Sex-specific prediction of interferon beta therapy response in relapsing-remitting multiple sclerosis

Charbit, Hanna; Benis, Arriel; Geyshis, Bella; Karussis, Dimitrios; Petrou, Panayota; Vaknin‐Dembinsky, Adi; Lavon, Iris

doi:10.1016/j.jocn.2014.11.027

Cited by 6 publications

(6 citation statements)

References 28 publications

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“…Numerous other molecular, genetic and quantitative radiological modifiers of treatment outcomes have been proposed (Bosca et al, 2010;Vosslamber et al, 2011;Horakova et al, 2012;Malhotra et al, 2013;Mahurkar et al, 2014;Matas et al, 2014;Uher et al, 2014;Charbit et al, 2015;Hegen et al, 2016;Kuhle et al, 2017). While these modifiers could add significantly to the predictive models by incorporating pathophysiological mechanisms of on-treatment multiple sclerosis activity, their availability in clinical practice is at the present time limited, as many are still awaiting validation.…”

Section: Modifiers Of Treatment Outcomesmentioning

confidence: 99%

Towards personalized therapy for multiple sclerosis: prediction of individual treatment response

Kalinčík

Manouchehrinia

Sobíšek

et al. 2017

Brain

103

102

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Timely initiation of effective therapy is crucial for preventing disability in multiple sclerosis; however, treatment response varies greatly among patients. Comprehensive predictive models of individual treatment response are lacking. Our aims were: (i) to develop predictive algorithms for individual treatment response using demographic, clinical and paraclinical predictors in patients with multiple sclerosis; and (ii) to evaluate accuracy, and internal and external validity of these algorithms. This study evaluated 27 demographic, clinical and paraclinical predictors of individual response to seven disease-modifying therapies in MSBase, a large global cohort study. Treatment response was analysed separately for disability progression, disability regression, relapse frequency, conversion to secondary progressive disease, change in the cumulative disease burden, and the probability of treatment discontinuation. Multivariable survival and generalized linear models were used, together with the principal component analysis to reduce model dimensionality and prevent overparameterization. Accuracy of the individual prediction was tested and its internal validity was evaluated in a separate, non-overlapping cohort. External validity was evaluated in a geographically distinct cohort, the Swedish Multiple Sclerosis Registry. In the training cohort (n = 8513), the most prominent modifiers of treatment response comprised age, disease duration, disease course, previous relapse activity, disability, predominant relapse phenotype and previous therapy. Importantly, the magnitude and direction of the associations varied among therapies and disease outcomes. Higher probability of disability progression during treatment with injectable therapies was predominantly associated with a greater disability at treatment start and the previous therapy. For fingolimod, natalizumab or mitoxantrone, it was mainly associated with lower pretreatment relapse activity. The probability of disability regression was predominantly associated with pre-baseline disability, therapy and relapse activity. Relapse incidence was associated with pretreatment relapse activity, age and relapsing disease course, with the strength of these associations varying among therapies. Accuracy and internal validity (n = 1196) of the resulting predictive models was high (>80%) for relapse incidence during the first year and for disability outcomes, moderate for relapse incidence in Years 2-4 and for the change in the cumulative disease burden, and low for conversion to secondary progressive disease and treatment discontinuation. External validation showed similar results, demonstrating high external validity for disability and relapse outcomes, moderate external validity for cumulative disease burden and low external validity for conversion to secondary progressive disease and treatment discontinuation. We conclude that demographic, clinical and paraclinical information helps predict individual response to disease-modifying therapies at the time of their commencement.

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Section: Modifiers Of Treatment Outcomesmentioning

confidence: 99%

Towards personalized therapy for multiple sclerosis: prediction of individual treatment response

Kalinčík

Manouchehrinia

Sobíšek

et al. 2017

Brain

103

102

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“…The Markov Chain Monte Carlo (MCMC) method was used to assess the sensitivity indices between inflammaging and MS. As a result, the 35 sensitive triples (by calculating the absolute difference frequency) were shown in Table 3 (Sarasin-Filipowicz et al, 2009 ; Chen and D'Mello, 2010 ; Bergbold and Lemberg, 2013 ; Liu et al, 2013 ; Malhotra et al, 2013 ; Wan, 2014 ; Charbit et al, 2015 ; Fusco et al, 2015 ; An et al, 2017 ; Arentsen et al, 2017 ; Maridas et al, 2017 ; Mathur et al, 2017 ; Xiao et al, 2019 ; Immler et al, 2020 ; Sato et al, 2020 ; Tong et al, 2020 ; Buhelt et al, 2021 ; Correale, 2021 ; Fadul et al, 2021 ; Ma et al, 2021 ; Peng et al, 2021 ; Bogacka et al, 2022 ; Franceschi et al, 2022 ; Hjæresen et al, 2022 ; Khurana and Goswami, 2022 ; Liu S. et al, 2022 ; Schebb et al, 2022 ; Watanabe et al, 2022 ; Saeidi et al, 2023 ). For example, the sensitive triple with maximum difference was “TMPRSS13-USP18-DCHS2” (the absolute difference value was 0.270469).…”

