Characterisation of the novel HLA-DPB1*04:02:24 allele by next-generation sequencing.

HLA-DPB1*04:02:24 differs from HLA-DPB1*04:02:01:01 by a single synonmous nucleotide substitution at position 639 G>A.

Identification of the novel HLA-DPB1*21:01:02 allele using nanopore sequencing technology.

HLA-DPB1*21:01:02 differs from HLA-DPB1*21:01:01 by one single nucleotide substitution at position 435 C>T in exon 3.

[Sequence analysis and identification of a novel HLA-DPB1*02:01:69 allele by third-generation sequencing].

OBJETIVE: To analyze the sequence of a novel HLA-DPB1 allele in an individual. METHODS: A individual identified from the database of blood donors for matched platelet transfusion at the Blood Center of Zhejiang Province in May 2022 was selected as the study subject. HLA genotype of the individual was determined by next-generation sequencing (NGS) on an Ion Torrent S5 platform. The sequence of the HLA-DPB1 locus was also determined by NGS on an Illumina Miseq platform and third-generation sequencing using Oxford Nanopore MinION. This study was approved by the Blood Center of Zhejiang Province (Ethics No. 2021-001). RESULTS: A novel HLA-DPB1*02 allele was identified in the specimen, for which the closest genotype was HLA-DPB1*02:new,17:01:01G, with the variant located in exon 3. Meanwhile, the NGS also revealed a novel HLA-DPB1*17 allele, with the closest genotype being HLA-DPB1*02:01,17:new. Both the HLA-DPB1*17:01:01:01 and HLA-DPB1*02 alleles were identified by third-generation sequencing. Compared with the HLA-DPB1*02:01:02:01 allele, the novel allele had a G>A variation at position 369 in the exon 3, which however did not result in amino acid change. CONCLUSION: A novel HLA-DPB1 allele has been identified and validated by both NGS and TGS, which has been named as HLA-DPB1*02:01:69 by the World Health Organization Committee on Nomenclature of Factors of the HLA System.

Characterisation of the novel HLA-DPB1*1618:01 allele by sequencing-based typing.

HLA-DPB1*1618:01 differs from HLA-DPB1*18:01:01:01 by one nucleotide substitution in codon 215 in exon 4.

The novel HLA-DPB1*04:02:01:44 allele identified in a volunteer bone marrow donor.

Nucleotide substitution in the intron 2 of HLA-DPB1*04:02:01:01 results in a novel allele, HLA-DPB1*04:02:01:44.

Identification of the novel HLA-DPB1*14:01:15 allele in a Brazilian bone marrow donor.

The novel HLA-DPB1*14:01:15 allele differs from DPB1*14:01:01:01 by change of C > T in exon 3.

Association between specific human leukocyte antigen alleles and development of thyroid immune-related adverse event.

Aim: Inherent variations in human leukocyte antigen (HLA) alleles have been revealed epidemiologically to influence the development of autoimmune diseases. HLA alleles may thus also be associated with the development of immune-related adverse events (irAEs), such as thyroid irAE.Materials & methods: In this case-control study, 71 cancer patients who received immune checkpoint inhibitors were enrolled and HLA-genotyped and the frequency of HLA alleles was compared.Results: A*26:01, DPA1*01:03 and DPB1*02:01 were significantly more frequent in patients with thyroid irAE than in patients without any irAEs (35.0 vs 3.2% [p = 0.004], 80.0 vs 45.2% [p = 0.020] and 55.0 vs 25.8% [p = 0.044], respectively).Conclusion: A*26:01, DPA1*01:03 and DPB1*02:01 appear to be associated with thyroid irAE.

Everyone has a unique combination of human leukocyte antigens (HLAs) in their body that help the immune system identify threats. HLAs were named from the fact that they were first identified on the surface of human leukocytes. Afterward, HLAs were also found on all human cells. HLAs present antigens to immune cells. These HLAs also influence how the immune system attacks cancer cells. Immune checkpoint inhibitors are drugs that can help the immune system fight cancer, but they sometimes cause severe adverse events. In this study, we investigated whether specific HLA genes are related to the development of an adverse event that affects the thyroid in cancer patients treated with immune checkpoint inhibitors. We found an association between three HLA genes (A*26:01, DPA1*01:03 and DPB1*02:01) and the development of the thyroid adverse event. However, larger studies are needed to confirm and generalize these initial exploratory findings.

HLA-DPB1 and DPA1 ~ DPB1 linkage mismatch affects the survival of recipients receiving HLA-14/14 matched unrelated donor HSCT.

