Positive Long-Term Outcome of Kidney Allocation via Acceptable Mismatch Program in Highly Sensitized Patients.

Introduction: Eurotransplant established the acceptable mismatch (AM) program to facilitate timely kidney transplantations of highly sensitized patients, but long-term granular clinical and immunological outcomes regarding overall graft survival and de novo DSA (dnDSA) formation are still intensively researched. The right choice of induction therapy in patients with differing immunological risk is not conclusively determined, as well as the impact of human leukocyte antigen (HLA) epitope matching on dnDSA formation. Methods: This monocentric, retrospective study analyzed 94 patients transplanted within the AM program between 2000 and 2019 compared to case-control matched cohorts of non- (PRA 0-5%; PRA-0) and intermediately sensitized (PRA 6-84%; PRA-6/84) patients transplanted through Eurotransplant Kidney Allocation System. Results: Estimated 10-year overall graft survival between the PRA-0 and AM cohorts was similar, whereas PRA-6/84 was significantly disadvantageous compared to PRA-0. Estimated 10-year incidence of antibody-mediated rejection rates was significantly lower in the PRA-0 group compared to AM and PRA-6/84 groups. Compared to the AM group, estimated incidence of de novo donor-specific antibody (dnDSA) was significantly lower in PRA-0 patients, with no differences between the AM and PRA-6/84 cohorts. The PRA-6/84 cohort was the only subgroup in which interleukin-2 receptor antagonist (IL2RA) induction was associated with longer overall graft survival, patient survival, and graft survival compared to depleting induction (ATG or OKT3). Broad HLA-A, -B, -DR mismatches (mmABDR) and HLA epitope mismatches determined by Eplets and PIRCHE-II were predictive for dnDSA formation in the total cohort, and the AM subgroup. Discussion: The high efforts expended on AM patients are justified to allow timely organ transplantation with acceptable risk profile and non-inferior outcomes. IL2RA induction in intermediately sensitized patients is associated with superior overall graft survival, patient survival, and graft survival compared to ATG/OKT3 induction, without negative effects on rejection episodes or dnDSA formation. In silico epitope matching might further help reduce dnDSA formation, particularly in high-risk AM patients.

Computational Eurotransplant kidney allocation simulations demonstrate the feasibility and benefit of T-cell epitope matching.

The EuroTransplant Kidney Allocation System (ETKAS) aims at allocating organs to patients on the waiting list fairly whilst optimizing HLA match grades. ETKAS currently considers the number of HLA-A, -B, -DR mismatches. Evidently, epitope matching is biologically and clinically more relevant. We here executed ETKAS-based computer simulations to evaluate the impact of epitope matching on allocation and compared the strategies. A virtual population of 400,000 individuals was generated using the National Marrow Donor Program (NMDP) haplotype frequency dataset of 2011. Using this population, a waiting list of 10,400 patients was constructed and maintained during simulation, matching the 2015 Eurotransplant Annual Report characteristics. Unacceptable antigens were assigned randomly relative to their frequency using HLAMatchmaker. Over 22,600 kidneys were allocated in 10 years in triplicate using Markov Chain Monte Carlo simulations on 32-CPU-core cloud-computing instances. T-cell epitopes were calculated using the www.pirche.com portal. Waiting list effects were evaluated against ETKAS for five epitope matching scenarios. Baseline simulations of ETKAS slightly overestimated reported average HLA match grades. The best balanced scenario maintained prioritisation of HLA A-B-DR fully matched donors while replacing the HLA match grade by PIRCHE-II score and exchanging the HLA mismatch probability (MMP) by epitope MMP. This setup showed no considerable impact on kidney exchange rates and waiting time. PIRCHE-II scores improved, whereas the average HLA match grade diminishes slightly, yet leading to an improved estimated graft survival. We conclude that epitope-based matching in deceased donor kidney allocation is feasible while maintaining equal balances on the waiting list.

Formation of donor-specific antibodies depends on the epitope load of mismatched HLAs in lung transplant recipients: A retrospective single-center study.

