Trends in Outcome of Hematopoietic Stem Cell Transplantation: 5000 Transplantations and 30 Years of Single-Center Experience.

In this single-center analysis, we evaluated the trends in 5185 hematopoietic cell transplantations performed between 1990 and 2022. The study group comprised 3237 allogeneic (alloHCT) and 1948 autologous (autoHCT) hematopoietic cell transplantations. In the multivariate analysis, there was an improvement in event-free-survival (EFS) after autoHCT (HR 0.6, 95% CI 0.4-0.7, p < 0.0001) due to reduced cumulative incidence of relapse in the last five years (56% in 2010-2014 vs. 38% in 2015-2022). An improvement in EFS after alloHCT over time was observed (HR 0.33, 95% CI 0.23-0.48, p < 0.0001), which was due to reduced non-relapse mortality. No difference in cumulative relapse incidence was observed over the last decade for allografted patients. Survival after autoHCT improved in Hodgkin's disease (HR 0.1, 95% CI 0.1-0.3), multiple myeloma (HR 0.4, 95% CI 0.2-0.7) and solid tumors (HR 0.2, 95% CI 0.2-0.4), while after alloHCT, improvement was observed in acute myeloid leukemia (HR 0.3, 95% CI 0.1-0.5), acute lymphoblastic leukemia (HR 0.2, 95% CI 0.1-0.5), Hodgkin's disease (HR 0.1, 95% CI 0.0-0.4), non-Hodgkin's lymphomas and chronic lymphocytic leukemia (HR 0.2, 95% CI 0.0-0.6), inborn diseases (HR 0.2, 95% CI 0.2-0.4) and acquired aplastic anemia with matched related donors and matched unrelated donors (HR 0.3, 95% CI 0.2-0.8).

A pilot study of implication of machine learning for relapse prediction after allogeneic stem cell transplantation in adults with Ph-positive acute lymphoblastic leukemia.

The posttransplant relapse in Ph-positive ALL increases the risk of death. There is an unmet need for instruments to predict the risk of relapse and plan prophylaxis. In this study, we analyzed posttransplant data by machine learning algorithms. Seventy-four Ph-positive ALL patients with a median age of 30 (range 18-55) years who previously underwent allo-HSCT, were retrospectively enrolled. Ninety-three percent of patients received prophylactic/preemptive TKIs after allo-HSCT. The values of the BCR::ABL1 level at serial assessments and over variables were collected in specified intervals after allo-HSCT. They were used to model relapse risk with several machine-learning approaches. GBM proved superior to the other algorithms and provided a maximal AUC score of 0.91. BCR::ABL1 level before and after allo-HSCT, prediction moment, and chronic GvHD had the highest value in the model. It was shown that after Day + 100, both error rates do not exceed 22%, while before D + 100, the model fails to make accurate predictions. As a result, we determined BCR::ABL1 levels at which the relapse risk remains low. Thus, the current BCR::ABL1 level less than 0.06% in patients with chronic GvHD predicts low risk of relapse. At the same time, patients without chronic GVHD after allo-HSCT should be classified as high risk with any level of BCR::ABL1. GBM model with posttransplant laboratory values of BCR::ABL1 provides a high prediction of relapse after allo-HSCT in the era of TKIs prophylaxis. Validation of this approach is warranted.

Genetic characteristics and treatment outcome in infants with KMT2A germline B-cell precursor acute lymphoblastic leukemia: Results of MLL-Baby protocol.

The aim of this study was to present the diagnostic and outcome characteristics of infants with germline status of KMT2A gene (KMT2A-g) B-cell precursor acute lymphoblastic leukemia (BCP-ALL) treated consistently according to the MLL-Baby protocol, a moderate-intensity protocol. Of the 139 patients enrolled in the MLL-Baby study, 100 (71.9%) carried different types of rearranged KMT2A (KMT2A-r), while the remaining 39 infants (28.1%) had KMT2A-g. KMT2A-g patients were generally older (77% older than 6 months), less likely to have a very high white blood cell count (greater than 100 × 109 /L), less likely to be central nervous system (CNS)-positive, and more likely to be CD10-positive. The 6-year event-free survival and overall survival rates for all 39 patients were 0.74 (standard error [SE] 0.07) and 0.80 (SE 0.07), respectively. Relapse was the most common adverse event (n = 5), with a cumulative incidence of relapse (CIR) of 0.13 (SE 0.06), while the incidence of a second malignancy (n = 1) and death in remission (n = 3) was 0.03 (SE 0.04) and 0.08 (SE 0.04), respectively. None of the initial parameters, including genetics and the presence of recently described fusions of NUTM1 and PAX5 genes, was able to distinguish patients with different outcomes. Only rapidity of response, measured as minimal residual disease (MRD) by flow cytometry, showed a statistically significant impact. Moderate-intensity therapy, as used in the MLL-Baby protocol in infants with KMT2A-g BCP-ALL, yields results comparable to other infant studies. Patients with a slow multicolor flow cytometry (MFC)-MRD response should be subjected to advanced therapies, such as targeted or immunotherapies.

Clinical significance of cytogenetic changes in childhood T-cell acute lymphoblastic leukemia: results of the multicenter group Moscow-Berlin (MB).

The prognostic significance of genetic lesions in T-cell ALL still needs to be elucidated. Karyotyping and FISH were performed in samples from 120 patients with T-cell ALL registered in the trial Moscow-Berlin 2008. Most frequent rearrangements were TLX3 (N = 29; 24%) and TAL1 (N = 18; 15%), followed by KMT2A (N = 6; 5%), TLX1 (N = 5; 4.2%), and 11p13-15 (N = 5; 4.2%). In 16.7% of patients, the karyotype was normal, and in 30.8% 'other' aberrations were seen. Patients with a normal karyotype, TAL1, or KMT2A rearrangements had the most favorable outcome (probability of event free survival (pEFS): 82% ± 6%), while prognosis for patients with TLX3 and TLX1 rearrangements and 'other' aberrations was less favorable (pEFS: 62% ± 6%). Worst outcome was observed for five patients with 11p rearrangements (pEFS: 20% ± 18%). In summary, three subgroups of patients with T-cell ALL with significantly different outcomes could be defined by cytogenetic profiling.