Quality Assessment of a Large Multi-Center Flow Cytometric Dataset of Acute Myeloid Leukemia Patients-A EuroFlow Study.
Anne E BrasSergio MatarrazStefan NierkensPaula FernándezJan PhilippéCarmen-Mariana AaneiFabiana Vieira de MelloLeire BurgosAlita J van der Sluijs-GellingGeorgiana Emilia GrigoreJacques J M van DongenAlberto OrfaoVincent H J van der Veldennull On Behalf Of The EuroFlow ConsortiumPublished in: Cancers (2022)
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.
Keyphrases
- acute myeloid leukemia
- data analysis
- bone marrow
- allogeneic hematopoietic stem cell transplantation
- big data
- mesenchymal stem cells
- emergency department
- case report
- end stage renal disease
- machine learning
- oxidative stress
- induced apoptosis
- acute lymphoblastic leukemia
- signaling pathway
- peritoneal dialysis
- chronic kidney disease
- single cell
- cell death
- human health
- pi k akt
- psychometric properties
- drug induced