Biomarkers formultiple sclerosis

Department of Neurosciences, Institute of Biomedical Research August Pi Sunyer, Hospital Clinic of Barcelona, Barcelona, Spain.
Drug News & Perspectives (Impact Factor: 3.13). 11/2010; 23(9):585-95. DOI: 10.1358/dnp.2010.23.9.1472300
Source: PubMed


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.

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Available from: Pablo Villoslada, May 29, 2015
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    • "Immunological markers are objective measures with clinical and scientific relevance. They can be useful in diagnostic definition/exclusion (e.g., anti acquaporin antibodies to differentiate MS from Neuromyelitis Ottica), to assess treatment response (e.g., anti IFN neutralizing antibodies in non-responder patients), but also to dissect pathogenetic mechanisms of disease [10-12]. Due to the complexity and redundancy of immune system, investigating the pattern of correlations among immune markers is a challenging task for traditional statistic. "
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    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.
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    ABSTRACT: Multiple sclerosis (MS) is a chronic, potentially disabling, immune-mediated neurodegenerative disease. Relapsing-remitting multiple sclerosis (RRMS) accounts for approximately 80-85% of all MS cases. It affects more women than men, and many of these women are of child-bearing age. Antiganglioside anti-bodies (AGA) have been reported in MS, nevertheless, clinical significance of AGA in MS has not been established. No immunological studies about the possible role of serum IgM anti-GM1 and anti-GD1a antibodies in pregnant women with MS have been performed so far. In this study, the diagnostic value of IgM anti-GM1 and anti-GD1a antibodies was evaluated by a standardized ELISA method. We used the serum of a patient with clinically defined RRMS before and during the pregnancy, as well as during a 3 month postpartum relapse and 7 and 18 months after delivery. Our findings of significantly elevated titres of serum IgM antibodies to GM1 and GD1a before pregnancy and during a neuroprotective treated relapse confirm the clinical significance of serum anti- ganglioside antibodies in MS. They suggest immune-mediated demyelination and neurodegeneration as an underlying pathogenetic mechanism. Unchanged IgM AGA titres during long-term postpartum disease period support the con- cept of possible beneficial effect of pregnancy on disease progression. Therefore, it is of critical importance to establish the role of IgM anti-GM1 and anti-GD1a antibodies as potential biomarkers for MS. This will help to develop differential therapeutic strategies in MS, which will prevent neurodegeneration and confer neuroprotection and allow treating pregnant women selectively according to their immunogenetic phenotype disease status.
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    ABSTRACT: New "omic" technologies and their application to systems biology approaches offer new opportunities for biomarker discovery in complex disorders, including multiple sclerosis (MS). Recent studies using massive genotyping, DNA arrays, antibody arrays, proteomics, glycomics, and metabolomics from different tissues (blood, cerebrospinal fluid, brain) have identified many molecules associated with MS, defining both susceptibility and functional targets (e.g., biomarkers). Such discoveries involve many different levels in the complex organizational hierarchy of humans (DNA, RNA, protein, etc.), and integrating these datasets into a coherent model with regard to MS pathogenesis would be a significant step forward. Given the dynamic and heterogeneous nature of MS, validating biomarkers is mandatory. To develop accurate markers of disease prognosis or therapeutic response that are clinically useful, combining molecular, clinical, and imaging data is necessary. Such an integrative approach would pave the way towards better patient care and more effective clinical trials that test new therapies, thus bringing the paradigm of personalized medicine in MS one step closer.
    Full-text · Article · Jan 2012 · Journal of neuroimmunology
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