Comparative pharmacogenetics of multiple sclerosis: IFN-β versus glatiramer acetate

Reshaping neuroimmunology: diagnosis and treatment in the era of precision medicine

Precision medicine has revolutionized the field of neuroimmunology, with innovative approaches that characterize diseases based on their biology, deeper understanding of the factors leading to heterogeneity within the same disease, development of targeted therapies, and strategies to tailor therapies to each patient. This review explores the impact of precision medicine on various neuroimmunological conditions, including multiple sclerosis (MS), neuromyelitis optica spectrum disorder (NMOSD), myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD), optic neuritis, autoimmune encephalitis, and immune-mediated neuropathies. We discuss advances in disease subtyping, recognition of novel entities, promising biomarkers, and the development of more selective monoclonal antibodies and cutting-edge synthetic cell-based immunotherapies in neuroimmunological disorders. In addition, we analyze the challenges related to affordability and equity in the implementation of these emerging technologies, especially in situations with limited resources.

ADAR Expression and Single Nucleotide Variants in Multiple Sclerosis Patients Affect the Response to Interferon Beta Therapy

Interferon (IFN)-β is the first-line disease management choice in multiple sclerosis (MS) with profound effects; however, in up to 50% of patients, clinical response does not occur. Ascertaining the responding state, need a long-term clinical follow-up, and this may lead to delay in use of other effective medications. IFN-induced cascade and its regulation is considered to play a major role in MS. Adenosine deaminase, RNA-specific (ADAR) dysregulation is important to IFN signaling pathway as an activity suppressor. Hence, we investigated the expression of ADAR and its single nucleotide variants of rs2229857 association with response to IFN-β in relapsing-remitting MS patients. mRNA levels and genotyping of rs2229857 in 167 MS patients were investigated via SYBR Green real-time (RT)-quantitative polymerase chain reaction and high-resolution melting RT PCR, respectively. The allele-A in rs2229857 and higher expression of ADAR were associated with poor response to IFN-β. Two response groups were significantly different in terms of annualized relapse rate, first symptoms, first extended disability status scale (EDSS), current EDSS, and the MS severity score. According to this study's findings, assessment of transcript levels and also variants in ADAR may be useful in identifying patients' response to IFN-β before starting treatment. Further investigations are needed to determine the potency of ADAR to be a predictive biomarker in drug responsiveness.

B-Cell Activity Predicts Response to Glatiramer Acetate and Interferon in Relapsing-Remitting Multiple Sclerosis

Objective

We investigated the predictive value of the enzyme-linked immunospot technique (ELISPOT) in identifying patients with relapsing-remitting multiple sclerosis (RRMS) who will respond to treatment with glatiramer acetate (GA) or interferon-β (IFN-β), based on the brain-reactive B-cell activity of peripheral blood cells.

Methods

In this retrospective, cross-sectional, real-world multicenter study, we identified patients with RRMS in the NeuroTransData MS registry and stratified them based on their documented treatment response (relapse-free in the first 12 months of treatment) to GA or IFN-β. The GA group comprised 73 patients who responded to GA and 35 nonresponders. The IFN-β group comprised 62 responders to IFN-β and 37 nonresponders. Patients with previous or current therapy affecting B-cell activity were excluded. We polyclonally stimulated mononuclear cells from peripheral blood samples (collected after participant selection) and investigated brain-reactive B-cell activity after incubation on brain tissue lysate-coated ELISPOT plates. Validity metrics of the ELISPOT testing results were calculated (Python 3.6.8) in relation to the clinical responsiveness in the 2 treatment groups.

Results

The ELISPOT B-cell activity assay showed a sensitivity of 0.74, a specificity of 0.76, a positive predictive value of 0.78, a negative predictive value of 0.28, and a diagnostic OR of 8.99 in predicting clinical response to GA vs IFN-β therapy in patients with RRMS.

Conclusion

Measurement of brain-reactive B-cell activity by ELISPOT provides clinically meaningful predictive probabilities of individual patients' treatment response to GA or IFN-β. The assay has the potential to improve the selection of optimal first-line treatment for individual patients with RRMS.

Classification of Evidence

This study provides Class II evidence that in patients with RRMS, the brain reactivity of their peripheral-blood B cells predicts clinical response to GA and IFN-β.

