Cytotoxic CD4+ T Cells Drive Multiple Sclerosis Progression.

Multiple sclerosis (MS) is the leading cause of chronic neurological disability in young adults. The clinical disease course of MS varies greatly between individuals, with some patients progressing much more rapidly than others, making prognosis almost impossible. We previously discovered that cytotoxic CD4+ T cells (CD4+ CTL), identified by the loss of CD28, are able to migrate to sites of inflammation and that they contribute to tissue damage. Furthermore, in an animal model for MS, we showed that these cells are correlated with inflammation, demyelination, and disability. Therefore, we hypothesize that CD4+ CTL drive progression of MS and have prognostic value. To support this hypothesis, we investigated whether CD4+ CTL are correlated with worse clinical outcome and evaluated the prognostic value of these cells in MS. To this end, the percentage of CD4+CD28null T cells was measured in the blood of 176 patients with relapsing-remitting MS (=baseline). Multimodal evoked potentials (EP) combining information on motoric, visual, and somatosensoric EP, as well as Kurtzke expanded disability status scale (EDSS) were used as outcome measurements at baseline and after 3 and 5 years. The baseline CD4+CD28null T cell percentage is associated with EP ( = 0.003,  = 0.28), indicating a link between these cells and disease severity. In addition, the baseline CD4+CD28null T cell percentage has a prognostic value since it is associated with EP after 3 years ( = 0.005,  = 0.29) and with EP and EDSS after 5 years ( = 0.008,  = 0.42 and  = 0.003,  = 0.27). To the best of our knowledge, this study provides the first direct link between the presence of CD4+ CTL and MS disease severity, as well as its prognostic value. Therefore, we further elaborate on two important research perspectives: 1° investigating strategies to block or reverse pathways in the formation of these cells resulting in new treatments that slow down MS disease progression, 2° including immunophenotyping in prediction modeling studies to aim for personalized medicine.