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Chulián S, Martínez-Rubio Á, Pérez-García VM, et al. High-Dimensional Analysis of Single-Cell Flow Cytometry Data Predicts Relapse in Childhood Acute Lymphoblastic Leukaemia. Cancers (Basel). 2020;13(1)doi: 10.3390/cancers13010017.
Chulián, S., Martínez-Rubio, �. �., Pérez-García, V. M., Rosa, M., Blázquez Goñi, C., Rodríguez Gutiérrez, J. F., Hermosín-Ramos, L., Molinos Quintana, �. �., Caballero-Velázquez, T., Ramírez-Orellana, M., Castillo Robleda, A., & Fernández-Martínez, J. L. (2020). High-Dimensional Analysis of Single-Cell Flow Cytometry Data Predicts Relapse in Childhood Acute Lymphoblastic Leukaemia. Cancers, 13(1), . https://doi.org/10.3390/cancers13010017
Chulián, Salvador, et al. "High-Dimensional Analysis of Single-Cell Flow Cytometry Data Predicts Relapse in Childhood Acute Lymphoblastic Leukaemia." Cancers vol. 13,1 (2020). doi: https://doi.org/10.3390/cancers13010017
Chulián S, Martínez-Rubio Á, Pérez-García VM, Rosa M, Blázquez Goñi C, Rodríguez Gutiérrez JF, Hermosín-Ramos L, Molinos Quintana Á, Caballero-Velázquez T, Ramírez-Orellana M, Castillo Robleda A, Fernández-Martínez JL. High-Dimensional Analysis of Single-Cell Flow Cytometry Data Predicts Relapse in Childhood Acute Lymphoblastic Leukaemia. Cancers (Basel). 2020 Dec 23;13(1). doi: 10.3390/cancers13010017. PMID: 33374500; PMCID: PMC7793064.
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Cancers (Basel). 2020 Dec 23;13(1). doi: 10.3390/cancers13010017.

High-Dimensional Analysis of Single-Cell Flow Cytometry Data Predicts Relapse in Childhood Acute Lymphoblastic Leukaemia.

Cancers

Salvador Chulián, Álvaro Martínez-Rubio, Víctor M Pérez-García, María Rosa, Cristina Blázquez Goñi, Juan Francisco Rodríguez Gutiérrez, Lourdes Hermosín-Ramos, Águeda Molinos Quintana, Teresa Caballero-Velázquez, Manuel Ramírez-Orellana, Ana Castillo Robleda, Juan Luis Fernández-Martínez

Affiliations

  1. Department of Mathematics, Universidad de Cádiz, Puerto Real, 11510 Cádiz, Spain.
  2. Biomedical Research and Innovation Institute of Cádiz (INiBICA), Hospital Universitario Puerta del Mar, 11009 Cádiz, Spain.
  3. Department of Mathematics, Mathematical Oncology Laboratory (MOLAB), Universidad de Castilla-La Mancha, 13071 Ciudad Real, Spain.
  4. Instituto de Matemática Aplicada a la Ciencia y la Ingeniería (IMACI), Universidad de Castilla-La Mancha, 13071 Ciudad Real, Spain.
  5. ETSI Industriales, Universidad de Castilla-La Mancha, 13071 Ciudad Real, Spain.
  6. Department of Paediatric Haematology and Oncology, 11407 Hospital de Jerez Cádiz, Spain.
  7. Department of Haematology, Hospital Vírgen del Rocío, 41103 Sevilla, Spain.
  8. Department of Haematology, Hospital Vírgen del Rocío/University of Sevilla, 41103 Sevilla, Spain.
  9. Department of Paediatric Haematology and Oncology, Hospital Infantil Universitario Niño Jesús, Instituto Investigación Sanitaria La Princesa, 28009 Madrid, Spain.
  10. Department of Mathematics, Group of Inverse Problems, Optimisation and Machine Learning, University of Oviedo, 33005 Oviedo, Spain.

PMID: 33374500 PMCID: PMC7793064 DOI: 10.3390/cancers13010017

Abstract

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.

Keywords: Acute Lymphoblastic Leukaemia; CD38; Fisher’s Ratio; flow cytometry data; mathematical oncology; personalised medicine; response biomarkers

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