Platelet transfusions in haploidentical haematopoietic stem cell allograft candidates: Protecting HLA-A and HLA-B antigens through eplet analysis

In patients awaiting an allogeneic haematopoietic stem cell transplantation, platelet transfusion is a risk factor for anti-HLA class I immunization because the resulting donor-specific antibodies complicate the allograft process. The objective of the present study was to determine the feasibility of a novel eplet-based strategy for identifying HLA class I mismatches between potential donors and the recipient when pre-allograft platelet transfusions were required. We included 114 recipient/haploidentical relative pairs. For each pair, we entered HLA-class I typing data into the HLA Eplet Mismatch calculator, defined the list of mismatched eplets (for the recipient versus donor direction) and thus identified the shared HLAs to be avoided. Using this list of HLAs, we defined the theoretical availability of platelet components (PCs) by calculating the virtual panel-reactive antibody (vPRA). We also determined the number of PCs actually available in France by querying the regional transfusion centre's database. The mean ± standard deviation number of highly/moderately exposed eplets to be avoided in platelet transfusions was 5.8 ± 3.3, which led to the prohibition of 38.5 ± 2 HLAs-A and -B. Taking into account the mismatched antigens and the eplet load, the mean ± standard deviation theoretical availability of PCs (according to the vPRA) was respectively 34.49% ± 1.95% for HLA-A and 80% ± 2.3% for HLA-B. A vPRA value below 94.9% for highly or moderately exposed eplets would predict that 10 PCs were actually available nationally. Although epitope protection of HLA molecules is feasible, it significantly restricts the choice of PCs.

Virtual platelet cross-matching as transfusion management for patients with immune platelet refractoriness

BACKGROUND AND OBJECTIVES

This study describes the use of the Epvix platform for virtual cross-matching (VC) of human leucocyte antigen (HLA)-compatible platelets for patients with immune platelet refractoriness, and demonstrates effectiveness of the selected platelets.

MATERIALS AND METHODS

A prospective cohort of haematological patients was evaluated from 2018 to 2022. HLA-typed donor bank profile was previously uploaded to the Epvix platform. Each patient's antibody reactivity panel (PRA) was included in the platform. Then, search, selection and VC were performed, and 24-h-corrected count increment (CCI) platelet transfusion was calculated (reference ≥2500).

RESULTS

Six patients were included (four female, two male), with mean age of 61 years. HLA antibodies were detected as the cause of immunity for all patients, whereas four patients also had non-immune causes. High percentage of alloimmunization was detected in all studied patients (mean PRA: 85.7%). Thirty different donors were able to schedule and perform platelet donations. The mean 24-h CCI count was 9882. All platelet transfusions achieved a satisfactory CCI count except for two transfusion events. Presence of non-immune causes identified in these two cases could account for the unsatisfactory CCI.

CONCLUSION

Epvix is a free application hosted on the Web and uses the HLAMatchmaker algorithm to generate histocompatibility reports. This study demonstrates the efficiency of VC performed by Epvix. However, physical cross-matching will still be necessary in some instances, as the platform does not support human platelet antigen polymorphism.

Improving HLA typing imputation accuracy and eplet identification with local next-generation sequencing training data

Assessing donor/recipient HLA compatibility at the eplet level requires second field DNA typings but these are not always available. These can be estimated from lower-resolution data either manually or with computational tools currently relying, at best, on data containing typing ambiguities. We gathered NGS typing data from 61,393 individuals in 17 French laboratories, for loci A, B, and C (100% of typings), DRB1 and DQB1 (95.5%), DQA1 (39.6%), DRB3/4/5, DPB1, and DPA1 (10.5%). We developed HaploSFHI, a modified iterative maximum likelihood algorithm, to impute second field HLA typings from low- or intermediate-resolution ones. Compared with the reference tools HaploStats, HLA-EMMA, and HLA-Upgrade, HaploSFHI provided more accurate predictions across all loci on two French test sets and four European-independent test sets. Only HaploSFHI could impute DQA1, and solely HaploSFHI and HaploStats provided DRB3/4/5 imputations. The improved performance of HaploSFHI was due to our local and nonambiguous data. We provided explanations for the most common imputation errors and pinpointed the variability of a low number of low-resolution haplotypes. We thus provided guidance to select individuals for whom sequencing would optimize incompatibility assessment and cost-effectiveness of HLA typing, considering not only well-imputed second field typing(s) but also well-imputed eplets.

