Protein-Based Classifier to Predict Conversion from Clinically Isolated Syndrome to Multiple Sclerosis

Machine learning model base on metabolomics and proteomics to predict cognitive impairment in Parkinson's disease

There is an urgent need to identify predictive biomarkers of Parkinson's disease (PD) with cognitive impairment (PDCI) in order to individualize patient management, ensure timely intervention, and improve prognosis. The aim of this study was to screen for these biomarkers by comparing the plasma proteome and metabolome of PD patients with or without cognitive impairment. Proteomics and metabolomics analyses were performed on a discover cohort. A machine learning model was used to identify candidate protein and metabolite biomarkers of PDCI, which were validated in an independent cohort. The predictive ability of these biomarkers for PDCI was evaluated by plotting receiver operating characteristic curves and calculating the area under the curve (AUC). Moreover, we assessed the predictive ability of these proteins in combination with neuroimaging. In the discover cohort (n = 100), we identified 25 protein features with best results in the machine learning model, including top-ranked PSAP and H3C15. The two-proteins were used for model construction, achieving an Area under the curve (AUC) of 0.951 in the train set and AUC of 0.981 in the test set. Similarly, the model gives a rank list of endogenous metabolite features, Glycocholic Acid and 6-Methylnicotinamide were two top features. Combining these two markers further got the AUC of 0.969 in train set and 0.867 in the test set. To validate the performance of the protein biomarkers, we performed targeted analysis of selected proteins (H3C15 and PSAP) and proteins likely associated with PDCI (NCAM2 and LAMB2) using parallel reaction monitoring in validation cohort (n = 116). The AUC of the classifier built with H3C15 and PSAP is 0.813. Moreover, when combining H3C15, PSAP, NCAM2, and LAMB2, the model achieved AUC of 0.983 in the train set, AUC of 0.981 in the test set, and AUC of 0.839 in the validation set. Furthermore, we verified that these protein markers we discovered can improve the predictive effect of neuroimaging on PDCI: the classifier built with neuroimaging features had AUC of 0.833, which improved to 0.905 when combined with H3C15. Taken together, our integrated proteomics and metabolomics analysis successfully identified potential biomarkers for PDCI. Additionally, H3C15 showed promise in enhancing the predictive performance of neuroimaging for cognitive impairment.

The Non-Targeted Lipidomic-Based Classifier Reveals Two Candidate Biomarkers for Ischemic Stroke in Hypertensive Individuals

Introduction

Traditional clinical risk factors are insufficient to estimate the residual risk of large-vessel ischemic stroke. Non-targeted lipidomic techniques provide an opportunity to evaluate these risks.

Methods

Plasma samples were collected from 113 hypertensive individuals, including 55 individuals at high risk of ischemic stroke and 58 matched individuals, in a prospective nested case-control cohort. To identify dysregulated lipid metabolites, we conducted multivariate and univariate analyses. A classifier based on a cross-validated procedure was employed to select the optimal combination of lipid species and their ratios.

Results

We identified 23 dysregulated lipid species in patients with and without ischemic stroke, including 16 (69.6%) up-regulated and 7 (30.4%) down-regulated lipid species. Through internal cross-validation, the optimal combination of two lipid features (phosphatidylcholine 34:2 and triglyceride 18:1/18:1/22:1 / phosphatidylcholine 34:2, referred to as ischemic stroke-related 2 lipid features - IS2LP) was selected, leading to a more precise prediction probability for ischemic stroke within 3.9 years. In the comparison of different risk factors, the traditional risk score, the IS2LP risk score, and the combination of the traditional risk score with IS2LP yield AUC values of 0.613(95% CI:0.509-0.717), 0.833(95% CI:0.755-0.911), and 0.843(95% CI:0.777-0.916), respectively. The combination of the traditional risk score and IS2LP exhibited significantly improved discriminative performance, with an integrated discrimination improvement (IDI) of 0.31 (p<0.001) and a continuous net reclassification improvement (NRI) of 1.06 (p < 0.001) compared to the traditional risk score.

Conclusion

We identified new lipidomic biomarkers associated with the futural event of large-vessel ischemic stroke. These lipid species could serve as potential blood biomarkers for assessing the residual risk of ischemic stroke in hypertensive individuals.

Identification of brain-enriched proteins in CSF as biomarkers of relapsing remitting multiple sclerosis

BACKGROUND

Multiple sclerosis (MS) is a clinically and biologically heterogenous disease with currently unpredictable progression and relapse. After the development and success of neurofilament as a cerebrospinal fluid (CSF) biomarker, there is reinvigorated interest in identifying other markers of or contributors to disease. The objective of this study is to probe the predictive potential of a panel of brain-enriched proteins on MS disease progression and subtype.

