Computational flow cytometry as a diagnostic tool in suspected-myelodysplastic syndromes.
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ABSTRACT:
SUBMITTER: Duetz C
PROVIDER: S-EPMC8453916 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Flow cytometric analysis is a recommended tool in the diagnosis of myelodysplastic syndromes. Current flow cytometric approaches evaluate the (im)mature myelo-/monocytic lineage with a median sensitivity and specificity of ~71% and ~93%, respectively. We hypothesized that the addition of erythroid lineage analysis could increase the sensitivity of flow cytometry. Hereto, we validated the analysis of erythroid lineage parameters recommended by the International/European LeukemiaNet Working Group for Flow Cytometry in Myelodysplastic Syndromes, and incorporated this evaluation in currently applied flow cytometric models. One hundred and sixty-seven bone marrow aspirates were analyzed; 106 patients with myelodysplastic syndromes, and 61 cytopenic controls. There was a strong correlation between presence of erythroid aberrancies assessed by flow cytometry and the diagnosis of myelodysplastic syndromes when validating the previously described erythroid evaluation. Furthermore, addition of erythroid aberrancies to two different flow cytometric models led to an increased sensitivity in detecting myelodysplastic syndromes: from 74% to 86% for the addition to the diagnostic score designed by Ogata and colleagues, and from 69% to 80% for the addition to the integrated flow cytometric score for myelodysplastic syndromes, designed by our group. In both models the specificity was unaffected. The high sensitivity and specificity of flow cytometry in the detection of myelodysplastic syndromes illustrates the important value of flow cytometry in a standardized diagnostic approach. The trial is registered at www.trialregister.nl as NTR1825; EudraCT n.: 2008-002195-10.
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Project description: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.
| S-EPMC6482988 | biostudies-literature
Project description:Cell cycle progression can be studied with computational models that allow to describe and predict its perturbation by agents as ionizing radiation or drugs. Such models can then be integrated in tools for pre-clinical/clinical use, e.g. to optimize kinetically-based administration protocols of radiation therapy and chemotherapy. We present a deterministic compartmental model, specifically reproducing how cells that survive radiation exposure are distributed in the cell cycle as a function of dose and time after exposure. Model compartments represent the four cell-cycle phases, as a function of DNA content and time. A system of differential equations, whose parameters represent transition rates, division rate and DNA synthesis rate, describes the temporal evolution. Initial model inputs are data from unexposed cells in exponential growth. Perturbation is implemented as an alteration of model parameters that allows to best reproduce cell-cycle profiles post-irradiation. The model is validated with dedicated in vitro measurements on human lung fibroblasts (IMR90). Cells were irradiated with 2 and 5 Gy with a Varian 6 MV Clinac at IRCCS Maugeri. Flow cytometry analysis was performed at the RadBioPhys Laboratory (University of Pavia), obtaining cell percentages in each of the four phases in all studied conditions up to 72 h post-irradiation. Cells show early [Formula: see text]-phase block (increasing in duration as dose increases) and later [Formula: see text]-phase accumulation. For each condition, we identified the best sets of model parameters that lead to a good agreement between model and experimental data, varying transition rates from [Formula: see text]- to S- and from [Formula: see text]- to M-phase. This work offers a proof-of-concept validation of the new computational tool, opening to its future development and, in perspective, to its integration in a wider framework for clinical use.
| S-EPMC7806866 | biostudies-literature
Project description:This review discusses the need for computational modeling in myelodysplastic syndromes (MDS) and early test results.As our evolving understanding of MDS reveals a molecularly complicated disease, the need for sophisticated computer analytics is required to keep track of the number and complex interplay among the molecular abnormalities. Computational modeling and digital drug simulations using whole exome sequencing data input have produced early results showing high accuracy in predicting treatment response to standard of care drugs. Furthermore, the computational MDS models serve as clinically relevant MDS cell lines for pre-clinical assays of investigational agents. MDS is an ideal disease for computational modeling and digital drug simulations. Current research is focused on establishing the prediction value of computational modeling. Future research will test the clinical advantage of computer-informed therapy in MDS.
| S-EPMC5670191 | biostudies-literature
Project description:Flow cytometry is an important technology for the study of microbial communities. It grants the ability to rapidly generate phenotypic single-cell data that are both quantitative, multivariate and of high temporal resolution. The complexity and amount of data necessitate an objective and streamlined data processing workflow that extends beyond commercial instrument software. No full overview of the necessary steps regarding the computational analysis of microbial flow cytometry data currently exists. In this review, we provide an overview of the full data analysis pipeline, ranging from measurement to data interpretation, tailored toward studies in microbial ecology. At every step, we highlight computational methods that are potentially useful, for which we provide a short nontechnical description. We place this overview in the context of a number of open challenges to the field and offer further motivation for the use of standardized flow cytometry in microbial ecology research.
