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

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.

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.

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.

Racial and ethnic disparities in treatment response and tolerability in multiple sclerosis: A comparative study

BACKGROUND

Inter-individual response and tolerability profiles associated with available disease-modifying treatments (DMTs) are an important aspect of the therapeutic decision-making process in multiple sclerosis (MS). In the absence of racially diverse clinical studies, the possible effect of race and ethnicity on treatment outcome remains uncertain. This study aims to compare disease outcome among Hispanic, Black, and White patients with MS, and assess the impact of race/ethnicity on long-term outcome.

METHODS

A retrospective review of electronic medical records was performed on a multiethnic cohort of MS patients treated in a large academic center. Sociodemographic characteristics, treatment regimens, and disability outcomes were compared between the three groups.

RESULTS

A total of 300 age- and gender-matched MS patients (100 Hispanic, 100 Black, and 100 White) were included in the study. When assessing the overall survival of MS patients without ambulatory disability 5 years from diagnosis, Hispanics and Blacks attained lower survival times compared to Whites (survival time ratio [STR] 0.17, p = 0.004; and 0.14, p = 0.002, respectively). Black patients had the highest rate of disease progression and treatment-limiting adverse events despite similar sociodemographic profiles and DMT exposure relative to Hispanics and Whites.

CONCLUSION

Racial/ethnic disparities in treatment outcome create an unmet need to identify tailored, multifaceted approaches to therapy selection in MS.

Significance of Single-Nucleotide Variants in Long Intergenic Non-protein Coding RNAs

Single-nucleotide variants (SNVs) are the most common genetic variants and universally present in the human genome. Genome-wide association studies (GWASs) have identified a great number of disease or trait-associated variants, many of which are located in non-coding regions. Long intergenic non-protein coding RNAs (lincRNAs) are the major subtype of long non-coding RNAs; lincRNAs play crucial roles in various disorders and cellular models via multiple mechanisms. With rapid growth in the number of the identified lincRNAs and genetic variants, there is great demand for an investigation of SNVs in lincRNAs. Hence, in this article, we mainly summarize the significant role of SNVs within human lincRNA regions. Some pivotal variants may serve as risk factors for the development of various disorders, especially cancer. They may also act as important regulatory signatures involved in the modulation of lincRNAs in a tissue- or disorder-specific manner. An increasing number of researches indicate that lincRNA variants would potentially provide additional options for genetic testing and disease risk assessment in the personalized medicine era.

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.

WITHDRAWN: Single nucleotide polymorphism, a putative driver for the role of long intergeneric non-coding RNA

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