Introduction Acute graft versus host disease (aGVHD) contributes to significant morbidity and mortality among allogeneic hematopoietic cell transplant (allo-HCT) recipients. Tacrolimus is a cornerstone of aGVHD prophylaxis, and at the University of North Carolina Medical Center (UNCMC), the institutional target range (ITR) for tacrolimus steady-state concentrations (Ctrough,ss) by day of transplant (D0) is 5-10 ng/mL. However, despite the use of therapeutic drug monitoring, the optimal tacrolimus dosing strategy in allo-HCT that associates with improved aGVHD outcomes has remained elusive, and a direct exposure-response relationship between oral tacrolimus Ctrough,ss and clinically significant grade II-IV aGVHD is still poorly characterized. Objectives To develop an exposure-response (ER) model that describes the relationship between oral tacrolimus Ctrough,ss and grade I only and grade II-IV aGVHD events. Methods Clinical and pharmacokinetics (PK) data from two studies (UNC16-1480 and UNC19-3328) supported ER model development. The exposure metric was tacrolimus Ctrough,ss on D-1 or D0. The Glucksberg criteria was used to grade the severity of aGVHD events, and subjects were categorized either as 1) none, 2) grade I only, or 3) grade II-IV. The base ER model was used to predict the grade II-IV aGVHD event rate across a range of concentrations. Results 285 adult allo-HCT recipients were included. A majority were diagnosed with acute leukemia (55%) and had an HLA full-match donor (81%). By D+100, n=142 (50%) did not experience aGVHD, n=71 (25%) experienced grade I aGVHD only, and n=72 (25%) experienced grade II-IV aGVHD. At the first therapeutic drug monitoring measurement, 187 (66%) subjects had Ctrough,ss values below 5 ng/mL. The likelihood of aGVHD events, compared to no aGVHD, were described by log-linear ER relationships with tacrolimus Ctrough,ss. Plots of the empirical relationships depicted a constant likelihood of grade I aGVHD events across a wide range of Ctrough,ss (0.5-28 ng/mL; Figure 1A), and an overall risk reduction of 18% in grade II-IV aGVHD events over that same range (Figure 1B). The base ER model predicted achieving a tacrolimus Ctrough,ss of at least 5 ng/mL (i.e., the lower bound of the UNCMC tacrolimus ITR) would reduce the risk grade II-IV aGVHD by ≥ 20% (i.e., a baseline of >45% to <25%; Figure 1C). Conclusions Using model-informed dosing strategies to optimize tacrolimus use, by including factors that contribute to PK and exposure variability (e.g., age, organ function, CYP3A5 metabolizer phenotype, etc.), is likely to reduce the risk of grade II-IV GVHD. The current ER model describes the relationship between aGVHD and tacrolimus Ctrough,ss well, which supports the use of model-informed tacrolimus dosing; however, further investigation into factors that predict response is warranted.

The rapid advances in HLA genotyping technology and the massive amounts of associated data have created a demand for better and more efficient laboratory data management practices. However, while some standards have been developed, there is a need for comprehensive guidelines that include all laboratory data-related processes such as messaging, storage and retention, documentation, reporting, validation and quality control. An important consideration in developing these recommendations is the feasibility of application in a laboratory setting without posing a substantial staff and cost burden for implementation and long-term maintenance and the availability of publicly available tools. This article presents evidence-based recommendations for multiple laboratory general data practices, focusing on HLA genotyping data and associated meta-data. These recommendations are compiled by experts in the fields of histocompatibility and immunogenetics (H&I) and representation from multiple H&I worldwide professional society leadership with the long-term goal of adopting these recommendations in future laboratory accreditation requirements.

