Graph-Based Imputation Methods and Their Applications to Single Donors and Families.

The outcome of Hematopoietic Stem Cell (HSCT) and organ transplant is strongly affected by the matching of the HLA alleles of the donor and the recipient. However, donors and sometimes recipients are often typed at low resolution, with some alleles either missing or ambiguous. Thus, imputation methods are required to detect the most probably high-resolution HLA haplotypes consistent with a typing. Such imputation algorithms require predefined haplotype frequencies. As such, the phasing of the typing is required for both imputation and frequency generation.We have developed a new approach to HLA haplotype and genotype imputation, where first all candidate phases of a typing are explicated, and then the ambiguity within each phase is solved. This ambiguity is solved through a graph structure of all partial haplotypes and the haplotypes consistent with them.This phasing approach was used to produce an imputation algorithm (GRIMM-Graph Imputation and Matching). GRIMM was then combined with the possibility of combining information from multiple races to produce MR-GRIMM (Multi-Race GRIMM). When family information is available, the phasing of each family member can be restricted by the others. We propose GRAMM (GRaph-bAsed faMily iMputation) to phase alleles in family pedigree HLA typing data and in mother-cord blood unit pairs. Finally, we combined MR-GRIMM with an expectation-maximization (EM) algorithm to estimate haplotype frequencies sharing information between races to produce MR-GRIMME (MR-GRIMM EM).We have shown that these algorithms naturally combine information between races and family members. The accuracy of each of these algorithms is significantly better than its current parallel methods. MR-GRIMM leads to high accuracy in matching predictions. GRAMM better imputes family members than either MR-GRIMM or any existing algorithm and has practically no phasing errors. MR-GRIMME obtains a higher likelihood than existing algorithms.MR-GRIMM, MR-GRIMME, and GRAMM are available as servers or through stand-alone versions in GITHUB and PyPi, as detailed in the appropriate sections.

Optimal Donor Selection Across Multiple Outcomes For Hematopoietic Stem Cell Transplantation By Bayesian Nonparametric Machine Learning.

Allogeneic hematopoietic cell transplantation (HCT) is one of the only curative treatment options for patients suffering from life-threatening hematologic malignancies; yet, the possible adverse complications can be serious even fatal. Matching between donor and recipient for 4 of the HLA genes is widely accepted and supported by the literature. However, among 8/8 allele matched unrelated donors, there is less agreement among centers and transplant physicians about how to prioritize donor characteristics like additional HLA loci (DPB1 and DQB1), donor sex/parity, CMV status, and age to optimize transplant outcomes. This leads to varying donor selection practice from patient to patient or via center protocols. Furthermore, different donor characteristics may impact different post transplant outcomes beyond mortality, including disease relapse, graft failure/rejection, and chronic graft-versus-host disease (components of event-free survival, EFS). We develop a general methodology to identify optimal treatment decisions by considering the trade-offs on multiple outcomes modeled using Bayesian nonparametric machine learning. We apply the proposed approach to the problem of donor selection to optimize overall survival and event-free survival, using a large outcomes registry of HCT recipients and their actual and potential donors from the Center for International Blood and Marrow Transplant Research (CIBMTR). Our approach leads to a donor selection strategy that favors the youngest male donor, except when there is a female donor that is substantially younger.

Systematic evaluation of donor-KIR/recipient-HLA interactions in HLA-matched hematopoietic cell transplantation for AML.

