Establishment of an HLA-DPA1 and DPB1 linkage prediction model based on NGS technology and validation of its clinical application value
10.3760/cma.j.cn114452-20240702-00349
- VernacularTitle:基于NGS技术建立HLA-DPA1和DPB1连锁预测模型及验证其临床应用价值
- Author:
Tengteng ZHANG
1
;
Shuang LIU
;
Xiaoni YUAN
;
Yang LI
;
Xue JIANG
;
Tianjie YANG
;
Xiaojing BAO
;
Jun HE
Author Information
1. 苏州大学附属第一医院，江苏省血液研究所HLA配型实验室，苏州 215031
- Keywords:
Next-generation sequencing;
Human leukocyte antigen;
DPA1;
DPB1;
linkage;
Hematopoietic stem cell transplantation
- From:
Chinese Journal of Laboratory Medicine
2024;47(11):1292-1298
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To establish a linkage prediction model for human leukocyte antigen (HLA) DPA1-DPB1 and validate it by using clinical data and follow-up data from unrelated allogeneic hematopoietic stem cell transplantation donors and recipients, and to explore the clinical application value of the prediction model in transplantation prognosis.Methods:This is a retrospective study. Leveraging the artificial neural network algorithm of NetMHCⅡpan and the DPA1-DPB1 haplotype linkage database of the Chinese population established in our previous research, and incorporating the amino acid FASTA data of DPA1-DPB1 of all known sequences newly published by the Latest International Immunogenetics/Human Leukocyte Antigens, 47 DPA1-DPB1 linkage models were established. Employing next-generation sequencing technology based on the hybridization capture library construction method, HLA genotyping tests for HLA-A, -B, -C, DRB1, DQB1, DQA1, DRB3/4/5, DPB1, and DPA1 (9 loci) were performed on 250 donor-recipients pairs who underwent unrelated-donor hematopoietic stem cell transplantation in the Department of Hematology of the First Affiliated Hospital of Soochow University between January 2016 and September 2021. HLA typing data and clinical information of transplant donors and recipients were retrospectively analyzed to assess and predict the impact of permissive and non-permissive linkage mismatches of DPA1-DPB1 on transplantation prognosis. The Kaplan-Meier method with the log-rank test was applied to compare the survival curves of overall survival (OS) rates between different groups. Additionally, a competing risks model was utilized to compare the cumulative incidence of grade Ⅱ-Ⅳ acute graft-versus-host disease and non-relapse mortality (NRM) across groups. The area under the receiver operating characteristic curve was employed to compare the predictive performance of the established prediction model with that of the T-cell epitope (TCE) model.Results:According to the different hydrophilic and hydrophobic properties of amino acids, the DPA1-DPB1 linkage model is categorized into types Ⅰ-Ⅳ: type I consists of 6 hydrophobic types at P1-P8 plus hydrophilic type at P9; type Ⅱ includes 17 hydrophobic types; type Ⅲ comprises 9 amphiphilic types; and type Ⅳ consists of 15 hydrophilic types. According to the prediction model, DPA1-matched and DPB1-mismatched donor-recipient cases were classed into P1-matched or P1-mismatched groups. Compared with fully matched DPA1 and DPB1 cases, P1-mismatched patients had a 2-year OS rate of 75% (12/16) versus 96.2%(25/26) (χ2=4.13, P=0.04), and a NRM rate of 4/16 versus 0 (χ2=7.05, P<0.01). However, there was no statistically significant difference in the 2-year OS and NRM rates compared to DPA1 and DPB1 cases ( P>0.05). The prediction model established in this study demonstrated a larger area under the receiver operating characteristic curve for predicting the 2-year OS rate compared with the DPB1 TCE model ( Z=0.71, P=0.48). In donor-recipient cases where both DPA1 and DPB1 were mismatched, the 2-year OS rates decreased and the NRM increased in both P1-matched and P1-mismatched cases compared with fully matched DPA1 and DPB1. Moreover, P1-mismatched patients had a worse prognosis compared to P1-matched patients. Conclusion:The DPA1-DPB1 linkage prediction model established based on high-throughput next-generation sequencing technology can be used to predict the impact of HLA-DP mismatches on OS and NRM in transplantation, and the prediction performance is superior to the TCE model.
