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.

肾移植为慢性肾衰竭患者提供了最好的生活质量。抗体和t细胞 介导的排斥反应（ABMR和TCMR）是决定移植物存活的关键因素。目前，诊断 排斥反应的鉴别诊断依赖于组织病理学检查， 主观解释，有限的可重复性，以及需要专业的移植病理学家。有一个 未满足的需求，开发更具体和定量的分子测试，可以补充和提高 常规组织学评估。在分子检测中，蛋白质组分析比 基因组和转录组分析，其受到许多翻译后和表观遗传 监管机制。此外，形态学变化形成了不同同种异体移植物分类的基础 疾病因此，移植界需要投资于基于活组织检查的测定， 其他人正在开发的基于血液/尿液的检测方法。本研究的目的是全面绘制 使用液体常规处理的福尔马林固定石蜡包埋（FFPE）活检的变化 色谱-串联质谱（LC-MS/MS）平台。满足个性化需求 该平台使用一种新的策略和机器学习来同时测量 一组靶向生物标志物以及数千种非靶向蛋白质的表达水平。中央 假设LC-MS/MS可用于使用发现数据集来定义疾病特异性生物标志物， 然后可以通过验证数据集来跟踪，以确定是否可以 在临床实践中实施。在目前的项目中，我们将重点发展ABMR的分子检测方法 因为抗体导致60%患者的移植物丢失。提出了两个目标。在目标1中，定量 蛋白质组学策略将用于绘制一组ABMR活检发现的蛋白质组水平变化， 其模拟物，如急性肾小管损伤（ATI）、TCMR、BK病毒肾病（BKVN）、间质纤维化/肾小管 萎缩（IFTA）和稳定的肾功能（STA）。目的是确定潜在的蛋白质生物标志物， 将ABMR与其模拟物区分开来。在目标#2中，目标#1中获得的潜在ABMR生物标志物将是 在独立的验证数据集中进行验证和优化。使用混合蛋白质组学平台 靶向，鸟枪蛋白质组学和机器学习，潜在蛋白质绝对定量信息 将收集生物标志物和数千种其他蛋白质，以建立肾移植蛋白质图谱， 发展这项研究的成功完成具有很大的潜力，可以转化为临床试验， 增强ABMR与其他疾病的诊断，这些疾病可以模拟该病理。它也将作为一个 开发新一代临床诊断测试的模型，将使用常规固定的活检材料。 这将消除对密集的生物库工作的需要，这种工作阻碍了更广泛的 将分子诊断应用于移植临床。

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