A pharmacogenetic signature of high response to Copaxone in late-phase clinical-trial cohorts of multiple sclerosis

Circular RNA expression profile and functional analysis of circUvrag in light-induced photoreceptor degeneration

BACKGROUND

Circular RNAs (circRNAs) are implicated in retinal pathophysiology; however, their expression profiles and functions in photoreceptor apoptosis are largely unknown. We explored circRNA-expression profiles and circUvrag (host gene: Uvrag, ultraviolet radiation resistance associated gene) function in light-induced photoreceptor apoptosis.

METHODS

Sprague-Dawley rats and 661 W photoreceptor cells were exposed to blue light to establish light-induced photoreceptor degeneration. Differentially expressed circRNAs were identified using microarrays. Potential functions of dysregulated circRNAs were analysed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. CircUvrag expression and localization were evaluated using quantitative RT-PCR and fluorescence in situ hybridization, respectively. CircUvrag overexpression and knockdown were induced using a plasmid and a small interfering RNA, respectively, and retinal function and structure were assessed using scotopic electroretinography, haematoxylin-eosin staining, and TUNEL staining. Microglial migration was assessed using IBA1 immunostaining. The apoptosis ratio of photoreceptor cells in vitro was detected using flow cytometry.

RESULTS

We identified 764 differentially expressed circRNAs, which were potentially related with the development of retinal structures, including neurons, dendrites, and synapses, and might participate in nervous-system pathophysiology. Light exposure enriched circUvrag in the cytoplasm of photoreceptors in the outer nuclear layer (ONL). CircUvrag knockdown decreased photoreceptor apoptosis and microglial migration to the ONL after light exposure, preserving ONL thickness and a-wave amplitude. In vitro, circUvrag knockdown inhibited photoreceptor apoptosis, although circUvrag overexpression slightly promoted photoreceptor apoptosis.

CONCLUSIONS

CircUvrag knockdown attenuated light-induced photoreceptor apoptosis, and might be a potential target in retinal degeneration.

Combining Clinical and Genetic Data to Predict Response to Fingolimod Treatment in Relapsing Remitting Multiple Sclerosis Patients: A Precision Medicine Approach

A personalized approach is strongly advocated for treatment selection in Multiple Sclerosis patients due to the high number of available drugs. Machine learning methods proved to be valuable tools in the context of precision medicine. In the present work, we applied machine learning methods to identify a combined clinical and genetic signature of response to fingolimod that could support the prediction of drug response. Two cohorts of fingolimod-treated patients from Italy and France were enrolled and divided into training, validation, and test set. Random forest training and robust feature selection were performed in the first two sets respectively, and the independent test set was used to evaluate model performance. A genetic-only model and a combined clinical-genetic model were obtained. Overall, 381 patients were classified according to the NEDA-3 criterion at 2 years; we identified a genetic model, including 123 SNPs, that was able to predict fingolimod response with an AUROC= 0.65 in the independent test set. When combining clinical data, the model accuracy increased to an AUROC= 0.71. Integrating clinical and genetic data by means of machine learning methods can help in the prediction of response to fingolimod, even though further studies are required to definitely extend this approach to clinical applications.

Hepatotoxicity of Drugs Used in Multiple Sclerosis, Diagnostic Challenge, and the Role of HLA Genotype Susceptibility

Multiple sclerosis (MS) is a chronic demyelinating disease of the central nervous system and the association with other autoimmune diseases is well-documented. There are many therapeutic options for the treatment of MS. Most of the available drugs cause drug-induced liver injury (DILI) to variable extents with heterogeneous clinical and biological manifestations, including liver injury with or without signs of hypersensitivity and autoimmunity. The diagnosis of DILI may be particularly difficult because MS is frequently associated with idiopathic autoimmune hepatitis. Recent advances suggest that MS and immune-mediated DILI could be promoted by genetic factors, including HLA genotype. In addition, some of these drugs may promote hepatitis B virus reactivation. This review explores the potential hepatotoxicity of drugs used to treat MS and the criteria to distinguish DILI from idiopathic autoimmune hepatitis associated with MS. The role of susceptible genes both promoting MS and causing the hepatotoxicity of the drug used for MS treatment is also discussed.