| S-EPMC7820666 | biostudies-literature
Project description:BackgroundWe sought to enhance the cytometric analysis of myelodysplastic syndromes (MDS) by performing a pilot study of a single cell mass cytometry (MCM) assay to more comprehensively analyze patterns of surface marker expression in patients with MDS.MethodsTwenty-three MDS and five healthy donor bone marrow samples were studied using a 34-parameter mass cytometry panel utilizing barcoding and internal reference standards. The resulting data were analyzed by both traditional gating and high-dimensional clustering.ResultsThis high-dimensional assay provided three major benefits relative to traditional cytometry approaches: First, MCM enabled detection of aberrant surface maker at high resolution, detecting aberrancies in 27/31 surface markers, encompassing almost every previously reported MDS surface marker aberrancy. Additionally, three previously unrecognized aberrancies in MDS were detected in multiple samples at least one developmental stage: increased CD321 and CD99; and decreased CD47. Second, analysis of the stem and progenitor cell compartment (HSPCs), demonstrated aberrant expression in 21 of the 23 MDS samples, which were not detected in three samples from patients with idiopathic cytopenia of undetermined significance. These immunophenotypically abnormal HSPCs were also the single most significant distinguishing feature between clinical risk groups. Third, unsupervised clustering of high-parameter MCM data allowed identification of abnormal differentiation patterns associated with immunophenotypically aberrant myeloid cells similar to myeloid derived suppressor cells.ConclusionsThese results demonstrate that high-parameter cytometry methods that enable simultaneous analysis of all bone marrow cell types could enhance the diagnostic utility of immunophenotypic analysis in MDS.
| S-EPMC9292828 | biostudies-literature
Project description:BackgroundImmunophenotypic analysis of the bone marrow (BM) cells has proven to be helpful in the diagnosis of Myelodysplastic Syndromes (MDS). However, the usefulness of flow cytometry (FCM) for the detection of myelodysplasia in the peripheral blood (PB) still needs to be investigated. The aim of this pilot study was to evaluate the value of FCM-based PB neutrophil and monocyte immunophenotyping for the diagnosis of lower risk MDS (LR-MDS).MethodsWe evaluated by 8-color FCM the expression of multiple cell surface molecules (CD10, CD11b, CD11c, CD13, CD14, CD15, CD16, CD34, CD45, CD56, CD64 and HLA-DR) in PB neutrophils and monocytes from a series of 14 adult LR-MDS patients versus 14 normal individuals.ResultsPeripheral blood neutrophils from patients with LR-MDS frequently had low forward scatter (FSC) and side scatter (SSC) values and low levels of CD11b, CD11c, CD10, CD16, CD13 and CD45 expression, in that order, as compared to normal neutrophils. In addition, patients with LR-MDS commonly display a higher fraction of CD14+CD56+ and a lower fraction of CD14+CD16+ monocytes in the PB. Based on these results, we proposed an immunophenotyping score based on which PB samples from patients with LR-MDS could be distinguished from normal PB samples with a sensitivity 93% and a specificity of 100%. In addition, we used this score to construct the MDS Thermometer, a screening tool for detection and monitoring of MDS in the PB in clinical practice.ConclusionsPeripheral blood neutrophil and monocyte immunophenotyping provide useful information for the diagnosis of LR-MDS, as a complement to cytomorphology. If validated by subsequent studies in larger series of MDS patients and extended to non-MDS patients with cytopenias, our findings may improve the diagnostic assessment and avoid invasive procedures in selected groups of MDS patients.
| S-EPMC5850915 | biostudies-literature
Project description:Complex karyotype (CK) is a poor prognosis factor in hematological malignancies. Studies have shown that the presence of CK in myelodysplastic syndrome (MDS) can be associated with MDS progression to acute myeloid leukemia. The goal of this review was to examine the relationship between different types of CK with MDS, as well as its possible role in the deterioration and progression of MDS to leukemia. The content used in this paper has been obtained by a PubMed and Google Scholar search of English language papers (1975-2018) using the terms complex karyotype and myelodysplastic syndromes. A single independent abnormality can be associated with a good prognosis. However, the coexistence of a series of abnormalities can lead to CK, which is associated with the deterioration of MDS and its progression to leukemia. Therefore, CK may be referred to as a prognostic factor in MDS. The detection of independent cytogenetic disorders that altogether can result in CK could be used as a prognostic model for laboratory and clinical use.
| S-EPMC6379782 | biostudies-literature
Project description:The myelodysplastic syndromes are a diverse group of clonal stem cell disorders characterized by ineffective hematopoiesis, peripheral cytopenias, and an increased propensity to evolve to acute myeloid leukemia. The molecular pathogenesis of these disorders is poorly understood, but recurring chromosomal abnormalities occur in approximately 50% of cases and are the focus of much investigation. The availability of newer molecular techniques has allowed the identification of additional genetic aberrations, including mutations and epigenetic changes of prognostic and potential therapeutic importance. This review focuses on the key role of cytogenetic analysis in myelodysplastic syndromes in the context of the diagnosis, prognosis, and pathogenesis of these disorders.
| S-EPMC4340482 | biostudies-literature
Project description:IntroductionThe information content of multiparametric flow cytometry experiments is routinely underexploited given the paucity of adequate tools for unbiased comprehensive data analysis that can be applied successfully and independently by immunologists without computational training.MethodsWe aimed to develop a tool that allows straightforward access to the entire information content of any given flow cytometry panel for immunologists without special computational expertise. We used a data analysis approach which accounts for all mathematically possible combinations of markers in a given panel, coded the algorithm and applied the method to mined and self-generated data sets.ResultsWe developed Flow Plex, a straightforward computational tool that allows unrestricted access to the information content of a given flow cytometry panel, enables classification of human samples according to distinct immune phenotypes, such as different forms of autoimmune uveitis, acute myeloid leukemia vs "healthy", "old" vs "young", and facilitates the identification of cell populations with potential biologic relevance to states of disease and health.ConclusionsWe provide a tool that allows immunologists and other flow cytometry users with limited bioinformatics skills to extract comprehensive, unbiased information from flow cytometry data sets.
| S-EPMC6688088 | biostudies-literature