Solid organ transplantation is the definitive treatment for eligible patients with end stage organ failure (1). Among all solid organs, kidney transplants are the most frequent. Yet, premature graft failure makes re-transplantation a common event. Of the various causes of premature kidney graft failure, rejection is the leading cause (2). Rejection involves the immunological recognition of the graft as foreign and its attack by the host’s immune system. While this process can be mitigated by induction and maintenance immunosuppression, poor adherence, or dose reduction render patients more vulnerable to developing immune injury because of incompatibility of human leukocyte antigens (HLA) with their donors.

Immune-related injury is less likely when HLA compatibility is greater between patients and donors. Yet, the fact that more than 32,000 HLA alleles have been identified makes complete HLA allelic matching of unrelated donors and patients exceedingly difficult (3). In recent decades, algorithms developed to study the degree of molecular HLA mismatch between donors and recipients have gained traction (4). HLAMatchmaker, for example, considers eplets, which represent polymorphic residues within a radius of 3.0-3.5 Å of a given sequence position on the molecular surface (5, 6). Eplet mismatches (EMM) were found to be predictive of immune injury and graft failure (7–10). Efforts to optimize compatibility at the level of HLA eplets offer the advantage of avoiding the incompatibilities that tend to inform antibody development. Also, the likelihood of identifying eplet compatible donors is increased by virtue of eplets being common to HLA alleles of the same locus and across loci (11, 12). While the literature primarily considered cumulative load of EMM as a predictor of transplant outcomes, EMM may differ in their association with inferior transplant outcomes (13), with individual high-risk eplet mismatches (and/or EMM co-expressed with them) informing graft failure risk with greater discrimination than the overall EMM load (14).

It is also important to recall that most EMM were envisioned to explain immune recognition by B-cells, which represents only part of the sequence of events leading to donor-specific antibody (DSA) development and allograft rejection. While the B-cell response is pivotal in DSA development, the T-cell response is also of importance (15). To address the indirect immune response by T-cells, a complementary approach was developed in the form of the Predicted Indirectly ReCognizable HLA Epitopes (PIRCHE-II) algorithm (16). The PIRCHE-II approach uses the protein sequence of HLA alleles to predict the allogeneic foreign peptides that can be presented within the recipients’ HLA Class II groove. Several reports suggest that the mismatch load of donor-derived peptides, which are absent in the recipient self-peptidome, but expressed by recipient HLA, are predictive of immune response and graft failure (17–19). As the peptides used for PIRCHE-II represent theoretical T-cell epitopes, it is important to verify their association with outcomes like graft failure. We posit that while the load of PIRCHE-II may be predictive of graft failure, individual PIRCHE-II peptides, or T-cell molecular mismatch (TcEMM), might carry different risks.

Like EMM, the analysis of PIRCHE-II is highly susceptible to the complexities of high dimensionality and relatedness between PIRCHE-II peptides. The relatedness (measured by correlation) is, at least in part, due to the tendency of HLA genes to be in linkage disequilibrium, and for PIRCHE-II peptides to be present in relation to multiple HLA molecules (20). Consequently, a large cohort is necessary to disentangle which of the mismatched PIRCHE-II peptides are most predictive of transplant outcomes. While both EMM and TcEMM may interact to inform transplant outcomes, one must first evaluate TcEMM as independent predictors. We thus conducted a retrospective cohort study to verify the independent association of mismatched PIRCHE-II peptides and death-censored graft failure (DCGF), assess the relatedness between mismatched peptides, and determine TcEMM most predictive of DCGF.

Upon application of the exclusion criteria, we identified 118,309 eligible KTR (see Supplementary Figure 1 , Supplementary Table 1 for the study flow diagram and baseline characteristics). The median follow-up for the analytic cohort was 6.39 years (Interquartile range (IQR) 3.12 - 10.01 years) and a total of 19,945 (~ 17%) DCGF events occurred within a median of 3.12 years (IQR 1.42 - 6.54) post-transplant. In this cohort, log-transformed PIRCHE-II scores were associated with an increased risk for DCGF, with significant HRs of 1.16 (95% Confidence Interval [95%CI] = 1.14-1.17) and 1.14 (95%CI = 1.12-1.15) for univariable and multivariable models, respectively.