The development of donor-specific antibodies (DSA) has a significant impact on graft outcome in solid organ transplantation. Mismatched HLAs are recognized directly and indirectly by the recipient immune system. Both pathways occur in parallel and result in the generation of plasma cells, DSA, cytotoxic and T helper lymphocytes. Here, we present the results of an analysis of the epitope load of mismatched HLAs in a cohort of 220 lung transplant recipients using two in silico algorithms, HLAMatchmaker and PIRCHE-II (Predicted Indirectly ReCognizable HLA Epitopes). De novo DSA (dnDSA) were detected by single antigen bead assays. The percentage of recipients who developed dnDSA was significantly higher in the group of patients who received lung transplants with a mismatching score above the detected threshold than in the group of patients who received lung transplants with a mismatching score below the threshold. In a multivariate Cox proportional hazard analysis, the PIRCHE-II score appeared to be a superior predictor of dnDSA formation. In addition, PIRCHE-II technology was shown to be useful in predicting separate dnDSA1 and dnDSA2 formation. We conclude that both algorithms can be used for the evaluation of the epitope load of mismatched HLAs and the prediction of DSA development in lung transplant recipients.

What happens after graft loss? A large, long-term, single-center observation.

The number of patients returning to dialysis after graft failure increases. Surprisingly, little is known about the clinical and immunological outcomes of this cohort. We retrospectively analyzed 254 patients after kidney allograft loss between 1997 and 2017 and report clinical outcomes such as mortality, relisting, retransplantations, transplant nephrectomies, and immunization status. Of the 254 patients, 49% had died 5 years after graft loss, while 27% were relisted, 14% were on dialysis and not relisted, and only 11% were retransplanted 5 years after graft loss. In the complete observational period, 111/254 (43.7%) patients were relisted. Of these, 72.1% of patients were under 55 years of age at time of graft loss and only 13.5% of patients were ≥65 years. Age at graft loss was associated with relisting in a logistic regression analysis. In the complete observational period, 42 patients (16.5%) were retransplanted. Only 4 of those (9.5%) were ≥65 years at time of graft loss. Nephrectomy had no impact on survival, relisting, or development of dnDSA. Patients after allograft loss have a high overall mortality. Immunization contributes to long waiting times. Only a very limited number of patients are retransplanted especially when ≥65 years at time of graft loss.

Donor selection in a pediatric stem cell transplantation cohort using PIRCHE and HLA-DPB1 typing.

BACKGROUND: New strategies to optimize donor selection for hematopoietic stem cell transplantation (HSCT) have mainly been evaluated in adults, but the disease spectrum requiring HSCT differs significantly in children and has consequences for the risk of complications, such as graft-versus-host disease (GvHD). PROCEDURES: Here we evaluated whether HLA-DPB1 and Predicted Indirectly ReCognizable HLA-Epitope (PIRCHE) matching can improve donor selection and minimize risks specific for a pediatric cohort undergoing HSCT in Berlin between 2014 and 2016. RESULTS: The percentage of HLA-DPB1-mismatched HSCT in the pediatric cohort was in line with the general distribution among matched unrelated donor HSCT. Nonpermissive HLA-DPB1 mismatches were not associated with a higher incidence of GvHD, but the incidence of relapse was higher in patients undergoing HSCT from HLA-DPB1-matched transplantations. High PIRCHE-I scores were associated with a significantly higher risk for developing GvHD in patients undergoing HSCT from nine of ten matched unrelated donors. This finding persisted after including HLA-DPB1 into the PIRCHE analysis. CONCLUSIONS: Implementing PIRCHE typing in the donor selection process for HSCT in children could particularly benefit children with nonmalignant diseases and support further validation of PIRCHE-based donor selection in a larger number of children treated at different sites.

Pretransplant virtual PRA and long-term outcomes of kidney transplant recipients.