ABSTRACT: In acute myeloid leukemia (AML), donor natural killer cell killer immunoglobulin-like receptors (KIR) and recipient HLA interactions may contribute to the graft-versus-leukemia effect of allogeneic hematopoietic cell transplantation (HCT). Analyses of individual KIR/HLA interactions, however, have yielded conflicting findings, and their importance in the HLA-matched unrelated donor (MUD) setting remains controversial. We systematically studied outcomes of individual donor-KIR/recipient-HLA interactions for HCT outcomes and empirically evaluated prevalent KIR genotypes for clinical benefit. Adult patients with AML (n = 2025) who received HCT with MUD grafts in complete remission reported to the Center for International Blood and Marrow Transplantation were evaluated. Only the donor-2DL2+/recipient-HLA-C1+ pair was associated with reduced relapse (hazard ratio [HR], 0.79; 95% confidence interval [CI], 0.67-0.93; P = .006) compared with donor-2DL2-/recipient-HLA-C1+ pair. However, no association was found when comparing HLA-C groups among KIR-2DL2+-graft recipients. We identified 9 prevalent donor KIR genotypes in our cohort and screened them for association with relapse risk. Genotype 5 (G5) in all recipients and G3 in Bw4+ recipients were associated with decreased relapse risk (HR, 0.52; 95% CI, 0.35-0.78; P = .002; and HR, 0.32; 95% CI, 0.14-0.72; P = .006; respectively) and G2 (HR 1.63, 95% CI, 1.15-2.29; P = .005) with increased relapse risk in C1-homozygous recipients, compared with other patients with the same ligand. However, we could not validate these findings in an external data set of 796 AML transplants from the German transplantation registry. Neither a systematic evaluation of known HLA-KIR interactions nor an empiric assessment of prevalent KIR genotypes demonstrated clinically actionable associations; therefore, these data do not support these KIR-driven strategies for MUD selection in AML.

Expanding donor options: haploidentical transplant recipients are also highly likely to have a 7/8-matched unrelated donor.

ABSTRACT: The use of haploidentical related donor (HRD) hematopoietic cell transplants (HCTs) in the United States grew by more than fourfold in the last decade, driven mainly by use of posttransplant cyclophosphamide (PTCy)-based graft-versus-host-disease prophylaxis. However, not all patients have a suitable HRD available. In this study, we explored the existence of unrelated donors (URDs) on the National Marrow Donor Program (NMDP) registry at the 8/8- or 7/8-match level for patients receiving HRD HCT in the United States and reporting to the Center for International Blood and Marrow Transplant Research between 2013 and 2020. The data consist of 9696 HRD HCT recipients. The NMDP search prognosis score and a search simulation were used to estimate counts of URD matches on the registry. NMDP search prognosis varied by patient ancestry, with 27.5% non-Hispanic White having a good score compared with 4.6% of African American HRD HCT recipients. Overall, 34% of recipients had ≥1 8/8-matched URDs and 84% had ≥1 7/8 URDs. Recipients of older HRDs (≥35 years) had a likelihood of between 20%- 65% of having ≥5 existing 7/8-matched URDs who were aged ≤35 years. Donor-selection practices varied among the 10 highest-volume HRD centers: 6 had >20% chance of an existing 8/8-matched URD for their HRD recipients, whereas 4 centers had low likelihood of identifying an 8/8-matched URD. In conclusion, although most US patients undergoing HRD HCT do not have an existing 8/8 URD, the majority have an existing 7/8-matched URD. Studies comparing outcomes in patients receiving either HRD or 7/8-matched URD HCT and PTCy-based graft-versus-host disease prophylaxis may be warranted.

Combined imputation of HLA genotype and self-identified race leads to better donor-recipient matching.

Allogeneic Hematopoietic Cell Transplantation (HCT) is a curative therapy for hematologic disorders and often requires human leukocyte antigen (HLA)-matched donors. Donor registries have recruited donors utilizing evolving technologies of HLA genotyping methods. This necessitates in-silico ambiguity resolution and statistical imputation based on haplotype frequencies estimated from donor data stratified by self-identified race and ethnicity (SIRE). However, SIRE has limited genetic validity and presents a challenge for individuals with unknown or mixed SIRE. We present MR-GRIMM "Multi-Race Graph IMputation and Matching" that simultaneously imputes the race/ethnic category and HLA genotype using a SIRE based prior. Additionally, we propose a novel method to impute HLA typing inconsistent with current haplotype frequencies. The performance of MR-GRIMM was validated using a dataset of 170,000 donor-recipient pairs. MR-GRIMM has an average 20 % lower matching error (1-AUC) than single-race imputation. The recall metric (sensitivity) of the race/ethnic category imputation from HLA was measured by comparing the imputed donor race with the donor-provided SIRE. Accuracies of 0.74 and 0.55 were obtained for the prediction of 5 broad and 21 detailed US population groups respectively. The operational implementation of this algorithm in a registry search could help improve match predictions and access to HLA-matched donors.

