Enhanced Diagnosis of Antibody-Mediated Kidney Rejection by Machine Learning and Hybrid Targeted-Shotgun Proteomics

通过机器学习和混合靶向鸟枪蛋白质组学增强抗体介导的肾脏排斥的诊断

• 批准号: 10055012
• 负责人: PARMJEET S RANDHAWA
• 金额: $ 23.4万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-03 至 2021-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10055012
• 关键词: Acute; Address; Algorithms; Allografting; Amino Acids; Antibodies; Atlases; Atrophic; BK Virus; Bioinformatics; Biological Assay; Biological Markers; Biopsy; Blood; Caring; Cell Culture Techniques; Cell physiology; Chronic Kidney Failure; Clinic; Clinical; Comb animal structure; Communities; Complement; Data; Data Discovery; Data Set; Diagnosis; Diagnostic; Diagnostic tests; Differential Diagnosis; Diffuse; Disease; End stage renal failure; Epigenetic Process; Fibrosis; Formalin; Generations; Genomics; Goals; Gold; Graft Rejection; Graft Survival; Histologic; Histology; Hybrids; Inflammatory; Injury; Kidney; Kidney Diseases; Kidney Transplantation; Label; Lesion; Life; Liquid Chromatography; Longevity; Machine Learning; Maps; Measures; Mediating; Messenger RNA; Methods; Modeling; Molecular; Molecular Diagnostic Testing; Monitor; Morphology; Nucleic Acids; Paraffin Embedding; Pathologist; Pathology; Pathway interactions; Patients; Performance; Phenotype; Procedures; Process; Protein Analysis; Proteins; Proteome; Proteomics; Quality of life; RNA; RNA analysis; Reaction; Renal function; Reporting; Reproducibility; Research; Risk; Running; Sampling; Sensitivity and Specificity; Shotguns; Stable Isotope Labeling; Survival Rate; T-Lymphocyte; Techniques; Technology; Testing; Tissue Embedding; Tissue Sample; Tissues; Translating; Transplantation; Tubular formation; Urine; Validation; assay development; base; biobank; biomarker discovery; biomarker panel; burden of illness; candidate marker; clinical Diagnosis; clinical practice; complement C4d; cost; design; differential expression; effective therapy; follow-up; histological specimens; histopathological examination; improved; interstitial; knowledge base; machine learning algorithm; molecular diagnostics; non-invasive monitor; novel; novel strategies; outcome forecast; personalized medicine; preservation; protein biomarkers; protein expression; proteomic signature; screening; specific biomarkers; synthetic peptide; tandem mass spectrometry; targeted biomarker; transcriptome; transcriptomics

Kidney transplantation offers the best quality of life for patients with chronic kidney failure. Antibody and T-cell mediated rejection (ABMR and TCMR) are key factors that determine graft survival. Currently, the diagnosis and differential diagnosis of rejection relies on histopathologic examination which has known limitations such as subjective interpretations, limited reproducibility, and the need for expert transplant pathologists. There is an unmet need to develop more specific and quantitative molecular tests that can complement and enhance conventional histologic assessment. Among the molecular assays, proteome profiling is more attractive than genomic and transcriptomic profiling which are subjected to numerous post-translational and epigenetic regulatory mechanisms. Moreover, morphologic changes form the basis of classifying different allograft diseases. Therefore, the transplant community needs to invest in biopsy-based assays in addition to the blood/urine-based assays that are being developed by others. This study is aimed to fully map the proteomic changes in routinely processed formalin fixed paraffin embedded (FFPE) biopsies using a liquid chromatography–tandem mass spectrometry (LC-MS/MS) platform. To meet the needs of personalized medicine, this platform uses a novel strategy and machine learning to simultaneously measure the absolute expression levels of a panel of targeted biomarkers as well as thousands of untargeted proteins. The central hypothesis is that LC-MS/MS can be used to define disease-specific biomarkers using a discovery data set, which can then be followed up by a validation data set to determine if LC-MS/MS based tests can be implemented in clinical practice. In the current project, we will focus on developing molecular assays for ABMR since antibody contributes to graft loss in 60% of patients. Two Aims are proposed. In Aim #1, quantitative proteomic strategies will be used to map proteome-level changes in a discovery set of biopsies with ABMR and its mimics, such as acute tubular injury (ATI), TCMR, BK virus nephropathy (BKVN), interstitial fibrosis/tubular atrophy (IFTA), and stable renal function (STA). The goal is to identify potential protein biomarkers that can distinguish ABMR from its mimics. In Aim #2, the potential ABMR biomarkers obtained in Aim #1 will be validated and optimized in an independent validation data set. Using a hybrid proteomic platform combing targeted, shotgun proteomics and machine learning, information on absolute quantitation of potential protein biomarkers and thousands of other proteins will be collected to build a kidney transplant Protein Atlas for assay development. Successful completion of this study has great potential to be translated into clinical tests that will enhance the diagnosis of ABMR from other diseases that can mimic that pathology. It will also serve as a model for developing a new generation of clincal diagnositc tests that will use routinely fixed biopsy materials. This will eliminate the need for intense biobanking efforts, which have hampered the more widespread implementation of molecular diagnostics into the transplant clinic.

