Myelodysplastic neoplasms dissected into indolent, leukaemic and unfavourable subtypes by computational clustering of haematopoietic stem and progenitor cells.

Myelodysplastic neoplasms (MDS) encompass haematological malignancies, which are characterised by dysplasia, ineffective haematopoiesis and the risk of progression towards acute myeloid leukaemia (AML). Myelodysplastic neoplasms are notorious for their heterogeneity: clinical outcomes range from a near-normal life expectancy to leukaemic transformation or premature death due to cytopenia. The Molecular International Prognostic Scoring System made progress in the dissection of MDS by clinical outcomes.

Immunophenotypic aberrant hematopoietic stem cells in myelodysplastic syndromes: a biomarker for leukemic progression.

Myelodysplastic syndromes (MDS) comprise hematological disorders that originate from the neoplastic transformation of hematopoietic stem cells (HSCs). However, discrimination between HSCs and their neoplastic counterparts in MDS-derived bone marrows (MDS-BMs) remains challenging. We hypothesized that in MDS patients immature CD34CD38 cells with aberrant expression of immunophenotypic markers reflect neoplastic stem cells and that their frequency predicts leukemic progression.

Characterization of myelodysplastic syndromes hematopoietic stem and progenitor cells using mass cytometry.

Background: Myelodysplastic syndromes (MDS) at risk of transformation to acute myeloid leukemia (AML) are difficult to identify. The bone marrows of MDS patients harbor specific hematopoietic stem and progenitor cell (HSPC) abnormalities that may be associated with sub-types and risk-groups. Leukemia-associated characteristics of such cells may identify MDS patients at risk of progression to AML and provide insight in the pathobiology of MDS.

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).

Prognostic relevance of morphological classification models for myelodysplastic syndromes in an era of the revised International Prognostic Scoring System.

Numerous morphological classification models have been developed to organise the heterogeneous spectrum of myelodysplastic syndromes (MDS). While the 2008 update of the World Health Organisation (WHO) is the current standard, the publication of the revised International Prognostic Scoring System (IPSS-R) has illustrated the need for supplemental prognostic information. The aim of this study was to investigate whether morphological classification models for MDS - of both the French-American-British (FAB) group and WHO - provide reliable criteria for their classification into homogeneous and clinically relevant categories with prognostic relevance beyond the IPSS-R.