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

膜性肾病精准医学诊断工具的开发

• 批准号: 10324016
• 负责人: Christopher P Larsen
• 金额: $ 24.78万
• 依托单位: NEPHROPATHOLOGY ASSOCIATES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-15 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10324016
• 关键词: Accounting; Algorithms; Antibodies; Antigen-Antibody Complex; Antigens; Archives; Autoantibodies; Autoantigens; Autoimmune; Autoimmune Responses; Biological Markers; Biopsy; Biopsy Specimen; Data; Data Set; Databases; Deposition; Development; Diagnosis; Diagnostic; Disease; Early Diagnosis; Evaluation; Explosion; Flare; Freezing; Funding; Future; G-substrate; Glomerular Capillary; Goals; Immune; Immunoassay; Immunoglobulin G; Immunologics; Incubated; Kidney; Kidney Diseases; Laboratories; Literature; Lupus Nephritis; Machine Learning; Mass Spectrum Analysis; Measures; Membranous Glomerulonephritis; Methods; Modeling; Monitor; Morphology; Nephritis; Pathogenicity; Pathology; Patient Care; Patients; Peptides; Phase; Process; Prognosis; Proteins; Proteinuria; Proteomics; Publishing; Pythons; Reporting; Reproducibility; Resources; Sampling; Statistical Data Interpretation; Techniques; Testing; Time; Tissue Extracts; Tissues; Training; United States National Institutes of Health; analytical method; base; classification algorithm; clinical Diagnosis; clinical diagnostics; clinical practice; cohort; cost; data acquisition; data analysis pipeline; data pipeline; disease diagnosis; feature selection; kidney biopsy; learning classifier; novel; pathogenic autoantibodies; podocyte; precision medicine; predictive modeling; rapid diagnosis; tool; treatment response

Summary/Abstract The goal of this project is to develop a precision medicine approach to the rapid diagnosis of membranous nephropathy (MN) using automated statistical analysis of proteomic data obtained from kidney biopsies. This approach uses data-independent acquisition mass spectrometry (DIA-MS) and an algorithmic data pipeline capable of efficiently determining the most likely MN antigen types present in kidney biopsy tissue. MN is a heterogenous autoimmune kidney disease that is caused in most cases by the presence of circulating pathogenic autoantibodies that react with podocyte antigens leading to the formation and accumulation of pathogenic immune complexes around glomerular capillary loops. Using the example of PLA2R-type MN, determination of antigen type has been shown to be important for diagnosis, monitoring response to treatment and early detection of disease flares. Historically, determination of MN antigen type has been performed by immunostaining; however, this has become impractical due to the discovery of at least 17 antigen types. There often is not enough tissue in the biopsy sample to conduct this number of immunostains, and moreover the immunostaining process is both time and resource intensive. The use of DIA-MS provides a novel proteomics approach to antigen typing in which immune complexes are captured by elution from frozen biopsy tissue, digested into tryptic peptides, and then measured by DIA-MS. Candidate MN antigens are identified using algorithmic classification and then validated in a final immunostaining step to confirm the candidate antigen. Our preliminary studies indicate that this is a robust approach; however, the method is not scalable without a similarly robust data analysis pipeline. In this Phase I project, we will optimize the DIA-MS method and then collect quantitative data from known cases of the most common types of MN that can be used to develop, train, test and optimize algorithmic classification models using a machine learning (ML) approach. In order to train the ML models, we will collect DIA-MS protein abundance data from 50 samples each of PLA2R, THSD7A and Exostosin types of MN, as well as 50 samples that are negative for each of these antigens as controls. In the Phase II, we will build complete datasets for all known antigen types of MN and optimize the ML classifier model for diagnostic workflows. Successful completion of these aims will result in the development a comprehensive method to efficiently classify MN cases of any antigen type. These tools will advance the practice of renal pathology from a largely morphology-based approach of diagnosing disease to a precision medicine-based proteomics approach that will efficiently provide actionable information to clinicians caring for patients with MN.

摘要/摘要 该项目的目的是开发一种精确的医学方法来快速诊断膜。 肾病（MN）使用从肾脏活检获得的蛋白质组学数据的自动统计分析。这 方法使用独立于数据的采集质谱（DIA-MS）和算法数据管道 能够有效地确定肾脏活检组织中最可能的MN抗原类型。 Mn是一个 异质自身免疫性肾脏疾病，这在大多数情况下是由于循环致病性而引起的 与足细胞抗原反应的自身抗体，导致致病性形成和积累 肾小球毛细管环周围的免疫复合物。使用PLA2R型MN的示例，确定 抗原类型已被证明对于诊断，监测对治疗的反应和早期检测很重要 疾病耀斑。从历史上看，通过免疫染色进行了MN抗原类型的测定。 但是，由于发现至少17种抗原类型，这变得不切实际。通常没有足够的 活检样品中的组织进行这种免疫抑制剂，此外，免疫染色过程 是时间和资源密集型。 DIA-MS的使用提供了一种新型的蛋白质组学方法来抗原分类 其中通过冷冻活检组织洗脱捕获免疫复合物，被消化成胰蛋白酶肽， 然后通过DIA-MS测量。使用算法分类鉴定候选MN抗原，然后 在最终免疫染色步骤中进行了验证，以确认候选抗原。我们的初步研究表明 这是一种强大的方法；但是，如果没有类似鲁棒的数据分析管道，该方法是不可扩展的。 在此阶段I项目中，我们将优化DIA-MS方法，然后从已知情况中收集定量数据 可用于开发，训练，测试和优化算法分类的最常见的MN类型 使用机器学习（ML）方法的模型。为了训练ML模型，我们将收集DIA-MS蛋白 来自PLA2R，THSD7A和Exostosin类型的50个样本的丰度数据以及50个样品 这些抗原中的每一种都为对照。在第二阶段，我们将为所有人构建完整的数据集 MN的已知抗原类型并优化用于诊断工作流程的ML分类器模型。成功完成 这些目的将导致开发一种综合方法，以有效地对任何案件进行分类 抗原类型。这些工具将从基于形态的方法中推进肾脏病理的实践 将疾病诊断为基于精确药物的蛋白质组学方法，该方法将有效地提供可行的 向关心MN患者的临床医生提供的信息。