HLA-EP-RESOLVER: A Novel Web-based Bioinformatic Tool to Determine the Presence or Absence of DSA Against Rare HLA Alleles in the Era of Rapid High-resolution Deceased Donor HLA Genotyping
Year: 2025
Type: article
Abstract: BACKGROUND: Patients awaiting a solid organ transplant require pretransplant testing including high-resolution HLA typing and screening of HLA antibodies. However, the standard HLA antibody testing cannot encompass all the HLA alleles available in the Australian donor pool. With the advent of high-resolution HLA typing for organ allocation, there is an increased prevalence of detecting uncommon HLA alleles. It is challenging to manually determine whether patients have donor-specific HLA antibodies against these antigens. To address this issue, a bioinformatic pipeline, HLA-EP-RESOLVER, was developed to assess eplet reactivity for rare HLA unacceptable antigens (UAs). METHODS: The HLA-EP-RESOLVER pipeline was validated against sensitized patients, and a list of candidate UAs was identified for each patient. The candidate UAs must meet the following criteria: (1) match at the first field with existing UAs, (2) share eplets or serological types with the current UAs, (3) do not share the patient's self-eplets or eplets on negative beads, and/or (4) in the exception rules. The Australian deceased donor pool was used as the initial model. The predicted UAs were then confirmed with physical crossmatching. RESULTS: The pipeline proved to be highly accurate at predicting high-risk antigens by utilizing eplet analysis through HLA Matchmaker, and the HLA data extracted from the Australian donor population. The reactivity of several candidate UAs were confirmed by cellular verification. CONCLUSIONS: The HLA-EP-RESOLVER program was shown to be rapid, useful, and highly accurate in determining acceptable or unacceptable HLA alleles in the new era of rapid high-resolution donor typing before allocation.
Source: Transplantation
Language: English
Subfield: Transplantation
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4409652113