Omics approaches to understanding the efficacy and safety of disease-modifying treatments in multiple sclerosis

From the perspective of precision medicine, the challenge for the future is to improve the accuracy of diagnosis, prognosis, and prediction of therapeutic responses through the identification of biomarkers. In this framework, the omics sciences (genomics, transcriptomics, proteomics, and metabolomics) and their combined use represent innovative approaches for the exploration of the complexity and heterogeneity of multiple sclerosis (MS). This review examines the evidence currently available on the application of omics sciences to MS, analyses the methods, their limitations, the samples used, and their characteristics, with a particular focus on biomarkers associated with the disease state, exposure to disease-modifying treatments (DMTs), and drug efficacies and safety profiles.

Rs205764 and rs547311 in linc00513 may influence treatment responses in multiple sclerosis patients: A pharmacogenomics Egyptian study

Background

Multiple sclerosis (MS) is characterized by a complex etiology that is reflected in the lack of consistently predictable treatment responses across patients of seemingly similar characteristics. Approaches to demystify the underlying predictors of aberrant treatment responses have made use of genome-wide association studies (GWAS), with imminent progress made in identifying single nucleotide polymorphisms (SNPs) associated with MS risk, disease progression, and treatment response. Ultimately, such pharmacogenomic studies aim to utilize the approach of personalized medicine to maximize patient benefit and minimize rate of disease progression.

Objective

Very limited research is available around the long intergenic non-coding RNA (linc)00513, recently being reported as a novel positive regulator of the type-1 interferon (IFN) pathway, following its overexpression in the presence of two polymorphisms: rs205764 and rs547311 in the promoter region of this gene. We attempt to provide data on the prevalence of genetic variations at rs205764 and rs547311 in Egyptian MS patients, and correlate these polymorphisms with the patients' responses to disease-modifying treatments.

Methods

Genomic DNA from 144 RRMS patients was isolated and analyzed for genotypes at the positions of interest on linc00513 using RT-qPCR. Genotype groups were compared with regards to their response to treatment; additional secondary clinical parameters including the estimated disability status score (EDSS), and onset of the disease were examined in relation to these polymorphisms.

Results

Polymorphisms at rs205764 were associated with a significantly higher response to fingolimod and a significantly lower response to dimethylfumarate. Moreover, the average EDSS of patients carrying polymorphisms at rs547311 was significantly higher, whereas no correlation appeared to exist with the onset of MS.

Conclusion

Understanding the complex interplay of factors influencing treatment response is pivotal in MS. One of the factors contributing to a patient's response to treatment, as well as disease disability, may be polymorphisms on non-coding genetic material, such as rs205764 and rs547311 on linc00513. Through this work, we propose that genetic polymorphisms may partially drive disease disability and inconsistent responses to treatment in MS; we also aim to draw attention towards genetic approaches, such as screening for specific polymorphisms, to possibly direct treatment choices in such a complex disease.

Influence of Genetic Polymorphisms on Clinical Outcomes of Glatiramer Acetate in Multiple Sclerosis Patients

Multiple sclerosis (MS) is a chronic, inflammatory, demyelinating disease of autoimmune origin, in which inflammation and demyelination lead to neurodegeneration and progressive disability. Treatment is aimed at slowing down the course of the disease and mitigating its symptoms. One of the first-line treatments used in patients with MS is glatiramer acetate (GA). However, in clinical practice, a response rate of between 30% and 55% is observed. This variability in the effectiveness of the medication may be influenced by genetic factors such as polymorphisms in the genes involved in the pathogenesis of MS. Therefore, this review assesses the impact of genetic variants on the response to GA therapy in patients diagnosed with MS. The results suggest that a relationship exists between the effectiveness of the treatment with GA and the presence of polymorphisms in the following genes: CD86, CLEC16A, CTSS, EOMES, MBP, FAS, TRBC1, IL1R1, IL12RB2, IL22RA2, PTPRT, PVT1, ALOX5AP, MAGI2, ZAK, RFPL3, UVRAG, SLC1A4, and HLA-DRB1*1501. Consequently, the identification of polymorphisms in these genes can be used in the future as a predictive marker of the response to GA treatment in patients diagnosed with MS. Nevertheless, there is a lack of evidence for this and more validation studies need to be conducted to apply this information to clinical practice.