Using a data driven approach, we studied a retrospective cohort of KTR from the SRTR to identify selected TcEMM most predictive of DCGF. In contrast to the traditional TcEMM analysis, which yields PIRCHE-II scores (akin to TcEMM load), our analyses sought to distinguish between TcEMMs associated with an increased risk of DCGF and those that were not. AFT models followed by penalized regression models and post-selection inference informed an up to 20-fold reduction in the number of TcEMMs associated with DCGF. These findings pave the way to more targeted experimental verification of TcEMMs. Such experimental verification will then lend support to their consideration in decisions on organ allocation and personalized management of KTR.

There is a need for novel biomarkers informing risk for immune and non-immune-mediated outcomes in kidney transplantation (34, 35). With the wider acceptance of molecular mismatch (5), or EMM load, as a predictor of transplant outcomes, there has been a growing interest in determining how the T-cell counterpart may contribute to transplant outcomes. Prior publications proposed TcEMM score thresholds for risk stratification of kidney transplant recipients (17–19, 36, 37). To further refine the assessment of risk by TcEMM, we sought to study how the identity of TcEMM informs risk. Sufficient statistical power was needed to study high dimensional data and disentangle which of the interrelated TcEMMs represent the most important predictors. Recently, in a study of child-specific antibodies in pregnant women, Niemann and colleagues identified a subset of TcEMM proposed to be highly immunogenic (19). Challenged by a small sample size, none of the TcEMMs were found to significantly associate with DSA development. This is despite previously observed associations between TcEMM score and child-specific antibody development (19). Interestingly, of the 60 peptides highlighted in the analysis by Niemann and colleagues, 41 were also observed in our cohort, and 14 of these were selected by one or more of the analyses reported in this manuscript. However, it should be noted that 56 of the 60 TcEMM identified by Niemann and colleagues originated from or presented by HLA-DRB3/4/5, -DQA1, -DPB1, or -DPA1, which are unavailable in the analytic dataset. There are several methodological differences, including the strategy in TcEMM ascertainment, the context of the sensitizing event studied (pregnancy vs. transplantation), and outcomes studied (DSA vs. DCGF). Niemann et al. also identified that position 4 of the peptides was different between high and low-risk TcEMM, with serine and glutamic acid being more frequent in high-risk TcEMM and phenylalanine and aspartic acid more frequent in low-risk TcEMM. When using DiffLogo in our dataset, we were not able to observe significant differences at any amino-acid position for TcEMM selected by Lasso, to support the notion that enrichment of amino acid differences distinguishes high-risk TcEMM.

It has been proposed that the frequency of TcEMM among donor:recipient pairs may affect the likelihood of it being predictive of DCGF. Inevitably, rare albeit high risk TcEMM are likely to yield imprecise relative risk estimates ( Figure 2 ). Importantly, the high-risk for DCGF observed with certain TcEMMs is more likely informed by the properties of molecular mismatches rather than the frequency with which they are observed. This impact of rare TcEMM emphasizes the need to study large and well specified prospective multicenter cohorts with unambiguous allele-level HLA types with information on longitudinal exposure to immunosuppression. When planning for future clinical applications, donor:recipient incompatibility due to frequently observed high-risk TcEMMs can be readily avoided as kidneys with high-risk cores could be allocated to candidates expressing similar cores. In the case of rare high risk TcEMM on the other hand, the likelihood of finding a compatible candidate is lower, and it may be important to plan for national organ sharing as a strategy to allocate these organs to HLA compatible candidates.