Existence of HLA-Mismatched Unrelated Donors Closes the Gap in Donor Availability Regardless of Recipient Ancestry.

In patients without a matched sibling donor (MSD) or well-matched unrelated donor (MUD), hematopoietic cell transplantation (HCT) can still be successful when using an HLA-mismatched unrelated donor (MMUD) in combination with post-transplantation cyclophosphamide (PTCy), abatacept, or other novel approaches. This may allow clinicians to choose a suitable donor from a wide range of donor options while optimizing other donor selection characteristics, including donor age. We hypothesized that allowing for a 5/8 HLA match level considering high-resolution matching at HLA-A, -B, -C and -DRB1, there is a potential to close the donor availability gap for all patients regardless of their race/ethnicity. In this work, we estimate the likelihood of matching for all racial/ethnic groups at different HLA match thresholds. Our study aimed to assess the potential for identifying an available MUD or MMUD in the National Marrow Donor Program (NMDP)/Be The Match (BTM) donor registry for 21 detailed and 5 broad racial/ethnic groups, using high-resolution HLA matching for HLA-A, -B, -C, and -DRB1 at various levels (8/8, 7/8, 6/8, and 5/8). We used donor registry population data from the NMDP/BTM in 2020 and redistributed the donor registry data according to existing population ratios, accounting for demonstrated donor availability. Finally, we used a genetic model at the population level to estimate the match likelihood for detailed and broad racial/ethnic groups. Likelihood of 8/8 HLA match ranging from 16% to 74% were obtained for various detailed racial/ethnic groups with available donors age ≤35 years. When considering more mismatches in the HLA loci, registry coverage became >99% with a 5/8 HLA match level for donors of all ages or those age ≤35 years, with HLA-DPB1 T cell epitope permissive matching, or when searching for donors outside of their racial/ethnic group. Our registry models demonstrate the potential for using MMUDs at various HLA match levels to study whether this will expand access to HCT across racial/ethnic groups. Expanded donor options may erase the donor availability gap for all patients while allowing for selection of MMUDs with favorable characteristics, such as younger age.

A common allele of HLA is associated with asymptomatic SARS-CoV-2 infection.

Studies have demonstrated that at least 20% of individuals infected with SARS-CoV-2 remain asymptomatic1-4. Although most global efforts have focused on severe illness in COVID-19, examining asymptomatic infection provides a unique opportunity to consider early immunological features that promote rapid viral clearance. Here, postulating that variation in the human leukocyte antigen (HLA) loci may underly processes mediating asymptomatic infection, we enrolled 29,947 individuals, for whom high-resolution HLA genotyping data were available, in a smartphone-based study designed to track COVID-19 symptoms and outcomes. Our discovery cohort (n = 1,428) comprised unvaccinated individuals who reported a positive test result for SARS-CoV-2. We tested for association of five HLA loci with disease course and identified a strong association between HLA-B*15:01 and asymptomatic infection, observed in two independent cohorts. Suggesting that this genetic association is due to pre-existing T cell immunity, we show that T cells from pre-pandemic samples from individuals carrying HLA-B*15:01 were reactive to the immunodominant SARS-CoV-2 S-derived peptide NQKLIANQF. The majority of the reactive T cells displayed a memory phenotype, were highly polyfunctional and were cross-reactive to a peptide derived from seasonal coronaviruses. The crystal structure of HLA-B*15:01-peptide complexes demonstrates that the peptides NQKLIANQF and NQKLIANAF (from OC43-CoV and HKU1-CoV) share a similar ability to be stabilized and presented by HLA-B*15:01. Finally, we show that the structural similarity of the peptides underpins T cell cross-reactivity of high-affinity public T cell receptors, providing the molecular basis for HLA-B*15:01-mediated pre-existing immunity.

