Acute myeloid leukemia (AML) comprises 32% of adult leukemia cases, with a 5-year survival rate of only 20-30%. Here, the immunophenotypic landscape of this heterogeneous malignancy is explored in a single-center cohort using a novel quantitative computational pipeline. For 122 patients who underwent induction treatment with intensive chemotherapy, leukemic cells were identified at diagnosis, computationally preprocessed, and quantitatively subtyped. Computational analysis provided a broad characterization of inter- and intra-patient heterogeneity, which would have been harder to achieve with manual bivariate gating. Statistical testing discovered associations between CD34, CD117, and HLA-DR expression patterns and genetic abnormalities. We found the presence of CD34+ cell populations at diagnosis to be associated with a shorter time to relapse. Moreover, CD34- CD117+ cell populations were associated with a longer time to AML-related mortality. Machine learning (ML) models were developed to predict 2-year survival, European LeukemiaNet (ELN) risk category, and inv(16) or NPM1mut, based on computationally quantified leukemic cell populations and limited clinical data, both readily available at diagnosis. We used explainable artificial intelligence (AI) to identify the key clinical characteristics and leukemic cell populations important for our ML models when making these predictions. Our findings highlight the importance of developing objective computational pipelines integrating immunophenotypic and genetic information in the risk stratification of AML.

Hematopoiesis is driven by molecular mechanisms that induce differentiation and proliferation of hematopoietic stem cells and their progeny. This involves the activity of various transcription factors, such as members of the Hairy/Enhancer of Split (HES) family, and important roles for both HES1 and HES4 have been shown in normal and malignant hematopoiesis. Here, we investigated the role of HES6 in human hematopoiesis using in vitro and in vivo models. Using bulk and single-cell RNA-sequencing data, we show that HES6 is expressed during erythroid/megakaryocyte and plasmacytoid dendritic cell development, as well as in multipotent precursors and at specific stages of T- and B-cell development following pre-B-cell receptor and pre-T-cell receptor signaling, respectively. Consistently, knockdown of HES6 in cord blood-derived hematopoietic precursors in well-defined in vitro differentiation assays resulted in reduced differentiation of human hematopoietic precursors towards megakaryocytes, erythrocytes, plasmacytoid dendritic cells, B cells and T cells. In addition, HES6 knockdown hematopoietic stem and progenitor cells displayed reduced colony-forming unit capacity in vitro and impaired potential to reconstitute hematopoiesis in vivo in a competitive transplantation assay. We demonstrate that loss of HES6 expression has an impact on cell cycle progression during erythroid differentiation and provide evidence for potential downstream target genes that affect these perturbations. Thus, our study provides new insights into the role of HES6 in human hematopoiesis.

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.

Supplementary Figures 1-5 from Small-Molecule MDM2 Antagonists as a New Therapy Concept for Neuroblastoma

Supplementary Data Figure S1. Determination of IgLV3-21 positivity by real-time PCR. Supplementary Data Figure S2: CLL-OS analysis including only CLL-related deaths. Supplementary Data Figure S3. Validation of the poor prognosis associated with IgLV3-21 usage in population C. Supplementary Data Figure S4. Validation of the poor prognosis of IgLV3-21 usage in population D. Supplementary Data Figure S5: prognostic impact of IgLV3-21 usage in the different CLL-IPI subgroups. Supplementary Data Figure S6. Immunogenetic features of IgLV3-21 patients. Supplementary Data Figure S7. CXCR4 surface expression is downregulated in IgLV3-21 patients.

