Publications by authors named "Bachas C"

Background: The proportion of residual leukemic blasts after chemotherapy assessed by multiparameter flow cytometry, is an important prognostic factor for the risk of relapse and overall survival in acute myeloid leukemia (AML). This measurable residual disease (MRD) is used in clinical trials to stratify patients for more or less intensive consolidation therapy. However, an objective and reproducible analysis method to assess MRD status from flow cytometry data is lacking, yet is highly anticipated for broader implementation of MRD testing.

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The most important reason for dismal outcomes in acute myeloid leukemia (AML) is the development of relapse. Leukemia stem cells (LSCs) are hypothesized to initiate relapse, and high CD34+CD38- LSC load is associated with poor prognosis. In 10% of AML patients, CD34 is not or is low expressed on the leukemic cells (<1%), and CD34+CD38- LSCs are absent.

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Many tumours are organised in a hierarchical structure with at its apex a cell that can maintain, establish, and repopulate the tumour-the cancer stem cell. The haematopoietic stem cell (HSC) is the founder cell for all functional blood cells. Like HSCs, the leukaemia stem cells (LSC) are hypothesised to be the leukaemia-initiating cells, which have features of stemness such as self-renewal, quiescence, and resistance to cytotoxic drugs.

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Measurable residual disease (MRD) measured in the bone marrow (BM) of acute myeloid leukemia (AML) patients after induction chemotherapy is an established prognostic factor. Hemodilution, stemming from peripheral blood (PB) mixing within BM during aspiration, can yield false-negative MRD results. We prospectively examined hemodilution by measuring MRD in BM aspirates obtained from three consecutive 2 mL pulls, along with PB samples.

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Background: Measurable residual disease (MRD) assessed by multiparametric flow cytometry (MFC) has gained importance in clinical decision-making for acute myeloid leukemia (AML) patients. However, complying with the recent In Vitro Diagnostic Regulations (IVDR) in Europe and Food and Drug Administration (FDA) guidance in the United States requires rigorous validation prior to their use in investigational clinical trials and diagnostics. Validating AML MRD-MFC assays poses challenges due to the unique underlying disease biology and paucity of patient specimens.

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Universal properties of two-dimensional conformal interfaces are encoded by the flux of energy transmitted and reflected during a scattering process. We develop an innovative method that allows us to use results for the energy transmission in thin-brane holographic models to find the energy transmission for general smooth domain-wall solutions of three-dimensional gravity. Our method is based on treating the continuous geometry as a discrete set of branes.

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Although most modern techniques and analysis methods in multiparameter flow cytometry (MFC) allow for increased dimensionality for the characterization and quantification of cell populations, most MFC applications depend on flow cytometers measuring relatively small (<16) numbers of parameters. When more markers than the available parameters need to be acquired, these are commonly distributed over multiple independent measurements that include a backbone of common markers. Several methods have been proposed to impute values for combinations of markers that were not measured simultaneously.

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Measurable residual disease (MRD) measured using multiparameter flow-cytometry (MFC) has proven to be an important prognostic biomarker in acute myeloid leukemia (AML). In addition, MRD is increasingly used to guide consolidation treatment towards a non-allogenic stem cell transplantation treatment for MRD-negative patients in the ELN-2017 intermediate risk group. Currently, measurement of MFC-MRD in bone marrow is used for clinical decision making after 2 cycles of induction chemotherapy.

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In acute myeloid leukemia (AML), a small cell population that contains stem cell features such as lack of differentiation, self-renewal potential, and drug resistance, can be identified. These so-called leukemic stem cells (LSCs) are thought to be responsible for relapse initiation after initial treatment leading to successful eradication of the bulk AML cell population. Since many studies have aimed to characterize and eliminate LSCs to prevent relapse and increase survival rates of patients, LSCs are one of the best characterized cancer stem cells.

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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.

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Measurable residual disease (MRD) quantified by multiparameter flow cytometry (MFC) is a strong and independent prognostic factor in acute myeloid leukemia (AML). However, several technical factors may affect the final read-out of the assay. Experts from the MRD Working Party of the European LeukemiaNet evaluated which aspects are crucial for accurate MFC-MRD measurement.

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Measurable residual disease (MRD) is an important biomarker in acute myeloid leukemia (AML) that is used for prognostic, predictive, monitoring, and efficacy-response assessments. The European LeukemiaNet (ELN) MRD Working Party evaluated standardization and harmonization of MRD in an ongoing manner and has updated the 2018 ELN MRD recommendations based on significant developments in the field. New and revised recommendations were established during in-person and online meetings, and a 2-stage Delphi poll was conducted to optimize consensus.

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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).

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Scattering from conformal interfaces in two dimensions is universal in that the flux of reflected and transmitted energy does not depend on the details of the initial state. In this Letter, we present the first gravitational calculation of energy reflection and transmission coefficients for interfaces with thin-brane holographic duals. Our result for the reflection coefficient depends monotonically on the tension of the dual string anchored at the interface and obeys the lower bound recently derived from the averaged-null-energy condition in conformal field theory.

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Novel treatment strategies are of paramount importance to improve clinical outcomes in pediatric AML. Since chemotherapy is likely to remain the cornerstone of curative treatment of AML, insights in the molecular mechanisms that determine its cytotoxic effects could aid further treatment optimization. To assess which genes and pathways are implicated in tumor drug resistance, we correlated ex vivo drug response data to genome-wide gene expression profiles of 73 primary pediatric AML samples obtained at initial diagnosis.

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Leukaemic stem cells (LSC) have been experimentally defined as the leukaemia-propagating population and are thought to be the cellular reservoir of relapse in acute myeloid leukaemia (AML). Therefore, LSC measurements are warranted to facilitate accurate risk stratification. Previously, we published the composition of a one-tube flow cytometric assay, characterised by the presence of 13 important membrane markers for LSC detection.

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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).

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Limited data are available on the incidence and impact of TP53 alterations and TP53 pathway deregulation in paediatric acute myeloid leukaemia (AML). We analysed TP53 alterations in bone marrow samples of 229 patients with de novo paediatric AML, and detected heterozygous missense exon mutations in two patients (1%) and 17p deletions of the TP53 gene in four patients (2%). These patients more frequently had complex karyotype (50% vs.

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Head and neck squamous cell carcinomas (HNSCCs) coincide with poor survival rates. The lack of driver oncogenes complicates the development of targeted treatments for HNSCC. Here, we follow-up on two previous genome-wide RNA and microRNA interference screens in HNSCC to cross-examine tumor-specific lethality by targeting ATM, ATR, CHEK1, or CHEK2.

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Background: Reproducibility of hits from independent CRISPR or siRNA screens is poor. This is partly due to data normalization primarily addressing technical variability within independent screens, and not the technical differences between them.

Results: We present "rscreenorm", a method that standardizes the functional data ranges between screens using assay controls, and subsequently performs a piecewise-linear normalization to make data distributions across all screens comparable.

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Development of relapse remains a problem for further improvements in the survival of pediatric AML patients. While virtually all patients show a good response to initial treatment, more patients respond poorly when treated at relapse. The cellular characteristics of leukemic blast cells that allow survival of initial treatment, relapse development and subsequent resistance to salvage treatment remain largely elusive.

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