Project

GR4MS

Integration of clinical and multi-omics multiple sclerosis data into a predictive algorithm of disease activity to accelerate personalized medicine
Application Areas
Research Areas
Research Lines
Staff
WEBSITE

ITB Principal Investigator

Name

Integration of clinical and multi-omics multiple sclerosis data into a predictive algorithm of disease activity to accelerate personalized medicine

Acronym

GR4MS

Location

Segrate

Start Date

2018

End Date

2022

Funder

Ministero della Salute - Bando Ricerca Finalizzata 2016

Partners

Ospedale San Raffaele - Institute of Experimental Neurology/Division of Neuroscience/Laboratory of Genetics of Complex Neurological Disorders Area Milano 4 - Marco Moscatelli, Matteo Gnocchi

In the present project we propose an integrated multi–omics approach combining clinical data with genetic variants, transcriptomic signatures and T lymphocyte repertoires to disentangle the biological basis of multiple sclerosis (MS) inflammatory activity and disease severity. We plan to study 220 MS patients with relapsing remitting (RR) disease course, who have been sampled early in the disease course, before any disease modifying treatments (DMTs) start.
  • Mascia, E. et al. (2025) “Genetic Contribution to Medium-Term Disease Activity in Multiple Sclerosis,” Molecular Neurobiology, 62(1), pp. 322–334. Available at: https://doi.org/10.1007/s12035-024-04264-8.
  • Mosca, E. et al. (2021) “Characterization and comparison of gene-centered human interactomes,” Briefings in Bioinformatics, 22(6). Available at: https://doi.org/10.1093/bib/bbab153.
  • Di Nanni, N. et al. (2020) “Gene relevance based on multiple evidences in complex networks,” Bioinformatics, 36(3), pp. 865–871. Available at: https://doi.org/10.1093/bioinformatics/btz652.
  • Di Nanni, N. et al. (2019) “isma: an R package for the integrative analysis of mutations detected by multiple pipelines,” BMC Bioinformatics, 20(1), p. 107. Available at: https://doi.org/10.1186/s12859-019-2701-0.