肾脏移植为慢性肾衰竭患者提供了最佳的生活质量。抗体和T细胞 介导的排斥（ABMR和TCMR）是确定移植物存活率的关键因素。目前，诊断 拒绝的鉴别诊断依赖于组织病理学检查，该检查已知有局限性 作为主观解释，有限的可重复性以及对专家移植病理学家的需求。有一个 未满足的需要开发更具体和定量的分子测试，以补充和增强 常规的组织学评估。在分子评估中，蛋白质组分析比 基因组和转录组学分析，经过大量翻译后和表观遗传学 监管机制。此外，形态变化构成了分类不同合金的基础 疾病。因此，移植社区还需要对基于活检的测定法进行投资 其他人正在开发的基于血/尿液的阿萨斯。这项研究旨在完全绘制蛋白质组学 使用液体的常规加工福尔马林固定石蜡（FFPE）活检的变化 色谱 - 潮流质谱法（LC-MS/MS）平台。满足个性化的需求 医学平台使用新颖的策略和机器学习来同时测量绝对 一组目标生物标志物以及数千种非靶向蛋白的表达水平。中央 假设是LC-MS/MS可用于使用发现数据集定义疾病特异性生物标志物， 然后可以进行验证数据集后跟进，以确定基于LC-MS/MS的测试是否可以是 在临床实践中实施。在当前项目中，我们将重点介绍为ABMR开发分子测定 由于抗体在60％的患者中导致移植物损失。提出了两个目标。在AIM＃1中，定量 蛋白质组学策略将用于绘制具有ABMR和ABMR的活检集中的蛋白质组级变化 它的模拟物，例如急性管状损伤（ATI），TCMR，BK病毒肾病（BKVN），间质纤维化/管状 萎缩（IFTA）和稳定的肾功能（STA）。目的是确定可以 与其模仿不同的ABMR。在AIM＃2中，AIM＃1获得的潜在ABMR生物标志物将是 在独立的验证数据集中验证和优化。使用混合蛋白质组学平台组合 有针对性的shot弹枪蛋白质组学和机器学习，有关潜在蛋白质绝对定量的信息 将收集生物标志物和数千种其他蛋白质以构建肾移植蛋白地图集进行测定 发展。这项研究的成功完成具有很大的潜力，可以转化为临床测试 从其他可以模仿该病理学的疾病中增强ABMR的诊断。它也将作为 开发新一代斜角诊断测试的模型，该测试将使用常规固定活检材料。 这将消除对强烈的生物群体的需求，这阻碍了更广泛的范围 将分子诊断的实施到移植诊所中。

项目成果

Diagnosis of T-cell-mediated kidney rejection by biopsy-based proteomic biomarkers and machine learning.

• DOI: 10.3389/fimmu.2023.1090373
• 发表时间: 2023
• 期刊: FRONTIERS IN IMMUNOLOGY
• 影响因子: 7.3
• 作者: Fang, Fei;Liu, Peng;Song, Lei;Wagner, Patrick;Bartlett, David;Ma, Liane;Li, Xue;Rahimian, M. Amin;Tseng, George;Randhawa, Parmjeet;Xiao, Kunhong
• 通讯作者: Xiao, Kunhong