Our study is the first to demonstrate an association between PIRCHE-II and DCGF in SRTR as well as the first attempt to identify TcEMM most predictive of DCGF. Nevertheless, we would like to acknowledge several limitations of the analyses and outline future research directions. First, while leveraging the analytical power enabled by the large SRTR dataset, which includes diverse populations of KTR, compensating for the sparsity of TcEMMs, it is vital to acknowledge that the TcEMM estimation is dependent on IMGT/HLA version considered and in the case of our analytic cohort relies on imputation (38). Unavailability of HLA-DRB3/4/5, -DQA1, -DPA1, and -DPB1 could lead to under- or over-estimation of TcEMM. Also, the analysis is limited considering the recipients’ presenting HLA was restricted to the HLA-DRB1 locus. Despite this, it is important to recall that there is a recognized utility for imputation, especially in the context of solid organ transplantation (22). Until large datasets with complete HLA genotypes at the allele-level of both donors and recipients are made available, our analyses can be used to inform analytical strategies when managing sparse data and considering all TcEMM, including rare ones. As the identity of rare peptides may vary from one cohort to another, both internal and external validation may be challenging. The consideration of rare HLA alleles, and those for which the complete sequence is not yet known, introduces another level of uncertainty. For these alleles, the PIRCHE algorithms considers the closest fully sequenced allele to inform on TcEMMs. While most such alleles differ from the closest alleles in intronic or noncoding regions, there could be differences between the assumed and true TcEMMs. Second, interrelatedness makes identification of TcEMMs that are causally related to graft failure challenging. Approximately a third of the TcEMM (38.14%) observed in our cohort were represented within highly correlated profiles ( Figure 3 ). Future efforts for experimental verification of TcEMM must consider not only the Lasso selected high risk TcEMM but also those represented in TcEMM profiles. Also, as there is currently no direct measure of response to TcEMM, confirming the immunogenicity of high-risk TcEMM remains elusive (39). In lieu of experimental verification, demonstration that high risk TcEMM (or co-expressed TcEMM) associate with biopsy proven rejection and graft loss in future prospective cohort studies and clinical trials, will lend support to their clinical relevance. Third, the PIRCHE-II algorithm predicts TcEMM based on likelihood of binding by HLA Class II. It is important to recall that the chain of processes between peptide production, selection, and presentation may also be at play but these are currently not well elucidated. Last, our understanding of the interplay between TcEMM and EMM as determinants of transplant outcomes is also in evolution. T-cell and B-cell molecular mismatches have been shown to associate with DSA and antibody-mediated rejection (ABMR) in a small cohort of pediatric heart transplant patients, as well as in adult kidney recipients (40, 41). The study showed that patients with high TcEMM and EMM loads were at greatest risk. This would be in line with the immunological perspective behind humoral responses, with both B- and T-cell responses being involved in antibody production and class-switch. Future studies should assess whether combinations of TcEMM and high-risk EMM may better inform suboptimal transplant outcomes (13).

In conclusion, we sought to assess the contribution of individual TcEMM as determinants of DCGF and were able to distinguish increasingly smaller numbers of TcEMM most predictive of this endpoint through various approaches. Further validation of these TcEMM as determinants of transplant outcomes in large independent cohorts with allele-level HLA genotypes is needed. Furthermore, investigation of the properties that may contribute to immune injury are also needed. While TcEMM is not ready for clinical application, our observations provide the foundation for future studies assessing whether these high risk TcEMM (or the TcEMM co-expressed with them) inform other hard clinical endpoints like biopsy proven rejection. Such verification would justify their avoidance during organ allocation or modifications of posttransplant management when mismatches cannot be avoided. Importantly, with a better differentiation of risk profiles of particular EMM and TcEMM, future research is needed to clarify how they interact and inform long-term transplant outcomes.

The data analyzed in this study was obtained from the SRTR. To learn about the applicable licenses/restrictions and submit requests to access these datasets, please consult: https://www.srtr.org/requesting-srtr-data/data-requests/.

The studies involving human participants were reviewed and approved by McGill University Health Center research ethics board. Written informed consent from the participants’ legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.

Study conception and design: WL, RS-P. Acquisition of data: RS-P. Analysis and interpretation of data: WL, DF, AY, and RS-P. Drafting of manuscript: WL and RS-P Critical revision: WL, AY, MN, KO, WK, LR, CP, RL, FC, HG, PK, AL, RS-P. All authors contributed to the article and approved the submitted version.

This research was enabled thanks to support provided by Genome Canada Large Scale Applied Research Program Award “Precision Medicine CanPREVENT AMR” funded by Genome Quebec, Genome British Columbia, Genome Alberta, and Canadian Institutes of Health Research, Calcul Québec (https://www.calculquebec.ca) and Compute Canada (https://www.computecanada.ca) through a Resource Allocation for Research groups (RAC cna-921-ac), and a MITACS Accelerate grant. RS-P is supported by Fonds de recherche du Quebec—Santé chercheur boursier clinicien award (grant no. 254386).

The authors thank James Hanley, Sahir Bhatnagar, and Jesse Islam for their input on the analyses conducted.

MN is an employee of PIRCHE AG that runs the PIRCHE web-portal.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

The data reported here have been supplied by the Hennepin Healthcare Research Institute (HHRI) as the contractor for the Scientific Registry of Transplant Recipients (SRTR). The interpretation and reporting of these data are the responsibility of the author(s) and in no way should be seen as an official policy of or interpretation by the SRTR or the U.S. Government.