Biomarkers for multiple sclerosis.
ABSTRACT The pursuit of personalized medicine requires the development of biomarkers to predict disease course, monitor disease evolution, stratify patient subgroups by disease activity and to predict and monitor response to therapies. Multiple sclerosis (MS) is a common neurological disease in young adults with an unpredictable course that may be associated with significant disability, diminishing the patient's quality of life. Currently, disease prognosis is based on clinical information (relapse rate and disability scales) and diagnostic tests (brain MRI or the presence of oligoclonal bands in the cerebrospinal fluid). However, the ability of neurologists to make an accurate prognosis is very limited based on such information, a situation perceived by patients as one of their biggest concerns. Although many recent studies have identified different molecules and imaging techniques associated with the course of MS, in most cases the diagnostic accuracy of such technologies has not been properly assessed. This shortcoming is partly due to the failure to validate such biomarkers, which impedes their application in clinical practice. However, the recent validation of anti-aquaporin-4 antibodies for Devic's disease and the development of optic coherent tomography for MS, are examples of the benefits that the development of MS biomarkers can offer. Indeed, it may currently be necessary to redress the bias in research towards clinical validation rather than discovery in order to promote translational research and improve patient's quality of life.
- SourceAvailable from: Mario Clerici[show abstract] [hide abstract]
ABSTRACT: BACKGROUND: Multiple Sclerosis (MS) is a multi-factorial disease, where a single biomarker unlikely can provide comprehensive information. Moreover, due to the non-linearity of biomarkers, traditional statistic is both unsuitable and underpowered to dissect their relationship. Patients affected with primary (PP=14), secondary (SP=33), benign (BB=26), relapsing-remitting (RR=30) MS, and 42 sex and age matched healthy controls were studied. We performed a depth immune-phenotypic and functional analysis of peripheral blood mononuclear cell (PBMCs) by flow-cytometry. Semantic connectivity maps (AutoCM) were applied to find the natural associations among immunological markers. AutoCM is a special kind of Artificial Neural Network able to find consistent trends and associations among variables. The matrix of connections, visualized through minimum spanning tree, keeps non linear associations among variables and captures connection schemes among clusters. RESULTS: Complex immunological relationships were shown to be related to different disease courses. Low CD4IL25+ cells level was strongly related (link strength, ls=0.81) to SP MS. This phenotype was also associated to high CD4ROR+ cells levels (ls=0.56). BB MS was related to high CD4+IL13 cell levels (ls=0.90), as well as to high CD14+IL6 cells percentage (ls=0.80). RR MS was strongly (ls=0.87) related to CD4+IL25 high cell levels, as well indirectly to high percentages of CD4+IL13 cells. In this latter strong (ls=0.92) association could be confirmed the induction activity of the former cells (CD4+IL25) on the latter (CD4+IL13). Another interesting topographic data was the isolation of Th9 cells (CD4IL9) from the main part of the immunological network related to MS, suggesting a possible secondary role of this new described cell phenotype in MS disease. CONCLUSIONS: This novel application of non-linear mathematical techniques suggests peculiar immunological signatures for different MS phenotypes. Notably, the immune-network displayed by this new method, rather than a single marker, might be viewed as the right target of immunotherapy. Furthermore, this new statistical technique could be also employed to increase the knowledge of other age-related multifactorial disease in which complex immunological networks play a substantial role.Immunity & Ageing 01/2013; 10(1):1.