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IntroductionThe association of polymorphisms in molecules involved in the immune response (checkpoint inhibitors) with the clinical outcome after allogeneic transplantation (alloHSCT) has been described. Lymphocyte Activation 3 (LAG3) is a surface protein that plays a regulatory role in immunity as an inhibitory immune checkpoint molecule.MethodsTo determine its role in the alloHSCT setting, we analyzed 797 patients transplanted from HLA-identical sibling donors. The LAG3 rs870849 C>T polymorphism was genotyped in donors.ResultsWe detected a higher incidence of severe acute GVHD in patients transplanted from donors with TT genotype (p: 0.047, HR 1.64; 95% CI 1.01 – 2.67). Overall survival (OS) was worse for patients transplanted from donors with the rs870849 CT/TT genotype (0.020; HR, 1.44; 95% CI 1.06 – 1.96), as well as disease-free survival (DFS) (p: 0.002; HR 1.58, 95%CI: 1.18 – 2.14) and transplant-related mortality (TRM) (p< 0.001; HR: 1.88, 95% CI 1.29 – 2.74). When combining the LAG3 rs870849 and the PDCD1 rs36084323 genotypes of the donor, three genetic groups were well defined, allowing a good stratification of the risk of acute GVHD, TRM, OS and DFS.DiscussionWe conclude that the LAG3 genotype of the donor may be considered in donors’ selection. As this selection may be limited in the HLA-identical sibling donor scenario, further studies exploring the impact of LAG3 genotype of the donor in unrelated transplantation are warranted.

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.