs presents a promising tool for biomarker development in allo-HSCT. Certainly, these findings warrant further exploration in larger studies, which will have significant implications not only for biomarker studies in transplantation but also for the broad field of EV-based biomarker discovery.

Differential Expression Analysis of Long Noncoding RNAs.

Long noncoding RNA (lncRNA) expression data have been increasingly used in identifying diagnostic and prognostic biomarkers in clinical studies. Low-expression genes are commonly observed in lncRNA and need to be effectively accommodated in differential expression analysis. In this chapter, we describe a protocol based on existing R packages for lncRNA differential expression analysis, including lncDIFF, ShrinkBayes, DESeq2, edgeR, and zinbwave, and provide an example application in a cancer study. In order to establish guidelines for proper application of these packages, we also compare these tools based on the implemented core algorithms and statistical models. We hope that this chapter will provide readers with a practical guide on the analysis choices in lncRNA differential expression analysis.

Protocols for Preparation and Mass Spectrometry Analysis of Clinical Urine Samples to Identify Candidate Biomarkers of Schistosoma-Associated Bladder Cancer.

Advances in mass spectrometry instrumentation have revolutionized analytical capability in clinical proteomics. In parallel, various sample preparation methods have been developed to try to address the inherent complexity and dynamic range of clinical samples, typically involving a combination of depletion of abundant proteins followed by extensive prefractionation. However, the depth of coverage routinely achieved in discovery proteomics experiments on peripheral fluids such as serum, still leaves something to be desired, especially if no depletion or prefractionation is done in order to increase the throughput of clinical samples. Remarkably, despite being an easily accessible, typically sterile and diagnostically rich clinical sample, urine is often overlooked and as such has received less development effort. As an ultrafiltrate of blood, urine contains proteins and protein fragments originating from all parts of the body which may have diagnostic or prognostic potential if accurately and reproducibly quantified. Here, we describe an efficient and simple method for the concentration of urine samples by methanol-chloroform precipitation and subsequent in-solution tryptic digestion prior to discovery or targeted mass spectrometry analysis. We exemplify this method by reference to the discovery of novel candidate urinary biomarkers of schistosomiasis. Importantly, the methods described here have been used to identify >1900 protein groups in human urine by label-free discovery proteomics, without requiring any prior depletion or prefractionation, making this approach amenable to high throughput clinical biomarker studies in many diseases.

Multi-Omics Investigation of Innate Navitoclax Resistance in Triple-Negative Breast Cancer Cells.

Simple SummaryTriple negative breast cancer is a disease with limited treatment options and the poorest outcome across all breast cancer subtypes, thus the need for new effective therapies is high. We recently found that navitoclax displays synergistic anti-proliferative and apoptotic activities with other drugs in treatment of triple negative breast cancer cells, but the resistance to treatment is still a limiting factor. Therefore, we investigated the effects of navitoclax treatment on the transcriptome, genome and epigenome in vitro to better understand the process of developing resistance. We discovered and validated a list of multiple, previously unknown markers of drug resistance that can help in patient selection in future clinical trials involving navitoclax.Cancer cells employ various defense mechanisms against drug-induced cell death. Investigating multi-omics landscapes of cancer cells before and after treatment can reveal resistance mechanisms and inform new therapeutic strategies. We assessed the effects of navitoclax, a BCL2 family inhibitor, on the transcriptome, methylome, chromatin structure, and copy number variations of MDA-MB-231 triple-negative breast cancer (TNBC) cells. Cells were sampled before treatment, at 72 h of exposure, and after 10-day drug-free recovery from treatment. We observed transient alterations in the expression of stress response genes that were accompanied by corresponding changes in chromatin accessibility. Most of these changes returned to baseline after the recovery period. We also detected lasting alterations in methylation states and genome structure that suggest permanent changes in cell population composition. Using single-cell analyses, we identified 2350 genes significantly upregulated in navitoclax-resistant cells and derived an 18-gene navitoclax resistance signature. We assessed the navitoclax-response-predictive function of this signature in four additional TNBC cell lines in vitro and in silico in 619 cell lines treated with 251 different drugs. We observed a drug-specific predictive value in both experiments, suggesting that this signature could help guiding clinical biomarker studies involving navitoclax.

