Implementation of Eplet Mismatch Analysis in Pediatric Kidney Transplantation

Eplet 错配分析在小儿肾移植中的实施

• 批准号: 10739126
• 负责人: Olga Charnaya
• 金额: $ 17.52万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10739126
• 关键词: Address; Age; Algorithms; Allografting; Amino Acid Sequence; Antibodies; Antibody Formation; Antibody Response; Biometry; Characteristics; Child; Childhood; Chronic; Clinical; Clinical Data; Clinical Research; Competence; Computer Models; Computer software; Conduct Clinical Trials; Data; Data Analyses; Development; Dialysis procedure; Doctor of Philosophy; Elements; End stage renal failure; Ensure; Epitopes; Equity; Etiology; Failure; Genotype; Geography; Goals; Graft Survival; HLA Antigens; Histocompatibility Testing; Immune response; Immune system; Immunogenetics; Immunologics; Incidence; Injury; Intervention; Kidney; Kidney Transplantation; Learning; Mediating; Modality; Modeling; Molecular; Morbidity - disease rate; Nature; Organ; Organ Donor; Organ Procurements; Outcome; Output; Policies; Public Health Schools; Race; Research; Research Design; Research Methodology; Research Personnel; Resolution; Retrospective Studies; Risk; Risk Factors; Risk Reduction; Site; System; Techniques; Technology; Testing; Time; Training; Transplant Recipients; Transplantation; Transplantation Immunology; Uncertainty; United States; Wait Time; Waiting Lists; Work; access disparities; antibody detection; burden of illness; career; clinical development; clinical practice; cohort; data management; design; dissemination science; effective therapy; geographic disparity; graft failure; hazard; high risk; immunogenicity; implementation science; improved; improved outcome; novel; organ allocation; organ procurement transplantation network; racial disparity; skills; tool; transplant centers

Kidney transplant (KT) offers a significant survival and morbidity benefit over dialysis, making it the preferred treatment modality for end-stage kidney disease. While late allograft failure has a multifactorial etiology, one of the largest contributors is the development of donor specific HLA antibodies (dnDSA), leading to allograft loss at a median 3-5 years post detection of antibodies. Donor specific HLA antibodies develop against short amino acid sequences within the HLA antigen. Each HLA antigen has multiple epitopes that can interact with the recipient immune system, and antibody-verified epitopes are termed “eplets”. Mismatched epitopes can be identified and enumerated using various molecular mismatch software packages. However, not all epitopes are equally likely to induce an antibody response in the recipient, as specific “high-risk” eplet mismatches were found to be disproportionally associated with dnDSA formation. Avoidance of high-risk mismatches between donor and recipient at the time of organ allocation is one way to improve long-term allograft survival because it would reduce the number of potential dnDSA targets. Variable immunogenicity is an accepted concept however details about which mismatches are high risk has not been well established. I propose to establish a multi-site pediatric kidney transplant (KT) cohort with full HLA genotyping on recipients and donors to perform such an analysis. This will inform the development of an adaptive allocation model, that can better account for the entangled and dynamic nature of allocation systems. The Organ Procurement and Transplant Network (OPTN) has mandated the development of a new allocation model, to develop a composite allocation scoring system that can account for dynamic changes in multiple recipient and donor characteristics. There is insufficient data to inform such a model on how to handle HLA mismatch on an epitope-level. My work with the multi-site cohort will inform how to best inform incorporate molecular mismatch analysis and high-resolution tissue typing data into an adaptive allocation model. My career goal is to become an independent clinical researcher focused on improving outcomes for KT recipients by studying the adaptive and humoral immune response to the allograft and conducting clinical trials to test interventions to reduce the burden of disease. By completion of the proposed research and didactic training at the Johns Hopkins School of Public Health, I will obtain a PhD in Clinical Research Methodology and develop a unique skillset that will allow me to establish an independent research career in transplant immunology. Specifically, I will gain expertise in multi-site study design and execution, large data management and analysis, advanced computational modeling, and application of immunogenetics to clinical practice.

肾移植（KT）提供了一个显着的生存和发病率的好处超过透析，使其成为 终末期肾病的首选治疗方式。虽然晚期同种异体移植失败有多因素， 病因学，最大的贡献者之一是供体特异性HLA抗体（dnDSA）的发展， 在检测到抗体后的中位3-5年时发生同种异体移植物丢失。供体特异性HLA抗体产生 针对HLA抗原内的短氨基酸序列。每个HLA抗原具有多个表位， 与受体免疫系统相互作用，并且抗体验证的表位被称为“eplets”。错配 表位可以使用各种分子错配软件包来鉴定和计数。然而，在这方面， 并非所有的表位都同样可能在受体中诱导抗体应答， 发现错配与dnDSA的形成相关。避免高风险 在器官分配时供体和受体之间的不匹配是改善长期 因为它会减少潜在的dnDSA靶点的数量。 可变免疫原性是一个公认的概念，但关于哪些错配是高风险的细节 还没有很好地建立起来。我建议建立一个多中心儿科肾移植（KT）队列， 对受体和供体进行HLA基因分型以进行这样的分析。这将有助于制定一项 自适应分配模型，可以更好地解释分配系统的纠缠和动态性质。 器官采购和移植网络（OPTN）已授权制定新的分配方案， 模型，以开发一个综合分配评分系统，可以考虑到多个 接受者和捐赠者的特点。没有足够的数据来告知这种模型如何处理HLA 表位水平上的失配。我与多站点队列的工作将告知如何最好地告知合并 将分子错配分析和高分辨率组织分型数据整合到自适应分配模型中。 我的职业目标是成为一名独立的临床研究人员，专注于改善KT的结果 通过研究对同种异体移植物的适应性和体液免疫反应并进行临床试验， 来测试减少疾病负担的干预措施。通过完成拟议的研究和教学 在约翰霍普金斯公共卫生学院接受培训后，我将获得临床研究方法学博士学位 并发展一套独特的技能，使我能够在移植领域建立一个独立的研究生涯， 免疫学具体而言，我将获得多中心研究设计和执行，大数据管理方面的专业知识 和分析，先进的计算建模，并应用免疫遗传学的临床实践。