Trans-population graph-based coverage optimization of allogeneic cellular therapy.

Background: Pre-clinical development and in-human trials of 'off-the-shelf' immune effector cell therapy (IECT) are burgeoning. IECT offers many potential advantages over autologous products. The relevant HLA matching criteria vary from product to product and depend on the strategies employed to reduce the risk of GvHD or to improve allo-IEC persistence, as warranted by different clinical indications, disease kinetics, on-target/off-tumor effects, and therapeutic cell type (T cell subtype, NK, etc.). Objective: The optimal choice of candidate donors to maximize target patient population coverage and minimize cost and redundant effort in creating off-the-shelf IECT product banks is still an open problem. We propose here a solution to this problem, and test whether it would be more expensive to recruit additional donors or to prevent class I or class II HLA expression through gene editing. Study design: We developed an optimal coverage problem, combined with a graph-based algorithm to solve the donor selection problem under different, clinically plausible scenarios (having different HLA matching priorities). We then compared the efficiency of different optimization algorithms - a greedy solution, a linear programming (LP) solution, and integer linear programming (ILP) -- as well as random donor selection (average of 5 random trials) to show that an optimization can be performed at the entire population level. Results: The average additional population coverage per donor decrease with the number of donors, and varies with the scenario. The Greedy, LP and ILP algorithms consistently achieve the optimal coverage with far fewer donors than the random choice. In all cases, the number of randomly-selected donors required to achieve a desired coverage increases with increasing population. However, when optimal donors are selected, the number of donors required may counter-intuitively decrease with increasing population size. When comparing recruiting more donors vs gene editing, the latter was generally more expensive. When choosing donors and patients from different populations, the number of random donors required drastically increases, while the number of optimal donors does not change. Random donors fail to cover populations different from their original populations, while a small number of optimal donors from one population can cover a different population. Discussion: Graph-based coverage optimization algorithms can flexibly handle various HLA matching criteria and accommodate additional information such as KIR genotype, when such information becomes routinely available. These algorithms offer a more efficient way to develop off-the-shelf IECT product banks compared to random donor selection and offer some possibility of improved transparency and standardization in product design.

Assessment of HLA-DPB1 genetic variation using an HLA-DP tool and its implications in clinical transplantation.

HLA-DP is a classic transplantation antigen that mediates alloreactivity through T-cell epitope (TCE) diversity and expression levels. A current challenge is to integrate these functional features into the prospective selection of unrelated donor candidates for transplantation. Genetically, HLA-DPB1 exon 2 defines the permissive and nonpermissive TCE groups, and exons 2 and 3 (in linkage with rs9277534) indicate low- and high-expression allotypes. In this study, we analyzed 356 272 exon 2-exon 3-phased sequences from individuals across 5 self-identified race and ethnicity categories: White, Hispanic, Asian or Pacific Islander, Black or African American, and American Indian or Alaskan Native. This sequence data set revealed the complex relationship between TCE and expression models and the importance of exon 3 sequence data. We also studied archived donor search lists for 2545 patients who underwent transplantation from an HLA-11/12 unrelated donor mismatched for a single HLA-DPB1 allele. Depending on the order in which the TCE and expression criteria were considered, some patients had different TCE- and expression-favorable donors. In addition, this data set revealed that many expression-favorable alternatives existed in the search lists. To improve the selection of candidate donors, we provide, disseminate, and automate our findings through our multifaceted tool called Expression of HLA-DP Assessment Tool, consisting of a public web application, Python package, and analysis pipeline.