Abstract 2016: Comparison of HTG-edge targeted RNA sequencing platform with whole transcriptome RNA sequencing for clinical biomarker studies

Abstract Purpose HTG EdgeSeq Next Generation Sequencing (NGS) technology is a targeted RNA Sequencing (RNA-Seq) platform based on quantitative nuclease protection chemistry, which requires substantially less sample input than RNA-Seq (one formalin-fixed paraffin-embedded (FFPE) slide versus 4-6 slides). The automated library processor allows fast downstream NGS analysis, making it a promising technology for clinical application and companion diagnostics, especially in samples with limited availability. Given the differences from standard RNA-Seq protocols, it is critical to investigate the performance of HTG EdgeSeq compared to whole transcriptome RNA-Seq technology. We investigated the concordance between HTG EdgeSeq and RNA-Seq on a set of FFPE samples. Procedure The HTG EdgeSeq Oncology Biomarker Panel (OBP) is comprised of 2558 genes including genes relevant to immuno-oncology and oncology pathways. To evaluate the concordance between the two platforms, 57 FFPE samples from diverse tumor types and varying tumor content (16-100%) were analyzed. One slide was analyzed per sample with the HTG EdgeSeq OBP, while four to six slides were analyzed with whole transcriptome analysis using the Illumina TruSeq Total RNA library kit. A subset of samples and genes were selected for analysis by RT-qPCR using a customized Taqman 15 gene array. Data from the three platforms was analyzed and compared for concordance. Data Summary Fifty-three samples passed QC on the HTG EdgeSeq platform, while only 29 samples passed a distribution-based QC of the RNA-Seq. For samples that passed QC using both technologies, the median Spearman correlation was 0.73 (25th percentile was 0.70), indicating good, but not linear correlation. In the samples that failed RNA-Seq QC, the median Spearman correlation was 0.30. Probes with low correlation (<0.25) between HTG EdgeSeq and RNA-Seq had an average interquartile range of expression less than 2 log2(CPM) indicating low dynamic range of expression. The HTG EdgeSeq platform showed a tendency towards more detection of low abundance genes indicating either higher sensitivity or higher background. Comparison of RT-qPCR to HTG EdgeSeq showed good sample to sample correlation however, in many cases, genes with low expression in HTG EdgeSeq were undetectable in RT-qPCR leaving the low-end sensitivity of HTG EdgeSeq unclear. Conclusions HTG EdgeSeq produced fewer QC failures compared to whole transcriptome RNA-Seq while using less tissue. The resulting data showed sound correlation between RNA-Seq, HTG EdgeSeq, and RT-qPCR platforms, especially for genes with moderate to high expression, enabling relatively low risk translation between biomarker platforms. Further, these results were consistent across various cancer types. The characteristics demonstrated in this study support the potential use of HTG EdgeSeq for clinical biomarker applications. Citation Format: Dennis O'Rourke, Jorge F. Sanchez-Garcia, P. Alexander Rolfe, Alice Huang, Danyi Wang, Juergen Scheuenpflug, Zheng Feng. Comparison of HTG-edge targeted RNA sequencing platform with whole transcriptome RNA sequencing for clinical biomarker studies [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2016.

Estimated insulin sensitivity in Type 1 diabetes adults using clinical and research biomarkers

AimsInsulin resistance in people with type 1 diabetes (T1D) is associated with increased risk of chronic complications and death. The gold standard to quantify insulin sensitivity, a euglycaemic hyperinsulinaemic clamp, is not applicable to clinical practice. We have employed clamp studies to develop a panel of formulae to estimate insulin sensitivity in adults with T1D for use in clinical practice and trials. MethodsClamps were conducted in 28 adults with T1D, who were also characterised with 38 clinical and research biomarkers. Exhaustive search analysis was used to derive equations correlating with clamp-quantified glucose disposal rate (GDR), GDR/plasma insulin (M/I) and log10M/I. ResultsMeasured insulin sensitivity correlated with BMI, WHR, HDL-C, adipokines and inflammation markers on univariate analysis. Exhaustive search analysis derived three formulae correlating with clamp-derived GDR and logM/I (p < 0.0001), accounting for ≈62% of their variability. A formula using gender, age, HDL-C, pulse pressure and WHR performed as well as those containing inflammation and adipokine measures. ConclusionsThe performance of formulae using routinely available parameters with/without research biomarkers in clinical studies and trials, particularly related to future complications, relevant lifestyle interventions, insulin delivery modes and insulin sensitisers is merited.

