Dismal outcome of refractory or relapsing patients with myelodysplasia-related acute myeloid leukemia partially alleviated by intensive chemotherapy.

BACKGROUND: Acute myeloid leukemia (AML) with myelodysplasia-related characteristics is a heterogeneous subset of AML that has been challenged throughout the history of myeloid malignancies classifications, considered to have similar outcomes as intermediate- or adverse-risk AML depending on the subgroup. However, little is known about the fate of these patients in refractory or relapsed situation (R/R) after first line therapy. METHODS: A large series of R/R AML patients, recorded in the French DATAML registry, have received either intensive chemotherapy (ICT), azacitidine (AZA) as single agent, or best supportive care (BSC). A cohort of 183 patients (median age 63-year-old) with what was called at the time AML-MRC has been explored, and data are reported here. RESULTS: Patient status was refractory for 93, while 90 had relapsed. Respectively, 88, 34, and 61 were included in the three treatment arms. The median OS of the whole cohort was 4.2 months (95%CI: 3.1-5.6) with a mean 1-year overall survival of 24% ± 3.2%. There was no significant survival difference between refractory and relapsed patients. The BSC group had overall a significantly worse outcome (p = 0.0001), and this remained true in both refractory (p = 0.01) and relapsed (p = 0.002) patients. Similar survivals were observed in both groups comparing ICT and AZA. CONCLUSIONS: These data, reporting about an ill-explored population, indicate the poor prognosis of this condition where both ICT and AZA can be proposed. The latter, which was demonstrated here to be a feasible option, should be added to new targeted therapies.

Artificial intelligence-based prediction models for acute myeloid leukemia using real-life data: A DATAML registry study.

We designed artificial intelligence-based prediction models (AIPM) using 52 diagnostic variables from 3687 patients included in the DATAML registry treated with intensive chemotherapy (IC, N = 3030) or azacitidine (AZA, N = 657) for an acute myeloid leukemia (AML). A neural network called multilayer perceptron (MLP) achieved a prediction accuracy for overall survival (OS) of 68.5% and 62.1% in the IC and AZA cohorts, respectively. The Boruta algorithm could select the most important variables for prediction without decreasing accuracy. Thirteen features were retained with this algorithm in the IC cohort: age, cytogenetic risk, white blood cells count, LDH, platelet count, albumin, MPO expression, mean corpuscular volume, CD117 expression, NPM1 mutation, AML status (de novo or secondary), multilineage dysplasia and ASXL1 mutation; and 7 variables in the AZA cohort: blood blasts, serum ferritin, CD56, LDH, hemoglobin, CD13 and disseminated intravascular coagulation (DIC). We believe that AIPM could help hematologists to deal with the huge amount of data available at diagnosis, enabling them to have an OS estimation and guide their treatment choice. Our registry-based AIPM could offer a large real-life dataset with original and exhaustive features and select a low number of diagnostic features with an equivalent accuracy of prediction, more appropriate to routine practice.

Vitamin C and D supplementation in acute myeloid leukemia.

Recent studies have highlighted the role of vitamin C and D in acute myeloid leukemia (AML). In 2018, we changed our practices to add both vitamins to the supportive care for all consecutive patients with AML undergoing intensive chemotherapy. In this study, we compared the outcomes of patients treated before and after this change in practice. From 2015 to 2020, 431 patients were included, 262 of whom received no supplementation and 169 of whom received vitamin supplementation. Vitamin C and vitamin D was administered from day 10 of chemotherapy until hematologic recovery from induction and consolidation. Most patients presented at diagnosis with low levels of vitamin C and D. Upon recovery from induction, vitamin D levels among the vitamin C/D group significantly increased compared with those at diagnosis, and pretransplant levels were significantly higher in the vitamin C/D group compared with the control group (median of 33 vs 19 ng/mL; P < .0001). During induction, the rates of bacterial or fungal infection, hemorrhage, or macrophage activation syndrome were lower in the vitamin C/D group, whereas there was no difference in response rate, relapse incidence, and overall survival (OS). However, the multivariate analysis for OS showed a significant interaction between vitamin C/D and NPM1 mutation, meaning that vitamin C/D supplementation was significantly and independently associated with better OS in patients with NPM1 mutations (hazard ratio [HR], 0.52; 95% confidence interval [CI], 0.30-0.90; P = .019) compared with patients with wild-type NPM1 (HR, 1.01; 95% CI, 0.68-1.51; P = .95). In conclusion, vitamin C/D supplementation is safe and could influence the outcomes of patients with AML undergoing intensive chemotherapy.

