Donor whole blood DNA methylation is not a strong predictor of acute graft versus host disease in unrelated donor allogeneic haematopoietic cell transplantation

• Published in: Frontiers in genetics Vol. 15; p. 1242636
• Main Authors: Webster, Amy P, Ecker, Simone, Moghul, Ismail, Liu, Xiaohong, Dhami, Pawan, Marzi, Sarah, Paul, Dirk S, Kuxhausen, Michelle, Lee, Stephanie J, Spellman, Stephen R, Wang, Tao, Feber, Andrew, Rakyan, Vardhman, Peggs, Karl S, Beck, Stephan
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 03-04-2024
• Subjects: biomarker identification and validation; DNA methylation; epigenetics; Genetics; haematopoietic stem cell transplant; HCT (hematopoietic cell transplant); machine learning; DNA methylation; HCT (hematopoietic cell transplant); haematopoietic stem cell transplant; machine learning; biomarker identification and validation; epigenetics
• ISSN: 1664-8021; 1664-8021
• DOI: 10.3389/fgene.2024.1242636

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.