METHODS

This study includes 40 individuals with MS and 14 headache controls. The MS cohort consists of 20 relapsing remitting (RR) and 20 primary progressive (PP) patients. The CSF of all individuals was analyzed for 63 brain enriched proteins using a method of liquid-chromatography tandem mass spectrometry. Wilcoxon rank sum test, Kruskal-Wallis one-way ANOVA, logistic regression, and Pearson correlation were used to refine the list of candidates by comparing relative protein concentrations as well as relation to known imaging and molecular biomarkers.

RESULTS

We report 30 proteins with some relevance to disease, clinical subtype, or severity. Strikingly, we observed widespread protein depletion in the disease CSF as compared to control. We identified numerous markers of relapsing disease, including KLK6 (kallikrein 6, OR = 0.367, p < 0.05), which may be driven by active disease as defined by MRI enhancing lesions. Other oligodendrocyte-enriched proteins also appeared at reduced levels in relapsing disease, namely CNDP1 (carnosine dipeptidase 1), LINGO1 (leucine rich repeat and Immunoglobin-like domain-containing protein 1), MAG (myelin associated glycoprotein), and MOG (myelin oligodendrocyte glycoprotein). Finally, we identified three proteins-CNDP1, APLP1 (amyloid beta precursor like protein 1), and OLFM1 (olfactomedin 1)-that were statistically different in relapsing vs. progressive disease raising the potential for use as an early biomarker to discriminate clinical subtype.

CONCLUSIONS

We illustrate the utility of targeted mass spectrometry in generating potential targets for future biomarker studies and highlight reductions in brain-enriched proteins as markers of the relapsing remitting disease stage.

Prognostic models for predicting clinical disease progression, worsening and activity in people with multiple sclerosis

BACKGROUND

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system that affects millions of people worldwide. The disease course varies greatly across individuals and many disease-modifying treatments with different safety and efficacy profiles have been developed recently. Prognostic models evaluated and shown to be valid in different settings have the potential to support people with MS and their physicians during the decision-making process for treatment or disease/life management, allow stratified and more precise interpretation of interventional trials, and provide insights into disease mechanisms. Many researchers have turned to prognostic models to help predict clinical outcomes in people with MS; however, to our knowledge, no widely accepted prognostic model for MS is being used in clinical practice yet.

OBJECTIVES

To identify and summarise multivariable prognostic models, and their validation studies for quantifying the risk of clinical disease progression, worsening, and activity in adults with MS.

SEARCH METHODS

We searched MEDLINE, Embase, and the Cochrane Database of Systematic Reviews from January 1996 until July 2021. We also screened the reference lists of included studies and relevant reviews, and references citing the included studies.

SELECTION CRITERIA

We included all statistically developed multivariable prognostic models aiming to predict clinical disease progression, worsening, and activity, as measured by disability, relapse, conversion to definite MS, conversion to progressive MS, or a composite of these in adult individuals with MS. We also included any studies evaluating the performance of (i.e. validating) these models. There were no restrictions based on language, data source, timing of prognostication, or timing of outcome.

DATA COLLECTION AND ANALYSIS

Pairs of review authors independently screened titles/abstracts and full texts, extracted data using a piloted form based on the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS), assessed risk of bias using the Prediction Model Risk Of Bias Assessment Tool (PROBAST), and assessed reporting deficiencies based on the checklist items in Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD). The characteristics of the included models and their validations are described narratively. We planned to meta-analyse the discrimination and calibration of models with at least three external validations outside the model development study but no model met this criterion. We summarised between-study heterogeneity narratively but again could not perform the planned meta-regression.

MAIN RESULTS

We included 57 studies, from which we identified 75 model developments, 15 external validations corresponding to only 12 (16%) of the models, and six author-reported validations. Only two models were externally validated multiple times. None of the identified external validations were performed by researchers independent of those that developed the model. The outcome was related to disease progression in 39 (41%), relapses in 8 (8%), conversion to definite MS in 17 (18%), and conversion to progressive MS in 27 (28%) of the 96 models or validations. The disease and treatment-related characteristics of included participants, and definitions of considered predictors and outcome, were highly heterogeneous amongst the studies. Based on the publication year, we observed an increase in the percent of participants on treatment, diversification of the diagnostic criteria used, an increase in consideration of biomarkers or treatment as predictors, and increased use of machine learning methods over time. Usability and reproducibility All identified models contained at least one predictor requiring the skills of a medical specialist for measurement or assessment. Most of the models (44; 59%) contained predictors that require specialist equipment likely to be absent from primary care or standard hospital settings. Over half (52%) of the developed models were not accompanied by model coefficients, tools, or instructions, which hinders their application, independent validation or reproduction. The data used in model developments were made publicly available or reported to be available on request only in a few studies (two and six, respectively). Risk of bias We rated all but one of the model developments or validations as having high overall risk of bias. The main reason for this was the statistical methods used for the development or evaluation of prognostic models; we rated all but two of the included model developments or validations as having high risk of bias in the analysis domain. None of the model developments that were externally validated or these models' external validations had low risk of bias. There were concerns related to applicability of the models to our research question in over one-third (38%) of the models or their validations. Reporting deficiencies Reporting was poor overall and there was no observable increase in the quality of reporting over time. The items that were unclearly reported or not reported at all for most of the included models or validations were related to sample size justification, blinding of outcome assessors, details of the full model or how to obtain predictions from it, amount of missing data, and treatments received by the participants. Reporting of preferred model performance measures of discrimination and calibration was suboptimal.

