Related Experiment Video
Updated: Mar 25, 2026

Murine Model of Leukemia Relapse to Induction Chemotherapy for Acute Lymphoblastic Leukemia
Published on: October 17, 2025
New cellular markers at diagnosis are associated with isolated central nervous system relapse in paediatric B-cell
Vincent H J van der Velden1, Daphne de Launaij1, Jeltje F de Vries1
1Department of Immunology, Erasmus MC, University Medical Centre Rotterdam, Rotterdam, The Netherlands.
Abstract:
In childhood acute lymphoblastic leukaemia (ALL), central nervous system (CNS) involvement is rare at diagnosis (1-4%), but more frequent at relapse (~30%). Because of the significant late sequelae of CNS treatment, early identification of patients at risk of CNS relapse is crucial. Using microarray-analysis, we discovered multiple differentially expressed genes between B-cell precursor (BCP) ALL cells in bone marrow (BM) and BCP-ALL cells in cerebrospinal fluid (CSF) at the time of isolated CNS relapse. After confirmation by real-time quantitative polymerase chain reaction, selected genes (including SCD and SPP1) were validated at the protein level by flowcytometric analysis of BCP-ALL cells in CSF. Further flowcytometric validation showed that a subpopulation of BCP-ALL cells (>1%) with a 'CNS protein profile' (SCD positivity and increased SPP1 expression) was present in the BM at diagnosis in patients who later developed an isolated CNS relapse, whereas this subpopulation was <1% or absent in all other patients. These data indicate that the presence of a (small) subpopulation of BCP-ALL cells with a 'CNS protein profile' at diagnosis (particularly SCD-positivity) is associated with isolated CNS relapse. Such information can be used to design new diagnostic and treatment strategies that aim at prevention of CNS relapse with reduced toxicity.
More Related Videos
09:01Flow Cytometry to Estimate Leukemia Stem Cells in Primary Acute Myeloid Leukemia and in Patient-derived-xenografts, at Diagnosis and Follow Up
Published on: March 26, 2018
07:35Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018