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.

The genotype list string code syntax for exchanging nomenclature-level genotyping results in clinical and research data management and analysis systems.

The nomenclatures used to describe HLA and killer-cell immunoglobulin-like receptor (KIR) alleles distinguish unique nucleotide and peptide sequences, and patterns of expression, but are insufficient for describing genotyping results, as description of ambiguities and relations across loci require terminology beyond allele names. The genotype list (GL) String grammar describes genotyping results for genetic systems with defined nomenclatures, like HLA and KIR, documenting what is known and unknown about a given genotyping result. However, the accuracy of a GL String is dependent on the reference database version under which it was generated. Here, we describe the GL string code (GLSC) system, which associates each GL String with meta-data describing the specific reference context in which the GL String was created, and in which it should be interpreted. GLSC is a defined syntax for exchanging GL Strings in the context of a specific gene-family namespace, allele-name code-system, and pertinent reference database version. GLSC allows HLA and KIR genotyping data to be transmitted, parsed and interpreted in the appropriate context, in an unambiguous manner, on modern data-systems, including Health Level 7 Fast Healthcare Interoperability Resource systems. Technical specification for GLSC can be found at https://glstring.org.

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.

Genotype List String 1.1: Extending the Genotype List String grammar for describing HLA and Killer-cell Immunoglobulin-like Receptor genotypes.

The Genotype List (GL) String grammar for reporting HLA and Killer-cell Immunoglobulin-like Receptor (KIR) genotypes in a text string was described in 2013. Since this initial description, GL Strings have been used to describe HLA and KIR genotypes for more than 40 million subjects, allowing these data to be recorded, stored and transmitted in an easily parsed, text-based format. After a decade of working with HLA and KIR data in GL String format, with advances in HLA and KIR genotyping technologies that have fostered the generation of full-gene sequence data, the need for an extension of the GL String system has become clear. Here, we introduce the new GL String delimiter "?," which addresses the need to describe ambiguity in assigning a gene sequence to gene paralogs. GL Strings that do not include a "?" delimiter continue to be interpreted as originally described. This extension represents version 1.1 of the GL String grammar.

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.

Genomic characterization of HLA class I and class II genes in ethnically diverse sub-Saharan African populations: A report on novel HLA alleles.

HLA allelic variation has been well studied and documented in many parts of the world. However, African populations have been relatively under-represented in studies of HLA variation. We have characterized HLA variation from 489 individuals belonging to 13 ethnically diverse populations from rural communities from the African countries of Botswana, Cameroon, Ethiopia, and Tanzania, known to practice traditional subsistence lifestyles using next generation sequencing (Illumina) and long-reads from Oxford Nanopore Technologies. We identified 342 distinct alleles among the 11 HLA targeted genes: HLA-A, -B, -C, -DRB1, -DRB3, -DRB4, -DRB5, -DQA1, -DQB1, -DPA1, and -DPB1, with 140 of those alleles containing novel sequences that were submitted to the IPD-IMGT/HLA database. Sixteen of the 140 alleles contained novel content within the exonic regions of the genes, while 110 alleles contained novel intronic variants. Four alleles were found to be recombinants of already described HLA alleles and 10 alleles extended the sequence content of already described alleles. All 140 alleles include complete allelic sequence from the 5' UTR to the 3' UTR that are inclusive of all exons and introns. This report characterizes the HLA allelic variation from these individuals and describes the novel allelic variation present within these specific African populations.

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.

A new strategy for systematically classifying HLA alleles into serological specificities.