AUTHORS' CONCLUSIONS

The current evidence is not sufficient for recommending the use of any of the published prognostic prediction models for people with MS in clinical routine today due to lack of independent external validations. The MS prognostic research community should adhere to the current reporting and methodological guidelines and conduct many more state-of-the-art external validation studies for the existing or newly developed models.

Cellular and Molecular Evidence of Multiple Sclerosis Diagnosis and Treatment Challenges

Multiple sclerosis (MS) is a chronic autoimmune disease that impacts the central nervous system and can result in disability. Although the prevalence of MS has increased in India, diagnosis and treatment continue to be difficult due to several factors. The present study examines the difficulties in detecting and treating multiple sclerosis in India. A lack of MS knowledge among healthcare professionals and the general public, which delays diagnosis and treatment, is one of the significant issues. Inadequate numbers of neurologists and professionals with knowledge of MS management also exacerbate the situation. In addition, MS medications are expensive and not covered by insurance, making them inaccessible to most patients. Due to the absence of established treatment protocols and standards for MS care, India's treatment techniques vary. In addition, India's population diversity poses unique challenges regarding genetic variations, cellular and molecular abnormalities, and the potential for differing treatment responses. MS is more difficult to accurately diagnose and monitor due to a lack of specialized medical supplies and diagnostic instruments. Improved awareness and education among healthcare professionals and the general public, as well as the development of standardized treatment regimens and increased investment in MS research and infrastructure, are required to address these issues. By addressing these issues, it is anticipated that MS diagnosis and treatment in India will improve, leading to better outcomes for those affected by this chronic condition.

A combination of molecular and clinical parameters provides a new strategy for high-grade serous ovarian cancer patient management

BACKGROUND

High-grade serous carcinoma (HGSC) is the most common and deadly subtype of ovarian cancer. Although most patients will initially respond to first-line treatment with a combination of surgery and platinum-based chemotherapy, up to a quarter will be resistant to treatment. We aimed to identify a new strategy to improve HGSC patient management at the time of cancer diagnosis (HGSC-1LTR).

METHODS

A total of 109 ready-available formalin-fixed paraffin-embedded HGSC tissues obtained at the time of HGSC diagnosis were selected for proteomic analysis. Clinical data, treatment approach and outcomes were collected for all patients. An initial discovery cohort (n = 21) were divided into chemoresistant and chemosensitive groups and evaluated using discovery mass-spectrometry (MS)-based proteomics. Proteins showing differential abundance between groups were verified in a verification cohort (n = 88) using targeted MS-based proteomics. A logistic regression model was used to select those proteins able to correctly classify patients into chemoresistant and chemosensitive. The classification performance of the protein and clinical data combinations were assessed through the generation of receiver operating characteristic (ROC) curves.

RESULTS

Using the HGSC-1LTR strategy we have identified a molecular signature (TKT, LAMC1 and FUCO) that combined with ready available clinical data (patients' age, menopausal status, serum CA125 levels, and treatment approach) is able to predict patient response to first-line treatment with an AUC: 0.82 (95% CI 0.72-0.92).

CONCLUSIONS

We have established a new strategy that combines molecular and clinical parameters to predict the response to first-line treatment in HGSC patients (HGSC-1LTR). This strategy can allow the identification of chemoresistance at the time of diagnosis providing the optimization of therapeutic decision making and the evaluation of alternative treatment strategies. Thus, advancing towards the improvement of patient outcome and the individualization of HGSC patients' care.

An Update on Diagnostic Laboratory Biomarkers for Multiple Sclerosis

PURPOSE

For many patients, the multiple sclerosis (MS) diagnostic process can be lengthy, costly, and fraught with error. Recent research aims to address the unmet need for an accurate and simple diagnostic process through discovery of novel diagnostic biomarkers. This review summarizes recent studies on MS diagnostic fluid biomarkers, with a focus on blood biomarkers, and includes discussion of technical limitations and practical applicability.

RECENT FINDINGS

This line of research is in its early days. Accurate and easily obtainable biomarkers for MS have not yet been identified and validated, but several approaches to uncover them are underway. Continue efforts to define laboratory diagnostic biomarkers are likely to play an increasingly important role in defining MS at the earliest stages, leading to better long-term clinical outcomes.