Virtual panel-reactive antibodies (vPRA) have been implemented to gauge sensitization worldwide. It is unclear how it associates with long-term outcomes, and its correlation with peak (pPRA) or actual (aPRA) has not been studied. We retrospectively reviewed data from 18- to 65-year-old kidney-only transplant patients during 1.1.1996-31.7.2011 in our center. PRAs were calculated based on solid-phase techniques. Of the 726 qualified cases, regardless of the PRA type, sensitized patients (PRA > 5%) had more females and previous transplant. Highly sensitized (HS, PRA > 50%) had longer waiting time, lower transplant rate, less living donor, more delayed graft function, and acute rejection. The conformity between vPRA and pPRA in HS was 75%, 57% between pPRA and aPRA. Forty-three percent (61/142) patients whose pPRA was >5% had no detectable aPRA and maintained similar outcomes as sensitized patients. Multivariate analysis showed consistently lower death-censored graft survival in HS defined by vPRA [HR 2.086 (95% CI 1.078-4.037), P < 0.05] and pPRA [HR 2.139 (95% CI 1.024-4.487), P < 0.05]. Both vPRA and pPRA provided reliable way estimating sensitization and predicting long-term graft survival, while aPRA might underestimate true sensitization. vPRA might be the most objective parameter to gauge sensitization.

PIRCHE application major versions 3 and 4 lead to equivalent T cell epitope mismatch scores in solid organ and stem cell transplantation modules.

PIRCHE scores in organ and stem cell transplantation have been shown to correlate with increased risk of donor-specific HLA antibodies and graft-versus-host disease, respectively. With advancements of the PIRCHE application server, it is critical to compare the predicted scores with previous versions. This manuscript compares the newly introduced PIRCHE version 4.2 with its predecessor version 3.3, which was widely used in retrospective studies, using a virtual cohort of 10,000 transplant pairs. In the stem cell transplantation module, both versions yield identical results in 100% of the test population. In the solid organ module, 97% of the test population has identical PIRCHE scores. The deviating cases (3%) were attributed to refinements in the PIRCHE algorithm's specification. Furthermore, the magnitude of the difference is likely to be below the detection limit for clinical effects, confirming the equivalence in PIRCHE scores between versions 3.3 and 4.2.

Repeated local ellipsoid protrusion supplements HLA surface characterization.

Allorecognition of donor HLA is a major risk factor for long-term kidney graft survival. Although several molecular matching algorithms have been proposed that compare physiochemical and structural features of the donors' and recipients' HLA proteins in order to predict their compatibility, the exact underlying mechanisms are still not fully understood. We hypothesized that the ElliPro approach of single ellipsoid fitting and protrusion ranking lacks sensitivity for the characteristic shape of HLA molecules and developed a prediction pipeline named Snowball that is fitting smaller ellipsoids iteratively to substructures. Aggregated protrusion ranks of locally fitted ellipsoids were calculated for 712 publicly available HLA structures and 78 predicted structures using AlphaFold 2. Amino-acid sequence and protrusion ranks were used to train deep neural network predictors to infer protrusion ranks for all known HLA sequences. Snowball protrusion ranks appear to be more sensitive than ElliPro scores in fine parts of the HLA such as the helix structures forming the HLA binding groove in particular when the ellipsoids are fitted to substructures considering atoms within a 15 Å radius. A cloud-based web service was implemented based on amino-acid matching considering both protein- and position-specific surface area and protrusion ranks extending the previously presented Snowflake prediction pipeline.

PIRCHE-II Risk and Acceptable Mismatch Profile Analysis in Solid Organ Transplantation.

To optimize outcomes in solid organ transplantation, the HLA genes are regularly compared and matched between the donor and recipient. However, in many cases a transplant cannot be fully matched, due to widespread variation across populations and the hyperpolymorphism of HLA alleles. Mismatches of the HLA molecules in transplanted tissue can be recognized by immune cells of the recipient, leading to immune response and possibly organ rejection. These adverse outcomes are reduced by analysis using epitope-focused models that consider the immune relevance of the mismatched HLA.PIRCHE, an acronym for Predicted Indirectly ReCognizable HLA Epitopes, aims to categorize and quantify HLA mismatches in a patient-donor pair by predicting HLA-derived T cell epitopes. Specifically, the algorithm predicts and counts the HLA-derived peptides that can be presented by the host HLA, known as indirectly-presented T cell epitopes. Looking at the immune-relevant epitopes within HLA allows a more biologically relevant understanding of immune response, and provides an expanded donor pool for a more refined matching strategy compared with allele-level matching. This PIRCHE algorithm is available for analysis of single transplantations, as well as bulk analysis for population studies and statistical analysis for comparison of probability of organ availability and risk profiles.

