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。 介导的排斥反应（ABMR和TCMR）是目前决定移植物存活的关键因素。 排斥反应的鉴别诊断依赖于组织病理学检查，该检查具有已知的局限性，例如 由于主观解释、有限的再现性以及需要专家移植病理学家。 开发更具体和定量的分子测试来补充和增强的需求尚未得到满足 在分子检测中，蛋白质组分析比传统的组织学评估更有吸引力。 基因组和转录组分析受到大量翻译后和表观遗传学的影响 此外，形态变化构成了不同同种异体移植物分类的基础。 因此，除了活检之外，移植界还需要投资于基于活检的检测。 其他人正在开发基于血液/尿液的检测方法，该研究旨在全面绘制蛋白质组图谱。 使用液体进行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。 医学，该平台使用新颖的策略和机器学习来同时测量绝对值 一组目标生物标志物以及数千种非目标蛋白质的表达水平。 假设 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， 然后可以通过验证数据集来确定是否可以进行基于 LC-MS/MS 的测试 在当前的项目中，我们将重点开发 ABMR 的分子检测方法。 由于抗体导致 60% 的患者出现移植物丢失，因此在目标 1 中提出了两个目标：定量。 蛋白质组学策略将用于通过 ABMR 和 其类似物，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）和稳定肾功能（STA）的目标是识别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，在目标 #1 中获得的潜在 ABMR 生物标志物将是 使用混合蛋白质组平台在独立验证数据集中进行验证和优化。 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来构建肾移植蛋白质图谱进行分析 成功完成这项研究具有转化为临床测试的巨大潜力。 增强 ABMR 对可模仿该病理学的其他疾病的诊断。 开发新一代临床诊断测试的模型，该测试将使用常规固定的活检材料。 这将消除对生物样本库工作的需要，生物样本库工作阻碍了更广泛的研究 在移植诊所中实施分子诊断。

项目成果

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

通过基于活检的蛋白质组生物标志物和机器学习诊断 T 细胞介导的肾脏排斥。

• 发表时间: 2023
• 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