World Marrow Donor Association guidelines for the reporting of novel HLA alleles.

The guidelines for the implementation and reporting of HLA nomenclature for the World Marrow Donor Association have served as a reliable standard for communication of HLA data in the hematopoietic cell transplantation process. Wider use of next-generation sequencing made a special provision of the guidelines increasingly pertinent: how to communicate novel HLA alleles. Novel alleles need to be recognized by the WHO Nomenclature Committee for Factors of the HLA system to obtain official allele designations. Until then they have to be handled according to the specific rules. Leaving the actual rules basically unchanged we give some advice on how to communicate novel alleles to best facilitate the search process for cases where novel alleles are identified on donor or patient side.

GRAMM: A new method for analysis of HLA in families.

Recently, haplo-identical transplantation with multiple HLA mismatches has become a viable option for stem cell transplants. Haplotype sharing detection requires the imputation of donor and recipient. We show that even in high-resolution typing when all alleles are known, there is a 15% error rate in haplotype phasing, and even more in low-resolution typings. Similarly, in related donors, the parents' haplotypes should be imputed to determine what haplotype each child inherited. We propose graph-based family imputation (GRAMM) to phase alleles in family pedigree HLA typing data, and in mother-cord blood unit pairs. We show that GRAMM has practically no phasing errors when pedigree data are available. We apply GRAMM to simulations with different typing resolutions as well as paired cord-mother typings, and show very high phasing accuracy, and improved allele imputation accuracy. We use GRAMM to detect recombination events and show that the rate of falsely detected recombination events (false-positive rate) in simulations is very low. We then apply recombination detection to typed families to estimate the recombination rate in Israeli and Australian population datasets. The estimated recombination rate has an upper bound of 10%-20% per family (1%-4% per individual).

Nonparametric failure time: Time-to-event machine learning with heteroskedastic Bayesian additive regression trees and low information omnibus Dirichlet process mixtures.

Many popular survival models rely on restrictive parametric, or semiparametric, assumptions that could provide erroneous predictions when the effects of covariates are complex. Modern advances in computational hardware have led to an increasing interest in flexible Bayesian nonparametric methods for time-to-event data such as Bayesian additive regression trees (BART). We propose a novel approach that we call nonparametric failure time (NFT) BART in order to increase the flexibility beyond accelerated failure time (AFT) and proportional hazard models. NFT BART has three key features: (1) a BART prior for the mean function of the event time logarithm; (2) a heteroskedastic BART prior to deduce a covariate-dependent variance function; and (3) a flexible nonparametric error distribution using Dirichlet process mixtures (DPM). Our proposed approach widens the scope of hazard shapes including nonproportional hazards, can be scaled up to large sample sizes, naturally provides estimates of uncertainty via the posterior and can be seamlessly employed for variable selection. We provide convenient, user-friendly, computer software that is freely available as a reference implementation. Simulations demonstrate that NFT BART maintains excellent performance for survival prediction especially when AFT assumptions are violated by heteroskedasticity. We illustrate the proposed approach on a study examining predictors for mortality risk in patients undergoing hematopoietic stem cell transplant (HSCT) for blood-borne cancer, where heteroskedasticity and nonproportional hazards are likely present.

Unrelated Stem Cell Donor HLA Match Likelihood in the US Registry Incorporating HLA-DPB1 Permissive Mismatching.

