51. A novel predictive approach for GVHD after allogeneic SCT based on clinical variables and cytokine gene polymorphisms
- Author
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Martínez-Laperche C, Buces E, Aguilera-Morillo MC, Picornell A, González-Rivera M, Lillo R, Santos N, Martín-Antonio B, Guillem V, Nieto JB, González M, de la Cámara R, Brunet S, Jiménez-Velasco A, Espigado I, Vallejo C, Sampol A, Bellón JM, Serrano D, Kwon M, Gayoso J, Balsalobre P, Urbano-Izpizua Á, Solano C, Gallardo D, Díez-Martín JL, Romo J, Buño I, and GVHD/Immunotherapy Committee of the Spanish Group for Hematopoietic Transplantat
- Subjects
Adult ,Male ,Oncology ,medicine.medical_specialty ,Multivariate analysis ,Adolescent ,Graft vs Host Disease ,Single-nucleotide polymorphism ,Disease ,03 medical and health sciences ,0302 clinical medicine ,Lasso (statistics) ,immune system diseases ,hemic and lymphatic diseases ,Internal medicine ,Linear regression ,medicine ,Humans ,Child ,Bone Marrow Transplantation ,Aged ,Retrospective Studies ,Transplantation ,Polymorphism, Genetic ,Framingham Risk Score ,Models, Genetic ,business.industry ,Hematopoietic Stem Cell Transplantation ,Infant, Newborn ,Infant ,Retrospective cohort study ,Hematology ,Middle Aged ,Allografts ,Clinical trial ,surgical procedures, operative ,Child, Preschool ,Hematologic Neoplasms ,030220 oncology & carcinogenesis ,Commentary ,Cytokines ,Female ,business ,Follow-Up Studies ,Stem Cell Transplantation ,030215 immunology - 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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