A novel predictive approach for GVHD after allogeneic SCT based on clinical variables and cytokine gene polymorphisms Articles uri icon

authors

  • Martinez Laperche, Carolina
  • Buces, Elena
  • AGUILERA MORILLO, MARIA DEL CARMEN
  • Picornell, Antoni
  • Gonzalez Rivera, Milagros
  • LILLO RODRIGUEZ, ROSA ELVIRA
  • Santos, Nazly
  • Martín Antonio, Beatriz
  • Guillem, Vicent
  • Benito, Jose B.
  • González, Marcos
  • de la Cámara, Rafael
  • Diez Martin, Luis
  • Serrano, David
  • Kwon, Mi
  • Gayoso, Jorge
  • Balsalobre, Pascual
  • Buno, Ismael
  • ROMO URROZ, JUAN

publication date

  • July 2018

start page

  • 1719

end page

  • 1737

volume

  • 2

International Standard Serial Number (ISSN)

  • 2473-9529

Electronic International Standard Serial Number (EISSN)

  • 2473-9537

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.