Donor-recipient HLA matching at the DPB1 locus improves the outcomes of hematopoietic stem cell transplantation (HCT). Retrospective outcome studies found that in HCTs matched for all 8 alleles of the A, B, C, and DRB1 loci at high resolution (8/8 match), few transplantations were also allele-matched at the DPB1 locus. DPB1 allele matching was once thought to be logistically impractical; however, a DPB1-permissive mismatch model based on T cell epitope (TCE) reactivity expands the proportion of suitable donors. To understand the likelihood of finding a DPB1-permissive donor, we sought to expand population genetic match likelihood models for the US unrelated donor registry, the National Marrow Donor Program (NMDP). After extending HLA haplotype frequency estimates to include the DPB1 locus, our models found that the likelihood of having a DPB1-permissive donor was not much lower than likelihood of 8/8 matching. A maximum of 5 additional donors would need to be typed to find a more optimal DPB1-permissive donor at least 90% of the time. Linkage disequilibrium patterns between the DPB1 locus and other classical HLA loci varied markedly by haplotype and population, indicating that the known recombination hotspot between DQ and DP gene complexes has not had a uniform impact; thus, DPB1-permissive donors are easier to identify within minority populations. DPB1 TCE categories were highly predictable from HLA typing at other loci when imputed with extended haplotype frequency data. Our overall results indicate that registry search strategies that seek a more optimally matched HCT donor encompassing HLA-DPB1 permissibility are likely to be highly productive.

Limited Contribution of Donor Characteristics to One-Year Survival After Hematopoietic Stem Cell Transplantation.

A large number of association studies have related donor characteristics to survival after bone marrow transplantation, for leukemia in general and specifically for acute myeloid leukemia (AML) patients. However, population-based differences often do not hold at the single transplant level. We test whether transplantation outcomes can be predicted at the single-patient level and whether such predictions can be used to better choose donors. The analysis was performed on a mixture of different diseases or with AML only, and with either patient and donor information or donor information only. We analyzed 3671 8-of-8 HLA-matched AML donor-recipient pairs and tested whether the outcome, including 1-year total and event-free survival, can be predicted from patient and donor-related factors. We used multiple machine learning and survival analysis methods. The best method is a fully connected neural network. Multiple outcomes can be predicted, with area under the specificity-sensitivity curve (AUC) values between 0.54 and 0.67 for the different outcomes. The patient age has a strong impact on prediction. However, for a given patient, when only donor or transplant information is used, limited prediction accuracy of 0.54 to 0.56 AUC for event-free survival and survival is obtained. Graft-versus-host disease and rejection after 1 year have slightly higher AUC values of around 0.59, whereas the relapse prediction accuracy was random. All donors' characteristics have a limited influence on the quality of hematopoietic stem cell transplantation for fully matched donors. Many factors with a population effect on survival have a very limited effect when combined with all other factors in a single-donor predictive model.

Assessment of HLA-B genetic variation with an HLA-B leader tool and implications in clinical transplantation.

Sequence variation in the HLA-B gene is critically linked to differential immune responses. A dimorphism at -21 of HLA-B exon 1 gives rise to leader peptides that are markers for risk of acute graft-versus-host disease, relapse, and mortality after unrelated donor and cord blood transplantation. To optimize the selection of stem cell transplant sources based on the HLA-B leader, an HLA-BLeader Assessment Tool (BLEAT) was developed to automate the assignment of leader genotypes, define HLA-B leader match statuses, and rank order candidate stem cell sources according to clinical risk. The base cohort consisted of 9 417 614 registered donors from the Be The Match Registry with HLA-B typing. Among these donors, the performance of BLEAT was assessed in 1 098 358 donors with sequence data for HLA-B exon 1 (2 196 716 haplotypes). The accuracy of leader assignment was then assessed in a second cohort of 1259 patients and their unrelated transplant donors. We furthermore established the frequencies of HLA-B leader genotype (MM, MT, TT) representations in broad racial categories in the 9.42 million donors. BLEAT has direct applications for the selection of optimal stem cell sources for transplantation and broad utility in basic and clinical research in pharmacogenomics, vaccine development, and cancer and infectious disease studies of human populations.

Predicting HLA-DPB1 permissive probabilities through a DPB1 prediction service towards the optimization of HCT donor selection.

