Development of a Precision Medicine-based Diagnostic Tool for Membranous Nephropathy

项目摘要 该计划的目标是启动基于质谱学(MS)的工作流程的商业化 膜性肾病(MN)的诊断和分类是肾病综合征的主要原因， 导致三分之一的患者出现肾衰竭。自20世纪50年代S以来，大多数形式的MN被认为是特发性的 然而，根据近年来大量的出版物，现在人们普遍认为MN是由 通过针对大约20种不同的内源性人类蛋白的自身抗体。最常见的移动电话号码 自身抗原是磷脂酶A2受体(PLA2R)，并对自身抗体效价进行血清学分析 这种蛋白已被证明是监测疾病进展、严重程度和 缓解率(1-3)。Arkana和其他人在MN病例(4-9例)中发现了其他新的自身抗原，并给予了 PLA2R血清学检测在个体化治疗中的成功、特征和临床验证 迫切需要剩余的MN自身抗原。此外，对阳性患者的随访调查 因为这些新发现的自身抗原与威胁生命的合并症有关，包括癌症和 特定的自身免疫性疾病。因为根据MN自身抗原对患者进行分类可能会显示出严重的 基础条件，PLA2R血清学只能识别70%的原发病例，Arkana与 阿肯色州大学医学科学(UAMS)的蛋白质组学专家开发了一种无偏见的MS- 对所有抗原类型的MN进行分类的方法。I期研究评估了近300例组织活检 具有PLA2R、凝血酶反应蛋白-1结构域等自身抗原的MN患者 含有7A(THSD7A)(10例)和exostosin1/2(EXT1/2)(11例)，还包括三重阴性病例。之后 评估一系列计算、统计和人工智能数据分析模式，排名Z- 分数被发现是一个有效的自身抗原分类的指标，正确地对超过95%的 样本。在拟议的第二阶段计划期间，Arkana将继续与UAMS合作翻译 为临床实践和商业应用提供了有力的手段。将为以下项目制定质量控制指标 包括/排除传入样本，以确保MS分析和支持的可靠性和重复性 自身抗原分类的验证。分析管道也将扩大，以满足 其他自身抗原靶标，其中许多是通过Arkana最近对数百个 生物库MN样本。最后，该方法将被转移到Arkana的CLIA实验室，并在 与UAMS的比较研究包括大约300个盲样，代表整个广度 MN自身抗原类。将此工作流程作为商业可用的服务推出不仅将为临床医生提供 MN患者的诊断和治疗细节前所未有，但也揭示了潜在的生命威胁 上一代战略无法发现的共病。

Project Summary The goal of this program is to initiate commercialization of a mass spectrometry (MS)-based workflow for the diagnosis and classification of membranous nephropathy (MN), a leading cause of nephrotic syndrome, leading to kidney failure in a third of patients. Since the 1950’s, most forms of MN were considered idiopathic in origin, however, based on numerous publications in recent years, it is now broadly accepted that MN is caused by autoantibodies against approximately 20 different endogenous human proteins. The most common MN autoantigen is the phospholipase A2 receptor (PLA2R), and serological analysis of autoantibody titers against this protein has proven to be an indispensable serological tool for monitoring disease progression, severity, and remission (1-3). Arkana and others have identified additional novel autoantigens in MN cases (4-9), and given the success of PLA2R serological testing for personalized treatment, the characterization and clinical validation of the remaining MN autoantigens is urgently required. Furthermore, follow-up investigation of patients positive for these newly identified autoantigens have been linked to life-threatening comorbidities including cancer and specific autoimmune diseases. Because classifying patients based on MN autoantigen may reveal severe underlying conditions, and PLA2R serology will identify only 70% of primary cases, Arkana has partnered with proteomics experts at the University of Arkansas for Medical Sciences (UAMS) to develop an unbiased, MS- based approach to classifying MN of all antigen types. Phase I studies evaluated nearly 300 tissue biopsies from MN patients with previously determined autoantigens including PLA2R, thrombospondin type-1 domain containing 7A (THSD7A) (10), and exostosin1/2 (EXT1/2) (11), with triple negative cases also included. After evaluating a range of computational, statistical, and artificial intelligence data analysis modalities, ranked Z- scores were found to be an effective metric for autoantigen classification, correctly classifying over 95% of samples. During the proposed Phase II program, Arkana will continue collaborating with UAMS to translate this powerful method to clinical practice and commercial availability. Quality control metrics will be developed for the inclusion/exclusion of incoming samples, to ensure the reliability and repeatability of MS analysis and support verification of autoantigen classifications. The analytical pipeline will also be broadened to accommodate additional autoantigen targets, many of which were identified by Arkana’s recent MS analysis of hundreds of biobanked MN samples. Finally, the method will be transferred to Arkana’s CLIA laboratory and validated in a comparative study with UAMS comprising approximately 300 blinded samples representing the full breadth of MN autoantigen classes. Launching this workflow as a commercially available service will not only offer clinicians unprecedented detail for the diagnosis and treatment of MN patients, but also reveal potentially life-threatening comorbidities undetectable by prior generation strategies.