1. A novel predictive approach for GVHD after allogeneic SCT based on clinical variables and cytokine gene polymorphisms
- Author
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Carolina Martínez-Laperche, Elena Buces, M. Carmen Aguilera-Morillo, Antoni Picornell, Milagros González-Rivera, Rosa Lillo, Nazly Santos, Beatriz Martín-Antonio, Vicent Guillem, José B. Nieto, Marcos González, Rafael de la Cámara, Salut Brunet, Antonio Jiménez-Velasco, Ildefonso Espigado, Carlos Vallejo, Antonia Sampol, José María Bellón, David Serrano, Mi Kwon, Jorge Gayoso, Pascual Balsalobre, Álvaro Urbano-Izpizua, Carlos Solano, David Gallardo, José Luis Díez-Martín, Juan Romo, and Ismael Buño
- Subjects
Specialties of internal medicine ,RC581-951 - Abstract
Abstract: Despite considerable advances in our understanding of the pathophysiology of graft-versus-host disease (GVHD), its prediction remains unresolved and depends mainly on clinical data. The aim of this study is to build a predictive model based on clinical variables and cytokine gene polymorphism for predicting acute GVHD (aGVHD) and chronic GVHD (cGVHD) from the analysis of a large cohort of HLA-identical sibling donor allogeneic stem cell transplant (allo-SCT) patients. A total of 25 SNPs in 12 cytokine genes were evaluated in 509 patients. Data were analyzed using a linear regression model and the least absolute shrinkage and selection operator (LASSO). The statistical model was constructed by randomly selecting 85% of cases (training set), and the predictive ability was confirmed based on the remaining 15% of cases (test set). Models including clinical and genetic variables (CG-M) predicted severe aGVHD significantly better than models including only clinical variables (C-M) or only genetic variables (G-M). For grades 3-4 aGVHD, the correct classification rates (CCR1) were: 100% for CG-M, 88% for G-M, and 50% for C-M. On the other hand, CG-M and G-M predicted extensive cGVHD better than C-M (CCR1: 80% vs. 66.7%, respectively). A risk score was calculated based on LASSO multivariate analyses. It was able to correctly stratify patients who developed grades 3-4 aGVHD (P < .001) and extensive cGVHD (P < .001). The novel predictive models proposed here improve the prediction of severe GVHD after allo-SCT. This approach could facilitate personalized risk-adapted clinical management of patients undergoing allo-SCT.
- Published
- 2018
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