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      Multiple sclerosis endophenotypes identified by high-dimensional blood signatures are associated with distinct disease trajectories

      1 , 1 , 1 , 1 , 1 , 2 , 3 , 4 , 5 , 6 , 2 , 3 , 4 , 1 , 3 , 7 , 8 , 1 , 9 , 2 , 2 , 10 , 11 , 12 , 13 , 1 , 1 , 1 , 1 , 2 , 1 , 10 , 1 , 14 , 5 , 1 , 2 , 1 , 14 , 3 , 4 , 5 , 11 , 12 , 10 , 15 , 2 , 1 , 1 , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , German Competence Network Multiple Sclerosis (KKNMS)
      Science Translational Medicine
      American Association for the Advancement of Science (AAAS)

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          Abstract

          One of the biggest challenges in managing multiple sclerosis is the heterogeneity of clinical manifestations and progression trajectories. It still remains to be elucidated whether this heterogeneity is reflected by discrete immune signatures in the blood as a surrogate of disease pathophysiology. Accordingly, individualized treatment selection based on immunobiological principles is still not feasible. Using two independent multicentric longitudinal cohorts of patients with early multiple sclerosis ( n = 309 discovery and n = 232 validation), we were able to identify three distinct peripheral blood immunological endophenotypes by a combination of high-dimensional flow cytometry and serum proteomics, followed by unsupervised clustering. Longitudinal clinical and paraclinical follow-up data collected for the cohorts revealed that these endophenotypes were associated with disease trajectories of inflammation versus early structural damage. Investigating the capacity of immunotherapies to normalize endophenotype-specific immune signatures revealed discrete effect sizes as illustrated by the limited effect of interferon-β on endophenotype 3–related immune signatures. Accordingly, patients who fell into endophenotype 3 subsequently treated with interferon-β exhibited higher disease progression and MRI activity over a 4-year follow-up compared with treatment with other therapies. We therefore propose that ascertaining a patient’s blood immune signature before immunomodulatory treatment initiation may facilitate prediction of clinical disease trajectories and enable personalized treatment decisions based on pathobiological principles.

          Abstract

          Three discrete immunological endophenotypes in early multiple sclerosis correlate with distinct disease trajectories and treatment responses.

          Editor’s summary

          The autoimmune disease multiple sclerosis (MS) is a highly heterogeneous disease with many different treatment options. However, it is not clear whether certain features of MS are associated with distinct immune signatures or would benefit from particular therapies. Here, Gross et al. used peripheral blood mononuclear cells and serum collected from two independent cohorts of patients with MS to identify three endophenotypes of the disease. These peripheral blood immune signatures distinguished patients with distinct clinical disease trajectories and efficacy of interferon-β treatment. These data suggest that peripheral blood analysis could be used to guide personalized treatment regimens for patients with MS. —Courtney Malo

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          Regularization Paths for Generalized Linear Models via Coordinate Descent

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            The sva package for removing batch effects and other unwanted variation in high-throughput experiments.

            Heterogeneity and latent variables are now widely recognized as major sources of bias and variability in high-throughput experiments. The most well-known source of latent variation in genomic experiments are batch effects-when samples are processed on different days, in different groups or by different people. However, there are also a large number of other variables that may have a major impact on high-throughput measurements. Here we describe the sva package for identifying, estimating and removing unwanted sources of variation in high-throughput experiments. The sva package supports surrogate variable estimation with the sva function, direct adjustment for known batch effects with the ComBat function and adjustment for batch and latent variables in prediction problems with the fsva function.
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              Adjusting batch effects in microarray expression data using empirical Bayes methods.

              Non-biological experimental variation or "batch effects" are commonly observed across multiple batches of microarray experiments, often rendering the task of combining data from these batches difficult. The ability to combine microarray data sets is advantageous to researchers to increase statistical power to detect biological phenomena from studies where logistical considerations restrict sample size or in studies that require the sequential hybridization of arrays. In general, it is inappropriate to combine data sets without adjusting for batch effects. Methods have been proposed to filter batch effects from data, but these are often complicated and require large batch sizes ( > 25) to implement. Because the majority of microarray studies are conducted using much smaller sample sizes, existing methods are not sufficient. We propose parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples. We illustrate our methods using two example data sets and show that our methods are justifiable, easy to apply, and useful in practice. Software for our method is freely available at: http://biosun1.harvard.edu/complab/batch/.
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                Journal
                Science Translational Medicine
                Sci. Transl. Med.
                American Association for the Advancement of Science (AAAS)
                1946-6234
                1946-6242
                March 27 2024
                March 27 2024
                : 16
                : 740
                Affiliations
                [1 ]Department of Neurology with Institute of Translational Neurology, University Hospital of Münster, University of Münster, 48149 Münster, Germany.
                [2 ]Department of Neurology, Focus Program Translational Neuroscience (FTN) and Immunotherapy (FZI), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany.
                [3 ]Experimental and Clinical Research Center, a Cooperation between the Max Delbrück Center for Molecular Medicine in the Helmholtz Association and Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany.
                [4 ]Neuroscience Clinical Research Center, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117 Berlin, Germany.
                [5 ]Department of Neurology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany.
                [6 ]Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA.
                [7 ]NeuroCure Clinical Research Center, Charité – Univeritäsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany.
                [8 ]Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, 10117 Berlin, Germany.
                [9 ]Institute of Biostatistics and Clinical Research, University of Münster, 48149 Münster, Germany.
                [10 ]Department of Neurology, St. Josef-Hospital, Ruhr-University Bochum, 44791 Bochum, Germany.
                [11 ]Department of Neurology, University of Heidelberg, 69120 Heidelberg, Germany.
                [12 ]Institute of Clinical Neuroimmunology, University Hospital and Biomedical Center (BMC), Faculty of Medicine, Ludwig Maximilians University of Munich, 81377 Munich, Germany.
                [13 ]Munich Cluster for Systems Neurology (SyNergy), 81377 Munich, Germany.
                [14 ]Department of Neurology, Medical Faculty, Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany.
                [15 ]Institute for Translational Psychiatry, University Hospital of Münster, University of Münster, 48149 Münster, Germany.
                Article
                10.1126/scitranslmed.ade8560
                b9756339-d362-4bc7-bc4f-7ba63ecc8c3b
                © 2024

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