Phenotypic profiling of CD34+ cells by advanced flow cytometry improves diagnosis of juvenile myelomonocytic leukemia.

Diagnostic criteria for juvenile myelomonocytic leukemia (JMML) are currently well defined, however in some patients diagnosis still remains a challenge. Flow cytometry is a well established tool for diagnosis and follow-up of hematological malignancies, nevertheless it is not routinely used for JMML diagnosis. Herewith, we characterized the CD34+ hematopoietic precursor cells collected from 31 children with JMML using a combination of standardized EuroFlow antibody panels to assess the ability to discriminate JMML cells from normal/reactive bone marrow cell as controls (n=29) or from cells of children with other hematological diseases mimicking JMML (n=9). CD34+ precursors in JMML showed markedly reduced B-cell and erythroid-committed precursors compared to controls, whereas monocytic and CD7+ lymphoid precursors were significantly expanded. Moreover, aberrant immunophenotypes were consistently present in CD34+ precursors in JMML, while they were virtually absent in controls. Multivariate logistic regression analysis showed that combined assessment of the number of CD34+CD7+ lymphoid precursors and CD34+ aberrant precursors or erythroid precursors had a great potential in discriminating JMMLs versus controls. Importantly our scoring model allowed highly efficient discrimination of truly JMML versus patients with JMML-like diseases. In conclusion, we show for the first time that CD34+ precursors from JMML patients display a unique immunophenotypic profile which might contribute to a fast and accurate diagnosis of JMML worldwide by applying an easy to standardize single eight-color antibody combination.

Sphingosine 1-phosphate protective effect on human proximal tubule cells submitted to an in vitro ischemia model: the role of JAK2/STAT3.

Acute kidney injury is a serious public health problem worldwide, being ischemia and reperfusion (I/R) the main lesion-aggravating factor that contributes to the evolution towards chronic kidney disease. Nonetheless, intervention approaches currently available are just considered palliative options. In order to offer an alternative treatment, it is important to understand key factors involved in the development of the disease including the rescue of the affected cells and/or the release of paracrine factors that are crucial for tissue repair. Bioactive lipids such as sphingosine 1-phosphate (S1P) have significant effects on the modulation of signaling pathways involved in tissue regeneration, such as cell survival, proliferation, differentiation, and migration. The main objective of this work was to explore the protective effect of S1P using human kidney proximal tubule cells submitted to a mimetic I/R lesion, via ATP depletion. We observed that the S1P pre-treatment increases cell survival by 50% and preserves the cell proliferation capacity of injured cells. We showed the presence of different bioactive lipids notably related to tissue repair but, more importantly, we noted that the pre-treatment with S1P attenuated the ischemia-induced effects in response to the injury, resulting in higher endogenous S1P production. All receptors but S1PR3 are present in these cells and the protective and proliferative effect of S1P/S1P receptors axis occur, at least in part, through the activation of the SAFE pathway. To our knowledge, this is the first time that S1PR4 and S1PR5 are referred in these cells and also the first indication of JAK2/STAT3 pathway involvement in S1P-mediated protection in an I/R renal model.

Quality Assessment of a Large Multi-Center Flow Cytometric Dataset of Acute Myeloid Leukemia Patients-A EuroFlow Study.

Flowcytometric analysis allows for detailed identification and characterization of large numbers of cells in blood, bone marrow, and other body fluids and tissue samples and therefore contributes to the diagnostics of hematological malignancies. Novel data analysis tools allow for multidimensional analysis and comparison of patient samples with reference databases of normal, reactive, and/or leukemia/lymphoma patient samples. Building such reference databases requires strict quality assessment (QA) procedures. Here, we compiled a dataset and developed a QA methodology of the EuroFlow Acute Myeloid Leukemia (AML) database, based on the eight-color EuroFlow AML panel consisting of six different antibody combinations, including four backbone markers. In total, 1142 AML cases and 42 normal bone marrow samples were included in this analysis. QA was performed on 803 AML cases using multidimensional analysis of backbone markers, as well as tube-specific markers, and data were compared using classical analysis employing median and peak expression values. Validation of the QA procedure was performed by re-analysis of >300 cases and by running an independent cohort of 339 AML cases. Initial evaluation of the final cohort confirmed specific immunophenotypic patterns in AML subgroups; the dataset therefore can reliably be used for more detailed exploration of the immunophenotypic variability of AML. Our data show the potential pitfalls and provide possible solutions for constructing large flowcytometric databases. In addition, the provided approach may facilitate the building of other databases and thereby support the development of novel tools for (semi)automated QA and subsequent data analysis.

Immunophenotypic Analysis of Acute Megakaryoblastic Leukemia: A EuroFlow Study.

Acute megakaryoblastic leukemia (AMKL) is a rare and heterogeneous subtype of acute myeloid leukemia (AML). We evaluated the immunophenotypic profile of 72 AMKL and 114 non-AMKL AML patients using the EuroFlow AML panel. Univariate and multivariate/multidimensional analyses were performed to identify most relevant markers contributing to the diagnosis of AMKL. AMKL patients were subdivided into transient abnormal myelopoiesis (TAM), myeloid leukemia associated with Down syndrome (ML-DS), AML-not otherwise specified with megakaryocytic differentiation (NOS-AMKL), and AMKL-other patients (AML patients with other WHO classification but with flowcytometric features of megakaryocytic differentiation). Flowcytometric analysis showed good discrimination between AMKL and non-AMKL patients based on differential expression of, in particular, CD42a.CD61, CD41, CD42b, HLADR, CD15 and CD13. Combining CD42a.CD61 (positive) and CD13 (negative) resulted in a sensitivity of 71% and a specificity of 99%. Within AMKL patients, TAM and ML-DS patients showed higher frequencies of immature CD34+/CD117+ leukemic cells as compared to NOS-AMKL and AMKL-Other patients. In addition, ML-DS patients showed a significantly higher expression of CD33, CD11b, CD38 and CD7 as compared to the other three subgroups, allowing for good distinction of these patients. Overall, our data show that the EuroFlow AML panel allows for straightforward diagnosis of AMKL and that ML-DS is associated with a unique immunophenotypic profile.