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Recurrence is one of the most intriguing questions in acute lymphoblastic leukemia (ALL), the leading common cancer in childhood. Prediction of the early relapse in this and other cancer types is a fundamental asset to address therapy and risk stratification.
In a recent preprint, researchers from MOLAB at Universities of Castilla-La Mancha and Cádiz have analyzed data from pediatric B-cell acute lymphoblastic leukemia patients obtained on diagnosis. Data from Hospital Niño Jesús (Madrid, Spain) and Hospital Virgen del Rocío (Seville, Spain) were available for the study. This kind of data can be exploited with computational, including artificial intelligence, methods.
MOLAB researchers designed an intuitive algorithm allowing to identify on diagnosis patients with potential or relapse versus those with no risk of relapse in B-cell childhood ALL. Even several biomarkers were found as relevant, one of the most interesting results was the association between a lower expression of CD38 in B cells marker and relapse.
The implications of this research are that the obtained metrics can anticipate which patients are not likely to respond to first-line chemotherapies. Thus alternative therapies could be used on those patients whithout waiting for first-line chemotherapies failure and avoiding the toxicity of an ineffective treatment.
High-dimensional analysis of single-cell flow cytometry data predicts relapse in Childhood Acute Lymphoblastic Leukemia
S. Chulián, Á.Martínez-Rubio, V. Pérez-García, M. Rosa, C. Blázquez-Goñi, J. F. Rodríguez-Gutiérrez, L. Hermosín-Ramos, Á.Molinos-Quintana, T. Caballero-Velázquez, M. Ramírez-Orellana, A. Castillo-Robleda, J.L Fernández-Martínez
Preprints 202010.0557.v1 submitted to Cancers
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