Acute myeloid leukemia (AML) comprises 32% of adult leukemia cases with a five-year survival rate of only 20–30%. Here, the immunophenotypic landscape of this heterogeneous malignancy is explored in a single-center cohort using a novel quantitative computational pipeline. For 122 patients who underwent induction treatment with intensive chemotherapy, leukemic cells were identified at diagnosis, computationally preprocessed and quantitatively subtyped. Computational analysis provided a broad characterization of inter- and intra-patient heterogeneity, unachievable with manual bivariate gating. Statistical testing discovered associations between CD34, CD117 and HLA-DR expression patterns and genetic abnormalities. We found presence of CD34 + cell populations at diagnosis to be associated with a shorter time-to-relapse. Moreover, CD34- CD117 + cell populations were associated with a longer time to AML-related mortality. Machine learning (ML) models were developed to predict two-year survival, European LeukemiaNet (ELN) risk category and inv(16) or NPM1 mut , based on computationally quantified leukemic cell populations and limited clinical data, both readily available at diagnosis. We used explainable artificial intelligence (AI) to identify the key clinical characteristics and leukemic cell populations important for our ML models when making these predictions. Our findings highlight the importance of developing objective computational pipelines integrating immunophenotypic and genetic information in the risk stratification of AML.

The aim of this study was to explore the relationship of iron and zinc deficiencies and anemia in children aged under 5 years living in malaria endemic area of South Kivu/DRC. We conducted a cross-sectional study in the health zone of Miti Murhesa in South Kivu/DRC. A total of 1088 children in good general health were included in this study. Almost 40% of children were anemic. The prevalence of iron deficiency (ID) was found in 34.9% and 49.1% children based on ferritin or free erythrocyte protoporphyrin (FEP), respectively. If anemia is present, we found iron deficiency anemia (IDA) according to the WHO-criteria (ferritin) in 31%, and according to FEP in 66% of children. The overall prevalence of zinc deficiency was 17.6%. If anemia is present, zinc deficiency was found in 24.4% of children. Inflammation/infection, based upon CRP, was present in 39.7% children. The independent factors associated with anemia were recent illness, middle upper arm circumference, weight-for-height, ID according to FEP, zinc deficiency, and submicroscopic Plasmodium infection. A high prevalence of ID was observed in children in South Kivu according to FEP. Ferritin as acute phase protein was less suited in this population due to a high frequency of infection/inflammation. Iron and zinc deficiencies were found to be significantly associated with anemia in this population.

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.

Background:Ferroportin (FPN) is known as an iron exporter and its effect on RBC iron could therefore hamper the growth of malaria parasites, since parasites are in need of iron. The aim of this study was to examine the prevalence of FPN Q248H in South Kivu/DRC and to evaluate its role in Plasmodium infected children and to explore its relationship with anemia.Materials and methods:We conducted a cross-sectional study in the health zone of Miti Murhesa in South Kivu/DRC. 1088 children aged under five years were included. The FPN Q248H mutation was analyzed by PCR (N = 1071). Allele frequency was calculated based on Hardy-Weinberg equation. Plasmodium infection was assessed by LAMP malaria assay (N = 1057). Statistical analysis was done using Medcalc® software. P-values < 0.05 were considered significant.Results:We found 11.4% FPN Q248H mutation. T allele frequency was estimated to be 0.0588 ± 0.0052. No significant differences for frequencies of anemia and malaria were observed between FPN Q248H mutation and FPN wild type. However, Plasmodium infected carriers of the FPN Q248H mutation had lower hemoglobin values than wild type children.Conclusion:Even though FPN Q248H mutation is associated with lower hemoglobin values in Plasmodium infected children, it was not found to be protective against malaria and anemia in children under 5 years living in malaria endemic area of South Kivu/Democratic Republic of Congo.