High-resolution HLA genotyping improves PIRCHE-II assessment of molecular mismatching in kidney transplantation.

HLA matching in solid organ transplant is performed with the aim of assessing immunologic compatibility in order to avoid hyperacute rejection and assess the risk of future rejection events. Molecular mismatch algorithms are intended to improve granularity in histocompatibility assessment and risk stratification. PIRCHE-II uses HLA genotyping to predict indirectly presented mismatched donor HLA peptides, though most clinical validation studies rely on imputing high resolution (HR) genotypes from low resolution (LR) typing data. We hypothesized that use of bona fide HR typing could overcome limitations in imputation, improving accuracy and predictive ability for donor-specific antibody development and acute rejection. We performed a retrospective analysis of adult and pediatric kidney transplant donor/recipient pairs (N = 419) with HR typing and compared the use of imputed LR genotyping verses HR genotyping for PIRCHE-II analysis and outcomes. Imputation success was highly dependent on the reference population used, as using historic Caucasian reference populations resulted in 10 % of pairs with unsuccessful imputation while multiethnic reference populations improved successful imputation with only 1 % unable to be imputed. Comparing PIRCHE-II analysis with HR and LR genotyping produced notably different results, with 20 % of patients discrepantly classified to immunologic risk groups. These data emphasize the importance of using multiethnic reference panels when performing imputation and indicate HR HLA genotyping has clinically meaningful benefit for PIRCHE-II analysis compared to imputed LR typing.

Using cloud infrastructure to facilitate data collection and conversion of HLA diagnostic data for the 18th International HLA and Immunogenetics Workshop.

The International HLA and Immunogenetics Workshop (IHIW) is a recurring gathering of researchers, technologists and clinicians where participants contribute to collaborative projects with a variety of goals, and come to consensus on definitions and standards for representing HLA and immunogenic determinants. The collaborative and international nature of these workshops, combined with the multifaceted goals of several specific workshop components, necessitates the collection and curation of a wide assortment of data, as well as an adaptable platform for export and analysis. With the aim of ensuring data quality and creation of reusable datasets, specific standards and nomenclature conventions are continuously being developed, and are an integral part of IHIW. Here we present the 18th IHIW Database, a purpose-built and extensible cloud-based file repository and web application for collecting and analyzing project-specific data. This platform is based on open-source software and uses established HLA data standards and web technologies to facilitate de-centralized data repository ownership, reduce duplicated efforts, and promote continuity for future IHIWs.

NephroCAGE-German-Canadian Consortium on AI for Improved Kidney Transplantation Outcome: Protocol for an Algorithm Development and Validation Study.

BACKGROUND: Recent advances in hardware and software enabled the use of artificial intelligence (AI) algorithms for analysis of complex data in a wide range of daily-life use cases. We aim to explore the benefits of applying AI to a specific use case in transplant nephrology: risk prediction for severe posttransplant events. For the first time, we combine multinational real-world transplant data, which require specific legal and technical protection measures. OBJECTIVE: The German-Canadian NephroCAGE consortium aims to develop and evaluate specific processes, software tools, and methods to (1) combine transplant data of more than 8000 cases over the past decades from leading transplant centers in Germany and Canada, (2) implement specific measures to protect sensitive transplant data, and (3) use multinational data as a foundation for developing high-quality prognostic AI models. METHODS: To protect sensitive transplant data addressing the first and second objectives, we aim to implement a decentralized NephroCAGE federated learning infrastructure upon a private blockchain. Our NephroCAGE federated learning infrastructure enables a switch of paradigms: instead of pooling sensitive data into a central database for analysis, it enables the transfer of clinical prediction models (CPMs) to clinical sites for local data analyses. Thus, sensitive transplant data reside protected in their original sites while the comparable small algorithms are exchanged instead. For our third objective, we will compare the performance of selected AI algorithms, for example, random forest and extreme gradient boosting, as foundation for CPMs to predict severe short- and long-term posttransplant risks, for example, graft failure or mortality. The CPMs will be trained on donor and recipient data from retrospective cohorts of kidney transplant patients. RESULTS: We have received initial funding for NephroCAGE in February 2021. All clinical partners have applied for and received ethics approval as of 2022. The process of exploration of clinical transplant database for variable extraction has started at all the centers in 2022. In total, 8120 patient records have been retrieved as of August 2023. The development and validation of CPMs is ongoing as of 2023. CONCLUSIONS: For the first time, we will (1) combine kidney transplant data from nephrology centers in Germany and Canada, (2) implement federated learning as a foundation to use such real-world transplant data as a basis for the training of CPMs in a privacy-preserving way, and (3) develop a learning software system to investigate population specifics, for example, to understand population heterogeneity, treatment specificities, and individual impact on selected posttransplant outcomes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/48892.

