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.