To analyse the effect of HLA-DPA1 and HLA-DPB1 allelic mismatches on the outcomes of unrelated donor haematopoietic stem cell transplantation (URD-HSCT), we collected 258 recipients with haematological disease who underwent HLA-10/10 matched URD-HSCT. HLA-A, -B, -C, -DRB1, -DQB1, -DRB3/4/5, -DQA1, -DPA1 and -DPB1 typing was performed for the donors and recipients using next-generation sequencing (NGS) technology. After excluding 8 cases with DQA1 or DRB3/4/5 mismatches, we included 250 cases with HLA-14/14 matching for further analysis. Our results showed that the proportion of matched DPA1 and DPB1 alleles was only 10.4% (26/250). The remaining 89.6% of donors and recipients demonstrated DPA1 or DPB1 mismatch. In the DPA1 matched and DPB1 mismatched group, accounting for 18.8% (47/250) of the cohort, DPB1*02:01/DPB1*03:01 allelic mismatches were associated with decreased 2-year OS and increased NRM. DPB1*02:02/DPB1*05:01 and DPB1*02:01/DPB1*05:01 mismatches showed no impact on outcomes. Moreover, the specific allelic mismatches observed were consistent with the DPB1 T-cell epitope (TCE) classification as permissive and non-permissive. We innovatively established an analysis method for DPA1 ~ DPB1 linkage mismatch for cases with both DPA1 and DPB1 mismatched, accounting for 70% (175/250) of the total. DPA1*02:02 ~ DPB1*05:01/DPA1*02:01 ~ DPB1*17:01 linkage mismatches were associated with lower 2-year OS, especially among AML/MDS recipients. DPA1*02:02 ~ DPB1*05:01/DPA1*01:03 ~ DPB1*02:01 linkage mismatches showed no impact on outcomes. In conclusion, applying the DPA1 ~ DPB1 linkage mismatch analysis approach can identify different types of mismatches affecting transplant outcomes and provide valuable insight for selecting optimal donors for AML/MDS and ALL recipients.

The novel HLA-DPB1*1553:01 allele, identified by Sanger dideoxy nucleotide sequencing in a Chinese individual.

HLA-DPB1*1553:01 differs from HLA-DPB1*09:01:01:01 by one nucleotide in exon 3.

The novel HLA-DPB1*05:01:20 allele, identified by Sanger dideoxy nucleotide sequencing in a Chinese individual.

HLA-DPB1*05:01:20 differs from HLA-DPB1*05:01:01:01 by one nucleotide in exon 3.

Identification of the novel HLA-DPB1*1591:01 allele by next-generation sequencing.

The novel HLA-DPB1*1591:01 allele was detected during the HLA typing for kidney transplantation.

Characterisation of the novel HLA-DPB1*1437:01 and HLA-DPB1*1438:01 alleles by next-generation sequencing.

The novel HLA-DPB1*1437:01 and HLA-DPB1*1438:01 alleles first identified in the Chinese individuals.

The novel HLA-DPB1*1467:01 allele characterised by two different sequencing-based typing techniques.

The novel allele HLA-DPB1*1467:01 differs from HLA-DPB1*09:01:01:01 by one non-synonymous nucleotide substitution in exon 2.

Revised HLA-DP TCE-Core Permissiveness Model Better Defines Relapse Risk and Survival following Haploidentical Transplant.

The presence of an HLA-DPB1 nonpermissive mismatch (NPMM) by the TCE-3 model has been associated with improved survival following haploidentical donor transplantation (HIDT) using post-transplantation cyclophosphamide (PTCy). With the development of a revised model (TCE-Core) that further separates TCE-3 "group 3" alleles into "core" (C) and "noncore" (NC) alleles, a formerly permissive mismatch (PMM) resulting from group 3 alleles in both donor and recipient is now considered a C-NPMM if 1 or more of those alleles is NC. We aimed to study the additional effect of HLA-DPB1 C-NPMM according to the TCE-Core algorithm, as well as the directional vector of the mismatch, on outcomes following HIDT. To this end, we analyzed 242 consecutive HIDT recipients with acute leukemia or myelodysplastic syndrome who underwent transplantation between 2005 and 2021 (median age, 51 years; range, 19 to 80 years). The median follow-up was 62 months (range, 23 to 199 months). Of the 136 HIDTs classified as PMM by TCE-3, 73 were reclassified as a C-NPMM by the TCE-Core algorithm, of which 36 were in the graft-versus host (GVH) vector (37 were host-versus-graft [HVG] only). Given comparable survival between conventional NPMM and C-NPMM, GVH/bidirectional were analyzed together (nonpermissive). HVG-only C-NPMM were combined with HLA-DPB1-matched and PMM (permissive) because of similar outcomes. The presence of a TCE-Core-defined nonpermissive HLA-DP mismatch resulted in superior 5-year overall survival (OS) (66% versus 47%) and disease-free survival (DFS) (60% versus 43%). Compared to the conventional TCE-3 algorithm, TCE-Core identified a higher percentage of nonpermissive transplants (38% versus 23%) and better discriminated outcomes between nonpermissive and permissive status, with a larger difference in survival outcomes using TCE-Core compared to TCE-3 (OS Δ, 18.3% versus 12.7%; DFS Δ, 16.5% versus 8.5%). In multivariable analysis (MVA), a nonpermissive TCE-Core mismatch led to improved OS (hazard ratio [HR], .54; P = .003) and DFS (HR, .62; P = .013), largely due to decreased relapse risk (HR, .63; P = .049). In contrast, nonrelapse mortality (NRM) and graft-versus-host disease (GVHD) outcomes were not significantly impacted. In summary, the presence of nonpermissive TCE-Core HLA-DP mismatch strongly predicts survival following PTCy-based HIDT, owing to a reduction in relapse risk without a corresponding increase in GVHD or NRM. As a donor selection tool, TCE-Core appears to better discriminate HIDT outcomes while at the same time identifying a larger percentage of the potential donor pool.

