Chronic myelomonocytic leukemia (CMML) is a myelodysplastic/myeloproliferative neoplasm, characterized by persistent monocytosis and dysplasia in at least one myeloid cell lineage. This persistent monocytosis should be distinguished from the reactive monocytosis which is sometimes observed in a context of infections or solid tumors. In 2015, Selimoglu-Buet et al. observed an increased percentage of classical monocytes (CD14+/CD16− >94%) in the peripheral blood (PB) of CMML patients. In this study, using multiparametric flow cytometry (MFC), we assessed the monocytic distribution in PB samples and in bone marrow aspirates from 63 patients with monocytosis or CMML suspicion, and in seven follow-up blood samples from CMML patients treated with hypomethylating agents (HMA). A control group of 12 healthy age-matched donors was evaluated in parallel in order to validate the analysis template. The CMML diagnosis was established in 15 cases in correlation with other clinical manifestations and biological tests. The MFC test for the evaluation of the repartition of monocyte subsets, as previously described by Selimoglu-Buet et al. showed a specificity of 97% in blood and 100% in marrow samples. Additional information regarding the expression of intermediate MO2 monocytes percentage improved the specificity to 100% in blood samples allowing the screening of abnormal monocytosis. The indicative thresholds of CMML monocytosis were different in PB compared to BM samples (classical monocytes >95% for PB and >93% for BM). A decrease of monocyte levels in PB and BM, along with a normalization of monocytes distribution, was observed after treatment in 4/7 CMML patients with favorable evolution. No significant changes were observed in 3/7 patients who did not respond to HMA therapy and also presented unfavorable molecular prognostic factors at diagnosis (ASXL1, TET2, and IDH2 mutations). Considering its simplicity and robustness, the monocyte subsets evaluation by MFC provides relevant information for CMML diagnosis.

Myelodysplastic syndromes (MDSs) are clonal disorders of hematopoiesis that exhibit heterogeneous clinical presentation and morphological findings, which complicates diagnosis, especially in early stages. Recently, refined definitions and standards in the diagnosis and treatment of MDS were proposed, but numerous questions remain. Multiparameter flow cytometry (MFC) is a helpful tool for the diagnostic workup of patients with suspected MDS, and various scores using MFC data have been developed. However, none of these methods have achieved the sensitivity that is required for a reassuring diagnosis in the absence of morphological abnormalities. One reason may be that each score evaluates one or two lineages without offering a broad view of the dysplastic process. The combination of two scores (e.g., Ogata and Red Score) improved the sensitivity from 50–60 to 88%, but the positive (PPV) and negative predictive values (NPV) must be improved. There are prominent differences between study groups when these scores are tested. Further research is needed to maximize the sensitivity of flow cytometric analysis in MDS. This review focuses on the application of flow cytometry for MDS diagnosis and discusses the advantages and limitations of different approaches.

The pathogenic role of mesenchymal stromal cells (MSCs) in myelodysplastic syndromes (MDS) development and progression has been investigated by numerous studies, yet, it remains controversial in some aspects (1, 2). In the present study, we found distinct features of MSCs from low-risk (LR)-MDS stromal microenvironment as compared to those from healthy subjects. At the molecular level, focal adhesion kinase, a key tyrosine kinase in control of cell proliferation, survival, and adhesion process, was found profoundly suppressed in expression and activation in LR-MDS MSC. At a functional level, LR-MDS MSCs showed impaired growth and clonogenic capacity, which were independent of cellular senescence and apoptosis. The pro-adipogenic differentiation and attenuated osteogenic capacity along with reduced SDF-1 expression could be involved in creating an unfavorable microenvironment for hematopoiesis. In conclusion, our experiments support the theory that the stromal microenvironment is fundamentally altered in LR-MDS, and these preliminary data offer a new perspective on LR-MDS pathophysiology.

