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.

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.

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.

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.

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.

Competing risks with missing covariates: effect of haplotypematch on hematopoietic cell transplant patients.

In this paper we consider a problem from hematopoietic cell transplant (HCT) studies where there is interest on assessing the effect of haplotype match for donor and patient on the cumulative incidence function for a right censored competing risks data. For the HCT study, donor's and patient's genotype are fully observed and matched but their haplotypes are missing. In this paper we describe how to deal with missing covariates of each individual for competing risks data. We suggest a procedure for estimating the cumulative incidence functions for a flexible class of regression models when there are missing data, and establish the large sample properties. Small sample properties are investigated using simulations in a setting that mimics the motivating haplotype matching problem. The proposed approach is then applied to the HCT study.