Snowflake: A deep learning-based human leukocyte antigen matching algorithm considering allele-specific surface accessibility.

Histocompatibility in solid-organ transplantation has a strong impact on long-term graft survival. Although recent advances in matching of both B-cell epitopes and T-cell epitopes have improved understanding of allorecognition, the immunogenic determinants are still not fully understood. We hypothesized that HLA solvent accessibility is allele-specific, thus supporting refinement of HLA B-cell epitope prediction. We developed a computational pipeline named Snowflake to calculate solvent accessibility of HLA Class I proteins for deposited HLA crystal structures, supplemented by constructed HLA structures through the AlphaFold protein folding predictor and peptide binding predictions of the APE-Gen docking framework. This dataset trained a four-layer long short-term memory bidirectional recurrent neural network, which in turn inferred solvent accessibility of all known HLA Class I proteins. We extracted 676 HLA Class-I experimental structures from the Protein Data Bank and supplemented it by 37 Class-I alleles for which structures were predicted. For each of the predicted structures, 10 known binding peptides as reported by the Immune Epitope DataBase were rendered into the binding groove. Although HLA Class I proteins predominantly are folded similarly, we found higher variation in root mean square difference of solvent accessibility between experimental structures of different HLAs compared to structures with identical amino acid sequence, suggesting HLA's solvent accessible surface is protein specific. Hence, residues may be surface-accessible on e.g. HLA-A*02:01, but not on HLA-A*01:01. Mapping these data to antibody-verified epitopes as defined by the HLA Epitope Registry reveals patterns of (1) consistently accessible residues, (2) only subsets of an epitope's residues being consistently accessible and (3) varying surface accessibility of residues of epitopes. Our data suggest B-cell epitope definitions can be refined by considering allele-specific solvent-accessibility, rather than aggregating HLA protein surface maps by HLA class or locus. To support studies on epitope analyses in organ transplantation, the calculation of donor-allele-specific solvent-accessible amino acid mismatches was implemented as a cloud-based web service.

Immunologic risk stratification of pediatric heart transplant patients by combining HLAMatchmaker and PIRCHE-II.

BACKGROUND: Molecular-level human leukocyte antigen (HLA) mismatch is a powerful biomarker of rejection; however, few studies have explored its use in heart transplant recipients, and none have attempted to use the results of separate algorithms synergistically. Here we tested the hypothesis that a combination of HLAMatchmaker and Predicted Indirectly Recognizable HLA Epitopes (PIRCHE-II) can be used to identify more patients at low risk of rejection. METHODS: We studied 274 recipient/donor pairs enrolled in the Clinical Trials in Organ Transplantation in Children (CTOTC) performing class I and II HLA genotyping by next-generation sequencing to determine eplet mismatch (epMM) load and PIRCHE-II score. Correlation with clinical outcomes was performed on 131 cases. RESULTS: Of the 131 patients, 100 without pre-formed donor specific antibody (DSA) were used to identify cutoffs for the Class I, HLA-DR, and HLA-DQ epMM load and PIRCHE-II score for risk of developing post-transplant DSA (epMM: Class I/DR/DQ = 9/9/6; PIRCHE-II: 141/116/111) and antibody-mediated rejection (ABMR) (epMM: 9/8/8; PIRCHE-II: 157/80/201). Patients with above cut-off epMM load appear to be less likely to develop DSA and ABMR if their PIRCHE-II score is below cut-off (high epMM/high PIRCHE-II: 12.3%-20.3% DSA and 9%-13.5% ABMR vs high epMM/low PIRCHE-II: 4%-10% DSA and 0%-2% ABMR). CONCLUSION: For the first time in a pediatric heart transplant cohort, immunologic risk cut-offs for DSA and ABMR have been established. When used together, epMM load and PIRCHE-II score allow us to reclassify a portion of cases with high epMM load as having a lower risk for developing DSA and ABMR.

