A Tool for the Assessment of HLA-DQ Heterodimer Variation in Hematopoietic Cell Transplantation: HLA-DQ Heterodimers Tool.

BACKGROUND: When optimizing transplants, clinical decision-makers consider HLA-A, -B, -C, -DRB1 (8 matched alleles out of 8), and sometimes HLA-DQB1 (10 out of 10) matching between the patient and donor. HLA-DQ is a heterodimer formed by the ß chain product of HLA-DQB1 and an α chain product of HLA-DQA1. In addition to molecules defined by the parentally-inherited cis haplotypes, α-ß trans-dimerization is possible between certain alleles, leading to unique molecules and a potential source of mismatched molecules. Recently, researchers uncovered that clinical outcome after HLA-DQB1-mismatched unrelated donor HCT depends on the total number of HLA-DQ molecule mismatches and the specific α-ß heterodimer mismatch. OBJECTIVE: Our objective in this study is to develop an automated tool for analyzing HLA-DQ heterodimer data and validating it through numerous datasets and analyses. By doing so, we provide an HLA-DQ heterodimer tool for DQα-DQß trans-heterodimer evaluation, HLA-DQ imputation, and HLA-DQ-featured source selection to the transplant field. STUDY DESIGN: In our study, we leverage 352,148 high-confidence, statistically-phased (via a modified expectation-maximization algorithm) HLA-DRB1∼DQA1∼DQB1 haplotypes, 1,052 pedigree-phased HLA-DQA1∼DQB1 haplotypes, and 13,663 historical transplants to characterize HLA-DQ heterodimers data. RESULTS: Using our developed QLASSy (HLA-DQA1 and HLA-DQB1 Heterodimers Assessment) tool, we first assessed the data quality of HLA-DQ heterodimers in our data for trans-dimers, missing HLA-DQA1 typing, and unexpected HLA-DQA1 and HLA-DQB1 combinations. Since trans-dimers enable up to four unique HLA-DQ molecules in individuals, we provide in-silico validations for 99.7% of 275 unique trans-dimers generated by 176,074 U.S. donors with HLA-DQA1 and HLA-DQB1 data. Many individuals lack HLA-DQA1 typing, so we developed and validated high-confidence HLA-DQ annotation imputation via HLA-DRB1 with >99% correct predictions in 23,698 individuals. A select few individuals displayed unexpected HLA-DQ combinations. We revisited the typing of 61 donors with unexpected HLA-DQ combinations based on their HLA-DQA1 and HLA-DQB1 typing and corrected 22 out of 61 (36%) cases of donors through data review or retyping and used imputation to resolve unexpected combinations. After verifying the data quality of our datasets, we analyzed our datasets further: we explored the frequencies of observed HLA-DQ combinations to compare HLA-DQ across populations (for instance, we found more high-risk molecules in Asian/Pacific Islander and Black/African American populations), demonstrated the effect of HLA-DQA1 and HLA-DQB1 mismatching on HLA-DQ molecular mismatches, and highlighted where donor selections could be improved at the time of search for historical transplants with this new HLA-DQ information (where 51.9% of G2-mismatched transplants had lower-risk, G2-matched alternatives). CONCLUSION: We encapsulated our findings into a tool that imputes missing HLA-DQA1 as needed, annotates HLA-DQ (mis)matches, and highlights other important HLA-DQ data to consider for the present and future. Altogether, these valuable datasets, analyses, and a culminating tool serve as actionable resources to enhance donor selection and improve patient outcomes.

Leveraging Hematopoietic Cell Transplant Data and Biorepository Resources at the Center for International Blood and Marrow Transplant Research to Improve Patient Outcomes.

Hematopoietic cell transplantation (HCT) has undergone many advances over the decades. Trends in HCT utilization have been impacted by research based on the data and samples collected by the Center for International Blood and Marrow Transplant Research (CIBMTR). Here, we provide a summary report of the CIBMTR Biorepository resource and describe the biospecimen inventory along with collection and request procedures. The diversity captured in this inventory reflects transplant activity, and these samples can be leveraged for secondary analyses to generate more data and insights to advance the field. We describe how our resources have already impacted HCT practice and elaborate on possibilities for further collaboration and utilization to maximize capabilities and research opportunities. Hematopoietic cell transplant data and biorepository resources at the CIBMTR have been and continue to be leveraged to improve patient outcomes.