Specificity of HLA monoclonal antibodies and their use to determine HLA expression on lymphocytes and peripheral blood stem cells

HLA Class I and II expression are known to differ locus-to-locus, however, HLA expression on the cell-surface is frequently reported as the total amount of HLA Class I or II antigens. This is despite evidence that indicates the differential expression of HLA can influence patient outcomes post-transplantation. Although numerous commercially available HLA monoclonal antibodies (mAbs) exist to characterize HLA expression, there is currently a lack of detailed information regarding their reactivities to HLA specificities. The specificities of locus-specific HLA mAbs (nine Class I and four Class II mAbs) were evaluated by two solid-phase Luminex single antigen bead assays. The reactivity patterns of these mAbs were then confirmed by flow cytometry using lymphocytes and PBSCs (peripheral blood stem cells). Out of the 13 HLA mAbs tested, only four (one Class I and three Class II mAbs) displayed intra-locus reactivity without also reacting to inter-locus specificities. Epitope analysis revealed the presence of shared epitopes across numerous HLA loci, explaining much of the observed inter-locus reactivity. The specificity of the HLA mAbs seen in solid-phase assays was confirmed against PBSCs and lymphocytes by flow cytometry. Using this method, we observed differences in the cell surface expression of HLA-C, HLA-DR, HLA-DQ, and HLA-DP between PBSCs and lymphocytes. Our results emphasize the need to characterize the reactivity patterns of HLA mAbs using solid-phase assays before their use on cells. Through understanding the reactivity of these HLA mAbs, the cellular expression of HLA can be more accurately assessed in downstream assays.

Corrigendum: Molecular HLA mismatching for prediction of primary humoral alloimmunity and graft function deterioration in paediatric kidney transplantation

[This corrects the article DOI: 10.3389/fimmu.2023.1092335.].

Molecular HLA mismatching for prediction of primary humoral alloimmunity and graft function deterioration in paediatric kidney transplantation

Introduction

Rejection remains the main cause of allograft failure in paediatric kidney transplantation and is driven by donor-recipient HLA mismatching. Modern computational algorithms enable assessment of HLA mismatch immunogenicity at the molecular level (molecular-mismatch, molMM). Whilst molMM has been shown to correlate with alloimmune outcomes, evidence demonstrating improved prediction performance against traditional antigen mismatching (antMM) is lacking.

Methods

We analysed 177 patients from the CERTAIN registry (median follow-up 4.5 years). molMM scores included Amino-Acid-Mismatch-Score (AAMS), Electrostatic-Mismatch-Score (EMS3D) and netMHCIIpan (netMHC1k: peptide binding affinity ≤1000 nM; netMHC: binding affinity ≤500 nM plus rank <2%). We stratified patients into high/low-risk groups based on risk models of DSA development.

Results

Donor-specific HLA antibodies (DSA) predominantly targeted the highest scoring molMM donor antigen within each HLA locus. MolMM scores offered superior discrimination versus antMM in predicting de novo DSA for all HLA loci; the EMS3D algorithm had particularly consistent performance (area under the receiver operating characteristic curve (AUC) >0.7 for all HLA loci vs. 0.52-0.70 for antMM). ABMR (but not TCMR) was associated with HLA-DQ molMM scores (AAMS, EMS3D and netMHC). Patients with high-risk HLA-DQ molMM had increased risk of graft function deterioration (50% reduction in baseline eGFR (eGFR50), adjusted HR: 3.5, 95% CI 1.6-8.2 high vs. low EMS3D). Multivariable modelling of the eGFR50 outcome using EMS3D HLA-DQ stratification showed better discrimination (AUC EMS3D vs. antMM at 2 years: 0.81 vs. 0.77, at 4.5 years: 0.72 vs. 0.64) and stratified more patients into the low-risk group, compared to traditional antMM.

Conclusion

Molecular mismatching was superior to antigen mismatching in predicting humoral alloimmunity. Molecular HLA-DQ mismatching appears to be a significant prognostic factor for graft function deterioration in paediatric kidney transplantation.

An Approach to Identify HLA Class II Immunogenic Epitopes in the Greek Population through Machine Learning Algorithms

Current pre-transplantation routine matching involves serum anti-HLA antibodies quantification but cannot always preclude unfavorable graft outcomes. Epitope-based matching is proposed as a more precise approach, but to date no epitope-matching algorithm provides a satisfactory predictive tool for transplantation outcomes. In this study, anti-HLA-II loci responses from 1748 patients were analyzed with unsupervised machine learning algorithms, namely principal component analysis (PCA) and antigenic distances, projected as dendrograms. PCA for anti-HLA-DR anti-bodies revealed three main clusters of responses: anti-HLA-DR51 combined with anti-HLA-DRB1*01, anti-HLA-DR52 combined with anti-HLA-DRB1*08 and anti-HLA-DR53 combined with anti-HLA-DRB1*10. The dendrogram for anti-HLA-DR confirmed the pattern and showed further bisection of each cluster. Common epitopes present exclusively in all HLA molecules of each cluster were determined following the HLA epitope registry. Thus, we propose that 19 out of 123 HLA-DR epitopes are those that mainly lead anti-HLA-DR responses in the studied population. Likewise, we identified 22 out of 83 epitopes responsible for anti-HLA-DQ and 13 out of 62 responsible for anti-HLA-DP responses. Interpretation of these results may elucidate mechanisms of interlocus cross-reactivity, providing an alternative way of estimating the significance of each epitope in a population and thus suggesting a novel strategy towards optimal donor selection.

