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Next Generation Sequencing data of leukodystrophy gene panel analysis and segregation study data Sudy supported by Italian Ministry of Health. Grant Numbers: GR2016_02363337, RF2016_02361285

Objective: To assess the onset of ocrelizumab efficacy on brain magnetic resonance imaging (MRI) measures of disease activity in the Phase II study in relapsing-remitting multiple sclerosis (RRMS), and relapse rate in the pooled Phase III studies in relapsing multiple sclerosis (RMS). Methods: Brain MRI activity was determined in the Phase II trial at monthly intervals in patients with RRMS receiving placebo, ocrelizumab (600 mg), or intramuscular interferon (IFN) β-1a (30 μg). Annualized relapse rate (ARR; over various epochs) and time to first relapse were analyzed in the pooled population of the Phase III OPERA I and OPERA II trials in patients with RMS receiving ocrelizumab (600 mg) or subcutaneous IFN β-1a (44 μg). Results: In patients with RRMS, ocrelizumab reduced the number of new T1 gadolinium-enhancing lesions by Week 4 vs placebo (p=0.042) and by Week 8 vs intramuscular IFN β-1a (p<0.001). Ocrelizumab also reduced the number of new or enlarging T2 lesions appearing between Weeks 4 and 8 vs both placebo and IFN β-1a (both p<0.001). In patients with RMS, ocrelizumab significantly reduced ARR (p=0.005), and the probability of time to first protocol-defined relapse (p=0.014) vs subcutaneous IFN β-1a within the first 8 weeks. Conclusion: Epoch analysis of MRI-measured lesion activity in the Phase II study and relapse rate in the Phase III studies consistently revealed a rapid suppression of acute MRI and clinical disease activity following treatment initiation with ocrelizumab in patients with RRMS and RMS, respectively. Classification of evidence: This study provides Class II evidence that for patients with RRMS and RMS, ocrelizumab suppressed MRI activity within 4 weeks and clinical disease activity within 8 weeks. Figure e-1Phase II study of ocrelizumab in patients with RRMSSup fig 1_v1b.jpgFigure e-2Phase III OPERA I and OPERA II studies of ocrelizumab in patients with RMSSup fig 2_v1b.jpgFigure e-3The effect of treatment with ocrelizumab on B-cell counts in the pooled OPERA populationSup fig 3_v1b.jpgFigure e-4The number of new T2 lesions in the Phase II populationSup fig 4_v1c.jpgFigure e-5The number of enlarging T2 lesions in the Phase II populationSup fig 5_v1c.jpg

Based on an extensive simulation study, McGregor and colleagues recently recommended the use of surrogate variable analysis (SVA) to control for the confounding effects of cell-type heterogeneity in DNA methylation association studies in scenarios where no cell-type proportions are available. As their recommendation was mainly based on simulated data, we sought to replicate findings in two large-scale empirical studies. In our empirical data, SVA did not fully correct for cell-type effects, its performance was somewhat unstable, and it carried a risk of missing true signals caused by removing variation that might be linked to actual disease processes. By contrast, a reference-based correction method performed well and did not show these limitations. A disadvantage of this approach is that if reference methylomes are not (publicly) available, they will need to be generated once for a small set of samples. However, given the notable risk we observed for cell-type confounding, we argue that, to avoid introducing false-positive findings into the literature, it could be well worth making this investment. CellType_Means.csvsee readMe file for details.Sample1.tarSample2.tarSample3.tarSample4.tarSample5.tarSample6.tar

The mammalian MRI (MaMI) data set is a comprehensive database that encompasses high-resolution ex vivo diffusion and structural (T1- and T2-weighted) MRI scans of 124 mammalian species, and a total of 225 scans including replicas. This data set was originally used by Yaniv Assaf (Assaf, Y. et al., 2020, Nat. Neurosci.; doi: https://doi.org/10.1038/s41593-020-0641-7). The version of the data set included in this repository only includes the network matrices obtained from the MRI scans. Details on the preprocessing of the data and the reconstruction of the matrices can be found in Suarez, LE. et al., 2022, bioRxiv; doi: https://doi.org/10.1101/2022.03.11.483995.