Implementation of Eplet Mismatch Analysis in Pediatric Kidney Transplantation

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.

Grant Number: 5K08DK134762-03
NIH Institute/Center: NIH
Principal Investigator: Olga Charnaya