Pediatric acute myeloid leukemia (pedAML) is a heterogeneous blood cancer that affects children. Although survival rates have significantly improved over the past few decades, 20–30% of children will succumb due to treatment-related toxicity or relapse. The molecular characterization of the leukemic stem cell, shown to be responsible for relapse, is needed to improve treatment options and survival. Recently, it has become clear that non-coding RNAs, including long non-coding RNAs (lncRNAs) and microRNAs (miRNAs), play a role in the development of human diseases, including pediatric cancer. Nevertheless, non-coding RNA expression data in pedAML are scarce. Here, we explored lncRNA (n = 30,168) and miRNA (n = 627) expression in pedAML subpopulations (leukemic stem cells (LSCs) and leukemic blasts (L-blasts)) and their normal counterparts (hematopoietic stem cells and control myeloblasts). The potential regulatory activity of differentially expressed lncRNAs in LSCs (unique or shared with the L-blast comparison) on miRNAs was assessed. Moreover, pre-ranked gene set enrichment analyses of (anti-) correlated protein-coding genes were performed to predict the functional relevance of the differentially upregulated lncRNAs in LSCs (unique or shared with the L-blast comparison). In conclusion, this study provides a catalog of non-coding RNAs with a potential role in the pathogenesis of pedAML, paving the way for further translational research studies.

Objective interpretation of FC results may still be hampered by limited technical standardization. The EuroFlow consortium conducted a series of experiments to determine the impact of different variables on the relative distribution and the median fluorescence intensity (MFI) of markers stained on different cell populations, from both healthy donors and patients’ samples with distinct hematological malignancies. The use of different anticoagulants; the time interval between sample collection, preparation, and acquisition; pH of washing buffers; and the use of cell surface membrane-only (SM) vs. cell surface plus intracytoplasmic (SM+CY) staining protocols, were evaluated. Our results showed that only monocytes were represented at higher percentages in EDTA- vs. heparin-anticoagulated samples. Application of SM or SM+CY protocols resulted in slight differences in the percentage of neutrophils and debris determined only with particular antibody combinations. In turn, storage of samples for 24 h at RT was associated with greater percentage of debris and cell doublets when the plasma cell disorder panel was used. Furthermore, 24 h storage of stained cells at RT was selectively detrimental for MFI levels of CD19 and CD45 on mature B- and T-cells (but not on leukemic blasts, clonal B- and plasma cells, neutrophils, and NK cells). The obtained results showed that the variables evaluated might need to be tailored for sample and cell type(s) as well as to the specific markers compared; however, defining of well-balanced boundaries for storage time, staining-to-acquisition delay, and pH of washing buffer would be a valid recommendation for most applications and circumstances described herein.

IntroductionMultiparameter flow cytometry (FCM) immunophenotyping is an important tool in the diagnostic screening and classification of primary immunodeficiencies (PIDs). The EuroFlow Consortium recently developed the PID Orientation Tube (PIDOT) as a universal screening tool to identify lymphoid-PID in suspicious patients. Although PIDOT can identify different lymphoid-PIDs with high sensitivity, clinical validation in a broad spectrum of patients with suspicion of PID is missing. In this study, we investigated the diagnostic performance of PIDOT, as part of the EuroFlow diagnostic screening algorithm for lymphoid-PID, in a daily practice at a tertiary reference center for PID.MethodsPIDOT was tested in 887 consecutive patients suspicious of PID at the Ghent University Hospital, Belgium. Patients were classified into distinct subgroups of lymphoid-PID vs. non-PID disease controls (non-PID DCs), according to the IUIS and ESID criteria. For the clinical validation of PIDOT, comprehensive characterization of the lymphoid defects was performed, together with the identification of the most discriminative cell subsets to distinguish lymphoid-PID from non-PID DCs. Next, a decision-tree algorithm was designed to guide subsequent FCM analyses.ResultsThe mean number of lymphoid defects detected by PIDOT in blood was 2.87 times higher in lymphoid-PID patients vs. non-PID DCs (p < 0.001), resulting in an overall sensitivity and specificity of 87% and 62% to detect severe combined immunodeficiency (SCID), combined immunodeficiency with associated or syndromic features (CID), immune dysregulation disorder (ID), and common variable immunodeficiency (CVID). The most discriminative populations were total memory and switched memory B cells, total T cells, TCD4+cells, and naive TCD4+cells, together with serum immunoglobulin levels. Based on these findings, a decision-tree algorithm was designed to guide further FCM analyses, which resulted in an overall sensitivity and specificity for all lymphoid-PIDs of 86% and 82%, respectively.ConclusionAltogether, our findings confirm that PIDOT is a powerful tool for the diagnostic screening of lymphoid-PID, particularly to discriminate (S)CID, ID, and CVID patients from other patients suspicious of PID. The combination of PIDOT and serum immunoglobulin levels provides an efficient guide for further immunophenotypic FCM analyses, complementary to functional and genetic assays, for accurate PID diagnostics.

