The ABNL-MARRO 001 study: a phase 1-2 study of randomly allocated active myeloid target compound combinations in MDS/MPN overlap syndromes.

BACKGROUND: Myelodysplastic/myeloproliferative neoplasms (MDS/MPN) comprise several rare hematologic malignancies with shared concomitant dysplastic and proliferative clinicopathologic features of bone marrow failure and propensity of acute leukemic transformation, and have significant impact on patient quality of life. The only approved disease-modifying therapies for any of the MDS/MPN are DNA methyltransferase inhibitors (DNMTi) for patients with dysplastic CMML, and still, outcomes are generally poor, making this an important area of unmet clinical need. Due to both the rarity and the heterogeneous nature of MDS/MPN, they have been challenging to study in dedicated prospective studies. Thus, refining first-line treatment strategies has been difficult, and optimal salvage treatments following DNMTi failure have also not been rigorously studied. ABNL-MARRO (A Basket study of Novel therapy for untreated MDS/MPN and Relapsed/Refractory Overlap Syndromes) is an international cooperation that leverages the expertise of the MDS/MPN International Working Group (IWG) and provides the framework for collaborative studies to advance treatment of MDS/MPN and to explore clinical and pathologic markers of disease severity, prognosis, and treatment response. METHODS: ABNL MARRO 001 (AM-001) is an open label, randomly allocated phase 1/2 study that will test novel treatment combinations in MDS/MPNs, beginning with the novel targeted agent itacitinib, a selective JAK1 inhibitor, combined with ASTX727, a fixed dose oral combination of the DNMTi decitabine and the cytidine deaminase inhibitor cedazuridine to improve decitabine bioavailability. DISCUSSION: Beyond the primary objectives of the study to evaluate the safety and efficacy of novel treatment combinations in MDS/MPN, the study will (i) Establish the ABNL MARRO infrastructure for future prospective studies, (ii) Forge innovative scientific research that will improve our understanding of pathogenetic mechanisms of disease, and (iii) Inform the clinical application of diagnostic criteria, risk stratification and prognostication tools, as well as response assessments in this heterogeneous patient population. TRIAL REGISTRATION: This trial was registered with ClinicalTrials.gov on August 19, 2019 (Registration No. NCT04061421).

Evaluation of the prognostic significance of eosinophilia and basophilia in a larger cohort of patients with myelodysplastic syndromes.

BACKGROUND: Lineage involvement and maturation arrest are considered to have prognostic significance in patients with myelodysplastic syndromes (MDS). However, although the prognostic value of neutropenia, thrombocytopenia, and monocytosis have been documented, little is known about the impact of eosinophils and basophils. METHODS: The authors examined the prognostic significance of eosinophils and basophils in 1008 patients with de novo MDS. Patients were enrolled from 3 centers of the Austrian-German MDS Working Group and were analyzed retrospectively. Blood eosinophils and basophils were quantified by light microscopy, and their impact on survival and leukemia-free survival was calculated by using Cox regression. RESULTS: Eosinophilia (eosinophils >350/microL) and basophilia (basophils >250/microL) predicted a significantly reduced survival (P < .05) without having a significant impact on leukemia-free survival. In multivariate analysis, eosinophilia and basophilia were identified as lactate dehydrogenase (LDH)-independent prognostic variables with International Prognostic Scoring System (IPSS)-specific impact. Although elevated LDH was identified as a major prognostic determinant in IPSS low-risk, intermediate-1 risk, and high-risk subgroups, the condition "eosinophilia and/or basophilia" was identified as a superior prognostic indicator in the IPSS intermediate-2 risk subgroup. CONCLUSIONS: The evaluation of eosinophils and basophils in patients with MDS was helpful and may complement the spectrum of variables to optimize prognostication in MDS.