Natural killer (NK) cells are key innate immunity effectors that play a major role in malignant cell destruction. Based on expression patterns of CD16, CD56, CD57, and CD94, three distinct NK cell maturation stages have been described, which differ in terms of cytokine secretion, tissue migration, and the ability to kill target cells. Our study addressed NK cell maturation in bone marrow under three conditions: a normal developmental environment, during pre-leukemic state (myelodysplastic syndrome, MDS), and during leukemic transformation (acute myeloblastic leukemia, AML). In this study, we used a new tool to perform multicolor flow cytometry data analysis, based on principal component analysis, which allowed the unsupervised, accurate discrimination of immature, mature, and hypermature NK subpopulations. An impaired NK/T cell distribution was observed in the MDS bone marrow microenvironment compared with the normal and AML settings, and a phenotypic shift from the mature to the immature state was observed in NK cells under both the MDS and AML conditions. Furthermore, an impaired NK cell antitumor response, resulting in changes in NK cell receptor expression (CD159a, CD158a, CD158b, and CD158e1), was observed under MDS and AML conditions compared with the normal condition. The results of this study provide evidence for the failure of this arm of the immune response during the pathogenesis of myeloid malignancies. NK cell subpopulations display a heterogeneous and discordant dynamic on the spectrum between normal and pathological conditions. MDS does not appear to be a simple, intermediate stage but rather serves as a decisive step for the mounting of an efficient or ineffective immune response, leading to either the removal of the tumor cells or to malignancy.

Myelodysplastic syndromes are a heterogeneous group of clonal hematopoietic disorders. However, the therapies used against the hematopoietic stem cells clones have limited efficacy; they slow the evolution toward acute myeloid leukemia rather than stop clonal evolution and eradicate the disease. The progress made in recent years regarding the role of the bone marrow microenvironment in disease evolution may contribute to progress in this area. This review presents the recent updates on the role of the bone marrow microenvironment in myelodysplastic syndromes pathogenesis and tries to find answers regarding how this information could improve myelodysplastic syndromes diagnosis and therapy.

Acute myeloid leukemias (AMLs) are hematologic malignancies with varied molecular and immunophenotypic profiles, making them difficult to diagnose and classify. High-dimensional analysis algorithms might increase the utility of multicolor flow cytometry for AML diagnosis and follow-up. The objective of the present study was to assess whether a Compass database-guided analysis can be used to achieve rapid and accurate diagnoses. We conducted this study to determine whether this method could be employed to pilote the genetic and molecular tests and to objectively identify different-from-normal (DfN) patterns to improve measurable residual disease follow-up in AML. Three Compass databases were built using Infinicyt 2.0 software, including normal myeloid-committed hematopoietic precursors (n = 20) and AML blasts harboring the most frequent recurrent genetic abnormalities (n = 50). The diagnostic accuracy of the Compass database-guided analysis was evaluated in a prospective validation study (125 suspected AML patients). This method excluded AML associated with the following genetic abnormalities: t(8;21), t(15;17), inv(16), and KMT2A translocation, with 92% sensitivity [95% confidence interval (CI): 78.6%–98.3%] and a 98.5% negative predictive value (95% CI: 90.6%–99.8%). Our data showed that the Compass database-guided analysis could identify phenotypic differences between AML groups, representing a useful tool for the identification of DfN patterns.

No abstract

Acute myeloid leukemias (AMLs) are a group of hematologic malignancies that are heterogeneous in their molecular and immunophenotypic profiles. Identification of the immunophenotypic differences between AML blasts and normal myeloid hematopoietic precursors (myHPCs) is a prerequisite to achieving better performance in AML measurable residual disease follow-ups. In the present study, we applied high-dimensional analysis algorithms provided by the Infinicyt 2.0 and Cytobank software to evaluate the efficacy of antibody combinations of the EuroFlow AML/myelodysplastic syndrome panel to distinguish AML blasts with recurrent genetic abnormalities (n = 39 AML samples) from normal CD45low CD117+ myHPCs (n = 23 normal bone marrow samples). Two types of scores were established to evaluate the abilities of the various methods to identify the most useful parameters/markers for distinguishing between AML blasts and normal myHPCs, as well as to distinguish between different AML groups. The Infinicyt Compass database-guided analysis was found to be a more user-friendly tool than other analysis methods implemented in the Cytobank software. According to the developed scoring systems, the principal component analysis based algorithms resulted in better discrimination between AML blasts and myHPCs, as well as between blasts from different AML groups. The most informative markers for the discrimination between myHPCs and AML blasts were CD34, CD36, human leukocyte antigen-DR (HLA-DR), CD13, CD105, CD71, and SSC, which were highly rated by all evaluated analysis algorithms. The HLA-DR, CD34, CD13, CD64, CD33, CD117, CD71, CD36, CD11b, SSC, and FSC were found to be useful for the distinction between blasts from different AML groups associated with recurrent genetic abnormalities. This study identified both benefits and the drawbacks of integrating multiple high-dimensional algorithms to gain complementary insights into the flow-cytometry data.