CKLF and IL1B transcript levels at diagnosis are predictive of relapse in children with pre‐B‐cell acute lymphoblastic leukaemia

Abstract

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.