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

Tomas Kalincik, Ali Manouchehrinia, Lukas Sobisek, Vilija Jokubaitis, Tim Spelman, Dana Horakova, Eva Havrdova, Maria Trojano, Guillermo Izquierdo, Alessandra Lugaresi, Marc Girard, Alexandre Prat, Pierre Duquette, Pierre Grammond, Patrizia Sola, Raymond Hupperts, Francois Grand'Maison, Eugenio Pucci, Cavit Boz, Raed AlroughaniVincent Van Pesch, Jeannette Lechner-Scott, Murat Terzi, Roberto Bergamaschi, Gerardo Iuliano, Franco Granella, Daniele Spitaleri, Vahid Shaygannejad, Celia Oreja-Guevara, Mark Slee, Radek Ampapa, Freek Verheul, Pamela McCombe, Javier Olascoaga, Maria Pia Amato, Steve Vucic, Suzanne Hodgkinson, Cristina Ramo-Tello, Shlomo Flechter, Edgardo Cristiano, Csilla Rozsa, Fraser Moore, Jose Luis Sanchez-Menoyo, Maria Laura Saladino, Michael Barnett, Jan Hillert, Helmut Butzkueven, MS Base Study Group

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    30 Citations (Scopus)

    Abstract

    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 (480%) 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.

    Original languageEnglish
    Pages (from-to)2426-2443
    Number of pages18
    JournalBrain
    Volume140
    Issue number9
    DOIs
    Publication statusPublished - 1 Sep 2017

    Keywords

    • Disability
    • Multiple sclerosis
    • Precision medicine
    • Prediction
    • Relapses

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  • Cite this

    Kalincik, T., Manouchehrinia, A., Sobisek, L., Jokubaitis, V., Spelman, T., Horakova, D., Havrdova, E., Trojano, M., Izquierdo, G., Lugaresi, A., Girard, M., Prat, A., Duquette, P., Grammond, P., Sola, P., Hupperts, R., Grand'Maison, F., Pucci, E., Boz, C., ... MS Base Study Group (2017). Towards personalized therapy for multiple sclerosis: Prediction of individual treatment response. Brain, 140(9), 2426-2443. https://doi.org/10.1093/brain/awx185