A Scoping Review on Body Fluid Biomarkers for Prognosis and Disease Activity in Patients with Multiple Sclerosis

Multiple sclerosis (MS) is a complex demyelinating disease of the central nervous system, presenting with different clinical forms, including clinically isolated syndrome (CIS), which is a first clinical episode suggestive of demyelination. Several molecules have been proposed as prognostic biomarkers in MS. We aimed to perform a scoping review of the potential use of prognostic biomarkers in MS clinical practice. We searched MEDLINE up to 25 November 2021 for review articles assessing body fluid biomarkers for prognostic purposes, including any type of biomarkers, cell types and tissues. Original articles were obtained to confirm and detail the data reported by the review authors. We evaluated the reliability of the biomarkers based on the sample size used by various studies. Fifty-two review articles were included. We identified 110 molecules proposed as prognostic biomarkers. Only six studies had an adequate sample size to explore the risk of conversion from CIS to MS. These confirm the role of oligoclonal bands, immunoglobulin free light chain and chitinase CHI3L1 in CSF and of serum vitamin D in the prediction of conversion from CIS to clinically definite MS. Other prognostic markers are not yet explored in adequately powered samples. Serum and CSF levels of neurofilaments represent a promising biomarker.

Proteomics in Multiple Sclerosis: The Perspective of the Clinician

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

Separation of Serum and Plasma Proteins for In-Depth Proteomic Analysis

There are probably no biological samples that did more to spur interest in proteomics than serum and plasma. The belief was that comparing the proteomes of these samples obtained from healthy and disease-affected individuals would lead to biomarkers that could be used to diagnose conditions such as cancer. While the continuing development of mass spectrometers with greater sensitivity and resolution has been invaluable, the invention of strategic strategies to separate circulatory proteins has been just as critical. Novel and creative separation techniques were required because serum and plasma probably have the greatest dynamic range of protein concentration of any biological sample. The concentrations of circulating proteins can range over twelve orders of magnitude, making it a challenge to identify low-abundance proteins where the bulk of the useful biomarkers are believed to exist. The major goals of this article are to (i) provide an historical perspective on the rapid development of serum and plasma proteomics; (ii) describe various separation techniques that have made obtaining an in-depth view of the proteome of these biological samples possible; and (iii) describe applications where serum and plasma proteomics have been employed to discover potential biomarkers for pathological conditions.

Molecular biomarkers in multiple sclerosis

Multiple sclerosis (MS) is a highly heterogenous disease regarding radiological, pathological, and clinical characteristics and therapeutic response, including both the efficacy and safety profile of treatments. Accordingly, there is a high demand for biomarkers that sensitively and specifically apprehend the distinctive aspects of the MS heterogeneity, and that can aid in better understanding of the disease diagnosis, prognosis, prediction of the treatment response, and, finally, in the development of new treatments. Currently, clinical characteristics (e.g., relapse rate and disease progression) and magnetic resonance imaging play the most important role in the clinical classification of MS and assessment of its course. Molecular biomarkers (e.g., immunoglobulin G (IgG) oligoclonal bands, IgG index, anti-aquaporin-4 antibodies, neutralizing antibodies against interferon-beta and natalizumab, anti-varicella zoster virus and anti-John Cunningham (JC) virus antibodies) complement these markers excellently. This review provides an overview of exploratory, validated and clinically useful molecular biomarkers in MS which are used for prediction, diagnosis, disease activity and treatment response.

Prognostication and contemporary management of clinically isolated syndrome

Clinically isolated syndrome (CIS) patients present with a single attack of inflammatory demyelination of the central nervous system. Recent advances in multiple sclerosis (MS) diagnostic criteria have expanded the number of CIS patients eligible for a diagnosis of MS at the onset of the disease, shrinking the prevalence of CIS. MS treatment options are rapidly expanding, which is driving the need to recognise MS at its earliest stages. In CIS patients, finding typical MS white matter lesions on the patient's MRI scan remains the most influential prognostic investigation for predicting subsequent diagnosis with MS. Additional imaging, cerebrospinal fluid and serum testing, information from the clinical history and genetic testing also contribute. For those subsequently diagnosed with MS, there is a wide spectrum of long-term clinical outcomes. Detailed assessment at the point of presentation with CIS provides fewer clues to calculate a personalised risk of long-term severe disability.Clinicians should select suitable CIS cases for steroid treatment to speed neurological recovery. Unfortunately, there are still no neuroprotection or remyelination strategies available. The use of MS disease modifying therapy for CIS varies among clinicians and national guidelines, suggesting a lack of robust evidence to guide practice. Clinicians should focus on confirming MS speedily and accurately with appropriate investigations. Diagnosis with CIS provides an opportune moment to promote a healthy lifestyle, in particular smoking cessation. Patients also need to understand the link between CIS and MS. This review provides clinicians an update on the contemporary evidence guiding prognostication and management of CIS.