EVALUATION OF MS-DIAL AND MZMINE2 SOFTWARES FOR CLINICAL LIPIDOMICS ANALYSIS

Lipidomics covers analysis of all lipid species in an organism. Lipid metabolism is one of the key factors to understand cellular processes at molecular level. Lipidomics has been used to find diagnostic and prognostic biomarkers in clinical sample (plasma, serum, urine, tissue). Today mass spectroscopy based approach dominates lipidomics and several computational platforms have been developed to process raw mass spectra data. However, there is no routine procedure for data processing in lipidomics. In present work, two different bioinformatics platforms, which are MS-DIAL and MZmine2, was compared for lipidomics analysis of plasma sample. Peak detection, identification and quantification parameters were investigated to understand advantages and disadvantages. In peak detection process, it was observed that MZmine2 detected more peak than MS-DIAL at same threshold level. In identification process, Lipidmaps database was used for identification. MZmine2 identifies more lipid than MS-DIAL. Semi-quantification is very important to find differentially expressed lipid species and biomarkers in clinical studies. MS-DIAL and MZmine2 calculated normalized peak intensities and results were compared to understand reproducibility. Average relative standard deviation of all peaks was calculated and results showed that MS-DIAL gives more reproducible results than MZmine2. In conclusion, MZmine2 and MS-DIAL could be used in clinical lipidomics studies.

National Institutes of Health StrokeNet During the Time of COVID-19 and Beyond.

National Institutes of Health StrokeNet During the Time of COVID-19 and Beyond.

Biological Markers as a Novel Approach in Clinical Diagnosis and Management of Diseases

The adoption and use of biological marker also known as biomarkers in clinical studies and management of disease conditions spans over 40 years. Biological marker are measurable biochemical or molecular alterations that can be adopted as indicator of normal biological or responses to the action of pathogen or pharmacological responses to a therapeutic intervention. Molecules, macro and micro molecules, metabolites, and chemical compounds from the body adopted for use in predicting and monitoring health outcomes are constant from individual to another which qualify them as standard biomarker parameters. Use of biological markers spans clinical screening of an underlying cause of disease, prognosis, predisposition to a disease condition and predicting outcomes of treatment. In the environment, biological markers can be applied to determine the effects of xenobiotic chemicals and reaction effect on living cell. A good marker while being specific, should be easy to measure, cheap, provide correct measurement and outcomes. The objective of this review paper is highlight the different types of biological markers and areas where they are applicable.

Alzheimer's Disease in Down Syndrome: Progress in the Design and Conduct of Drug Prevention Trials.

Individuals with Down syndrome (DS) are at high risk for developing Alzheimer's disease (AD) pathology and this has provided significant insights into our understanding of the genetic basis of AD. The present review summarizes recent clinical, neuropathologic, imaging, and fluid biomarker studies of AD in DS (DSAD), highlighting the striking similarities, as well as some notable differences, between DSAD and the more common late-onset form of AD (LOAD) in the general population, as well as the much rarer, autosomal-dominant form of AD (ADAD). There has been significant progress in our understanding of the natural history of AD biomarkers in DS and their relationship to clinically meaningful changes. Additional work is needed to clearly define the continuum of AD that has been described in the general population, such as the preclinical, prodromal, and dementia stages of AD. Multiple therapeutic approaches, including those targeting not only β-amyloid but also tau and the amyloid precursor protein itself, require consideration. Recent developments in the field are presented within the context of such efforts to conduct clinical trials to treat and potentially prevent AD in DS.

Platform comparison of HTG EdgeSeq and RNA-Seq for gene expression profiling of tumor tissue specimens.

3566 Background: Clinical biomarker studies are often hindered by the availability of tissue specimens of sufficient quality and quantity. While RNA-Seq is often considered the gold standard for measuring mRNA expression levels in cancer tissue, it typically requires multiple formalin-fixed paraffin-embedded (FFPE) tissue sections to extract a sufficient amount of quality RNA for subsequent gene expression profiling analysis. The HTG EdgeSeq technology is a gene expression profiling platform that combines quantitative nuclease protection assay technology with next-generation sequencing detection. Unlike RNA-Seq, the HTG EdgeSeq technology does not require RNA extraction, and can use small amounts of tissue material, typically several mm2, to generate reproducible gene expression profiles. Methods: This study compares the performance of RNA-Seq and HTG's profiling panel, the HTG EdgeSeq Precision Immuno-Oncology Panel (PIP), which is designed to measure expression levels of 1,392 genes focused on tumor/immune interaction. Approximately 1,200 samples from three tumor indications (gastric cancer, colorectal cancer and ovarian cancer) were tested using both technologies. Results: Up to four FFPE slides were used for RNA extraction to support RNA-Seq testing; out of the 1,202 samples processed, 1,099 generated extracted RNA of sufficient quality and quantity (as measured by RNA concentration, RIN score and %DV200) to proceed to sequencing, which resulted in a pass rate of 91.4% for RNA-Seq. The HTG EdgeSeq PIP panel resulted in a pass rate of 97.3% (samples passing QC metrics) when the same 1,200 samples were tested, and required only a single FFPE section owing to the small sample requirement. The t-SNE (a non-linear dimensionality reduction method) analysis of the common 1,358 genes revealed similar clustering of the three cancer indications between the two methods. Correlations across individual genes by sample resulted in the mean Spearman correlation coefficient of 0.73 (95% confidence interval of 0.61 - 0.80). Additionally, gene-wise comparisons across all samples were also evaluated. Conclusions: These data demonstrate that HTG EdgeSeq gene expression panels can be used as a competitive alternative to RNA-Seq, generating equivalent gene expression results, while offering the added benefits of a small sample size requirement, lack of RNA extraction bias, and fully automated data analysis pipeline.

