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.

与透析相比，肾移植(KT)提供了显著的存活率和发病率优势，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方面的因素 病因学上，最大的贡献之一是供者特异性人类白细胞抗原抗体(DnDSA)的发展，导致 在检测到抗体后的中位数3-5年发生同种异体移植物丢失。供体特异性人类白细胞抗原抗体研制成功 对抗人类白细胞抗原内的短氨基酸序列。每个HLA抗原都有多个表位，可以 与受体免疫系统相互作用，抗体验证的表位被称为“eplet”。不匹配 可以使用各种分子失配软件包来识别和列举表位。然而， 并不是所有的表位都同样有可能在受体体内引起抗体反应，因为它们是特定的“高危”表位。 错配与dnDSA的形成不成比例地相关。避免高风险 器官分配时供受者之间的不匹配是改善长期存活率的一种方法 同种异体移植物存活，因为这将减少潜在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 一直没有得到很好的确立。我建议建立一个多部位儿童肾移植(KT)队列， 对受者和捐赠者进行人类白细胞抗原基因分型以进行这样的分析。这将为开发一个 自适应分配模型，能够更好地考虑分配系统的纠缠性和动态性。 器官采购和移植网络(OPTN)已授权开发一项新的分配 模型，以开发一个综合分配评分系统，可以考虑多个 受者和捐赠者的特征。没有足够的数据来告知这样一个模型如何处理人类白细胞抗原 表位水平上的不匹配。我在多站点队列中的工作将告诉我如何最好地向Inc.提供信息 将分子错配分析和高分辨率组织分型数据转换为自适应分配模型。 我的职业目标是成为一名独立的临床研究员，专注于改善KT的结果 通过研究受者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 测试减轻疾病负担的干预措施。通过完成建议的研究和授课 在约翰霍普金斯大学公共卫生学院接受培训后，我将获得临床研究方法论博士学位 并开发了一套独特的技能，使我能够在移植领域建立独立的研究生涯 免疫学。具体地说，我将获得多站点研究设计和执行、大数据管理方面的专业知识 和分析，高级计算建模，以及免疫遗传学在临床实践中的应用。