Development of an immunogenicity score for HLA-DQ eplets: A conceptual study

Eplets are defined as distinct amino acid configurations on the surface of HLA molecules. The aim of this study was to estimate the immunogenicity of HLA‐DQ eplets in a cohort of 221 pregnancies with HLA‐DQ mismatches. We defined the immunogenicity of an eplet by the frequency of antibody responses against it. Around 90% of all listed DQB1 or DQA1 eplets were at least five times mismatched and thus included for the calculation of their immunogenicity. The DQB1 eplets with the five highest immunogenicity scores were 55PP, 52PR, 52PQ, 85VG and 45EV; 25% of all DQB1 eplets were not reacting. The DQA1 eplets with the five highest immunogenicity scores were 25YS, 47QL, 55RR, 187T and 18S; 17% of all DQA1 eplets were not reacting. The immunogenicity score had a slightly higher area under the curve to predict development of child‐specific antibodies than various molecular mismatch scores (eg, eplet mismatch load, amino acid mismatch load). Overlapping eplets were identified as a barrier to unambiguously assign the immunogenicity score based on HLA antibody reaction patterns. In this conceptual study, we explored the immunogenicity of HLA‐DQ eplets and created a map of potentially immunogenic regions on HLA‐DQ molecules, which requires validation in clinical transplant cohorts.

Eplet incompatibility in pediatric renal transplantation

Eplet incompatibility appears to be a better predictor of the de novo appearance of DSA post-Tx than HLA antigen matching in adults. We evaluated the HLA Matchmaker® software (version 2.1) in our pediatric cohort to predict the appearance of DSA post-Tx. We included 70 pediatric patients (26 girls, 10 living donors, mean age 11.2 ± 3.9 years) after a first R-Tx (January 2010-August 2016), without prior immunization, having complete HLA typing (A, B, C, DRB1 and DQB1) and DSA follow-up for at least one year. The mean of HLA and eplet incompatibilities was 4.7 ± 1.3 and 15.5 ± 6.1, respectively, with a correlation coefficient r² between these two variables of 0.34 (P < .001). The eplet load was 12.8 ± 5.0 in living donors vs 15.9 ± 6.2 in deceased donors (P = NS), 12.6 ± 6.1 in preemptive R-Tx (n = 14) vs 16.3 ± 5.9 for non-preemptive R-Tx (P = .04). Seven patients (10%) developed DSA during the 3.5 ± 1.2 years post-Tx. The eplet load was 13.7 ± 5.5 for those who developed DSA vs 15.7 ± 6.1 for the others (P = NS). In our single-center series of pediatric R-Tx with good HLA matching and lower eplet load than previously published series, eplet incompatibilities do not predict the development of DSA. The question of the HLA matching requirement and the daily interest of the HLA Matchmaker® software to help select the grafts remain open.

Different eplet software programs give discordant and incorrect results: An analysis of HLAMatchmaker vs Fusion Matchmaker Eplet calling software

HLA eplet matching is a novel approach to define acceptable HLA mismatches for transplant recipients. We performed an eplet analysis of three different transplant case-series to determine if the available software programs gave accurate results. Eplet analysis was performed for three different transplant case-series typed by NGS for all HLA class I and II loci. The three different HLA datasets were entered into both the HLAMatchmaker program (v2.1) and OLI Fusion MatchMaker (v4.2) software tools. Eplet results which were discordant were cross referenced against eplet registry and published HLA allele sequence data to determine the correct assignments. The comparison reveals that there was poor concordance between the two eplet programs. Analysis of the same donor/recipient pair often gave rise to different total eplet scores, incorrect eplet mismatches and antibody verification status, and both programs have eplets assigned to incorrect HLA alleles. Overall, the OLI Fusion MatchMaker eplet tool gave more accurate and useful eplet results. Eplet matching is still primarily a research tool. Before eplet matching can enter routine clinical practice further work is required to validate the accuracy of available eplet software programs. Incorrect eplet assignment could have serious adverse consequences in the clinical transplant setting.