Donor-specific anti-human leukocyte antigen (HLA) antibodies are a significant barrier to solid organ transplant and can lead to poor outcomes in the posttransplant setting. Physical crossmatch (PXM), including complement-dependent cytotoxicity crossmatch and flow cytometric crossmatch, has substantially reduced the incidence of hyperacute rejection and, for years, has been the gold standard for compatibility assessment before transplant. However, the advent of solid-phase anti-HLA antibody testing and molecular HLA typing has allowed the introduction and rise of virtual crossmatch (VXM). With a high negative predictive value, VXM has proven to be more sensitive than PXM in detecting donor-specific anti-HLA antibodies. Recent regulations by the Centers for Medicare & Medicaid Services now recognize VXM as a suitable alternative to PXM in pretransplant compatibility assessment. As VXM gains acceptance, it is imperative that we understand its complexities, such as its definition, application, advantages, and disadvantages. In this review, we examine the principles underlying VXM, including both generally accepted concepts and areas under debate. Furthermore, we highlight current applications and discuss opportunities for future improvement.

The purpose of this study was to enhance the prediction of solid-organ recipient and donor crossmatch compatibility by applying machine learning (ML). Prediction of crossmatch compatibility is complex and requires an understanding of the recipient and donor human leukocyte antigen (HLA) alleles and recipient HLA antibodies. An HLA allele imputation system that converts HLA antigens to alleles was developed to enhance the prediction’s performance. The imputed and known HLA alleles were combined for recipient and donor with a recipient’s HLA antibody profile. After processing, donor-specific antibodies were input into various ML models. Next, an ML model was developed and characterized based on determining donor-specific antibodies using the full HLA antibody profile of the recipient without laboratory interpretation. The models achieved an ROC-AUC of 0.975. These results demonstrate that the models can predict crossmatch reactivity and yield insight into the importance of specific HLA antibodies in the transplant-matching process. These data represent our understanding of personalized histocompatibility risk assessments.

Tacrolimus is a cornerstone of acute graft-versus-host disease (aGVHD) prophylaxis in allogeneic hematopoietic cell transplant (allo-HCT) recipients. However, a narrow therapeutic index and high interindividual variability in pharmacokinetics (PK) make starting dose selection a major challenge in clinical practice. Data from two PK studies conducted at the University of North Carolina Medical Center (UNCMC) were used to develop an oral tacrolimus population pharmacokinetic (popPK) model specific to adult allo-HCT recipients. Monte Carlo simulations were performed to compare the likelihood of achieving the UNCMC institutional target trough concentration range (ITR) (5–10 ng/mL) on the day of transplant (D0) under the current institutional dosing protocol, dosing recommendations from the Clinical Pharmacogenetics Implementation Consortium (CPIC), and model-derived dosing recommendations. In total, 290 allo-HCT recipients contributed a total of 906 PK samples to the final analysis. A two-compartment popPK model adequately described the PK data. Population typical values of apparent clearance (TVCL/F) for 70 kg individuals receiving reduced intensity conditioning were 0.33 L/h/kg for CYP3A5 poor metabolizers (PMs) and 0.70 L/h/kg for intermediate and normal metabolizers (IMs and NMs). The probability of the population-level average D0 trough concentration being within the UNCMC ITR under the current UNCMC weight-based dosing protocol, CPIC-based, and model-derived dosing strategies were estimated to be 37

IntroductionModern histocompatibility algorithms depend on the comparison and analysis of high-resolution HLA protein sequences and structures, especially when considering epitope-based algorithms, which aim to model the interactions involved in antibody or T cell binding. HLA genotype imputation can be performed in the cases where only low/intermediate-resolution HLA genotype is available or if specific loci are missing, and by providing an individuals’ race/ethnicity/ancestry information, imputation results can be more accurate. This study assesses the effect of imputing high-resolution genotypes on molecular mismatch scores under a variety of ancestry assumptions.MethodsWe compared molecular matching scores from “ground-truth” high-resolution genotypes against scores from genotypes which are imputed from low-resolution genotypes. Analysis was focused on a simulated patient-donor dataset and confirmed using two real-world datasets, and deviations were aggregated based on various ancestry assumptions.ResultsWe observed that using multiple imputation generally results in lower error in molecular matching scores compared to single imputation, and that using the correct ancestry assumptions can reduce error introduced during imputation.DiscussionWe conclude that for epitope analysis, imputation is a valuable and low-risk strategy, as long as care is taken regarding epitope analysis context, ancestry assumptions, and (multiple) imputation strategy.