Snowflake epitope matching correlates with child-specific antibodies during pregnancy and donor-specific antibodies after kidney transplantation.

Development of donor-specific human leukocyte antigen (HLA) antibodies (DSA) remains a major risk factor for graft loss following organ transplantation, where DSA are directed towards patches on the three-dimensional structure of the respective organ donor's HLA proteins. Matching donors and recipients based on HLA epitopes appears beneficial for the avoidance of DSA. Defining surface epitopes however remains challenging and the concepts underlying their characterization are not fully understood. Based on our recently implemented computational deep learning pipeline to define HLA Class I protein-specific surface residues, we hypothesized a correlation between the number of HLA protein-specific solvent-accessible interlocus amino acid mismatches (arbitrarily called Snowflake) and the incidence of DSA. To validate our hypothesis, we considered two cohorts simultaneously. The kidney transplant cohort (KTC) considers 305 kidney-transplanted patients without DSA prior to transplantation. During the follow-up, HLA antibody screening was performed regularly to identify DSA. The pregnancy cohort (PC) considers 231 women without major sensitization events prior to pregnancy who gave live birth. Post-delivery serum was screened for HLA antibodies directed against the child's inherited paternal haplotype (CSA). Based on the involved individuals' HLA typings, the numbers of interlocus-mismatched antibody-verified eplets (AbvEPS), the T cell epitope PIRCHE-II model and Snowflake were calculated locus-specific (HLA-A, -B and -C), normalized and pooled. In both cohorts, Snowflake numbers were significantly elevated in recipients/mothers that developed DSA/CSA. Univariable regression revealed significant positive correlation between DSA/CSA and AbvEPS, PIRCHE-II and Snowflake. Snowflake numbers showed stronger correlation with numbers of AbvEPS compared to Snowflake numbers with PIRCHE-II. Our data shows correlation between Snowflake scores and the incidence of DSA after allo-immunization. Given both AbvEPS and Snowflake are B cell epitope models, their stronger correlation compared to PIRCHE-II and Snowflake appears plausible. Our data confirms that exploring solvent accessibility is a valuable approach for refining B cell epitope definitions.

Dissecting the impact of molecular T-cell HLA mismatches in kidney transplant failure: A retrospective cohort study.

Introduction: Kidney transplantation is the optimal treatment in end-stage kidney disease, but de-novo donor specific antibody development continues to negatively impact patients undergoing kidney transplantation. One of the recent advances in solid organ transplantation has been the definition of molecular mismatching between donors and recipients' Human Leukocyte Antigens (HLA). While not fully integrated in standard clinical care, cumulative molecular mismatch at the level of eplets (EMM) as well as the PIRCHE-II score have shown promise in predicting transplant outcomes. In this manuscript, we sought to study whether certain T-cell molecular mismatches (TcEMM) were highly predictive of death-censored graft failure (DCGF). Methods: We studied a retrospective cohort of kidney donor:recipient pairs from the Scientific Registry of Transplant Recipients (2000-2015). Allele level HLA-A, B, C, DRB1 and DQB1 types were imputed from serologic types using the NMDP algorithm. TcEMMs were then estimated using the PIRCHE-II algorithm. Multivariable Accelerated Failure Time (AFT) models assessed the association between each TcEMM and DCGF. To discriminate between TcEMMs most predictive of DCGF, we fit multivariable Lasso penalized regression models. We identified co-expressed TcEMMs using weighted correlation network analysis (WGCNA). Finally, we conducted sensitivity analyses to address PIRCHE and IMGT/HLA version updates. Results: A total of 118,309 donor:recipient pairs meeting the eligibility criteria were studied. When applying the PIRCHE-II algorithm, we identified 1,935 distinct TcEMMs at the population level. A total of 218 of the observed TcEMM were independently associated with DCGF by AFT models. The Lasso penalized regression model with post selection inference identified a smaller subset of 86 TcEMMs (56 and 30 TcEMM derived from HLA Class I and II, respectively) to be highly predictive of DCGF. Of the observed TcEMM, 38.14% appeared as profiles of highly co-expressed TcEMMs. In addition, sensitivity analyses identified that the selected TcEMM were congruent across IMGT/HLA versions. Conclusion: In this study, we identified subsets of TcEMMs highly predictive of DCGF and profiles of co-expressed mismatches. Experimental verification of these TcEMMs determining immune responses and how they may interact with EMM as predictors of transplant outcomes would justify their consideration in organ allocation schemes and for modifying immunosuppression regimens.

