Association between megakaryocyte abnormalities on bone marrow smear and response to thrombopoietin receptor agonists in adult patients with primary immune thrombocytopenia.

Impaired platelet production is a mechanism of immune thrombocytopenia (ITP). Morphological abnormalities of megakaryocytes (MKs) are sometimes observed in this disease. Two studies have suggested an association between MK abnormalities and response to corticosteroids in primary ITP, but none have investigated this association for thrombopoietin-receptor agonists (TPO-RAs). This was the aim of this study. The source of population was the French CARMEN registry with prospective follow-up of adult patients with incident ITP. We included patients with primary ITP, treated by TPO-RA and with a bone marrow smear before initiating TPO-RA. MK abnormalities were categorized by the presence of dysplasia and by the stage of maturation. Among 451 patients screened, 38 were included in the analysis. There was no difference in the median percentage of dysplastic MKs between responders to TPO-RA (4.0%, 95% confidence interval - CI: 2.3-6.4) and non-responders (4.5%, 95% CI: 0.7-7.1). There was a slightly higher proportion of granular MKs (4.5%, 95% CI: 3-6) and basophilic MKs (30.1%, 95% CI: 21.9-39.1) in non-responders compared to responders (granular: 2.0%, 95% CI: 0-4.1; basophilic: 21.3%, 95% CI: 11.4-40.7). In conclusion, MK abnormalities were not associated with response achievement in ITP patients treated with TPO-RA in this series of 38 patients.

Dactinomycin in acute myeloid leukemia with NPM1 mutations.

OBJECTIVES: Complete responses have been observed in NPM1-mutated AML patients with dactinomycin, a nucleolar stress-inducing drug. Here, we report a single-center experience of compassionate use of dactinomycin in untreated or relapsed/ refractory NPM1-mutated AML. METHODS: From September 2015 to February 2019, 26 adult patients with NPM1-mutated AML received dactinomycin in different situations: front-line treatment in 4 unfit patients (16%); morphologic (n = 16, 62%), molecular relapses (n = 4, 16%), refractory disease (n = 1, 13%), or postremission therapy in second complete response (n = 1, 13%). RESULTS: Median age was 62.5 years. Median number of dactinomycin cycle was 1 (1-8), and 7 patients (27%) received more than 3 cycles. Three out of 17 patients (18%) in morphologic relapse or refractory to chemotherapy achieved complete remission after the first cycle of dactinomycin. None of the 4 patients unfit for intensive chemotherapy responded to dactinomycin as front-line therapy. Grade 3-4 adverse events were thrombocytopenia (n = 11, 42%), neutropenia (n = 11, 42%), GI toxicity (n = 6, 23%), mucositis (n = 5, 19%), lung infection (n = 5, 19%), and skin rash (n = 2, 7.6%). CONCLUSIONS: Dactinomycin is an inexpensive and easily available drug that may induce significant responses in few AML patients with NPM1 mutations with an acceptable safety profile.

Imatinib with intensive chemotherapy in AML with t(9;22)(q34.1;q11.2)/BCR::ABL1. A DATAML registry study.

Acute myeloid leukemia (AML) with t(9;22) (q34.1; q11.2)/BCR::ABL1, a distinct entity within the group of AML with defining genetic abnormalities, belong to the adverse-risk group of the 2022 ELN classification. However, there is little data on outcome since the era of tyrosine kinase inhibitors. Among 5819 AML cases included in the DATAML registry, 20 patients with de novo BCR::ABL1+AML (0.3%) were identified. Eighteen patients treated with standard induction chemotherapy were analyzed in this study. Imatinib was added to chemotherapy in 16 patients. The female-to-male ratio was 1.25 and median age was 54 years. The t(9;22) translocation was the sole chromosomal abnormality in 12 patients. Main gene mutations detected by NGS were ASXL1, RUNX1 and NPM1. Compared with patients with myeloid blast phase of chronic myeloid leukemia (CML-BP), de novo BCR::ABL1+AML had higher WBC, fewer additional chromosomal abnormalities, lower CD36 or CD7 expression and no ABL1 mutations. Seventeen patients (94.4%) achieved complete remission (CR) or CR with incomplete hematologic recovery. Twelve patients were allografted in first remission. With a median follow-up of 6.3 years, the median OS was not reached and 2-year OS was 77% (95% CI: 50-91). Four out of five patients who were not transplanted did not relapse. Comparison of BCR::ABL1+AML, CML-BP, 2017 ELN intermediate (n = 643) and adverse-risk patients (n = 863) showed that patients with BCR::ABL1+AML had a significant better outcome than intermediate and adverse-risk patients. BCR::ABL1+AML patients treated with imatinib and intensive chemotherapy should not be included in the adverse-risk group of current AML classifications.

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.