CSF levels of HoxB3 and YKL-40 may predict conversion from clinically isolated syndrome to relapsing remitting multiple sclerosis

INTRODUCTION

Multiple sclerosis (MS) often initiates with an acute episode of neurological disturbance, known as clinically isolated syndrome (CIS). There is an unmet need for biomarkers that differentiate patients who will convert to MS and who will remain as CIS after the first attack.

METHODS

First attack serum and cerebrospinal fluid (CSF) samples of 33 CIS patients were collected and these patients were divided as those who converted to MS (CIS-MS, n=17) and those who continued as CIS (CIS-CIS, n=16) in a 3-year follow-up period. Levels of homeobox protein Hox-B3 (HoxB3) and YKL-40 were measured by ELISA in samples of CIS-CIS, CIS-MS, relapsing remitting MS (RRMS) patients (n=15) and healthy controls (n=20).

RESULTS

CIS-CIS patients showed significantly reduced CSF levels of YKL-40 and increased serum/CSF levels of HoxB3 compared with CIS-MS and RRMS patients. CIS-MS and RRMS patients had comparable YKL-40 and HoxB3 level profiles. Receiver operating characteristic (ROC) curve analysis showed the highest sensitivity for CSF HoxB3 measurements in prediction of CIS-MS conversion. Kaplan-Meier analysis demonstrated that CIS patients with lower CSF HoxB3 (<3.678 ng/ml) and higher CSF YKL-40 (>654.9 ng/ml) displayed a significantly shorter time to clinically definite MS.

CONCLUSION

CSF levels of HoxB3 and YKL-40 appear to predict CIS to MS conversion, especially when applied in combination. HoxB3, which is a transcription factor involved in immune cell activity, stands out as a potential candidate molecule with biomarker capacity for MS.

Novel Protein Biomarkers of Monoamine Metabolism Defects Correlate with Disease Severity

BACKGROUND

Genetic defects of monoamine neurotransmitters are rare neurological diseases amenable to treatment with variable response. They are major causes of early parkinsonism and other spectrum of movement disorders including dopa-responsive dystonia.

OBJECTIVES

The objective of this study was to conduct proteomic studies in cerebrospinal fluid (CSF) samples of patients with monoamine defects to detect biomarkers involved in pathophysiology, clinical phenotypes, and treatment response.

METHODS

A total of 90 patients from diverse centers of the International Working Group on Neurotransmitter Related Disorders were included in the study (37 untreated before CSF collection, 48 treated and 5 unknown at the collection time). Clinical and molecular metadata were related to the protein abundances in the CSF.

RESULTS

Concentrations of 4 proteins were significantly altered, detected by mass spectrometry, and confirmed by immunoassays. First, decreased levels of apolipoprotein D were found in severe cases of aromatic L-amino acid decarboxylase deficiency. Second, low levels of apolipoprotein H were observed in patients with the severe phenotype of tyrosine hydroxylase deficiency, whereas increased concentrations of oligodendrocyte myelin glycoprotein were found in the same subset of patients with tyrosine hydroxylase deficiency. Third, decreased levels of collagen6A3 were observed in treated patients with tetrahydrobiopterin deficiency.

CONCLUSION

This study with the largest cohort of patients with monoamine defects studied so far reports the proteomic characterization of CSF and identifies 4 novel biomarkers that bring new insights into the consequences of early dopaminergic deprivation in the developing brain. They open new possibilities to understand their role in the pathophysiology of these disorders, and they may serve as potential predictors of disease severity and therapies. © 2020 International Parkinson and Movement Disorder Society.

Development of robust targeted proteomics assays for cerebrospinal fluid biomarkers in multiple sclerosis

Background

Verification of cerebrospinal fluid (CSF) biomarkers for multiple sclerosis and other neurological diseases is a major challenge due to a large number of candidates, limited sample material availability, disease and biological heterogeneity, and the lack of standardized assays. Furthermore, verification studies are often based on a low number of proteins from a single discovery experiment in medium-sized cohorts, where antibodies and surrogate peptides may differ, thus only providing an indication of proteins affected by the disease and not revealing the bigger picture or concluding on the validity of the markers. We here present a standard approach for locating promising biomarker candidates based on existing knowledge, resulting in high-quality assays covering the main biological processes affected by multiple sclerosis for comparable measurements over time.

Methods

Biomarker candidates were located in CSF-PR (proteomics.uib.no/csf-pr), and further filtered based on estimated concentration in CSF and biological function. Peptide surrogates for internal standards were selected according to relevant criteria, parallel reaction monitoring (PRM) assays created, and extensive assay quality testing performed, i.e. intra- and inter-day variation, trypsin digestion status over time, and whether the peptides were able to separate multiple sclerosis patients and controls.