Section: Resultsmentioning

confidence: 99%

“…TMPRSS13 (transmembrane serine protease 13) had an essential role in its zymogen autoactivation, proteolytic activity to ward the protein substrate prostasin and phosphorylation (Martin et al, 2021 ). USP18 (Ubiquitin-specific peptidase 18) was a deubiquitinating enzyme, and as a negative regulator of type I IFN (IFN-α and IFN-β) signaling pathway, where IFN-β was effective for treating MS (Sarasin-Filipowicz et al, 2009 ; Malhotra et al, 2013 ; Charbit et al, 2015 ). In short, USP18 was vital to MS pathogenesis (Malhotra et al, 2013 ).…”

Section: Resultsmentioning

confidence: 99%

Identification of crucial inflammaging related risk factors in multiple sclerosis

Xu,

Wang,

Ren

et al. 2024

Front. Mol. Neurosci.

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BackgroundMultiple sclerosis (MS) is an immune-mediated disease characterized by inflammatory demyelinating lesions in the central nervous system. Studies have shown that the inflammation is vital to both the onset and progression of MS, where aging plays a key role in it. However, the potential mechanisms on how aging-related inflammation (inflammaging) promotes MS have not been fully understood. Therefore, there is an urgent need to integrate the underlying mechanisms between inflammaging and MS, where meaningful prediction models are needed.MethodsFirst, both aging and disease models were developed using machine learning methods, respectively. Then, an integrated inflammaging model was used to identify relative risk factors, by identifying essential “aging-inflammation-disease” triples. Finally, a series of bioinformatics analyses (including network analysis, enrichment analysis, sensitivity analysis, and pan-cancer analysis) were further used to explore the potential mechanisms between inflammaging and MS.ResultsA series of risk factors were identified, such as the protein homeostasis, cellular homeostasis, neurodevelopment and energy metabolism. The inflammaging indices were further validated in different cancer types. Therefore, various risk factors were integrated, and even both the theories of inflammaging and immunosenescence were further confirmed.ConclusionIn conclusion, our study systematically investigated the potential relationships between inflammaging and MS through a series of computational approaches, and could present a novel thought for other aging-related diseases.

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“…Then, the authors validated the expression of their results on 47 external patients, 28 women and 19 men, that were confirmed for JAK1 , IFNAR2 and SKI . Considering the expression of the eight genes reported, it is possible to predict the therapy response but in a sex-specific manner [ 25 ]. Sferruzza, F. et al inspected the effects of fingolimod in the CD14+ monocytes of RRMS patients before and 6 months after treatment.…”

Section: Resultsmentioning

confidence: 99%

A Comprehensive Exploration of the Transcriptomic Landscape in Multiple Sclerosis: A Systematic Review

Chiricosta

Blando

D’Angiolini

et al. 2023

IJMS

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Multiple Sclerosis (MS) is, to date, an incurable disease of the nervous system characterized by demyelination. Several genetic mutations are associated with the disease but they are not able to explain all the diagnosticated cases. Thus, it is suggested that altered gene expression may play a role in human pathologies. In this review, we explored the role of the transcriptomic profile in MS to investigate the main altered biological processes and pathways involved in the disease. Herein, we focused our attention on RNA-seq methods that in recent years are producing a huge amount of data rapidly replacing microarrays, both with bulk and single-cells. The studies evidenced that different MS stages have specific molecular signatures and non-coding RNAs may play a key role in the disease. Sex-dependence was observed before and after treatments used to alleviate symptomatology activating different biological processes in a drug-dependent manner. New pathways, such as neddylation, were found deregulated in MS and inflammation was linked to neuron degeneration areas through spatial transcriptomics. It is evident that the use of RNA-seq in the study of complex pathologies, such as MS, is a valid strategy to shed light on new involved mechanisms.

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Sex-specific prediction of interferon beta therapy response in relapsing-remitting multiple sclerosis

Cited by 6 publications

References 28 publications

Towards personalized therapy for multiple sclerosis: prediction of individual treatment response

Towards personalized therapy for multiple sclerosis: prediction of individual treatment response

Identification of crucial inflammaging related risk factors in multiple sclerosis

A Comprehensive Exploration of the Transcriptomic Landscape in Multiple Sclerosis: A Systematic Review

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