Multivariate Analysis of Immune Reconstitution and Relapse Risk Scoring in Children Receiving Allogeneic Stem Cell Transplantation for Acute Leukemias. Issue 11 (November 2021) - Spadea, Manuela; Saglio, Francesco; Tripodi, Serena I.; Menconi, Mariacristina; Zecca, Marco; Fagioli, Franca
Abstract : Background: A timely and effective immune reconstitution after hematopoietic stem cell transplantation (HSCT) is of crucial importance to enhance graft-versus-leukemia reaction in hematological malignancies. Several factors can influence the yield of this process, and new mathematical models are needed to describe this complex phenomenon. Methods: We retrospectively analyzed immune reconstitution in the early post-HSCT period in a multicenter cohort of 206 pediatric patients affected by acute lymphoblastic leukemia, acute myeloblastic leukemia, and myelodysplastic syndrome who received their first allo-HSCT. All patients were in complete morphological remission at transplantation and were followed-up at least 26 mo post-HSCT. Blood samples for analysis of lymphocyte subset numbers were collected at day 100 (±20 d). Results: The 2-y cumulative incidence of relapse was 22.2% (95% confidence interval [CI], 17.3-27). Using principal component analysis, we identified based on 16 input variables a new multivariate model that enables patients' description in a low-dimensional model, consisting of the first 2 principal components. We found that the numbers of CD3 + /CD4 + /CD8 + lymphocyte subsets at day 100 post-HSCT and acute graft-versus-host disease had the greatest impact in preventing relapse. We ultimately derived a risk score defining high- or medium-low–risk groups with 2-y cumulative incidence of relapse: 35.3% (95% CI, 25.6-45) and 15.6% (95% CI, 10.1-20.7), respectively ( P   =  0.001*). Conclusions: Our model describes immune reconstitution and its main influencing factors in the early posttransplantation period, presenting as a reliable model for relapse risk prediction. If validated, this model could definitely serve as a predictive tool and could be used for clinical trials or for individualized patient counseling.