High-dimensional Analysis of Single-cell Flow Cytometry Data Predicts Relapse in Childhood Acute Lymphoblastic Leukemia
Chulián, S.; Martínez-Rubio, Á.; Pérez-García, V.M.; Rosa, M.; Blázquez-Goñi, C.; Rodríguez Gutiérrez, J.F.; Hermosín-Ramos, L.; Molinos-Quintana, Á.; Caballero-Velázquez, T.; Ramírez-Orellana, M.; Castillo Robleda, A.; Fernández-Martínez, J.L.
Cancers (submitted) (2020)
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Abstract
Artificial intelligence methods may help in unveliling information hidden in high-dimensional oncological data. Flow cytometry studies of haematological malignancies provide quantitative data with the potential to be used for the construction of response biomarkers. Many computational methods from the bioinformatics toolbox can be applied to these data but have not been exploited in their full potential in leukaemias, specifically for the case of childhood B-cell acute lymphoblastic leukemia. In this paper we analysed flow cytometry data obtained on diagnosis from 54 paediatric B-cell acute lymphoblastic leukemia patients from two local institutions. We constructed classifiers based on the Fisher’s Ratio to quantify differences in expression levels of immunophenotypical markers between patients with relapsing and non-relapsing disease. The distribution of the marker CD38 was found and validated to have a strong discriminating power between both patient cohorts, thus providing a classifier.