Despite its demonstrated importance in hematopoietic cell transplantation, the HLA-DPB1 locus is only typed in one in five unrelated donors in the United States. Addressing this issue, we developed a DPB1 Prediction Service that leverages seven-locus haplotype frequencies (HLA-A ∼ C ∼ B ∼ DRB3/4/5 ∼ DRB1 ∼ DQB1 ∼ DPB1) to extend the imputation of six-locus HLA typing (HLA-A ∼ C ∼ B ∼ DRB3/4/5 ∼ DRB1 ∼ DQB1) to the HLA-DPB1 locus, including the novel prediction of HLA-DPB1 TCE groups to calculate donor-recipient TCE permissive match probabilities. Simulations of current-day patient searches reveal the service can fill in missing gaps for another four in five donors that appears on lists. To validate its performance, samples of 206,328 registered donors and 5,218 donor-recipient pairs with known high-resolution HLA-DPB1 typing were used for predicted-versus-observed comparisons. These comparisons demonstrated that the predictions were correct for 11.9-19.7% of HLA-DPB1 genotypes, 64.9-70.0% of TCE groups, and 61.0% of permissive match categories. Although HLA-DPB1 match predictions must be confirmed by additional typing, knowledge of TCE match probabilities facilitates rapid and improved identification of best donor options, especially for populations of color. Thus, we developed the TCE Prediction Tool user interface for a pilot program with several transplant centers to preview the accuracy and utility of this prediction framework, which provides valuable upfront optimization of donor selection.

Optimal Donor Selection for Hematopoietic Cell Transplantation Using Bayesian Machine Learning.

PURPOSE: Donor selection practices for matched unrelated donor (MUD) hematopoietic cell transplantation (HCT) vary, and the impact of optimizing donor selection in a patient-specific way using modern machine learning (ML) models has not been studied. METHODS: We trained a Bayesian ML model in 10,318 patients who underwent MUD HCT from 1999 to 2014 to provide patient- and donor-specific predictions of clinically severe (grade 3 or 4) acute graft-versus-host disease or death by day 180. The model was validated in 3,501 patients from 2015 to 2016 with archived records of potential donors at search. Donor selection optimizing predicted outcomes was implemented over either an unlimited donor pool or the donors in the search archives. Posterior mean differences in outcomes from optimal donor selection versus actual practice were summarized per patient and across the population with 95% intervals. RESULTS: Event rates were 33% (training) and 37% (validation). Among donor features, only age affected outcomes, with the effect consistent regardless of patient features. The median (interquartile range) difference in age between the youngest donor at search and the selected donor was 6 (1-10) years, whereas the number of donors per patient younger than the selected donor was 6 (1-36). Fourteen percent of the validation data set had an approximate 5% absolute reduction in event rates from selecting the youngest donor at search versus the actual donor used, leading to an absolute population reduction of 1% (95% interval, 0 to 3). CONCLUSION: We confirmed the singular importance of selecting the youngest available MUD, irrespective of patient features, identified potential for improved HCT outcomes by selecting a younger MUD, and demonstrated use of novel ML models transferable to optimize other complex treatment decisions in a patient-specific way.

Stem cell donor HLA typing improves CPRA in kidney allocation.

The Organ Procurement and Transplantation Network (OPTN) Kidney Allocation System provides a priority to sensitized candidates based on the calculated panel reactive antibody (CPRA) value. The human leukocyte antigen (HLA) haplotype reference panel used for calculation of the CPRA by the United Network for Organ Sharing (UNOS), the OPTN contractor, has limitations. We derived a novel panel from the National Marrow Donor Program HLA haplotype data set and compared the accuracy of CPRA values generated with this panel (NMDP-CPRA) to those generated from the UNOS panel (UNOS-CPRA), using predicted and actual deceased donor kidney offers for a cohort of 24 282 candidates. The overall accuracy for kidney offers was similar using NMDP-CPRA and UNOS-CPRA. Accuracy was slightly higher for NMDP-CPRA than UNOS-CPRA for candidates in several highly sensitized CPRA categories, with deviations in linkage disequilibrium for Caucasians and the smaller size of the UNOS panel as contributing factors. HLA data derived from stem cell donors yields CPRA values that are comparable to those derived from deceased kidney donors while improving upon several problems with the current reference panel. Consideration should be given to using stem cell donors as the reference panel for calculation of CPRA to improve equity in kidney transplant allocation.

