Transplantation Direct (Nov 2021)

Multivariate Analysis of Immune Reconstitution and Relapse Risk Scoring in Children Receiving Allogeneic Stem Cell Transplantation for Acute Leukemias

  • Manuela Spadea, MD,
  • Francesco Saglio, MD,
  • Serena I. Tripodi, MD,
  • Mariacristina Menconi, MD,
  • Marco Zecca, MD,
  • Franca Fagioli, MD

DOI
https://doi.org/10.1097/TXD.0000000000001226
Journal volume & issue
Vol. 7, no. 11
p. e774

Abstract

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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.