Current review and next steps for artificial intelligence in multiple sclerosis risk research

In the last few decades, the prevalence of multiple sclerosis (MS), a chronic inflammatory disease of the nervous system, has increased, particularly in Northern European countries, the United States, and United Kingdom. The promise of artificial intelligence (AI) and machine learning (ML) as tools to address problems in MS research has attracted increasing interest in these methods. Bayesian networks offer a clear advantage since they can integrate data and causal knowledge allowing for visualizing interactions between dependent variables and potential confounding factors. A review of AI/ML research methods applied to MS found 216 papers using terms "Multiple Sclerosis", "machine learning", "artificial intelligence", "Bayes", and "Bayesian", of which 90 were relevant and recently published. More than half of these involve the detection and segmentation of MS lesions for quantitative analysis; however clinical and lifestyle risk factor assessment and prediction have largely been ignored. Of those that address risk factors, most provide only association studies for some factors and often fail to include the potential impact of confounding factors and bias (especially where these have causal explanations) that could affect data interpretation, such as reporting quality and medical care access in various countries. To address these gaps in the literature, we propose a causal Bayesian network approach to assessing risk factors for MS, which can address deficiencies in current epidemiological methods of producing risk measurements and makes better use of observational data.

Pathogenic Single Nucleotide Polymorphisms on Autophagy-Related Genes

In recent years, the study of single nucleotide polymorphisms (SNPs) has gained increasing importance in biomedical research, as they can either be at the molecular origin of a determined disorder or directly affect the efficiency of a given treatment. In this regard, sequence variations in genes involved in pro-survival cellular pathways are commonly associated with pathologies, as the alteration of these routes compromises cellular homeostasis. This is the case of autophagy, an evolutionarily conserved pathway that counteracts extracellular and intracellular stressors by mediating the turnover of cytosolic components through lysosomal degradation. Accordingly, autophagy dysregulation has been extensively described in a wide range of human pathologies, including cancer, neurodegeneration, or inflammatory alterations. Thus, it is not surprising that pathogenic gene variants in genes encoding crucial effectors of the autophagosome/lysosome axis are increasingly being identified. In this review, we present a comprehensive list of clinically relevant SNPs in autophagy-related genes, highlighting the scope and relevance of autophagy alterations in human disease.

Pharmacogenetics: A strategy for personalized medicine for autoimmune diseases

For many years, a considerable number of patients with autoimmune diseases (ADs) have suffered from a lack of drug response and drug-related toxicity. Despite the emergence of new therapeutic options such as biological agents, patients continue to struggle with these problems. Unfortunately, new challenges, including the paradoxical effects of biological drugs, have complicated the situation. In recent decades, efforts have been made to predict drug response as well as drug-related side effects. Thanks to the many advances in genetics, evaluation of markers to predict drug response/toxicity before the initiation of treatment may be an avenue toward personalizing treatments. Implementing pharmacogenetics and pharmacogenomics in the clinic could improve clinical care; however, obstacles remain to effective personalized medicine for ADs. The present study attempted to clarify the concept of pharmacogenetics/pharmacogenomics for ADs. After an overview on the pathogenesis of the most common types of treatments, this paper focuses on pharmacogenetic studies related to the selected ADs. Bridging the gap between pharmacogenetics and personalized medicine is also discussed. Moreover, the advantages, disadvantages and recommendations related to making personalized medicine practical for ADs have been addressed.

