RT Journal Article
T1 A novel predictive approach for GVHD after allogeneic SCT based on clinical variables and cytokine gene polymorphisms.
A1 Martinez-Laperche, Carolina
A1 Buces, Elena
A1 Aguilera-Morillo, M Carmen
A1 Picornell, Antoni
A1 Gonzalez-Rivera, Milagros
A1 Lillo, Rosa
A1 Santos, Nazly
A1 Martin-Antonio, Beatriz
A1 Guillem, Vicent
A1 Nieto, Jose B
A1 Gonzalez, Marcos
A1 de-la-Camara, Rafael
A1 Brunet, Salut
A1 Jimenez-Velasco, Antonio
A1 Espigado, Ildefonso
A1 Vallejo, Carlos
A1 Sampol, Antonia
A1 Bellon, Jose Maria
A1 Serrano, David
A1 Kwon, Mi
A1 Gayoso, Jorge
A1 Balsalobre, Pascual
A1 Urbano-Izpizua, Alvaro
A1 Solano, Carlos
A1 Gallardo, David
A1 Diez-Martin, Jose Luis
A1 Romo, Juan
A1 Buño, Ismael
K1 Graft-versus-host disease
K1 Acute graft-versus-host disease
K1 Allogeneic stem cell transplantation
K1 Predictive model
K1 Cytokine gene polymorphism
K1 Single nucleotide polymorphisms
K1 Genetic risk factors
K1 Clinical variables
K1 Risk stratification
AB 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.
PB American Society of Hematology
YR 2018
FD 2018-07-24
LK http://hdl.handle.net/10668/12734
UL http://hdl.handle.net/10668/12734
LA en
NO Martínez-Laperche C, Buces E, Aguilera-Morillo MC, Picornell A, González-Rivera M, Lillo R, et al. A novel predictive approach for GVHD after allogeneic SCT based on clinical variables and cytokine gene polymorphisms. Blood Adv. 2018 Jul 24;2(14):1719-1737.
NO The authors would like to thank the Centro Nacional de Genotipado for help with genotyping. They would also like to acknowledge the patients who participated in this study, as well as the staff of the Hematology Department, Hospital General Universitario Gregorio Marañón (Madrid, Spain), who made the study possible. This study was partially supported by Ministry of Economy and Competitiveness ISCIII-FIS Grants PI08/1463, PI11/00708, PI14/01731, PI17/01880, and RD12/0036/0061 and cofinanced by the European Regional Development Fund from the European Commission, the “A way of making Europe” initiative, and grants from Fundación LAIR and Asociación Madrileña de Hematología y Hemoterapia
DS RISalud
RD Sep 2, 2025