Results

Assays were developed for 25 proteins, represented by 72 peptides selected according to relevant guidelines and available literature and tested for assay peptide suitability. Stability testing revealed 64 peptides with low intra- and inter-day variations, with 44 also being stably digested after 16 h of trypsin digestion, and 37 furthermore showing a significant difference between multiple sclerosis and controls, thereby confirming literature findings. Calibration curves and the linear area of measurement have, so far, been determined for 17 of these peptides.

Conclusions

We present 37 high-quality PRM assays across 21 CSF-proteins found to be affected by multiple sclerosis, along with a recommended workflow for future development of new assays. The assays can directly be used by others, thus enabling better comparison between studies. Finally, the assays can robustly and stably monitor biological processes in multiple sclerosis patients over time, thus potentially aiding in diagnosis and prognosis, and ultimately in treatment decisions.

Challenges and Opportunities in Clinical Applications of Blood-Based Proteomics in Cancer

Simple Summary

The traditional approach in identifying cancer related protein biomarkers has focused on evaluation of a single peptide/protein in tissue or circulation. At best, this approach has had limited success for clinical applications, since multiple pathological tumor pathways may be involved during initiation or progression of cancer which diminishes the significance of a single candidate protein/peptide. Emerging sensitive proteomic based technologies like liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics can provide a platform for evaluating serial serum or plasma samples to interrogate secreted products of tumor–host interactions, thereby revealing a more “complete” repertoire of biological variables encompassing heterogeneous tumor biology. However, several challenges need to be met for successful application of serum/plasma based proteomics. These include uniform pre-analyte processing of specimens, sensitive and specific proteomic analytical platforms and adequate attention to study design during discovery phase followed by validation of discovery-level signatures for prognostic, predictive, and diagnostic cancer biomarker applications.

Abstract

Blood is a readily accessible biofluid containing a plethora of important proteins, nucleic acids, and metabolites that can be used as clinical diagnostic tools in diseases, including cancer. Like the on-going efforts for cancer biomarker discovery using the liquid biopsy detection of circulating cell-free and cell-based tumor nucleic acids, the circulatory proteome has been underexplored for clinical cancer biomarker applications. A comprehensive proteome analysis of human serum/plasma with high-quality data and compelling interpretation can potentially provide opportunities for understanding disease mechanisms, although several challenges will have to be met. Serum/plasma proteome biomarkers are present in very low abundance, and there is high complexity involved due to the heterogeneity of cancers, for which there is a compelling need to develop sensitive and specific proteomic technologies and analytical platforms. To date, liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics has been a dominant analytical workflow to discover new potential cancer biomarkers in serum/plasma. This review will summarize the opportunities of serum proteomics for clinical applications; the challenges in the discovery of novel biomarkers in serum/plasma; and current proteomic strategies in cancer research for the application of serum/plasma proteomics for clinical prognostic, predictive, and diagnostic applications, as well as for monitoring minimal residual disease after treatments. We will highlight some of the recent advances in MS-based proteomics technologies with appropriate sample collection, processing uniformity, study design, and data analysis, focusing on how these integrated workflows can identify novel potential cancer biomarkers for clinical applications.

What do we currently know about the clinically isolated syndrome suggestive of multiple sclerosis? An update

Multiple sclerosis (MS) is a chronic, demyelinating, not fully understood disease of the central nervous system. The first demyelinating clinical episode is called clinically isolated syndrome (CIS) suggestive of MS. Although the most common manifestations of CIS are long tracts dysfunction and unilateral optic neuritis, it can also include isolated brainstem syndromes, cerebellar involvement, and polysymptomatic clinical image. Recently, the frequency of CIS diagnosis has decreased due to the more sensitive and less specific 2017 McDonald criteria compared with the revisions from 2010. Not all patients with CIS develop MS. The risk of conversion can be estimated based on many predictive factors including epidemiological, ethnical, clinical, biochemical, radiological, immunogenetic, and other markers. The management of CIS is nowadays widely discussed among clinicians and neuroscientists. To date, interferons, glatiramer acetate, teriflunomide, cladribine, and some other agents have been evaluated in randomized, placebo-controlled, double-blind studies relying on large groups of patients with the first demyelinating event. All of these drugs were shown to have beneficial effects in patients with CIS and might be used routinely in the future. The goal of this article is to explore the most relevant topics regarding CIS as well as to provide the most recent information in the field. The review presents CIS definition, classification, clinical image, predictive factors, and management. What is more, this is one of very few reviews summarizing the topic in the light of the 2017 McDonald criteria.