Determination of cutoff values for biomarkers in clinical studies

Determination of cutoff values for biomarkers in clinical studies

Эффекты острого стресса у мышей, различающихся чувствительностью 5-НТ 1А -рецепторов к хронической активации с помощью 8-OH-DPAT

Recently the B6.CBA-D13Mit76C (B6-M76C) and B6.CBA-D13Mit76B (B6-M76B) recombinant mouse lines have been created, which differ in the sensitivity of the 5-HT1A-receptor to chronic activation by an 8-OH-DPAT (8-hydroxy-2- (di-n-propylamino) tetralin). 5-HT1A-receptor is the key regulator of the brain serotonin (5-HT) system that is implicated in the regulation of stress response. In this work, it was found that acute emotional stress (immobilization of 40 min) increases the expression of the c-fos gene, which is a marker of the response to stress, in the brain of both lines. The stress-induced increase c-fos expression in the striatum, hypothalamus, and cortex of B6-M76C mice was more pronounced. The reaction to emotional stress led to an increase in the ratio of the serotonin metabolite - 5-HIAA (5-hydroxyinodolacetic acid) to 5-HT in the midbrain and hippocampus in both lines. An increased 5-HIAA/5-HT ratio was detected in the hypothalamus B6-M76C and cortex B6-M76B of mice after stress. Moreover, the reaction to stress led to decrease in the level of norepinephrine in the hippocampus and hypothalamus in B6-M76C mice. The levels of dopamine, norepinephrine, epinephrine and the dopamine/norepinephrine ratio in the adrenal glands increased in response to the stress effect only in B6-M76C. The present study showed that the sympathoadrenal system is more sensitive to acute stress in the B6-M76C mice. Reaction to stress in B6-M76C mice leads to an increase in the content of norepinephrine and epinephrine in adrenal tissue. An increase in the adrenal dopamine/norepinephrine ratio may indicate a more rapid transition of catecholamine biosynthesis intermediates (dopamine) to norepinephrine and further epinephrine due to the acceleration of the biosynthetic process in response to stress in B6-M76C mice. In addition, an increased sensitivity of the hypothalamic neurons to the action of stress in B6-M76C was shown. Thus, B6-M76C mice are of significant interest for the study of the hypothalamic-pituitary-adrenal system with an overactive response to stress, and can contribute to the identification of new biomarkers for clinical studies

Two-Dimensional Gel Electrophoresis Combined with Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry Analysis of Eye Lens to Identify Biomarkers of Age-Related Cataract.

This chapter describes the application of two-dimensional gel electrophoresis (2DGE) combined with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) in the analysis of rat eye lens proteins. The main purpose was to identify proteins that may serve as potential biomarkers in age-related cataract formation. This includes the family of proteins known as the crystallins. Structural proteins and enzymes involved antioxidant activities. In addition, we also analyzed lenses from other species to illustrate the potential of using this technique in clinical and preclinical biomarker studies.

Multiplex Analysis of Circulating Hormone Levels in Rat Models of Age-Related Diseases.

This chapter describes the application of multiplex immunoassay of hormones which are known to be present at different levels during aging and may therefore be precipitous in age-related diseases. As an example, we have analyzed serum from 12-week-old rats using multiplexes for the hormones insulin, leptin, growth hormone, corticosterone, and testosterone, which have been implicated in sarcopenia. The same technique can be used in the investigation of other sarcopenia biomarkers as well as in other disease cases involving both clinical and preclinical biomarker studies.

