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，肾移植是终末期肾病患者的首选治疗方法 在患者生存、生活质量和成本方面。移植物过早丢失的最常见原因之一 肾移植后的免疫损伤是同种免疫介导的。HLA同种异体抗原是一个重要的屏障， 同种异体移植物的长期结局。肾移植失败通常是由于受者对外源性抗原的免疫识别引起的。 供体器官的人类白细胞抗原（HLA）蛋白。HLA基因复合体包括多个位点 并且具有高度的遗传多态性。HLA氨基酸（AA）多态性强烈影响 HLA分子的结构和功能特征，包括T细胞和同种抗体的同种识别。 以前的研究表明，HLA抗原的错配（HLA）与更差的结果有关， 移植失败（GF）的风险最高，特别是与HLA-DRB 1相关。然而，相对 由于器官分配系统尚未收集全面的数据，因此AA变异的影响尚不清楚 分子HLA分型数据。使用高分辨率HLA分型评估表面抗原的单中心研究- 暴露的氨基酸（称为“eplets”）已经表明，Ep的数量与去乙酰化酶的存在相关。 新生供体特异性抗体（dnDSA）和GF。然而，这些研究涉及的对象相对较少， 并不能代表不同种族的移植人群。此外，这些研究假设 单调的风险增加，我们计划在这个拨款申请中解决这个差距。 我们将使用我们开发的HLA插补方法来解锁利用SRTR的能力 AA MM类别与移植物失败的关联分析数据库。此外，我们将验证和 使用大型多中心肾移植队列进一步研究AA MM相关性， 解决HLA I类和II类分型是容易获得的。我们还将评估HLA-AA-MM 有dnDSA开发风险的修订。这个项目旨在回答几个悬而未决的问题， HLA配型与结果：（1）哪些HLA位点配型最重要？(2)除了抗原水平 氨基酸水平的错配是否可以进一步分层结果？(3)是AA的一些替代品， AA图案比其他图案更重要？我们的团队开发了一种机器学习功能 工程化以发现和优化与GF和dnDSA相关联的AA MM分组（bin）。改进 患者风险分层可能有助于确定哪些移植受者将受益于较低的侵略性 免疫抑制方案，并通过减少重复移植的数量，由于移植失败， 不匹配的捐赠者我们的方法广泛适用于其他器官移植， 造血细胞移植