Author Of 1 Presentation
P0431 - Assessment of clinical, genetic and immune repertoire data to predict disease activity and progression in RRMS (ID 1330)
Abstract
Background
Multiple Sclerosis (MS) has a highly heterogeneous clinical course and, given the broad spectrum of approved therapies, there is a strong need to identify parameters that can guide treatment choice
Objectives
The present study investigates clinical, genetic and immunological parameters associated with MS severity in order to combine them into a predictive model.
Methods
An “Extended” cohort of ~1,000 patients who started a first-line drug, with available clinical and genetic data, and a “Core” dataset of ~200 patients with clinical, genetic and immune repertoire information obtained before first-line treatment start were enrolled. The following outcomes were considered at the 4-year follow-up: NEDA-3 criterion, time to first relapse (TFR), EDSS and MS Severity Score (MSSS). A regression analysis was performed on both cohorts and results were meta-analyzed.
Results
A younger age at onset (AAO) and a shorter disease duration strongly correlate with higher inflammatory activity; a higher baseline EDSS and AAO are the best prognostic markers of disability increase. The genetic study identified some interesting signals with suggestive association: rs6925307 is associated with NEDA status (OR 0.55, p:1.53e-06) and has an eQTL effect on CLVS2 gene, required for normal morphology of endosomes and lysosomes in neurons. Rs9264731, an intronic variant in the HLA-C gene, is associated with TFR (HR 1.49, p:4.11e-06). T-cell receptor (TCR) sequencing is ongoing and immune repertoire data are already available for 123 patients: overall more than 16.000.000 sequences have been obtained, of which 81.5% are productive, corresponding on average to ~77.000 unique clonotypes per patient.
Conclusions
We confirmed the association of clinical parameters with disease severity and we identified some interesting genetic markers whose association need to be replicated. TCR data are being generated and will be integrated in a predictive model of disease activity.