Diagnostic Biomarkers in Liver Injury by Drugs, Herbs, and Alcohol: Tricky Dilemma after EMA Correctly and Officially Retracted Letter of Support.

Liver injuries caused by the use of exogenous compounds such as drugs, herbs, and alcohol are commonly well diagnosed using laboratory tests, toxin analyses, or eventually reactive intermediates generated during metabolic degradation of the respective chemical in the liver and subject to covalent binding by target proteins. Conditions are somewhat different for idiosyncratic drug induced liver injury (DILI), for which metabolic intermediates as diagnostic aids are rarely available. Although the diagnosis of idiosyncratic DILI can well be established using the validated, liver specific, structured, and quantitative RUCAM (Roussel Uclaf Causality Assessment Method), there is an ongoing search for new diagnostic biomarkers that could assist in and also confirm RUCAM-based DILI diagnoses. With respect to idiosyncratic DILI and following previous regulatory letters of recommendations, selected biomarkers reached the clinical focus, including microRNA-122, microRNA-192, cytokeratin analogues, glutamate dehydrogenase, total HMGB-1 (High Mobility Group Box), and hyperacetylated HMGB-1 proteins. However, the new parameters total HMGB-1, and even more so the acetylated HMGB-1, came under critical scientific fire after misconduct at one of the collaborating partner centers, leading the EMA to recommend no longer the exploratory hyperacetylated HMGB1 isoform biomarkers in clinical studies. The overall promising nature of the recommended biomarkers was considered by EMA as highly dependent on the outstanding results of the now incriminated biomarker hyperacetylated HMGB-1. The EMA therefore correctly decided to officially retract its Letter of Support affecting all biomarkers listed above. New biomarkers are now under heavy scrutiny that will require re-evaluations prior to newly adapted recommendations. With Integrin beta 3 (ITGB3), however, a new diagnostic biomarker may emerge, possibly being drug specific but tested in only 16 patients; due to substantial remaining uncertainties, final recommendations would be premature. In conclusion, most of the currently recommended new biomarkers have lost regulatory support due to scientific misconduct, requiring now innovative approaches and re-evaluation before they can be assimilated into clinical practice.

Identification of lncRNAs Associated With Neuroblastoma in Cross-Sectional Databases: Potential Biomarkers.

Long non-coding RNAs (lncRNAs) have emerged as an important regulatory control in biological systems. Though the field of lncRNA has been progressing rapidly, a complete understanding of the role of lncRNAs in neuroblastoma pathogenesis is still lacking. To identify the abrogated lncRNAs in primary neuroblastoma and in the metastasized as well as the relapsed form of neuroblastoma, we analyzed an RNA-seq dataset on neuroblastoma that is available online to identify the lncRNAs that could potentially be contributing to the biology of neuroblastoma. The identified lncRNAs were further scrutinized using a publicly available epigenetic dataset of neuroblastoma and a cancer database. After this cross-sectional study, we were able to identify three significant lncRNAs, CASC15, PPP1R26-AS1, and USP3-AS1, which could serve as potential biomarkers in clinical studies of neuroblastoma pathogenesis.

Pathological Influences on Clinical Heterogeneity in Lewy Body Diseases.

PD, PD with dementia, and dementia with Lewy bodies are clinical syndromes characterized by the neuropathological accumulation of alpha-synuclein in the CNS that represent a clinicopathological spectrum known as Lewy body disorders. These clinical entities have marked heterogeneity of motor and nonmotor symptoms with highly variable disease progression. The biological basis for this clinical heterogeneity remains poorly understood. Previous attempts to subtype patients within the spectrum of Lewy body disorders have centered on clinical features, but converging evidence from studies of neuropathology and ante mortem biomarkers, including CSF, neuroimaging, and genetic studies, suggest that Alzheimer's disease beta-amyloid and tau copathology strongly influence clinical heterogeneity and prognosis in Lewy body disorders. Here, we review previous clinical biomarker and autopsy studies of Lewy body disorders and propose that Alzheimer's disease copathology is one of several likely pathological contributors to clinical heterogeneity of Lewy body disorders, and that such pathology can be assessed in vivo. Future work integrating harmonized assessments and genetics in PD, PD with dementia, and dementia with Lewy bodies patients followed to autopsy will be critical to further refine the classification of Lewy body disorders into biologically distinct endophenotypes. This approach will help facilitate clinical trial design for both symptomatic and disease-modifying therapies to target more homogenous subsets of Lewy body disorders patients with similar prognosis and underlying biology. © 2019 International Parkinson and Movement Disorder Society.