Biomarker development in patients with HNPP

项目摘要/摘要 人外周髓鞘蛋白22基因杂合性缺失导致遗传性神经病 易患压力性瘫痪(HNPP)。HNPP的病理特征是已知的局灶性髓鞘增厚。 在周围神经中被称为“托马库拉”。HNPP患者通常表现为一过性局灶性感觉丧失和 轻微的机械按压可能引起的肌肉无力，不会影响健康的人体。我们的 在HNPP动物模型中测试小分子化合物的临床前研究表明，它可以阻止疾病 病理学。然而，将这种潜在的治疗方法转化为临床使用需要特定的验证结果 监测HNPP患者纵向治疗效果的措施。因此，这一目标是 这项研究是开发监测生物标记物，可以作为主要或次要结果测量 在HNPP临床试验中。这项研究将研究两种方法：定量磁共振成像(Qmri)。 和人类皮肤活检。轴突丧失的程度通常与神经系统疾病的严重程度相关。 精神错乱。因此，轴突丢失的测量通常是疾病进展的可靠生物标志物。传统上， 周围神经疾病的病理通过腓肠神经活检进行评估，这是一种侵入性手术， 需要手术切除神经，所以连续的腓肠神经活组织检查不可能用于纵向研究。 人类皮肤活组织检查是微创的，可以在同一对象中重复多次。初步 研究表明，真皮轴突和髓鞘中的病理可以通过 深度学习。这项研究将使用建立的基于深度学习的模型来自动化真皮神经 皮肤活检中的形态计量学。另一方面，使用qMRI来评估腿部肌肉脂肪分数(FF)，a 肌肉失神经的标志，间接反映轴突丢失，我们发现FF值在 HNPP。这项研究将确定个体肌肉中量化的FF是否可以用来跟踪 具有更好响应性的HNPP。此外，本研究将开发一种基于深度学习的方法来 使用qMRI自动对费力的个体肌肉FF进行量化。虽然肌肉内脂肪堆积 是HNPP中轴突丢失所致的终末病理改变，仍然需要对病变神经进行直接评估。 这项研究将使用战略性获得的梯度回波(阶段)成像技术来量化外周 神经，包括磁化传递率、质子密度、纵向和有效横向松弛 泰晤士报。由于HNPP患者临床表现的一过性、多灶性特点，现有的功能 结果指标，如Charcot-Marie-Tooth神经病评分或神经传导研究，可能不会 及时捕捉这些情节。然而，随着时间的积累，轴突丢失决定了最终的 HNPP患者的残疾。我们的总体假设是皮肤活检的定量数据与 QMRI可以可靠地测量HNPP患者的轴突丢失。

PROJECT SUMMARY/ABSTRACT Heterozygous deletion of human Peripheral Myelin Protein 22 (PMP22) gene results in hereditary neuropathy with liability to pressure palsies (HNPP). HNPP is characterized pathologically by focal myelin thickenings known as “tomacula” in peripheral nerves. Patients with HNPP typically present with transient focal sensory loss and muscle weakness that may be evoked by mild mechanical compressions that do not affect healthy humans. Our preclinical studies testing small molecule compound in HNPP animal model have been shown to arrest disease pathology. However, translation of this potential therapy to clinical use demand specific validated outcome measures to monitor the effects of treatment longitudinally in patients with HNPP. Therefore, the goal of this study is to develop monitoring biomarkers that may serve as either primary or secondary outcome measurements in HNPP clinical trials. The study will investigate two measures: quantitative magnetic resonance imaging (qMRI) and human skin biopsy. The degree of axonal loss is usually correlated with disease severity in neurological disorders. Therefore, measures of axonal loss are often reliable biomarkers for disease progression. Traditionally, pathology in peripheral nerve diseases has been evaluated by sural nerve biopsy, an invasive procedure that requires surgical removal of the nerve, so sequential sural nerve biopsies are not possible for longitudinal studies. Human skin biopsies are minimally invasive and can be repeated many times in the same subject. Preliminary studies have demonstrated that pathologies in dermal axon and myelin can be quantified automatically through deep learning. This study will use the established deep learning-based model to automate dermal nerve morphometrics in skin biopsy. On the other hand, using qMRI to assess muscular fat fraction (FF) in legs, a marker of muscle denervation that indirectly reflects axonal loss, we found that FF is increased in patients with HNPP. This study will determine whether FF quantified in individual muscle can be used to track progression of HNPP with better responsiveness. Furthermore, this study will develop a deep learning-based method to automate the laborious individual muscle FF quantification with qMRI. Although intramuscular fat accumulation is the end pathology resulting from axonal loss in HNPP, direct assessment of the diseased nerve is still needed. This study will use the strategically acquired gradient echo (STAGE) imaging technique to quantify peripheral nerves, including magnetization transfer ratio, proton density, longitudinal and effective transverse relaxation times. Due to the transient, multi-focal features of clinical presentation in patients with HNPP, existing function outcome measures, such as the Charcot-Marie-Tooth neuropathy score or nerve conduction studies, may not capture those episodes in a timely manner. However, axonal loss accumulated over time determines the final disabilities in patients with HNPP. Our overall hypothesis is that quantitative data from skin biopsy together with qMRI can reliably measure axonal loss in patients with HNPP.