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.

总结/摘要 该项目的目标是开发一种精确的医学方法来快速诊断膜性疾病。 使用从肾活检获得的蛋白质组学数据的自动统计分析来评估肾病（MN）。这 方法使用数据独立采集质谱（DIA-MS）和算法数据管道 能够有效地确定肾活检组织中存在的最可能的MN抗原类型。MN是第 一种异质性自身免疫性肾病，在大多数情况下是由循环病原体的存在引起的。 自身抗体与足细胞抗原反应，导致致病性 肾小球毛细血管袢周围的免疫复合物。以PLA 2 R型MN为例， 抗原分型对诊断、监测治疗反应和早期发现很重要 疾病的爆发。历史上，MN抗原类型的测定是通过免疫染色进行的; 然而，由于发现了至少17种抗原类型，这已经变得不切实际。往往没有足够的 组织中的活组织检查样品进行此数量的免疫染色，而且免疫染色过程 是时间和资源密集型的。DIA-MS的使用为抗原分型提供了一种新的蛋白质组学方法 其中免疫复合物通过从冷冻的活组织检查组织洗脱而被捕获，消化成胰蛋白酶肽， 使用算法分类鉴定候选MN抗原，然后 在最终的免疫染色步骤中验证以确认候选抗原。我们的初步研究表明， 这是一种稳健的方法;然而，如果没有类似稳健的数据分析流水线，该方法是不可缩放的。 在这个一期项目中，我们将优化DIA-MS方法，然后从已知病例中收集定量数据 最常见的MN类型，可用于开发，训练，测试和优化算法分类 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MN的PLA 2 R、THSD 7A和外生肌肽类型的各50个样品以及来自MN的50个样品的丰度数据 作为对照的这些抗原都是阴性的。在第二阶段，我们将为所有人建立完整的数据集。 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 这些目标将导致发展一种综合方法，有效地分类MN情况下，任何 抗原类型这些工具将推动肾脏病理学的实践，从主要基于形态学的方法 诊断疾病的精确医学为基础的蛋白质组学方法，将有效地提供可操作的 为MN患者提供护理的临床医生提供信息。