Detalle Publicación

ARTÍCULO
Automated database-guided expert-supervised orientation for immunophenotypic diagnosis and classification of acute leukemia
Autores: Lhermitte, L.; Mejstrikova, E.; van der Sluijs-Gelling, A. J.; Grigore, G. E. ; Sedek, L.; Bras, A. E.; Gaipa, G.; da Costa, E. S.; Novakova, M.; Sonneveld, E.; Buracchi, C.; Bacelar, T. D. ; Marvelde, J. G. T.; Trinquand, A.; Asnafi, V. ; Szczepanski, T.; Matarraz, S.; Lopez, A.; Vidriales, B.; Bulsa, J.; Hrusak, O.; Kalina, T.; Lecrevisse, Q.; Ayuso, M. M. ; Bruggemann, M.; Verde, J.; Fernandez, P. ; Burgos Rodríguez, Leire; Paiva, Bruno; Pedreira, C. E.; van Dongen, J. J. M.; Orfao, A. (Autor de correspondencia); van der Velden, V. H. J.
Título de la revista: LEUKEMIA
ISSN: 0887-6924
Volumen: 32
Número: 4
Páginas: 874 - 881
Fecha de publicación: 2018
Lugar: WOS
Resumen:
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 493% 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.