Use of a hybrid intelligence decision tree to identify mature B-cell neoplasms.

BACKGROUND: Mature B-cell neoplasms are challenging to diagnose due to their heterogeneity and overlapping clinical and biological features. In this study, we present a new workflow strategy that leverages a large amount of flow cytometry data and an artificial intelligence approach to classify these neoplasms. METHODS: By combining mathematical tools, such as classification algorithms and regression tree (CART) models, with biological expertise, we have developed a decision tree that accurately identifies mature B-cell neoplasms. This includes chronic lymphocytic leukemia (CLL), for which cytometry has been extensively used, as well as other non-CLL subtypes. RESULTS: The decision tree is easy to use and proposes a diagnosis and classification of mature B-cell neoplasms to the users. It can identify the majority of CLL cases using just three markers: CD5, CD43, and CD200. CONCLUSION: This approach has the potential to improve the accuracy and efficiency of mature B-cell neoplasm diagnosis.

Prognostic Impact of Unsupervised Early Assessment of Bulk and Leukemic Stem Cell Measurable Residual Disease in Acute Myeloid Leukemia.

PURPOSE: Acute myeloid leukemias (AML) are clonal diseases that develop from leukemic stem cells (LSC) that carry an independent prognostic impact on the initial response to induction chemotherapy, demonstrating the clinical relevance of LSC abundance in AML. In 2018, the European LeukemiaNet published recommendations for the detection of measurable residual disease (Bulk MRD) and suggested the exploration of LSC MRD and the use of multiparametric displays. EXPERIMENTAL DESIGN: We evaluated the performance of unsupervised clustering for the post-induction assessment of bulk and LSC MRD in 155 patients with AML who received intensive conventional chemotherapy treatment. RESULTS: The median overall survival (OS) for Bulk+ MRD patients was 16.7 months and was not reached for negative patients (HR, 3.82; P < 0.0001). The median OS of LSC+ MRD patients was 25.0 months and not reached for negative patients (HR, 2.84; P = 0.001). Interestingly, 1-year (y) and 3-y OS were 60% and 39% in Bulk+, 91% and 52% in Bulk-LSC+ and 92% and 88% in Bulk-LSC-. CONCLUSIONS: In this study, we confirm the prognostic impact of post-induction multiparametric flow cytometry Bulk MRD in patients with AML. Focusing on LSCs, we identified a group of patients with negative Bulk MRD but positive LSC MRD (25.8% of our cohort) with an intermediate prognosis, demonstrating the interest of MRD analysis focusing on leukemic chemoresistant subpopulations.

Phenotypically-defined stages of leukemia arrest predict main driver mutations subgroups, and outcome in acute myeloid leukemia.

Classifications of acute myeloid leukemia (AML) patients rely on morphologic, cytogenetic, and molecular features. Here we have established a novel flow cytometry-based immunophenotypic stratification showing that AML blasts are blocked at specific stages of differentiation where features of normal myelopoiesis are preserved. Six stages of leukemia differentiation-arrest categories based on CD34, CD117, CD13, CD33, MPO, and HLA-DR expression were identified in two independent cohorts of 2087 and 1209 AML patients. Hematopoietic stem cell/multipotent progenitor-like AMLs display low proliferation rate, inv(3) or RUNX1 mutations, and high leukemic stem cell frequency as well as poor outcome, whereas granulocyte-monocyte progenitor-like AMLs have CEBPA mutations, RUNX1-RUNX1T1 or CBFB-MYH11 translocations, lower leukemic stem cell frequency, higher chemosensitivity, and better outcome. NPM1 mutations correlate with most mature stages of leukemia arrest together with TET2 or IDH mutations in granulocyte progenitors-like AML or with DNMT3A mutations in monocyte progenitors-like AML. Overall, we demonstrate that AML is arrested at specific stages of myeloid differentiation (SLA classification) that significantly correlate with AML genetic lesions, clinical presentation, stem cell properties, chemosensitivity, response to therapy, and outcome.

Perls' Stain Guidelines from the French-Speaking Cellular Hematology Group (GFHC).

In order to standardize cellular hematology practices, the French-speaking Cellular Hematology Group (Groupe Francophone d'Hématologie Cellulaire, GFHC) focused on Perls' stain. A national survey was carried out, leading to the proposal of recommendations on insoluble iron detection and quantification in bone marrow. The criteria presented here met with a "strong professional agreement" and follow the suggestions of the World Health Organization's classification of hematological malignancies.