Peptides Derived From Mismatched Paternal Human Leukocyte Antigen Predicted to Be Presented by HLA-DRB1, -DRB3/4/5, -DQ, and -DP Induce Child-Specific Antibodies in Pregnant Women.

Predicted Indirectly ReCognizable Human Leukocyte Antigen (HLA) Epitopes (PIRCHE) are known to be a significant risk factor for the development of donor HLA-specific antibodies after organ transplantation. Most previous studies on PIRCHE limited their analyses on the presentation of the HLA-DRB1 locus, although HLA-DRB3/4/5, -DQ, and -DP are also known for presenting allopeptides to CD4+ T cells. In this study, we analyzed the impact of predicted allopeptides presented by these additional loci on the incidence of HLA-specific antibodies after an immunization event. We considered pregnancy as a model system of an HLA immunization and observed child-specific HLA antibody (CSA) development of 231 mothers during pregnancy by samples being taken at delivery. Our data confirm that PIRCHE presented by HLA-DRB1 along with HLA-DRB3/4/5, -DQ, and -DP are significant predictors for the development of CSA. Although there was limited peptidome overlap observed within the mothers' presenting HLA proteins, combining multiple presenting loci in a single predictor improved the model only marginally. Prediction performance of PIRCHE further improved when normalizing scores by the respective presenters' binding promiscuity. Immunogenicity analysis of specific allopeptides could not identify significant drivers of an immune response in this small cohort, suggesting confirmatory studies.

Can PIRCHE-II Matching Outmatch Traditional HLA Matching?

We analyzed in a cohort of 68,606 first deceased donor kidney transplantations reported to the Collaborative Transplant Study whether an epitope-based matching of donor-recipient pairs using the Predicted Indirectly ReCognizable HLA Epitopes algorithm (PIRCHE-II) is superior to currently applied HLA antigen matching. PIRCHE-II scores were calculated based on split antigen HLA-A, -B, -DRB1 typing and adjusted to the 0-6 range of HLA mismatches. PIRCHE-II scores correlated strongly with the number of HLA mismatches (Spearman ρ = 0.65, P < 0.001). In multivariable analyses both parameters were found to be significant predictors of 5-year death-censored graft loss with high prognostic power [hazard ratio (HR) per adjusted PIRCHE-II score = 1.102, per HLA mismatch = 1.095; z-value PIRCHE-II: 9.8, HLA: 11.2; P < 0.001 for both]. When PIRCHE-II scores and HLA mismatches were analyzed simultaneously, their predictive power decreased but remained significant (PIRCHE-II: P = 0.002; HLA: P < 0.001). Influence of PIRCHE-II was especially strong in presensitized and influence of HLA mismatches in non-sensitized recipients. If the level of HLA-incompatibility was low (0-3 mismatches), PIRCHE-II scores showed a low impact on graft survival (HR = 1.031) and PIRCHE-II matching did not have additional significant benefit (P = 0.10). However, if the level of HLA-incompatibility was high (4-6 mismatches), PIRCHE-II improved the positive impact of matching compared to applying the traditional HLA matching alone (HR = 1.097, P = 0.005). Our results suggest that the PIRCHE-II score is useful and can be included into kidney allocation algorithms in addition to HLA matching; however, at the resolution level of HLA typing that is currently used for allocation it cannot fully replace traditional HLA matching.