Identification of four novel HLA alleles: HLA-B*27:265, -B*35:569, -DRB1*08:117 and -DPB1*1435:01.

Identification of four new HLA alleles (B*27:265, B*35:569, DRB1*08:117, and DPB1*1435:01) in Brazilian bone marrow donors.

Identification of the novel null allele HLA-DPB1*1449:01N by next-generation sequencing.

The novel HLA-DPB1*1449:01N allele differs from HLA-DPB1*16:01:01:01 by a nucleotide at codon 92 in exon 2.

[Risk prediction and function evaluation by T-cell epitope model and expression model of HLA-DPB1 mismatching in unrelated-donor hematopoietic stem cell transplantations].

Objective: To evaluate the risk prediction and assessment function of HLA-DPB1 T-cell epitope (TCE) model and expression model in human leukocyte antigen (HLA)-matched unrelated hematopoietic stem cell transplantation (MUD-HSCT) with HLA-DPB1 mismatching. Methods: A total of 364 (182 pairs) potential MUD-HSCT donors and recipients confirmed by HLA high-resolution typing in Shaanxi Blood Center from 2016 to 2019 were analyzed retrospectively. Of the 182 recipients, there were 121 males and 61 females with an average age of (26.3±14.2) years. Of the 182 donors, there were 148 males and 34 females with an average age of (33.7±7.5) years. Polymerase chain reaction-sequence-based typing (PCR-SBT), next-generation sequencing (NGS) and polymerase chain reaction-sequence specific oligonucleotide probe (PCR-SSO) based on LABScan®3D platform were used for high-resolution typing of HLA-A, B, C, DRB1, DQB1, DPB1 gene, and PCR-SBT was used for single nucleotide polymorphism (SNP) typing. TCE model and expression model were used to predict and evaluate the HLA-DPB1 mismatch pattern and acute graft-versus-host-disease (aGVHD) risk. Results: A total of 26 HLA-DPB1 alleles and their 3'-UTR rs9277534 SNP genotypes were detected in this study population, and two new alleles HLA-DPB1*1052∶01 and HLA-DPB1*1119∶01 were found and officially named. The overall mismatch rate of HLA-DPB1 in MUD-HSCT donors and recipients was 90.66% (165/182). In TCE model, the HLA-DPB1 mismatch rates of permissible mismatch (PM) and non-permissible mismatch (non-PM) were 47.80% (87/182) and 42.86% (78/182), respectively. The non-PM in GvH direction was 13.73% (25/182), and which in HvG direction was 29.12% (53/182). A total of 73 pairs of donors and recipients in TCE model met the evaluation criteria of expression model. Among of TCE PM group, recipient DP5 mismatches accounted for 34.25% (25/73) were predicted as aGVHD high risk according to expression model. For the TCE non-PM group, both the recipient DP2 mismatches of 6.85% (5/73) and recipient DP5 mismatches of 10.86% (8/73) were predicted to be at high risk for aGVHD. Risk prediction by TCE model and expression model was 27.27% concordant and 16.97% unconcordant. Conclusions: TCE model and expression model are effective tools to predict aGVHD risk of MUD-HSCT. Comprehensive application of the two models is helpful to the hierarchical assessment of HSCT risk.

Characterization of the novel HLA-DPB1*1584:01 allele, identified by next-generation sequencing.

HLA-DPB1*1584:01 differs from HLA-DPB1*104:01:01:03 by one nucleotide substitution in exon 2.

Characterization of the novel HLA-DPB1*1516:01 allele by sequencing-based typing.

HLA-DPB1*1516:01 differs from HLA-DPB1*1229:01 by seven nucleotide substitutions in exon 3.

Next-generation sequencing identifies two novel HLA-DPB1 alleles: HLA-DPB1*1069:01 and DPB1*1072:01.

Characterization of two novel HLA-DPB1 alleles: HLA-DPB1*1069:01, and DPB1*1072:01 containing non-synonymous nucleotide substitutions.