Machine Learning Insights into HLA Noncoding Sequence Mismatches and Their Impact on DPB1 Matching in Hematopoietic Cell Transplantation

Medhat Askar, Timothy L. Mosbruger, Grace Tzun-Wen Shaw, Haedong Kim, Yuncheng Duan, Andrew S. Allen, Jamie L. Duke, Timothy S. Olson, Dimitri S. Monos, Tristan J. Hayeck
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Abstract

Purpose: HCT is vital for treating hematological malignancies, relying on HLA matching between unrelated patient-donor pairs to significantly reduce adverse outcomes. Recent studies recognize the potential impact of HLA-DPB1 mismatches on HCT outcomes. Multiple approaches focus on finding better-tolerated HLA-DPB1 mismatches. Additionally, recent studies suggest matching at noncoding HLA sequence may improve HCT outcomes. This study aims to evaluate different approaches for categorizing DPB1 mismatches in patient-donor pairs and explore the potential impact of noncoding mismatches (available in class I HLA genes) on clinical outcomes. Methods: A retrospective study of 5,106 HCT pairs using Cox proportional hazards models, weighted by a machine learning algorithm, evaluates the impact of particular combinations of HLA-DPB1 mismatches in the context of noncoding HLA class I mismatches on outcomes of HCT. HLA-DPB1 mismatch criteria included T-cell epitope permissive/non-permissive mismatches, expression markers, and evolutionary clade mismatches. Results: Two HLA-DPB1 mismatches, using multiple criteria, lead to significant hazards of acute graft versus host disease grades 2-4, in the T cell replete group. When HLA-DPB1 mismatches occurred across evolutionary clades (DP2 allele/low-expression patient vs DP5 allele/high-expression in the donor), the deplete group showed significant hazards for treatment-related mortality (TRM) (HR=1.94, p-value=8.9x10-7) and overall survival (OS) (HR=1.67, p-value=1.3 x10-5) for additive effects of noncoding mismatches with two HLA-DPB1 mismatches. Conclusion: Two HLA-DPB1 mismatches remain to predict worse outcomes. However, noncoding mismatches in HLA class I genes confer elevated hazards of TRM and OS in conjunction with mismatches across evolutionary branches of HLA-DPB1. Therefore, noncoding mismatches may inform donor selection in the presence of HLA-DPB1 mismatches and improve HCT outcomes, emphasizing the utility of comprehensive sequencing of HLA alleles in HCT settings.
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关于 HLA 非编码序列错配及其对造血细胞移植中 DPB1 配型影响的机器学习见解
目的:造血干细胞移植对治疗血液恶性肿瘤至关重要,它依赖于非亲缘患者-供体配对之间的 HLA 匹配,以显著减少不良后果。最近的研究认识到 HLA-DPB1 不匹配对 HCT 结果的潜在影响。多种方法都侧重于寻找耐受性更好的 HLA-DPB1 错配。此外,最近的研究表明,非编码 HLA 序列匹配可改善 HCT 结果。本研究旨在评估对患者-供体配对中的 DPB1 错配进行分类的不同方法,并探讨非编码错配(可在 I 类 HLA 基因中找到)对临床结果的潜在影响。研究方法对 5106 对 HCT 进行回顾性研究,采用机器学习算法加权的 Cox 比例危险模型,评估在非编码 HLA I 类错配的情况下,HLA-DPB1 错配的特定组合对 HCT 结局的影响。HLA-DPB1错配标准包括T细胞表位允许/非允许错配、表达标记和进化支系错配。结果:在T细胞完全组中,采用多种标准的两个HLA-DPB1错配会导致2-4级急性移植物抗宿主疾病的显著危害。当HLA-DPB1错配发生在不同进化支系(DP2等位基因/低表达的患者与DP5等位基因/高表达的供体)时,贫乏组的治疗相关死亡率(TRM)(HR=1.94,p-value=8.9x10-7)和总生存率(OS)(HR=1.67,p-value=1.3x10-5)在两个HLA-DPB1错配的非编码错配的叠加效应下显示出显著的危险性。结论两个 HLA-DPB1 基因错配仍可预测较差的预后。然而,HLA I类基因中的非编码错配与HLA-DPB1进化分支中的错配一起会增加TRM和OS的危险性。因此,非编码错配可为存在 HLA-DPB1 错配的供体选择提供参考,并改善 HCT 的预后,这强调了在 HCT 环境中对 HLA 等位基因进行全面测序的效用。
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