Automated database-guided expert-supervised orientation for immunophenotypic diagnosis and classification of acute leukemia
Keywords: 
Acute leukemia (AL)
AL orientation tube (ALOT)
B-cell precursor (BCP)
T-cell acute lymphoblastic leukemia (T-ALL)
Issue Date: 
2018
Publisher: 
Springer Nature
Project: 
info:eu-repo/grantAgreement/MINECO/Ayudas a la incorporación de nuevas áreas temáticas y nuevos grupos al Consorcio CIBER (AE de Salud 2016)/CB16/12/00400/ES/CANCER
ISSN: 
0887-6924
Note: 
This work is licensed under a Creative Commons AttributionNonCommercial-NoDerivs 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http:// creativecommons.org/licenses/by-nc-nd/4.0/
Citation: 
Lhermitte, L. (Ludovic); Mejstrikova, E. (Ester); Sluijs-Gelling, A. (Alita) van der; et al. "Automated database-guided expert-supervised orientation for immunophenotypic diagnosis and classification of acute leukemia". Leukemia. 32 (4), 2018, 874 - 881
Abstract
Precise classification of acute leukemia (AL) is crucial for adequate treatment. EuroFlow has previously designed an AL orientation tube (ALOT) to guide towards the relevant classification panel (T-cell acute lymphoblastic leukemia (T-ALL), B-cell precursor (BCP)-ALL and/or acute myeloid leukemia (AML)) and final diagnosis. Now we built a reference database with 656 typical AL samples (145 T-ALL, 377 BCP-ALL, 134 AML), processed and analyzed via standardized protocols. Using principal component analysis (PCA)-based plots and automated classification algorithms for direct comparison of single-cells from individual patients against the database, another 783 cases were subsequently evaluated. Depending on the database-guided results, patients were categorized as: (i) typical T, B or Myeloid without or; (ii) with a transitional component to another lineage; (iii) atypical; or (iv) mixed-lineage. Using this automated algorithm, in 781/783 cases (99.7%) the right panel was selected, and data comparable to the final WHO-diagnosis was already provided in >93% of cases (85% T-ALL, 97% BCP-ALL, 95% AML and 87% mixed-phenotype AL patients), even without data on the full-characterization panels. Our results show that database-guided analysis facilitates standardized interpretation of ALOT results and allows accurate selection of the relevant classification panels, hence providing a solid basis for designing future WHO AL classifications.

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