Institute of Cell and Molecular Pathology, Hannover Medical School, Hannover, Germany.
PLoS One. 2012;7(9):e44401. doi: 10.1371/journal.pone.0044401. Epub 2012 Sep 6.
Amyotrophic lateral sclerosis (ALS) is a fatal disorder of the motor neuron system with poor prognosis and marginal therapeutic options. Current clinical diagnostic criteria are based on electrophysiological examination and exclusion of other ALS-mimicking conditions. Neuroprotective treatments are, however, most promising in early disease stages. Identification of disease-specific CSF biomarkers and associated biochemical pathways is therefore most relevant to monitor disease progression, response to neuroprotective agents and to enable early inclusion of patients into clinical trials.
CSF from 35 patients with ALS diagnosed according to the revised El Escorial criteria and 23 age-matched controls was processed using paramagnetic bead chromatography for protein isolation and subsequently analyzed by MALDI-TOF mass spectrometry. CSF protein profiles were integrated into a Random Forest model constructed from 153 mass peaks. After reducing this peak set to the top 25%, a classifier was built which enabled prediction of ALS with high accuracy, sensitivity and specificity. Further analysis of the identified peptides resulted in a panel of five highly sensitive ALS biomarkers. Upregulation of secreted phosphoprotein 1 in ALS-CSF samples was confirmed by univariate analysis of ELISA and mass spectrometry data. Further quantitative validation of the five biomarkers was achieved in an 80-plex Multiple Reaction Monitoring mass spectrometry assay.
ALS classification based on the CSF biomarker panel proposed in this study could become a valuable predictive tool for early clinical risk stratification. Of the numerous CSF proteins identified, many have putative roles in ALS-related metabolic processes, particularly in chromogranin-mediated secretion signaling pathways. While a stand-alone clinical application of this classifier will only be possible after further validation and a multicenter trial, it could be readily used to complement current ALS diagnostics and might also provide new insights into the pathomechanisms of this disease in the future.
肌萎缩侧索硬化症（ALS）是一种运动神经元系统的致命疾病，预后较差，治疗选择有限。目前的临床诊断标准基于电生理学检查和排除其他类似 ALS 的情况。然而，神经保护治疗在疾病早期阶段最有希望。因此，鉴定疾病特异性 CSF 生物标志物和相关生化途径对于监测疾病进展、对神经保护剂的反应以及使患者能够早期纳入临床试验最为重要。
使用顺磁珠色谱法对根据修订后的埃尔埃斯科里亚尔标准诊断的 35 名 ALS 患者和 23 名年龄匹配的对照者的 CSF 进行处理，用于蛋白质分离，然后通过 MALDI-TOF 质谱分析。CSF 蛋白质图谱被整合到一个随机森林模型中，该模型由 153 个质量峰构建而成。将该峰集减少到前 25%后，构建了一个能够高度准确、敏感和特异性预测 ALS 的分类器。对鉴定出的肽进行进一步分析，得到了一组五个高度敏感的 ALS 生物标志物。通过 ELISA 和质谱数据的单变量分析，证实了 ALS-CSF 样本中分泌磷蛋白 1 的上调。在 80 重多重反应监测质谱测定中进一步对这五个生物标志物进行了定量验证。
基于本研究中提出的 CSF 生物标志物组进行的 ALS 分类可能成为早期临床风险分层的有价值的预测工具。在鉴定出的众多 CSF 蛋白中，许多蛋白在 ALS 相关代谢过程中具有潜在作用，特别是在嗜铬粒蛋白介导的分泌信号通路中。虽然在进一步验证和多中心试验之前，这种分类器的独立临床应用是不可能的，但它可以很容易地用于补充当前的 ALS 诊断，并可能为未来研究该疾病的发病机制提供新的见解。
