Molecular-based diagnosis of multiple sclerosis and its progressive stage
Molecular-based diagnosis of multiple sclerosis and its progressive stage
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DOI：
10.1002/ana.25083
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发表时间：
2017-11-01
影响因子：
11.2
通讯作者：
Bielekova, Bibiana
中科院分区：
文献类型：
--
作者：
Barbour, Christopher;Kosa, Peter;Bielekova, Bibiana
目的：在大多数内科学科中，生物标志物有助于诊断，允许廉价的治疗筛选，并指导患者特异性治疗方案的选择。相反，神经学缺乏对中枢神经系统（CNS）细胞的生理状态或功能障碍的有效测量。因此，患有慢性神经系统疾病的患者通常使用单一的疾病修饰疗法，而不了解患者特定的残疾驱动因素。因此，将多发性硬化症（MS）作为复杂的多基因神经系统疾病的一个例子，我们试图确定脑脊液（CSF）生物标志物是否在个体内稳定、细胞类型、疾病和/或过程特异性，以及对治疗干预的反应。方法：我们在一个建模队列（n = 225）中使用统计学习方法，从基于dna适配体的1,128个CSF蛋白的测量中开发诊断分类器。一个独立验证队列（n = 85）评估了衍生分类器的可靠性。生物学解释来自于原代或干细胞衍生的人类中枢神经系统细胞和细胞系的体外建模。鉴别MS与临床、病理生理和影像学模拟MS的中枢神经系统疾病的分类器在接受者工作特征曲线（AUROC）下的验证面积为0.98，而鉴别复发缓解型MS与进展型MS的分类器的AUROC验证面积为0.91。没有分类器能比随机猜测更好地区分原发性进行性MS和继发性进行性MS。治疗引起的生物标志物的变化大大超过了分析的个人和技术变异。脑脊液生物标志物反映的中枢神经系统生物学过程是稳健、稳定、疾病特异性的，甚至是疾病分期特异性的。这为CSF生物标志物在药物开发和CNS疾病精准医学中的广泛应用提供了机会。神经网络学报2017；82:795 - 812
ObjectiveBiomarkers aid diagnosis, allow inexpensive screening of therapies, and guide selection of patient-specific therapeutic regimens in most internal medicine disciplines. In contrast, neurology lacks validated measurements of the physiological status, or dysfunction(s) of cells of the central nervous system (CNS). Accordingly, patients with chronic neurological diseases are often treated with a single disease-modifying therapy without understanding patient-specific drivers of disability. Therefore, using multiple sclerosis (MS) as an example of a complex polygenic neurological disease, we sought to determine whether cerebrospinal fluid (CSF) biomarkers are intraindividually stable, cell type-, disease- and/or process-specific, and responsive to therapeutic intervention.MethodsWe used statistical learning in a modeling cohort (n = 225) to develop diagnostic classifiers from DNA-aptamer-based measurements of 1,128 CSF proteins. An independent validation cohort (n = 85) assessed the reliability of derived classifiers. The biological interpretation resulted from in vitro modeling of primary or stem cell-derived human CNS cells and cell lines.ResultsThe classifier that differentiates MS from CNS diseases that mimic MS clinically, pathophysiologically, and on imaging achieved a validated area under the receiver operating characteristic curve (AUROC) of 0.98, whereas the classifier that differentiates relapsing-remitting from progressive MS achieved a validated AUROC of 0.91. No classifiers could differentiate primary progressive from secondary progressive MS better than random guessing. Treatment-induced changes in biomarkers greatly exceeded intraindividual and technical variabilities of the assay.InterpretationCNS biological processes reflected by CSF biomarkers are robust, stable, disease specific, or even disease stage specific. This opens opportunities for broad utilization of CSF biomarkers in drug development and precision medicine for CNS disorders. Ann Neurol 2017;82:795-812