Automated database-guided expert-supervised orientation for immunophenotypic diagnosis and classification of acute leukemia
L Lhermitte, E Mejstrikova, A J van der Sluijs-Gelling, G E Grigore, L Sedek, A E Bras, G Gaipa, E Sobral da Costa, M Novakova, E Sonneveld, C Buracchi, T de Sá Bacelar, J G Te Marvelde, A Trinquand, V Asnafi, T Szczepanski, S Matarraz, A Lopez, B Vidriales, J Bulsa, O Hrusak, T Kalina, Q Lecrevisse, M Martin Ayuso, M Brüggemann, J Verde, P Fernandez, L Burgos, B Paiva, C E Pedreira, J J M van Dongen, A Orfao, V H J van der Velden
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.
CITATION Leukemia. 2018 Apr;32(4):874-881. doi: 10.1038/leu.2017.313. Epub 2017 Nov 1.