HLA Immunogenetics and kidney allograft outcomes

HLA 免疫遗传学和肾同种异体移植结果

• 批准号: 10566338
• 负责人: Malek Kamoun
• 金额: $ 85.26万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-11-21 至 2027-10-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10566338
• 关键词: Accounting; Acute; Address; Alloantigen; Allografting; Amino Acid Motifs; Amino Acids; Antibodies; Antibody Specificity; Antigens; Applications Grants; Binding; Categories; Clinical; Clinical Trials Design; Complex; Data; Databases; Development; Dialysis procedure; Drug toxicity; End stage renal failure; Engineering; Failure; Genes; Genetic Polymorphism; Genomic Segment; Goals; Grouping; HLA Antigens; Heterogeneity; Histocompatibility Antigens Class I; Immune; Immunogenetics; Immunosuppression; Injury; Isoantibodies; Kidney Transplantation; Linkage Disequilibrium; Machine Learning; Measures; Mediating; Methods; Modeling; Molecular; Organ Donor; Organ Transplantation; Outcome; Patient risk; Patients; Peptides; Population; Positioning Attribute; Proteins; Quality of life; Regimen; Resolution; Risk; Specificity; Statistical Data Interpretation; Surface; System; T-Lymphocyte; Testing; Titrations; Transplant Recipients; Transplantation; Treatment Efficacy; Variant; Work; clinically relevant; cohort; cost; data integration; donor-specific antibody; ethnic diversity; graft failure; hematopoietic cell transplantation; high risk; human leukocyte antigen testing; improved; kidney allograft; novel; organ allocation; post-transplant; premature; risk stratification; tool; transplant registry

Project Summary Kidney transplantation is the preferred treatment of patients with end-stage renal disease compared to dialysis in terms of patient survival, quality of life and cost. One of the most common causes of premature graft loss after kidney transplantation is alloimmune-mediated injury. HLA alloantigens represent a significant barrier to long-term allograft outcome. Kidney allograft failure is often caused by recipient immune recognition of foreign human leukocyte antigen (HLA) proteins of the donor organ. The HLA gene complex comprises multiple loci and has a high degree of genetic polymorphism. HLA amino acid (AA) polymorphisms strongly impact key structural and functional features of HLA molecules, including allorecognition by T cells and alloantibody. Previous studies have shown that mismatches (MMs) at HLA antigens are associated with worse outcomes, with the highest risk of graft failure (GF) particularly associated with HLA-DRB1 MMs. However, the relative impact of AA variation is unknown because organ allocation systems have not collected comprehensive molecular HLA typing data. Single-center studies using high resolution HLA typing to evaluate MMs at surface- exposed amino acids (termed “eplets”) have shown that the number of MMs correlates with the presence of de novo donor specific antibodies (dnDSA) and GF. However, these studies involved relatively few subjects and were not representative of the ethnically diverse transplant population. In addition, these studies assumed monotonic risk increase, a gap that we plan to address in this grant application. We will use HLA imputation methods that we have developed to unlock the capability to utilize the SRTR database for association analysis of AA MM categories with graft failure. In addition, we will validate and further investigate AA MM associations using a large multi-center cohort of kidney transplants wherein high resolution HLA class I and class II typing is readily available. We will also evaluate associations of HLA AA MM assortments with risk of dnDSA development. This project aims to answer several unresolved questions about HLA AA MMs and outcomes: (1) Which HLA loci are most important to match? (2) On top of antigen-level mismatches, can amino acid level mismatches further stratify outcomes? (3) Are some assortments of AA or AA motifs more important to match for than others? Our team has developed a machine learning feature engineering to discover and optimize AA MM groupings (bins) associated with GF and dnDSA. Improved patient risk stratification may help identify which transplant recipients would benefit from less aggressive immunosuppressive regimens and by reducing the number of repeat transplants due to graft failure with a poorly matched donor. Our approach generalizes broadly to other organ transplantation and possibly to hematopoietic cell transplantation.

项目摘要 与透析相比 就患者的生存，生活质量和成本而言。过早移植物损失的最常见原因之一 肾脏移植后是同种免疫性介导的损伤。 HLA同种剂代表了 长期同种异体结果。肾脏同种异体失败通常是由接受者的免疫认知引起的 供体器官的人白细胞抗原（HLA）蛋白。 HLA基因复合物包括多个基因座 并具有高度的遗传多态性。 HLA氨基酸（AA）多态性强烈影响钥匙 HLA分子的结构和功能特征，包括T细胞和同种抗体的同种异体认知。 先前的研究表明，HLA抗原的不匹配（MMS）与结果较差有关， 尤其是与HLA-DRB1 MMS有关的移植失败（GF）的最高风险。但是，亲戚 AA变化的影响尚不清楚，因为器官分配系统尚未全面收集 分子HLA键入数据。单中心研究使用高分辨率HLA键入来评估表面上的MMS 暴露的氨基酸（称为“ eplets”）表明，MMS的数量与DE的存在相关 Novo供体特异性抗体（DNDSA）和GF。但是，这些研究涉及相对较少的受试者和 不是代表种族的移植种群的代表。另外，这些研究假设 单调风险增加，我们计划在此赠款申请中解决的差距。 我们将使用已开发的HLA归纳方法来解锁使用SRTR的功能 用于与移植失败的AA MM类别的关联分析数据库。此外，我们将验证并 进一步研究AA MM关联，使用大型多中心肾移植物 分辨率HLA I类和II类打字很容易获得。我们还将评估HLA AA MM的关联 具有DNDSA开发风险的分类。该项目旨在回答几个未解决的问题 HLA AA MMS和结果：（1）哪个HLA本地最重要的是匹配？ （2）在抗原级别的顶部 不匹配，氨基酸水平不匹配能否进一步分层结果？ （3）是AA或 AA主题比其他图案更重要？我们的团队已经开发了机器学习功能 工程研究与GF和DNDSA相关的AA MM分组（BINS）。改进 患者风险分层可能有助于确定哪些移植受者将从不那么积极的情况下受益 免疫抑制方案，并通过减少由于A的移植失败而减少重复移植的数量 匹配不佳的捐助者。我们的方法广泛地概括为其他器官移植，并可能 造血细胞移植。