McDaniel Mims Brianyell, Furr Kathryn L, Enriquez Josue, Grisham Matthew B
Department of Oral Health Sciences, Medical University of South Carolina, Charleston, SC 29425, USA.
Department of Immunology and Molecular Microbiology, Texas Tech University Health Sciences Center, Lubbock, TX 79423, USA.
Dis Model Mech. 2025 Feb 1;18(2). doi: 10.1242/dmm.052084. Epub 2025 Feb 28.
The transplantation of allogeneic hematopoietic stem cells is a potentially curative treatment for hematological malignancies, inherited blood disorders and immune deficiencies. Unfortunately, 30-50% of patients receiving allogeneic hematopoietic stem cells will develop a potentially life-threatening inflammatory disease called acute graft-versus-host disease (aGVHD). In patients with aGVHD, graft-associated T cells, which typically target the skin, intestinal tract and liver, can also damage the lungs and lymphoid tissue. Damage to lymphoid tissue creates prolonged immunodeficiency that markedly increases the risk of infections and bleeding, resulting in considerable morbidity and mortality. Although mouse models of aGVHD have been instrumental to our understanding of this condition's pathogenesis, translation of preclinical data into new and more effective treatments for human disease has been limited for reasons that remain to be fully understood. However, evidence suggests that factors associated with mouse models of aGVHD likely contribute to these unsatisfactory results. In this Review, we identify and discuss the specific factors inherent to mouse models of aGVHD that may limit the translation of preclinical data to patient treatment, and suggest how to improve the translatability of these models.
异基因造血干细胞移植是治疗血液系统恶性肿瘤、遗传性血液疾病和免疫缺陷的一种潜在的治愈性疗法。不幸的是，30%至50%接受异基因造血干细胞移植的患者会发生一种可能危及生命的炎症性疾病，称为急性移植物抗宿主病（aGVHD）。在aGVHD患者中，通常靶向皮肤、肠道和肝脏的移植物相关T细胞也会损害肺部和淋巴组织。淋巴组织受损会导致长期免疫缺陷，显著增加感染和出血风险，从而导致相当高的发病率和死亡率。尽管aGVHD的小鼠模型对我们理解这种疾病的发病机制起到了重要作用，但由于一些尚未完全明确的原因，将临床前数据转化为针对人类疾病的新的、更有效的治疗方法仍然有限。然而，有证据表明，与aGVHD小鼠模型相关的因素可能导致了这些不尽人意的结果。在本综述中，我们识别并讨论了aGVHD小鼠模型中固有的可能限制临床前数据转化为患者治疗的具体因素，并提出了如何提高这些模型的可转化性。