Multiple sclerosis: disease modifying therapy and the human leukocyte antigen

OBJECTIVE

To investigate the potential relationship between the human leukocyte antigen (HLA) type (class I and II) and the response to several disease-modifying therapies (DMTs) in patients with multiple sclerosis (MS).

METHODS

We analyzed clinical data of 87 patients with MS at the beginning and end of each type of DMT including the disease duration, Expanded Disability Status Scale and Multiple Sclerosis Severity Score (MSSS). Genotyping of HLA-DRB1, HLA-DPB1, HLA-DQB1, HLA-A, HLA-B and HLA-C alleles were identified using high-resolution techniques. Statistical correlation between the HLA type and response to DMTs was done using the initial and final MSSS.

RESULTS

Statistical relationships (p < 0.05) were found for only 15 of 245 alleles tested. There was a reduction in the MSSS for patients treated with corticosteroids (DRB1*15:01, DPB1*04:01, DQB1*02:01 and DQB1*03:01), azathioprine (DRB1*03:01, DPB1*04:01, DQB1*03:02, DQB1*06:02, HLA-C*07:02), interferon β-1a 22 mcg (DRB1*11:04, DQB1*03:01 and DQB1*03:02), interferon β-1a 30 mcg (DPB1*02:01, HLA-C*05:01) and interferon β-1b (DQB1*02:01).

CONCLUSION

These findings suggest a few relationships between the HLA and response to DMTs in the disability for some types of HLA class I and II alleles in a specific subset of MS patients.

Pharmacogenomics of Multiple Sclerosis: A Systematic Review

Background: Over the past two decades, various novel disease-modifying drugs for multiple sclerosis (MS) have been approved. However, there is high variability in the patient response to the available medications, which is hypothesized to be partly attributed to genetics. Objectives: To conduct a systematic review of the current literature on the pharmacogenomics of MS therapy. Methods: A systematic literature search was conducted using PubMed/MEDLINE database searching for articles investigating a role of genetic variation in response to disease-modifying MS treatments, published in the English language up to October 9th, 2018. PRISMA guidelines for systematic reviews were applied. Studies were included if they investigated response or nonresponse to MS treatment defined as relapse rate, by expanded disability status scale score or based on magnetic resonance imaging. The following data were extracted: first author's last name, year of publication, PMID number, sample size, ethnicity of patients, method, genes, and polymorphisms tested, outcome, significant associations with corresponding P-values and confidence intervals, response criteria, and duration of the follow-up period. Results: Overall, 48 articles published up to October 2018, evaluating response to interferon-beta, glatiramer acetate, mitoxantrone, and natalizumab, met our inclusion criteria and were included in this review. Among those, we identified 42 (87.5%) candidate gene studies and 6 (12.5%) genome-wide association studies. Existing pharmacogenomic evidence is mainly based on the results of individual studies, or on results of multiple studies, which often lack consistency. In recent years, hypothesis-free approaches identified novel candidate genes that remain to be validated. Various study designs, including the definition of clinical response, duration of the follow-up period, and methodology as well as moderate sample sizes, likely contributed to discordances between studies. However, some of the significant associations were identified in the same genes, or in the genes involved in the same biological pathways. Conclusions: At the moment, there is no available clinically actionable pharmacogenomic biomarker that would enable more personalized treatment of MS. More large-scale studies with uniform design are needed to identify novel and validate existing pharmacogenomics findings. Furthermore, studies investigating associations between rare variants and treatment response in MS patients, using next-generation sequencing technologies are warranted.

An unsupervised machine learning method for discovering patient clusters based on genetic signatures

INTRODUCTION

Many chronic disorders have genomic etiology, disease progression, clinical presentation, and response to treatment that vary on a patient-to-patient basis. Such variability creates a need to identify characteristics within patient populations that have clinically relevant predictive value in order to advance personalized medicine. Unsupervised machine learning methods are suitable to address this type of problem, in which no a priori class label information is available to guide this search. However, it is challenging for existing methods to identify cluster memberships that are not just a result of natural sampling variation. Moreover, most of the current methods require researchers to provide specific input parameters a priori.

