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. 2021 Jan;34(1):59-69.
doi: 10.1038/s41379-020-00677-7. Epub 2020 Sep 30.

Automated identification of leukocyte subsets improves standardization of database-guided expert-supervised diagnostic orientation in acute leukemia: a EuroFlow study

Ludovic Lhermitte  1 , Sylvain Barreau #  1 , Daniela Morf #  2 , Paula Fernandez  2 , Georgiana Grigore  3 , Susana Barrena  3   4   5   6   7 , Maaike de Bie  8 , Juan Flores-Montero  4   5   6   7 , Monika Brüggemann  9 , Ester Mejstrikova  10 , Stefan Nierkens  11 , Leire Burgos  12 , Joana Caetano  13 , Giuseppe Gaipa  14 , Chiara Buracchi  14 , Elaine Sobral da Costa  15 , Lukasz Sedek  16 , Tomasz Szczepański  17 , Carmen-Mariana Aanei  18 , Alita van der Sluijs-Gelling  19 , Alejandro Hernández Delgado  3   4   5   6   7 , Rafael Fluxa  3 , Quentin Lecrevisse  4   5   6   7 , Carlos E Pedreira  20 , Jacques J M van Dongen  19 , Alberto Orfao  4   5   6   7 , Vincent H J van der Velden  21 ; EuroFlow Consortium
Collaborators, Affiliations

Automated identification of leukocyte subsets improves standardization of database-guided expert-supervised diagnostic orientation in acute leukemia: a EuroFlow study

Ludovic Lhermitte et al. Mod Pathol. 2021 Jan.

Abstract

Precise classification of acute leukemia (AL) is crucial for adequate treatment. EuroFlow has previously designed an AL orientation tube (ALOT) to guide toward the relevant classification panel and final diagnosis. In this study, we designed and validated an algorithm for automated (database-supported) gating and identification (AGI tool) of cell subsets within samples stained with ALOT. A reference database of normal peripheral blood (PB, n = 41) and bone marrow (BM; n = 45) samples analyzed with the ALOT was constructed, and served as a reference for the AGI tool to automatically identify normal cells. Populations not unequivocally identified as normal cells were labeled as checks and were classified by an expert. Additional normal BM (n = 25) and PB (n = 43) and leukemic samples (n = 109), analyzed in parallel by experts and the AGI tool, were used to evaluate the AGI tool. Analysis of normal PB and BM samples showed low percentages of checks (<3% in PB, <10% in BM), with variations between different laboratories. Manual analysis and AGI analysis of normal and leukemic samples showed high levels of correlation between cell numbers (r2 > 0.95 for all cell types in PB and r2 > 0.75 in BM) and resulted in highly concordant classification of leukemic cells by our previously published automated database-guided expert-supervised orientation tool for immunophenotypic diagnosis and classification of acute leukemia (Compass tool). Similar data were obtained using alternative, commercially available tubes, confirming the robustness of the developed tools. The AGI tool represents an innovative step in minimizing human intervention and requirements in expertise, toward a "sample-in and result-out" approach which may result in more objective and reproducible data analysis and diagnostics. The AGI tool may improve quality of immunophenotyping in individual laboratories, since high percentages of checks in normal samples are an alert on the quality of the internal procedures.

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Conflict of interest statement

JJMvD, AO, JFM, VHJvdV, LL, EM, and TS each report being one of the inventors on the EuroFlow-owned patent PCT/NL2010/050332 (Methods, reagents and kits for flowcytometric immunophenotyping of normal, reactive, and malignant leukocytes). The Infinicyt software is based on intellectual property (IP) of some EuroFlow laboratories (University of Salamanca in Spain and Federal University of Rio de Janeiro in Brazil) and the scientific input of other EuroFlow members. All above mentioned intellectual property and related patents are licensed to Cytognos (Salamanca, ES) and BD Biosciences (San José, CA), which companies pay royalties to the EuroFlow Consortium. These royalties are exclusively used for continuation of the EuroFlow collaboration and sustainability of the EuroFlow consortium. MB reports to belong to speakers bureau/honoraria (Amgen, Celgene, Janssen), Advisory board/committee (Amgen, Janssen), to be involved in consultancy (Amgen, Incyte, PRMA) and to receive research funding (Affimed, Amgen, Celgene, Regeneron). VHJvdV reports a Laboratory Services Agreement with BD Biosciences; all related fees are for the Erasmus MC. JJMvD and AO report an Educational Services Agreement from BD Biosciences (San José, CA) and a Scientific Advisor Agreement with Cytognos; all related fees and honoraria are for the involved university departments at Leiden University Medical Center and University of Salamanca. GG, SB, AHD, and RF are employees of Cytognos. The other authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Fig. 1
Fig. 1. Overview of the study.
a Construction of the ALOT databases. asmCD3 or cyCD3 (n = 8), general (n = 1); bsmCD3 or cyCD3 (n = 5), CD19 (n = 2), cyCD79a (n = 3), MPO (n = 18); CD45 (n = 3); CD7 and CD34 (n = 2). b Overall pipeline for data analysis of tested samples. An ALOT database of flow cytometry data files corresponding to normal PB and BM samples stained with ALOT was built to serve as a reference to use in combination with the automated gating and identification (AGI) tool (red box). Any leukemic sample analyzed using the ALOT and the EuroFlow standardized operating protocol (SOP) could serve as an input to the AGI tool (left panel). Every single event was assigned to normal populations according to the reference database. Events whose immunophenotypic pattern did not match the exact phenotype of normal populations were labeled as “checks” and were submitted to the expert for appropriate final classification into debris, doublets, normal subsets, or abnormal population. Abnormal (leukemic) population(s) were then processed by our previously published database-guided expert-supervised algorithm (Compass tool) to describe the phenotypic composition of the leukemic population(s) and guide toward the appropriate panel for complete characterization and diagnosis of the leukemia subtype (green box). Briefly, this process used a compass algorithm and a large ALOT reference database of 656 leukemic samples to compare the immunophenotypic patterns and provide an output. To evaluate the performance of the AGI tool, a manual analysis was run, followed by the Compass algorithm in parallel to this AGI and Compass analyses. Comparison of the two approaches was based on: the number and nature of checks, the phenotypic description of the leukemia bulk, and the final panel orientation as readouts.
Fig. 2
Fig. 2. Correlations between the number of events for different leukocyte subsets present in peripheral blood as analyzed by manual analysis vs. the AGI tool.
Pearson R2 are shown. Post-AGI review, all correlations had p values <0.001.
Fig. 3
Fig. 3. Correlations between number of events for different leukocyte subsets present in bone marrow as analyzed by manual analysis vs. the AGI tool.
Pearson R2 are shown. Post-AGI review, all correlations had p values <0.001.
Fig. 4
Fig. 4. Comparison of Compass tool data for acute leukemia cells in peripheral blood (PB, left) or bone marrow (BM, right), as identified by manual analysis (MA) or the AGI tool (AGI).
Representative examples are shown, additional cases are shown in the Supplementary materials.
Fig. 5
Fig. 5. Reproducibility of manual analysis (MA) and AGI analysis.
a Twenty-six samples were evaluated both manually and by AGI tool by seven independent experts to define the intra-person variability. b Ten samples were independently analyzed twice by three experts both manually and by AGI tool to define the inter-person variability. Data represent mean values of %CV values calculated for each of the 10 samples for the indicated cell populations.
See this image and copyright information in PMC

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