Long non-coding RNA signatures to track treatment responses in multiple sclerosis

摘要 早期发现多发性硬化症是限制神经损害的关键，但监测 患者的进展和对治疗的反应可以说是相似的，如果不是更重要的 疾病的慢性本质。此外，据报道，不坚持治疗的比率 在患者群体中高达25%到40%，这表明需要提供持续的 监测和选择最佳治疗方法。识别新的可操作的生物标记物将 为临床医生提供更多信息，用于诊断、预后、临床 亚型以及治疗的选择和监测。启动次优治疗 对患者的健康和经济状况都是有害的。 到目前为止，选择疾病修正治疗(DMT)的一般方法是权衡 在考虑疾病的侵袭性、药物的有效性和 以“反复试验”的方式进行治疗的潜在副作用。这种做法相当令人不安。 当了解到治疗失败或治疗不当会导致不可逆转的神经疾病时 损坏。此外，这些药物中有许多与严重的药物不良反应有关 例如心脏事件、机会性感染和继发性自身免疫。评选 针对特定患者的最佳治疗方法，以及确定特定患者是否/何时有效的能力 DMT缩小是非常可取的，并将在整个医疗保健中带来巨大好处 频谱。多发性硬化症的病程在所有患者中并不相同，也不是所有患者都是如此。 对治疗的反应也一样。确定可操作的生物标志物作为 替代特定疗法的疗效将使临床医生能够识别无反应 患者尽可能早，并有可能评估剂量或给药，以优化患者 结果。 我们以前的工作探索了lncRNA作为可以测量的候选生物标记物 为准确进行外周全血MS分型提供了初步数据支持 Fast Track应用突出了用机器分析lncRNA表达水平的潜力 学习不仅要对多发性硬化症进行分类，还要指出治疗反应。

ABSTRACT Early detection of multiple sclerosis is key to limiting neurological damage but monitoring patient progression and response to therapy is of arguably similar if not greater importance due to the chronic nature of disease. Moreover, rates of non-adherence to therapy has been reported to be as high as 25% to 40% in the patient population suggesting the need to provide continuous monitoring and selection of optimal therapy. Identification of novel actionable biomarkers would provide clinicians with additional information for the purposes of diagnosis, prognosis, clinical subtyping as well as for the selection and monitoring of therapy. Initiation of sub-optimal therapy can be both detrimental to the patient’s health and financial well-being. To date, the general approach to selecting a disease modifying treatment (DMT) is to weigh the risks and benefits while considering the aggressiveness of disease, efficacy of the drug and the potential side effects of treatment in a “trial and error” fashion. This approach is quite unsettling when understanding that treatment failure or inadequacy can cause irreversible neurological damage. Furthermore, many of these drugs are associated with serious adverse drug reactions such as cardiac events, opportunistic infections and secondary autoimmunity. Selection of the best therapy for a particular patient as well as the ability to identify if/when efficacy of a particular DMT dwindles is highly desirable and would be of great benefit throughout the healthcare spectrum. The course of MS disease does not manifest identically in all patients nor do all patients respond to treatment the same way. Identification of actionable biomarkers to serve as a surrogate for the efficacy of a particular therapy would allow clinicians to identify nonresponsive patients as early as possible and potentially evaluate dosing or administration to optimize patient outcomes. Our previous work has explored lncRNAs as candidate biomarkers that can be measured in peripheral whole blood to accurately classify MS. The preliminary data provided in support of our fast track application highlights the potential for lncRNA expression levels analyzed with machine learning to not only classify MS but also indicate treatment responses.