METHOD

This work presents an unsupervised machine learning method to cluster patients based on their genomic makeup without providing input parameters a priori. The method implements internal validity metrics to algorithmically identify the number of clusters, as well as statistical analyses to test for the significance of the results. Furthermore, the method takes advantage of the high degree of linkage disequilibrium between single nucleotide polymorphisms. Finally, a gene pathway analysis is performed to identify potential relationships between the clusters in the context of known biological knowledge.

DATASETS AND RESULTS

The method is tested with a cluster validation and a genomic dataset previously used in the literature. Benchmark results indicate that the proposed method provides the greatest performance out of the methods tested. Furthermore, the method is implemented on a sample genome-wide study dataset of 191 multiple sclerosis patients. The results indicate that the method was able to identify genetically distinct patient clusters without the need to select parameters a priori. Additionally, variants identified as significantly different between clusters are shown to be enriched for protein-protein interactions, especially in immune processes and cell adhesion pathways, via Gene Ontology term analysis.

CONCLUSION

Once links are drawn between clusters and clinically relevant outcomes, Immunochip data can be used to classify high-risk and newly diagnosed chronic disease patients into known clusters for predictive value. Further investigation can extend beyond pathway analysis to evaluate these clusters for clinical significance of genetically related characteristics such as age of onset, disease course, heritability, and response to treatment.

Pharmacogenetics of glatiramer acetate therapy for multiple sclerosis: the impact of genome-wide association studies identified disease risk loci

AIM

Association analysis of genome-wide association studies (GWAS) identified multiple sclerosis (MS) risk genetic variants with glatiramer acetate (GA) treatment efficacy.

PATIENTS & METHODS

SNPs in 17 GWAS-identified immune response loci were analyzed in 296 Russian MS patients as possible markers of optimal GA treatment response for at least 2 years.

RESULTS

Alleles/genotypes of EOMES, CLEC16A, IL22RA2, PVT1 and HLA-DRB1 were associated by themselves with event-free phenotype during GA treatment for at least 2 years (p _f  = 0.032 - 0.00092). The biallelic combinations including EOMES, CLEC16A, IL22RA2, PVT1, TYK2, CD6, IL7RA and IRF8 genes were associated with response to GA with increased significance level (p _f  = 0.0060 - 1.1 × 10^-5). The epistasic interactions or additive effects were observed between the components of the identified biallelic combinations.

CONCLUSION

We pinpointed the involvement of several GWAS-identified MS risk loci in GA therapy efficacy. These findings may be aggregated to predict the optimal GA response in MS patients.

Human Tregs Made Antigen Specific by Gene Modification: The Power to Treat Autoimmunity and Antidrug Antibodies with Precision

Human regulatory CD4⁺ T cells (Tregs) are potent immunosuppressive lymphocytes responsible for immune tolerance and homeostasis. Since the seminal reports identifying Tregs, vast research has been channeled into understanding their genesis, signature molecular markers, mechanisms of suppression, and role in disease. This research has opened the doors for Tregs as a potential therapeutic for diseases and disorders such as multiple sclerosis, type I diabetes, transplantation, and immune responses to protein therapeutics, like factor VIII. Seminal clinical trials have used polyclonal Tregs, but the frequency of antigen-specific Tregs among polyclonal populations is low, and polyclonal Tregs may risk non-specific immunosuppression. Antigen-specific Treg therapy, which uses genetically modified Tregs expressing receptors specific for target antigens, greatly mitigates this risk. Building on the principles of T-cell receptor cloning, chimeric antigen receptors (CARs), and a novel CAR derivative, called B-cell antibody receptors, our lab has developed different types of antigen-specific Tregs. This review discusses the current research and optimization of gene-modified antigen-specific human Tregs in our lab in several disease models. The preparations and considerations for clinical use of such Tregs also are discussed.