HLA-DQ heterodimers in hematopoietic cell transplantation

Abstract

HLA-DQ heterodimers increase the susceptibility to autoimmune diseases but their role in hematopoietic-cell transplantation is unknown. We tested the hypothesis that outcome after HLA-matched and HLA-DQ-mismatched hematopoietic-cell transplantation is influenced by HLA-DQ heterodimers. Heterodimers were defined in 5164 HLA-matched and 520 HLA-DQ-mismatched patients and their transplant donors according to well-established crystallographic criteria. Group 1 (G1) heterodimers are any DQA1*02/03/04/05/06α paired with any DQB1*02/03/04β. Group 2 (G2) heterodimers are DQA1*01α paired with any DQB1*05/06β. Multivariable models identified significantly higher relapse risk in G1G2 and G2G2 compared to G1G1 HLA-matched patients with malignant disease; risk increased with increasing numbers of G2-molecules. In HLA-DQ-mismatched transplantation for malignant diseases, matching or mismatching for G2 increased relapse risk. G2 lowered disease-free survival after both HLA-matched and HLA-DQ-mismatched transplantation. A paradigm based on HLA-DQ heterodimers provides a functional definition of the hematopoietic-cell transplantation barrier and a means to lower risks for future patients.

Machine learning reveals limited contribution of trans-only encoded variants to the HLA-DQ immunopeptidome by accurate and comprehensive HLA-DQ antigen presentation prediction

“…Furthermore, when examining the motif deconvolution of the DQheterozygous samples without trans-only molecules, our method was able to identify four distinct motifs in each sample (see supplementary figure 12C). resulting in their inefficient assembly, lack of stability and surface expression and therefore loss of function 12,14 . These results thus demonstrate how such rules can be learned directly from MS-immunopeptidome data using tailored data mining methods and rationally defined data sets, suggesting that similar types of analysis should be extended to HLA-DP to further our understanding of cis versus trans α-and β-chain pairing.…”

“…For example, the DQA1*02, 03, 04, 05, and 06 alleles form stable heterodimers only with the DQB1*02, 03, and 04 alleles. Likewise, DQA1*01 has only been detected to form stable heterodimers with DQB1*05 and 06 (Creary et al, 2021; Kwok et al, 1993; Petersdorf et al, 2022).…”

“…For example, DQA1*01 has only been detected to form stable heterodimers with DQB1*05 and 06 alleles. Likewise, the DQA1*02, 03, 04, 05, and 06 alleles form stable heterodimers only with the DQB1*02, 03, and 04 [12][13][14] .…”

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

“…Matching at DRB3/4/5, DQA1 and DPA1 has not yet been shown independently to be clinically relevant for HSCT matching, although alleles at these loci are potential targets of patient antibody mediated rejection 29 . Mismatches across certain DQA1∼DQB1 heterodimers 26 and low-expression loci 30 may also increase risks. Typing across all classical HLA loci has occurred for new NMDP donor recruits only since 2020, thus the proportion of registry donors typed at the DQA1 and DPA1 loci remains very low (1.5%).…”

“…We implemented the HLA-DP T-cell epitope permissibility paradigm (8-of-8 DP-TCE+ and 7-of-8 DP-TCE+) using methods described in detail in the Supplement. Current US clinical matching guidance does not call for HLA matching at the DQB1 locus 25 , however we also modeled 10-of-10 DP-TCE+ and 9-of-10 DP-TCE+ model for centers that prefer to match at this additional locus based on more recent evidence 26 . The models used NMDP registry sizes for actively listed US adult donors and cord blood units at the end of 2020.…”