IntroductionIn kidney transplant recipients (KTRs), steroid-free maintenance regimens are used to minimize the negative impact of steroid use. Studies comparing alemtuzumab (ALZ) and anti-thymocyte globulin (ATG) with rapid steroid withdrawal are limited. The aim of this study was to assess a composite outcome of incidence of de novo donor specific antibodies (dnDSAs), biopsy proven rejection (BPAR), graft failure, or death in KTRs receiving ALZ or ATG with a steroid-free maintenance regimen.MethodsA single-center, retrospective cohort study was conducted in adult KTRs who underwent rapid steroid withdrawal. There were two cohorts, KTRs who received induction with ALZ compared to ATG. The primary composite outcome was incidence of dnDSAs, BPAR, graft failure, or death. Secondary outcomes included renal function, cytomegalovirus (CMV) and BK viremia, and leukopenia.ResultsTwo hundred twenty-five KTRs were included where 146 received ALZ and 79 received ATG. The Cox proportional hazard of the primary composite outcome in the ALZ compared to ATG group was not significant (unadjusted model HR 1.37, 95% CI .74-2.55). Individual incidences of composite outcome were similar. There was a difference in estimated glomerular filtration rate at 12 months post-transplant (55.7 vs. 62.3 mL/min/1.73m2, p = .03) and leukopenia at 3 months (3.7 vs. 4.2 x 109/L, p = .03). Other secondary outcomes were similar.ConclusionsThere was no difference in composite outcome for dnDSAs, BPAR, graft failure, and death.

BACKGROUND:HLA antibody testing is essential for successful solid-organ allocation, patient monitoring post-transplant, and risk assessment for both solid-organ and hematopoietic transplant patients. Luminex solid-phase testing is the most common method for identifying HLA antibody specificities, making it one of the most complex immunoassays as each panel contains over 90 specificities for both HLA class I and HLA class II with most of the analysis being performed manually in the vendor-provided software. Principal component analysis (PCA), used in machine learning, is a feature extraction method often utilized to assess data with many variables.METHODS & FINDINGS:In our study, solid organ transplant patients who exhibited HLA donor-specific antibodies (DSAs) were used to characterize the utility of PCA-derived analysis when compared to a control group of post-transplant and pre-transplant patients. ROC analysis was utilized to determine a potential threshold for the PCA-derived analysis that would indicate a significant change in a patient's single antigen bead pattern. To evaluate if the algorithm could identify differences in patterns on HLA class I and HLA class II single antigen bead results using the optimized threshold, HLA antibody test results were analyzed using PCA-derived analysis and compared to the clinical results for each patient sample. The PCA-derived algorithm had a sensitivity of 100% (95% CI, 73.54%-100%), a specificity of 75% (95% CI, 56.30%-92.54%), with a PPV of 65% (95% CI, 52.50%-83.90%) and an NPV of 100%, in identifying new reactivity that differed from the patients historic HLA antibody pattern. Additionally, PCA-derived analysis was utilized to assess the potential over-reactivity of single antigen beads for both HLA class I and HLA class II antibody panels. This assessment of antibody results identified several beads in both the HLA class I and HLA class II antibody panel which exhibit over reactivity from 2018 to the present time.CONCLUSIONS:PCA-derived analysis would be ideal to help automatically identify patient samples that have an HLA antibody pattern of reactivity consistent with their history and those which exhibit changes in their antibody patterns which could include donor-specific antibodies, de novo HLA antibodies, and assay interference. A similar method could also be applied to evaluate the over-reactivity of beads in the HLA solid phase assays which would be beneficial for lot comparisons and instructive for transplant centers to better understand which beads are more prone to exhibiting over-reactivity and impact patient care.

