Research Article Computational Approaches to Facilitate Epitope-Based HLA Matching in Solid Organ Transplantation Kirsten Geneugelijk, 1 Jeroen Wissing, 1 Dirk Koppenaal, 1 Matthias Niemann, 2 and Eric Spierings 1 1 Laboratory of Translational Immunology, University Medical Center Utrecht, Utrecht, Netherlands 2 PIRCHE AG, Berlin, Germany Correspondence should be addressed to Eric Spierings; e.spierings@umcutrecht.nl Received 14 November 2016; Accepted 26 December 2016; Published 12 February 2017 Academic Editor: Mepur H. Ravindranath Copyright © 2017 Kirsten Geneugelijk et al. his is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Epitope-based HLA matching has been emerged over the last few years as an improved method for HLA matching in solid organ transplantation. he epitope-based matching concept has been incorporated in both the PIRCHE-II and the HLAMatchmaker algorithm to ind the most suitable donor for a recipient. For these algorithms, high-resolution HLA genotype data of both donor and recipient is required. Since high-resolution HLA genotype data is oten not available, we developed a computational method which allows epitope-based HLA matching from serological split level HLA typing relying on HLA haplotype frequencies. To validate this method, we simulated a donor-recipient population for which PIRCHE-II and eplet values were calculated when using both high-resolution HLA genotype data and serological split level HLA typing. he majority of the serological split level HLA-determined ln(PIRCHE-II)/ln(eplet) values did not or only slightly deviate from the reference group of high-resolution HLA- determined ln(PIRCHE-II)/ln(eplet) values. his deviation was slightly increased when HLA-C or HLA-DQ was omitted from the input and was substantially decreased when using two-ield resolution HLA genotype data of the recipient and serological split level HLA typing of the donor. hus, our data suggest that our computational approach is a powerful tool to estimate PIRCHE-II/eplet values when high-resolution HLA genotype data is not available. 1. Introduction Alloimmunity due to Human Leukocyte Antigens (HLA) mismatches between donor and recipient signiicantly impairs grat survival ater solid organ transplantation [1–3]. he risk on grat failure is signiicantly associated with the number of HLA mismatches [1, 4]. herefore, some allocation policies prefer deceased donors with zero mismatches at HLA-A, HLA-B, and HLA-DR, whereas others select deceased donors based on the number mismatches at these loci [5]. Although the number of HLA mismatches is a potent predictor of transplant outcome, not every HLA mismatch will have an equal efect on grat failure [6, 7]. Cumulating evidence suggest that some HLA mismatches may induce alloimmunity, whereas others are well-tolerated [6, 7]. his high variability in permissibility might be due to diferences in the antigenic load between diferent donor-recipient cou- ples [8, 9]. Each HLA antigen expresses a unique combination of epitopes, but some of these individual epitopes may be shared between diferent HLA antigens [8]. hese shared epi- topes will not induce alloimmunity, whereas those epitopes that are mismatched between donor and recipient may induce alloimmunity. hus, quantifying the antigenic load (i.e., the number of epitope mismatches) between donor and recipient instead of counting the number of HLA mismatches may be a better approach to predicting transplant outcome [9–12]. his concept of epitope-based HLA matching is an alternative method to deine the most suitable HLA mismatch for each patient, thereby reducing the risk on donor-speciic HLA antibody formation ater transplantation and grat failure. Two in silico methods, HLAMatchmaker and PIRCHE- II, have incorporated the epitope-based HLA matching con- cept in their algorithm to ind the most suitable donor for a Hindawi Publishing Corporation Journal of Immunology Research Volume 2017, Article ID 9130879, 9 pages https://doi.org/10.1155/2017/9130879