HLA Immunogenetics and kidney allograft outcomes

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 肾移植后是同种异体免疫介导的损伤。人类白细胞抗原同种异体抗原是一种重要的屏障 同种异体移植的长期结果。同种异体肾移植失败常由受体免疫识别引起 供体器官的人类白细胞抗原(HL A)蛋白。人类白细胞抗原基因复合体由多个基因座组成 并具有高度的遗传多态。人类白细胞抗原氨基酸(AA)基因多态性强烈影响Key 人类白细胞抗原分子的结构和功能特征，包括T细胞对同种异体的识别和同种异体抗体。 先前的研究表明，人类白细胞抗原的错配(MMS)与较差的预后相关。 移植物衰竭(GF)的风险最高，尤其与HLA-DRB1 MMS相关。然而，相对的 AA变异的影响未知，因为器官分配系统尚未收集 人类白细胞抗原分子分型数据。使用高分辨率人类白细胞抗原配型评估表面MMS的单中心研究- 暴露的氨基酸(称为“Eplet”)表明，MMS的数量与De的存在相关 新供体特异性抗体(DnDSA)和生长因子。然而，这些研究涉及的对象相对较少， 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，这是我们计划在这次赠款申请中解决的一个缺口。 我们将使用我们开发的人类白细胞抗原归属方法来解锁利用SRTR的能力 用于AA MM类别与移植失败的关联分析的数据库。此外，我们还将验证和 使用大型多中心肾移植队列进一步研究AA MM的相关性，其中高 HLAI类和II类分型的分辨率是现成的。我们还将评估人类白细胞抗原-AA-多发性骨髓瘤的相关性 与dnDSA发展风险有关的各种疾病。这个项目旨在回答几个悬而未决的问题 HLAAA MMS和结果：(1)哪些HLA位点配对最重要？(2)在抗原水平之上 不匹配，氨基酸水平不匹配会使结果进一步分层吗？(3)某些种类的氨基酸或 AA主题比其他主题更值得匹配吗？我们的团队开发了一种机器学习功能 工程设计，以发现和优化与GF和dnDSA相关的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 免疫抑制方案和减少因移植失败而重复移植的次数 配型不匹配的捐献者。我们的方法广泛地推广到其他器官移植，并可能 造血细胞移植。

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.