Role of HLA-B exon 1 in graft-versus-host disease after unrelated haemopoietic cell transplantation: a retrospective cohort study.

BACKGROUND: The success of unrelated haemopoietic cell transplantation (HCT) is limited by graft-versus-host disease (GVHD), which is the main post-transplantation challenge when HLA-matched donors are unavailable. A sequence dimorphism in exon 1 of HLA-B gives rise to leader peptides containing methionine (Met; M) or threonine (Thr; T), which differentially influence natural killer and T-cell alloresponses. The main aim of the study was to evaluate the role of the leader dimorphism in GVHD after HLA-B-mismatched unrelated HCT. METHODS: We did a retrospective cohort study of 33 982 patients who received an unrelated HCT done in Australia, Europe, Japan, North America, and the UK between Jan 1, 1988, and Dec 31, 2016. Data were contributed by participants of the International Histocompatibility Working Group in Hematopoietic Cell Transplantation. All cases were included and there were no exclusion criteria. Multivariate regression models were used to assess risks associated with HLA-A, HLA-B, HLA-C, HLA-DRB1, and HLA-DQB1 mismatching. Among the 33 982 transplantations, the risks of GVHD associated with HLA-B M and T leaders were established in 17 100 (50·3%) HLA-matched and 1457 (4·3%) single HLA-B-mismatched transplantations using multivariate regression models. Leader frequencies were defined in 2 004 742 BeTheMatch US registry donors. FINDINGS: Between Jan 20, 2017, and March 11, 2019, we assessed 33 982 HCTs using multivariate regression models for the role of HLA mismatching on outcome. Median follow-up was 1841 days (IQR 909-2963). Mortality and GVHD increased with increasing numbers of HLA mismatches. A single HLA-B mismatch increased grade 3-4 acute GVHD (odds ratio [OR] 1·89, 95% CI 1·53-2·33; p<0·0001). Among the single HLA-B-mismatched transplantations, acute GVHD risk was higher with leader mismatching than with leader matching (OR 1·73, 1·02-2·94; p=0·042 for grade 2-4) and with an M leader shared allotype compared with a T leader shared allotype (OR 1·98, 1·39-2·81; p=0·0001 for grade 3-4). The preferred HLA-B-mismatched donor is leader-matched and shares a T leader allotype. The majority (1 836 939 [91·6%]) of the 2 004 742 US registry donors have the TT or MT genotype. INTERPRETATION: The HLA-B leader informs GVHD risk after HLA-B-mismatched unrelated HCT and differentiates high-risk HLA-B mismatches from those with lower risk. The leader of the matched allotype could be considered to be as important as the leader of the mismatched allotype for GVHD. Prospective identification of leader-matched donors is feasible for most patients in need of a HCT, and could lower GVHD and increase availability of HCT therapy. These findings are being independently validated and warrant further research in prospective trials. FUNDING: The National Institutes of Health, USA.

Selection of unrelated donors and cord blood units for hematopoietic cell transplantation: guidelines from the NMDP/CIBMTR.

Allogeneic hematopoietic cell transplantation involves consideration of both donor and recipient characteristics to guide the selection of a suitable graft. Sufficient high-resolution donor-recipient HLA match is of primary importance in transplantation with adult unrelated donors, using conventional graft-versus-host disease prophylaxis. In cord blood transplantation, optimal unit selection requires consideration of unit quality, cell dose and HLA-match. In this summary, the National Marrow Donor Program (NMDP) and the Center for International Blood and Marrow Transplant Research, jointly with the NMDP Histocompatibility Advisory Group, provide evidence-based guidelines for optimal selection of unrelated donors and cord blood units.