HLA serological specificities were defined by the reactivity of HLA molecules with sets of sera and monoclonal antibodies. Many recently identified alleles defined by molecular typing lack their serotype assignment. We surveyed the literature describing the correlation of the reactivity of serologic reagents with AA residues. 20 - 25 AA residues determining epitopes (DEP) that correlated with 82 WHO serologic specificities were identified for HLA class I loci. Thirteen DEP each located in the beta-1 domains that correlated with 24 WHO serologic specificities were identified for HLA-DRB1 and -DQB1 loci. The designation of possible HLA-DPB1, -DQA1, -DPA1, and additional serological specificities that result from epitopes defined by residues located at both -DQA1 and -DQB1 subunits were also examined. HATS software was developed for automated serotype assignments to HLA alleles in one of the three hierarchical matching criteria: (1) all DEP (FULL); (2) selected DEP specific to each serological specificity (SEROTYPE); (3) one AA mismatch with one or more SEROTYPES (INCOMPLETE). Results were validated by evaluating the alleles whose serotypes do not correspond to the first field of the allele name listed in the HLA dictionary. Additional 85 and 21 DEP patterns that do not correspond to any WHO serologic specificities for common HLA class I and DRB1 alleles were identified, respectively. A comprehensive antibody identification panel would allow for accurate unacceptable antigen listing and compatibility predictions in solid organ transplantation. We propose that antibody-screening panels should include all serologic specificities identified in this study.

A common allele of HLA mediates asymptomatic SARS-CoV-2 infection.

Despite some inconsistent reporting of symptoms, studies have demonstrated that at least 20% of individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) will remain asymptomatic. Although most global efforts have focused on understanding factors underlying severe illness in COVID-19 (coronavirus disease of 2019), the examination of asymptomatic infection provides a unique opportunity to consider early disease and immunologic features promoting rapid viral clearance. Owing to its critical role in the immune response, we postulated that variation in the human leukocyte antigen (HLA) loci may underly processes mediating asymptomatic infection. We enrolled 29,947 individuals registered in the National Marrow Donor Program for whom high-resolution HLA genotyping data were available in the UCSF Citizen Science smartphone-based study designed to track COVID-19 symptoms and outcomes. Our discovery cohort (n=1428) was comprised of unvaccinated, self-identified subjects who reported a positive test result for SARS-CoV-2. We tested for association of five HLA loci (HLA-A, -B, -C, -DRB1, -DQB1) with disease course and identified a strong association of HLA-B*15:01 with asymptomatic infection, and reproduced this association 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 individuals carrying HLA-B*15:01 were reactive to the immunodominant SARS-CoV-2 S-derived peptide NQKLIANQF, and 100% of the reactive cells displayed memory phenotype. Finally, we characterize the protein structure of HLA-B*15:01-peptide complexes, demonstrating that the NQKLIANQF peptide from SARS-CoV-2, and the highly homologous NQKLIANAF from seasonal coronaviruses OC43-CoV and HKU1-CoV, share similar ability to be stabilized and presented by HLA-B*15:01, providing the molecular basis for T-cell cross-reactivity and HLA-B*15:01-mediated pre-existing immunity.

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.

Challenges for the standardized reporting of NGS HLA genotyping: Surveying gaps between clinical and research laboratories.

Next generation sequencing (NGS) is being applied for HLA typing in research and clinical settings. NGS HLA typing has made it feasible to sequence exons, introns and untranslated regions simultaneously, with significantly reduced labor and reagent cost per sample, rapid turnaround time, and improved HLA genotype accuracy. NGS technologies bring challenges for cost-effective computation, data processing and exchange of NGS-based HLA data. To address these challenges, guidelines and specifications such as Genotype List (GL) String, Minimum Information for Reporting Immunogenomic NGS Genotyping (MIRING), and Histoimmunogenetics Markup Language (HML) were proposed to streamline and standardize reporting of HLA genotypes. As part of the 17th International HLA and Immunogenetics Workshop (IHIW), we implemented standards and systems for HLA genotype reporting that included GL String, MIRING and HML, and found that misunderstanding or misinterpretations of these standards led to inconsistencies in the reporting of NGS HLA genotyping results. This may be due in part to a historical lack of centralized data reporting standards in the histocompatibility and immunogenetics community. We have worked with software and database developers, clinicians and scientists to address these issues in a collaborative fashion as part of the Data Standard Hackathons (DaSH) for NGS. Here we report several categories of challenges to the consistent exchange of NGS HLA genotyping data we have observed. We hope to address these challenges in future DaSH for NGS efforts.