RT Journal Article
T1 High-Dimensional Analysis of Single-Cell Flow Cytometry Data Predicts Relapse in Childhood Acute Lymphoblastic Leukaemia.
A1 Chulián, Salvador
A1 Martínez-Rubio, Álvaro
A1 Pérez-García, Víctor M
A1 Rosa, María
A1 Blázquez Goñi, Cristina
A1 Rodríguez Gutiérrez, Juan Francisco
A1 Hermosín-Ramos, Lourdes
A1 Molinos Quintana, Águeda
A1 Caballero-Velázquez, Teresa
A1 Ramírez-Orellana, Manuel
A1 Castillo Robleda, Ana
A1 Fernández-Martínez, Juan Luis
K1 Acute Lymphoblastic Leukaemia
K1 CD38
K1 Fisher’s Ratio
K1 flow cytometry data
K1 mathematical oncology
K1 personalised medicine
K1 response biomarkers
AB Artificial intelligence methods may help in unveiling information that is 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 they have not been exploited in their full potential in leukaemias, specifically for the case of childhood B-cell Acute Lymphoblastic Leukaemia. In this paper, we analysed flow cytometry data that were obtained at diagnosis from 56 paediatric B-cell Acute Lymphoblastic Leukaemia patients from two local institutions. Our aim was to assess the prognostic potential of immunophenotypical marker expression intensity. We constructed classifiers that are based on the Fisher's Ratio to quantify differences between patients with relapsing and non-relapsing disease. We also correlated this with genetic information. The main result that arises from the data was the association between subexpression of marker CD38 and the probability of relapse.
SN 2072-6694
YR 2020
FD 2020-12-23
LK https://hdl.handle.net/10668/25117
UL https://hdl.handle.net/10668/25117
LA en
DS RISalud
RD Sep 4, 2025