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) 具有显着的生存率和发病率优势，使其成为 终末期肾病的首选治疗方式，而晚期同种异体移植失败有多种因素。 病因学方面，最大的贡献者之一是供体特异性 HLA 抗体 (dnDSA) 的开发， 检测到抗体后平均 3-5 年会出现同种异体移植损失。 针对 HLA 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“不匹配的eplet”。 可以使用各种分子错配软件包来识别和计数表位。 并非所有表位都同样可能在受体中诱导抗体反应，因为特定的“高风险”eplet 发现错配与 dnDSA 的形成不成比例地相关。 器官分配时捐赠者和接受者之间的不匹配是改善长期状况的一种方法 同种异体移植物存活，因为它会减少潜在 dnDSA 目标的数量。 可变免疫原性是一个公认的概念，但有关哪些错配具有高风险的详细信息 我建议建立一个完整的多部位儿童肾移植（KT）队列。 对受者和捐献者进行 HLA 基因分型以进行此类分析，这将为开发提供信息。 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。 器官获取和移植网络 (OPTN) 已授权制定新的分配方案 模型，开发一个综合分配评分系统，可以考虑多个因素的动态变化 没有足够的数据来告知此类模型如何处理 HLA。 我与多位点队列的合作将指导如何最好地整合信息。 分子错配分析和高分辨率组织分型数据进入自适应分配模型。 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 通过完成拟议的研究和教学来测试减轻疾病负担的干预措施。 在约翰霍普金斯大学公共卫生学院接受培训，我将获得临床研究方法学博士学位 并培养一套独特的技能，使我能够在移植领域建立独立的研究生涯 具体来说，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、先进的计算模型以及免疫遗传学在临床实践中的应用。