Predictive impact of allele-matching and EBMT risk score for outcome after T-cell depleted unrelated donor transplantation in poor-risk acute leukemia and myelodysplasia

Abstract

Many parameters predict for outcome after unrelated donor (URD) allogeneic hematopoietic stem cell transplantation (alloSCT). High-resolution HLA-matching significantly impacts outcome and also the European Group of Blood and Marrow Transplantation (EBMT) risk score, based on patient age, disease stage, donor type, time from diagnosis to SCT and gender combination, may predict for non-relapse mortality and overall survival (OS). We evaluated the individual and combined effects of allele-matching and the EBMT risk score in 327 patients with poor-risk acute leukemia or myelodysplasia, who received a T-cell depleted URD alloSCT. Matching for HLA-A, -B, -C and -DRB1 alleles (8/8 match) was associated with a 5-year OS of 40% compared with 30% for mismatched (p7/8) pairs (P ¼ 0.02). Patients with EBMT risk scores of 1-2, 3, 4 and 5-7 had 5-year OS estimates of 53, 43, 30 and 20%, respectively (Po0.001). The favorable prognostic impact of an 8/8 donor was most pronounced if the EBMT risk score was low (1-2). Fiveyear OS was 74±8% vs 39±11% for fully matched patients with a low-risk EBMT score as compared with EBMT low-risk patients with p7/8 donors. These data underscore the importance of incorporating both the EBMT risk score and the degree of high-resolution HLA-matching in the risk assessment prior to URD alloSCT.

EBMT risk score can predict the outcome of leukaemia after unmanipulated haploidentical blood and marrow transplantation

“…3 The stage of the disease reflects the response to therapy and is an important risk factor that is widely used to predict the outcome after HSCT. 23,[27][28][29] For haploidentical HSCT, the significance of disease stage is also confirmed by other treatment centres in Europe. 30,31 A >12-month duration of time from diagnosis to transplant means more treatment is required to achieve remission, which may add to the pretransplant toxicity and may increase NRM.…”

“…[21][22][23][24][25][26] However, the validity of the EBMT risk score for haploidentical transplantation has not previously been evaluated. To our knowledge, this study is the first to report the association between the EBMT risk score and different outcomes of transplantation in a group of unmanipulated HBMT recipients (n = 502).…”

Functional distance between recipient and donor HLA-DPB1 determines nonpermissive mismatches in unrelated HCT

“…[2][3][4] It is well established that HLA disparity increases the risks of adverse clinical outcome including overall and nonrelapse mortality (NRM), as well as graft-versus-host disease (GVHD), although it can also mediate a beneficial graft-versusleukemia (GVL) effect. [5][6][7][8][9][10] The identification of clinically permissive, ie, well-tolerated HLA mismatches is the subject of intensive research efforts that include the search for high-risk mismatch combinations, [11][12][13] structural comparison of HLA molecules, 14,15 and the identification of shared T-cell epitopes (TCEs) in mismatched HLA-DPB1 alleles. 4,9,16,17 Considerable attention has also been given to the association between specific amino acid (AA) substitutions in mismatched HLA class I alleles and adverse outcome, 11,[18][19][20] resulting in a number of bio-informatic models for in silico outcome prediction.…”

Application of machine learning algorithms for clinical predictive modeling: a data-mining approach in SCT

“…For instance, prediction of overall survival, TRM, GVHD, GVHD related death and relapse related death at various time points post-transplant (e.g., 100 days, 1 year and 5 years). [48][49][50][51][52][53] In addition, applying data-mining techniques to databases with combined HSCT and nontransplant (for example, chemotherapy) data could potentially lead to the development of therapeutic decision-support systems, allowing better treatment allocation for each patient. SUMMARY DM is a promising approach for the development of prediction models.…”