Computational flow cytometry as a diagnostic tool in suspected-myelodysplastic syndromes

The diagnostic work-up of patients suspected for myelodysplastic syndromes is challenging and mainly relies on bone marrow morphology and cytogenetics. In this study, we developed and prospectively validated a fully computational tool for flow cytometry diagnostics in suspected-MDS. The computational diagnostic workflow consists of methods for pre-processing flow cytometry data, followed by a cell population detection method (FlowSOM) and a machine learning classifier (Random Forest). Based on a six tubes FC panel, the workflow obtained a 90% sensitivity and 93% specificity in an independent validation cohort. For practical advantages (e.g., reduced processing time and costs), a second computational diagnostic workflow was trained, solely based on the best performing single tube of the training cohort. This workflow obtained 97% sensitivity and 95% specificity in the prospective validation cohort. Both workflows outperformed the conventional, expert analyzed flow cytometry scores for diagnosis with respect to accuracy, objectivity and time investment (less than 2 min per patient)

Targeting Toll-like receptor 7/8 enhances uptake of apoptotic leukemic cells by monocyte-derived dendritic cells but interferes with subsequent cytokine-induced maturation

Therapeutic vaccination with dendritic cells (DC) is an emerging investigational therapy for eradication of minimal residual disease in acute myeloid leukemia. Various strategies are being explored in manufacturing DC vaccines ex vivo, e.g., monocyte-derived DC (MoDC) loaded with leukemia-associated antigens (LAA). However, the optimal source of LAA and the choice of DC-activating stimuli are still not well defined. Here, loading with leukemic cell preparations (harboring both unknown and known LAA) was explored in combination with a DC maturation-inducing cytokine cocktail (CC; IL-1β, IL-6, TNF-α, and PGE2) and Toll-like receptor ligands (TLR-L) to optimize uptake. Since heat shock induced apoptotic blasts were more efficiently taken up than lysates, we focused on uptake of apoptotic leukemic cells. Uptake of apoptotic blast was further enhanced by the TLR7/8-L R848 (20–30%); in contrast, CC-induced maturation inhibited uptake. CC, and to a lesser extent R848, enhanced the ability of MoDC to migrate and stimulate T cells. Furthermore, class II-associated invariant chain peptide expression was down-modulated after R848- or CC-induced maturation, indicating enhanced processing and presentation of antigenic peptides. To improve both uptake and maturation, leukemic cells and MoDC were co-incubated with R848 for 24 h followed by addition of CC. However, this approach interfered with CC-mediated MoDC maturation as indicated by diminished migratory and T cell stimulatory capacity, and the absence of IL-12 production. Taken together, our data demonstrate that even though R848 improved uptake of apoptotic leukemic cells, the sequential use of R848 and CC is counter-indicated due to its adverse effects on MoDC maturation

Mesenchymal inflammation drives genotoxic stress in hematopoietic stem cells and predicts disease evolution in human pre-leukemia

Mesenchymal niche cells may drive tissue failure and malignant transformation in the hematopoietic system but the molecular mechanisms and their relevance to human disease remain poorly defined. Here, we show that perturbation of mesenchymal cells in a mouse model of the preleukemic disorder Shwachman-Diamond syndrome induces mitochondrial dysfunction, oxidative stress and activation of DNA damage responses in hematopoietic stem and progenitor cells. Massive parallel RNA sequencing of highly purified mesenchymal cells in the mouse model and a range of human preleukemic syndromes identified p53-S100A8/9-TLR inflammatory signaling as a common driving mechanism of genotoxic stress. Transcriptional activation of this signaling axis in the mesenchymal niche predicted leukemic evolution and progression-free survival in myelodysplastic syndrome, the principal leukemia predisposition syndrome. Collectively, our findings reveal a concept of mesenchymal niche-induced genotoxic stress in heterotypic stem and progenitor cells through inflammatory signaling as an actionable determinant of disease outcome in human preleukemia

Clinical Implication of Multi-Parameter Flow Cytometry in Myelodysplastic Syndromes

Myelodysplastic syndromes (MDS) are a challenging group of diseases for clinicians and researchers, as both disease course and pathobiology are highly heterogeneous. In (suspected) MDS patients, multi-parameter flow cytometry can aid in establishing diagnosis, risk stratification and choice of therapy. This review addresses the developments and future directions of multi-parameter flow cytometry scores in MDS. Additionally, we propose an integrated diagnostic algorithm for suspected MDS

Computational analysis of flow cytometry data in hematological malignancies: future clinical practice?

PURPOSE OF REVIEW: This review outlines the advancements that have been made in computational analysis for clinical flow cytometry data in hematological malignancies. RECENT FINDINGS: In recent years, computational analysis methods have been applied to clinical flow cytometry data of hematological malignancies with promising results. Most studies combined dimension reduction (principle component analysis) or clustering methods (FlowSOM, generalized mixture models) with machine learning classifiers (support vector machines, random forest). For diagnosis and classification of hematological malignancies, many studies have reported results concordant with manual expert analysis, including B-cell chronic lymphoid leukemia detection and acute leukemia classification. Other studies, e.g. concerning diagnosis of myelodysplastic syndromes and classification of lymphoma, have shown to be able to increase diagnostic accuracy. With respect to treatment response monitoring, studies have focused on, for example, computational minimal residual disease detection in multiple myeloma and posttreatment classification of healthy or diseased in acute myeloid leukemia. The results of these studies are encouraging, although accurate relapse prediction remains challenging. To facilitate clinical implementation, collaboration and (prospective) validation in multicenter setting are necessary. SUMMARY: Computational analysis methods for clinical flow cytometry data hold the potential to increase ease of use, objectivity and accuracy in the clinical work-up of hematological malignancies

Employing the immunological synapse in AML: Development of leukemic dendritic cells for active specific immunization

Cytotoxic T cells directed against leukemic blasts have been observed in patients with acute myeloid leukemia (AML). However, generation of efficient T-cell responses is hampered due to several factors that enable AML blasts to protect themselves from the patients immune system. Improved immune responses can be established by the differentiation of AML blasts into AML-derived dendritic cells (DC) thereby conserving their intrinsic leukemia specific antigens and obtaining full capacity to present these antigens to naïve T cells. This review discusses increased immunogenicity of AML blasts by differentiation into AML-DC and describes ways to augment the AML-DC vaccination approach