T-cell responses to minor histocompatibility antigens (mHAs) mediate graft-versus-leukemia (GVL) effects and graft-versus-host disease (GVHD) in allogeneic hematopoietic cell transplantation. Therapies that boost T-cell responses improve allogeneic hematopoietic cell transplant (alloHCT) efficacy but are limited by concurrent increases in the incidence and severity of GVHD. mHAs with expression restricted to hematopoietic tissue (GVL mHAs) are attractive targets for driving GVL without causing GVHD. Prior work to identify mHAs has focused on a small set of mHAs or population-level single-nucleotide polymorphism-association studies. We report the discovery of a large set of novel GVL mHAs based on predicted immunogenicity, tissue expression, and degree of sharing among donor-recipient pairs (DRPs) in the DISCOVeRY-BMT data set of 3231 alloHCT DRPs. The total number of predicted mHAs varied by HLA allele, and the total number and number of each class of mHA significantly differed by recipient genomic ancestry group. From the pool of predicted mHAs, we identified the smallest sets of GVL mHAs needed to cover 100% of DRPs with a given HLA allele. We used mass spectrometry to search for high-population frequency mHAs for 3 common HLA alleles. We validated 24 predicted novel GVL mHAs that are found cumulatively within 98.8%, 60.7%, and 78.9% of DRPs within DISCOVeRY-BMT that express HLA-A∗02:01, HLA-B∗35:01, and HLA-C∗07:02, respectively. We confirmed the immunogenicity of an example novel mHA via T-cell coculture with peptide-pulsed dendritic cells. This work demonstrates that the identification of shared mHAs is a feasible and promising technique for expanding mHA-targeting immunotherapeutics.

Introduction While tens of thousands of HLA alleles have been identified by DNA sequencing, the contribution of alternative splicing to HLA diversity is not well characterized. In this study, we sought to determine if long-read sequencing could be used to accurately quantify allele-specific HLA transcripts in primary human lymphocytes. Methods cDNA libraries were prepared from peripheral blood lymphocytes from 12 donors and sequenced by nanopore long-read sequencing. HLA reads were aligned to donor-specific reference sequences based on the known type of each donor. Allele-specific exon utilization was calculated as the proportion of reads aligning to each allele containing known exons, and transcript isoforms were quantified based on patterns of exon utilization within individual reads. Results Splice variants were rare among class I HLA genes (median exon retention rate 99%–100%), except for several HLA-C alleles with exon 5 spliced out of up to 15% of reads. Splice variants were also rare among class II HLA genes (median exon retention rate 98%–100%), except for HLA-DQB1 . Consistent with previous work, exon 5 of HLA-DQB1 was spliced out in alleles with a mutated splice acceptor site at rs28688207. Surprisingly, a 28% loss of exon 5 was also observed in HLA-DQB1 alleles with an intact splice acceptor site at rs28688207. Discussion We describe a simple bioinformatic workflow to quantify allele-specific expression of HLA transcript isoforms. Further studies are warranted to characterize the repertoire of HLA transcripts expressed in different cell types and tissues across diverse populations.

HLA typing provides essential results for stem cell and solid organ transplants, as well as providing diagnostic benefits for various rheumatology, gastroenterology, neurology, and infectious diseases. It is becoming increasingly clear that understanding the expression of patient HLA transcripts can provide additional benefits for many of these same patient groups. Our study cohort was evaluated using a long-read RNA sequencing methodology to provide rapid HLA genotyping results and normalized HLA transcript expression. Our assay used NGSEngine to determine the HLA genotyping result and normalized mRNA transcript expression using Athlon2. The assay demonstrated an excellent concordance rate of 99.7%. Similar to previous studies, for the class I loci, patients demonstrated significantly lower expression of HLA-C than HLA-A and -B (Mann–Whitney U, p value = 0.0065 and p value = 0.0154, respectively). In general, the expression of class II transcripts was lower than that of class I transcripts. This study demonstrates a rapid high-resolution HLA typing assay using RNA-Seq that can provide accurate HLA genotyping and HLA allele-specific transcript expression in 7–8 h, a timeline short enough to perform the assay for deceased donors.

Histocompatibility testing is essential for donor identification and risk assessment in solid organ and hematopoietic stem cell transplant. Additionally, it is useful for identifying donor specific alleles for monitoring donor specific antibodies in post-transplant patients. Next-generation sequence (NGS) based human leukocyte antigen (HLA) typing has improved many aspects of histocompatibility testing in hematopoietic stem cell and solid organ transplant. HLA disease association testing and research has also benefited from the advent of NGS technologies. In this review we discuss the current impact and future applications of NGS typing on clinical histocompatibility testing for transplant and non-transplant purposes.