Allogeneic hematopoietic cell transplantation (HCT) is used to treat many blood-based disorders and malignancies, however it can also result in serious adverse events, such as the development of acute graft-versus-host disease (aGVHD). This study aimed to develop a donor-specific epigenetic classifier to reduce incidence of aGVHD by improving donor selection. Genome-wide DNA methylation was assessed in a discovery cohort of 288 HCT donors selected based on recipient aGVHD outcome; this cohort consisted of 144 cases with aGVHD grades III-IV and 144 controls with no aGVHD. We applied a machine learning algorithm to identify CpG sites predictive of aGVHD. Receiver operating characteristic (ROC) curve analysis of these sites resulted in a classifier with an encouraging area under the ROC curve (AUC) of 0.91. To test this classifier, we used an independent validation cohort (n = 288) selected using the same criteria as the discovery cohort. Attempts to validate the classifier failed with the AUC falling to 0.51. These results indicate that donor DNA methylation may not be a suitable predictor of aGVHD in an HCT setting involving unrelated donors, despite the initial promising results in the discovery cohort. Our work highlights the importance of independent validation of machine learning classifiers, particularly when developing classifiers intended for clinical use.

中文翻译：

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供体全血 DNA 甲基化并不是无关供体同种异体造血细胞移植中急性移植物抗宿主病的强有力预测因子

同种异体造血细胞移植（HCT）用于治疗许多血液疾病和恶性肿瘤，但它也可能导致严重的不良事件，例如急性移植物抗宿主病（aGVHD）的发生。本研究旨在开发一种供体特异性表观遗传分类器，通过改善供体选择来降低 aGVHD 的发生率。根据受者 aGVHD 结果选择的 288 名 HCT 捐献者的发现队列中评估了全基因组 DNA 甲基化；该队列由 144 例具有 III-IV 级 aGVHD 的病例和 144 例无 aGVHD 的对照组成。我们应用机器学习算法来识别可预测 aGVHD 的 CpG 位点。对这些位点的受试者工作特征 (ROC) 曲线分析得出的分类器的 ROC 曲线下面积 (AUC) 为 0.91，令人鼓舞。为了测试这个分类器，我们使用了一个独立的验证队列（n = 288），该验证队列是使用与发现队列相同的标准选择的。尝试验证分类器失败，AUC 降至 0.51。这些结果表明，尽管发现队列中的初步结果很有希望，但在涉及无关供体的 HCT 环境中，供体 DNA 甲基化可能不是 aGVHD 的合适预测因子。我们的工作强调了机器学习分类器独立验证的重要性，特别是在开发用于临床使用的分类器时。