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.

AbstractReproducible expert-independent flow-cytometric criteria for the differential diagnoses between mature B-cell neoplasms are lacking. We developed an algorithm-driven classification for these lymphomas by flow cytometry and compared it to the WHO gold standard diagnosis. Overall, 662 samples from 662 patients representing 9 disease categories were analyzed at 9 laboratories using the previously published EuroFlow 5-tube-8-color B-cell chronic lymphoproliferative disease antibody panel. Expression levels of all 26 markers from the panel were plotted by B-cell entity to construct a univariate, fully standardized diagnostic reference library. For multivariate data analysis, we subsequently used canonical correlation analysis of 176 training cases to project the multidimensional space of all 26 immunophenotypic parameters into 36 2-dimensional plots for each possible pairwise differential diagnosis. Diagnostic boundaries were fitted according to the distribution of the immunophenotypes of a given differential diagnosis. A diagnostic algorithm based on these projections was developed and subsequently validated using 486 independent cases. Negative predictive values exceeding 92.1% were observed for all disease categories except for follicular lymphoma. Particularly high positive predictive values were returned in chronic lymphocytic leukemia (99.1%), hairy cell leukemia (97.2%), follicular lymphoma (97.2%), and mantle cell lymphoma (95.4%). Burkitt and CD10+ diffuse large B-cell lymphomas were difficult to distinguish by the algorithm. A similar ambiguity was observed between marginal zone, lymphoplasmacytic, and CD10− diffuse large B-cell lymphomas. The specificity of the approach exceeded 98% for all entities. The univariate immunophenotypic library and the multivariate expert-independent diagnostic algorithm might contribute to increased reproducibility of future diagnostics in mature B-cell neoplasms.

Juvenile myelomonocytic leukemia (JMML), a rare myelodysplastic/myeloproliferative neoplasm of early childhood, is characterized by clonal growth of RAS signaling addicted stem cells. JMML subtypes are defined by specific RAS pathway mutations and display distinct gene, microRNA (miRNA) and long non-coding RNA expression profiles. Here we zoom in on circular RNAs (circRNAs), molecules that, when abnormally expressed, may participate in malignant deviation of cellular processes. CirComPara software was used to annotate and quantify circRNAs in RNA-seq data of a "discovery cohort" comprising 19 JMML patients and 3 healthy donors (HD). In an independent set of 12 JMML patients and 6 HD, expression of 27 circRNAs was analyzed by qRT-PCR. CircRNA-miRNA-gene networks were reconstructed using circRNA function prediction and gene expression data. We identified 119 circRNAs dysregulated in JMML and 59 genes showing an imbalance of the circular and linear products. Our data indicated also circRNA expression differences among molecular subgroups of JMML. Validation of a set of deregulated circRNAs in an independent cohort of JMML patients confirmed the down-regulation of circOXNAD1 and circATM, and a marked up-regulation of circLYN, circAFF2, and circMCTP1. A new finding in JMML links up-regulated circMCTP1 with known tumor suppressor miRNAs. This and other predicted interactions with miRNAs connect dysregulated circRNAs to regulatory networks. In conclusion, this study provides insight into the circRNAome of JMML and paves the path to elucidate new molecular disease mechanisms putting forward circMCTP1 up-regulation as a robust example.