Disease relapse is the greatest cause of treatment failure in paediatric B-cell acute lymphoblastic leukaemia (B-ALL). Current risk stratifications fail to capture all patients at risk of relapse. Herein, we used a machine-learning approach to identify B-ALL blast-secreted factors that are associated with poor survival outcomes. Using this approach, we identified a two-gene expression signature (CKLF and IL1B) that allowed identification of high-risk patients at diagnosis. This two-gene expression signature enhances the predictive value of current at diagnosis or end-of-induction risk stratification suggesting the model can be applied continuously to help guide implementation of risk-adapted therapies.
ASJC Scopus subject areas