A comprehensive review on the role of chemokines in the pathogenesis of multiple sclerosis

Multiple sclerosis (MS) as a chronic inflammatory disorder of the central nervous system (CNS) is thought to be caused by the abnormal induction of immune responses. Chemokines as molecules that can engage leukocytes into the location of inflammation, actively participate in the pathogenesis of MS. Several members of this family of chemo attractants have been shown to be dysregulated in the peripheral blood, cerebrospinal fluid or CNS lesions of MS patients. Studies in animal models of MS particularly experimental autoimmune encephalomyelitis have indicated the critical roles of chemokines in the pathophysiology of MS. In the current review, we summarize the data regarding the role of CCL2, CCL3, CCL4, CCL11, CCL20, CXCL1, CXCL2, CXCL8, CXCL10, CXCL12 and CXCL13 in the pathogenesis of MS.

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.

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

Background

Copaxone is an efficacious and safe therapy that has demonstrated clinical benefit for over two decades in patients with relapsing forms of multiple sclerosis (MS). On an individual level, patients show variability in their response to Copaxone, with some achieving significantly higher response levels. The involvement of genes (e.g., HLA-DRB1*1501) with high inter-individual variability in Copaxone’s mechanism of action (MoA) suggests the potential contribution of genetics to treatment response. This study aimed to identify genetic variants associated with Copaxone response in patient cohorts from late-phase clinical trials.

Methods

Single nucleotide polymorphisms (SNPs) associated with high and low levels of response to Copaxone were identified using genome-wide SNP data in a discovery cohort of 580 patients from two phase III clinical trials of Copaxone. Multivariable Bayesian modeling on the resulting SNPs in an expanded discovery cohort with 1171 patients identified a multi-SNP signature of Copaxone response. This signature was examined in 941 Copaxone-treated MS patients from seven independent late-phase trials of Copaxone and assessed for specificity to Copaxone in 310 Avonex-treated and 311 placebo-treated patients, also from late-phase trials.

Results

A four-SNP signature consisting of rs80191572 (in UVRAG), rs28724893 (in HLA-DQB2), rs1789084 (in MBP), and rs139890339 (in ZAK(CDCA7)) was identified as significantly associated with Copaxone response. Copaxone-treated signature-positive patients had a greater reduction in annualized relapse rate (ARR) compared to signature-negative patients in both discovery and independent cohorts, an effect not observed in Avonex-treated patients. Additionally, signature-positive placebo-treated cohorts did not show a reduction in ARR, demonstrating the predictive as opposed to prognostic nature of the signature. A 10% subset of patients, delineated by the signature, showed marked improvements across multiple clinical parameters, including ARR, MRI measures, and higher proportion with no evidence of disease activity (NEDA).

Conclusions

This study is the largest pharmacogenetic study in MS reported to date. Gene regions underlying the four-SNP signature have been linked with pathways associated with either Copaxone’s MoA or the pathophysiology of MS. The pronounced association of the four-SNP signature with clinical improvements in a ~10% subset of the MS patient population demonstrates the complex interplay of immune mechanisms and the individualized nature of response to Copaxone.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-017-0436-y) contains supplementary material, which is available to authorized users.

Clinical implications of neuropharmacogenetics

INTRODUCTION

Pharmacogenetics aims to identify the underlying genetic factors participating in the variability of drug response. Indeed, genetic variability at the DNA or RNA levels can directly or indirectly modify the pharmacokinetic or the pharmacodynamic parameters of a drug. The ultimate aim of pharmacogenetics is to move towards a personalised medicine by predicting responders and non-responders, adjusting the dose of the treatment, and identifying individuals at risk of adverse drug effects.

METHODS

A literature research was performed in which we reviewed all pharmacogenetic studies in neurological disorders including neurodegenerative diseases, multiple sclerosis, stroke and epilepsy.

RESULTS

Several pharmacogenetic studies have been performed in neurology, bringing insights into the inter-individual drug response variability and in the pathophysiology of neurological diseases. The principal implications of these studies for the management of patients in clinical practice are discussed.

CONCLUSION/DISCUSSION

Although several genetic factors have been identified in the modification of drug response in neurological disorders, most of them have a marginal predictive effect at the single gene level, suggesting mutagenic interactions as well as other factors related to drug interaction and disease subtypes. Most pharmacogenetic studies deserve further replication in independent populations and, ideally, in pharmacogenetic clinical trials to demonstrate their relevance in clinical practice.