MRI features associated with rapid disease activity in clinically isolated syndrome patients at high risk for multiple sclerosis

Clinically isolated syndrome (CIS) is a central nervous system inflammatory and demyelinating event that lasts at least 24 h and can represent the first episode of relapsing-remitting multiple sclerosis. MRI is an important imaging tool in the diagnosis and longitudinal monitoring of CIS progression. Accurate differential diagnosis of high-risk versus low-risk CIS is important because high-risk CIS patients could be treated early. Although a few studies have previously characterized CIS and explored possible imaging predictors of CIS conversion to MS, it remains unclear which amongst the commonly measured MRI features, if any, are good predictors of rapid disease progression in CIS patients. The goal of this review paper is to identify MRI features in high-risk CIS patients that are associated with rapid disease activity within 5 years as measured by clinical disability.

A roadmap to precision medicine for multiple sclerosis

Multiple sclerosis (MS) affects approximately 1 million persons in the United States, and is the leading cause of neurological disability in young adults. The concept of precision medicine is now being applied to MS and has the promise of improved care. MS patients experience a variety of neurological symptoms, and disease severity ranges from mild to severe, and the biological underpinnings of these phenotypes are now starting to be elucidated. Precision medicine involves the classification of disease subtypes based on the underlying biology, rather than clinical phenotypes alone, and may govern disease course and treatment response. Over 18 disease-modifying drugs have been approved for the treatment of MS, and several biomarkers of treatment response are emerging. This article provides an overview of the concepts of precision medicine and emerging biological markers and their evolving role in decision-making in MS management.

Protein-based cardiogenic shock patient classifier

Aims

Cardiogenic shock (CS) is associated with high short-term mortality and a precise CS risk stratification could guide interventions to improve patient outcome. Here, we developed a circulating protein-based score to predict short-term mortality risk among patients with CS.

Methods and results

Mass spectrometry analysis of 2654 proteins was used for screening in the Barcelona discovery cohort (n = 48). Targeted quantitative proteomics analyses (n = 51 proteins) were used in the independent CardShock cohort (n = 97) to derive and cross-validate the protein classifier. The combination of four circulating proteins (Cardiogenic Shock 4 proteins—CS4P), discriminated patients with low and high 90-day risk of mortality. CS4P comprises the abundances of liver-type fatty acid-binding protein, beta-2-microglobulin, fructose-bisphosphate aldolase B, and SerpinG1. Within the CardShock cohort used for internal validation, the C-statistic was 0.78 for the CardShock risk score, 0.83 for the CS4P model, and 0.84 (P = 0.033 vs. CardShock risk score) for the combination of CardShock risk score with the CS4P model. The CardShock risk score with the CS4P model showed a marked benefit in patient reclassification, with a net reclassification improvement (NRI) of 0.49 (P = 0.020) compared with CardShock risk score. Similar reclassification metrics were observed in the IABP-SHOCK II risk score combined with CS4P (NRI =0.57; P = 0.032). The CS4P patient classification power was confirmed by enzyme-linked immunosorbent assay (ELISA).

Conclusion

A new protein-based CS patient classifier, the CS4P, was developed for short-term mortality risk stratification. CS4P improved predictive metrics in combination with contemporary risk scores, which may guide clinicians in selecting patients for advanced therapies.

Targeted proteomics in urinary extracellular vesicles identifies biomarkers for diagnosis and prognosis of prostate cancer

Rapid and reliable diagnosis of prostate cancer (PCa) is highly desirable as current used methods lack specificity. In addition, identification of PCa biomarkers that can classify patients into high- and low-risk groups for disease progression at early stage will improve treatment decision-making. Here, we describe a set of protein-combination panels in urinary extracellular vesicles (EVs), defined by targeted proteomics and immunoblotting techniques that improve early non-invasive detection and stratification of PCa patients.We report a two-protein combination in urinary EVs that classifies benign and PCa patients (ADSV-TGM4), and a combination of five proteins able to significantly distinguish between high- and low-grade PCa patients (CD63-GLPK5-SPHM-PSA-PAPP). Proteins composing the panels were validated by immunohistochemistry assays in tissue microarrays (TMAs) confirming a strong link between the urinary EVs proteome and alterations in PCa tissues. Moreover, ADSV and TGM4 abundance yielded a high diagnostic potential in tissue and promising TGM4 prognostic power. These results suggest that the proteins identified in urinary EVs distinguishing high- and low grade PCa are a reflection of histological changes that may be a consequence of their functional involvement in PCa development. In conclusion, our study resulted in the identification of protein-combination panels present in urinary EVs that exhibit high sensitivity and specificity for PCa detection and patient stratification. Moreover, our study highlights the potential of targeted proteomic approaches–such as selected reaction monitoring (SRM)–as diagnostic assay for liquid biopsies via urinary EVs to improve diagnosis and prognosis of suspected PCa patients.

