HLA epitope mismatch analysis in kidney transplant patients
OA Version
Citation
Abstract
End Stage Renal Disease is the most common cause of kidney failure and is the most common diagnosis for the receival of a kidney transplant. Improvements in diagnostics, immunosuppression, post-operative monitoring, and organ allocation have allowed the procedure to become widespread in treating End Stage Renal Disease and have led to excellent post-operative outcomes. However, a disparity exists in access to and success of kidney transplantation between different racial and social groups. Success 10 years post-op has also been variable and demonstrates the need for further research in donor/recipient immune matching and immunosuppressive therapies. Human Leukocyte Antigen (HLA) Mismatch Analysis is the study and comparison of biomarkers between donors and recipients to ensure a “match” to prevent an immune response to foreign tissue. Eplet mismatch analysis further studies specific regions of these HLA proteins, which can help further ascertain the risk associated with matches. While epitope mismatch analysis has shown promise in predicting the risk of rejection post-transplant, there are many questions about its clinical use with low-resolution genotyping that is used for kidney transplantation. Comparing two surrogate groups that underwent either method of analysis, it was found in greater than 90% of pairs analyzed the immunological risk categorization resolution from molecular mismatch remained the same. A comparison was also made to understand the effect of racial classification on the number of epitope mismatches, which was also found not to have a significant difference among racially concordant and discordant groups. Such information provides an important look into the utility of tests done to determine the most effective donor/recipient match. The minimal difference between imputation and high-resolution genotyping has shown that low resolution genotyping can be used to provide clinical risk classification based on epitope mismatches.