What is targeted proteomics? A concise revision of targeted acquisition and targeted data analysis in mass spectrometry

Targeted proteomics has gained significant popularity in mass spectrometry-based protein quantification as a method to detect proteins of interest with high sensitivity, quantitative accuracy and reproducibility. However, with the emergence of a wide variety of targeted proteomics methods, some of them with high-throughput capabilities, it is easy to overlook the essence of each method and to determine what makes each of them a targeted proteomics method. In this viewpoint, we revisit the main targeted proteomics methods and classify them in four categories differentiating those methods that perform targeted data acquisition from targeted data analysis, and those methods that are based on peptide ion data (MS1 targeted methods) from those that rely on the peptide fragments (MS2 targeted methods).

The role of exosomes in CNS inflammation and their involvement in multiple sclerosis

Multiple sclerosis (MS) is a putative autoimmune disease of the central nervous system (CNS) in which autoreactive immune cells recognizing myelin antigens lead to demyelination and axonal injury. Mechanisms relevant to the pathogenesis of MS have not been fully elucidated, particularly those underlying initiation of immune system dysfunction. For example, it is not known how reactivity against CNS components is generated within the peripheral immune system. In this review, we propose that a significant contribution to the immunoregulatory events may derive from a cell-to-cell communication system involving the production, secretion and transfer of extracellular vesicles known as exosomes. Herein, we discuss in detail the biogenesis and roles of these cell surface-generated vesicles from the standpoint of receptors and their cargo, microRNA. It is well known that exosomes can cross the blood-brain barrier and thus may contribute to the spread of brain antigens to the periphery. Further understanding of exosome-dependent mechanisms in MS should provide a novel angle to the analysis of the pathogenesis of this disease. Finally, we launch the idea that exosomes and their contents may serve as biomarkers in MS.

Biomarkers of multiple sclerosis: current findings

Multiple sclerosis (MS) is an autoimmune disease affecting the brain and spinal cord that is associated with chronic inflammation leading to demyelination and neurodegeneration. With the recent increase in the number of available therapies for MS, optimal treatment will be based on a personalized approach determined by an individual patient's prognosis and treatment risks. An integral part of such therapeutic decisions will be the use of molecular biomarkers to predict disability progression, monitor ongoing disease activity, and assess treatment response. This review describes current published findings within the past 3 years in biomarker research in MS, specifically highlighting recent advances in the validation of cerebrospinal fluid biomarkers such as neurofilaments (light and heavy chains), chitinases and chitinase 3-like proteins, soluble surface markers of innate immunity, and oligoclonal immunoglobulin M antibodies. Current research in circulating miRNAs as biomarkers of MS is also discussed. Continued validation and testing will be required before MS biomarkers are routinely applied in a clinical setting.

CSF-PR 2.0: An Interactive Literature Guide to Quantitative Cerebrospinal Fluid Mass Spectrometry Data from Neurodegenerative Disorders

The rapidly growing number of biomedical studies supported by mass spectrometry based quantitative proteomics data has made it increasingly difficult to obtain an overview of the current status of the research field. A better way of organizing the biomedical proteomics information from these studies and making it available to the research community is therefore called for. In the presented work, we have investigated scientific publications describing the analysis of the cerebrospinal fluid proteome in relation to multiple sclerosis, Parkinson's disease and Alzheimer's disease. Based on a detailed set of filtering criteria we extracted 85 data sets containing quantitative information for close to 2000 proteins. This information was made available in CSF-PR 2.0 (http://probe.uib.no/csf-pr-2.0), which includes novel approaches for filtering, visualizing and comparing quantitative proteomics information in an interactive and user-friendly environment. CSF-PR 2.0 will be an invaluable resource for anyone interested in quantitative proteomics on cerebrospinal fluid.

In-Depth Cerebrospinal Fluid Quantitative Proteome and Deglycoproteome Analysis: Presenting a Comprehensive Picture of Pathways and Processes Affected by Multiple Sclerosis

In the current study, we conducted a quantitative in-depth proteome and deglycoproteome analysis of cerebrospinal fluid (CSF) from relapsing-remitting multiple sclerosis (RRMS) and neurological controls using mass spectrometry and pathway analysis. More than 2000 proteins and 1700 deglycopeptides were quantified, with 484 proteins and 180 deglycopeptides significantly changed between pools of RRMS and pools of controls. Approximately 300 of the significantly changed proteins were assigned to various biological processes including inflammation, extracellular matrix organization, cell adhesion, immune response, and neuron development. Ninety-six significantly changed deglycopeptides mapped to proteins that were not found changed in the global protein study. In addition, four mapped to the proteins oligo-myelin glycoprotein and noelin, which were found oppositely changed in the global study. Both are ligands to the nogo receptor, and the glycosylation of these proteins appears to be affected by RRMS. Our study gives the most extensive overview of the RRMS affected processes observed from the CSF proteome to date, and the list of differential proteins will have great value for selection of biomarker candidates for further verification.