CKLF and IL1B transcript levels at diagnosis are predictive of relapse in children with pre‐B‐cell acute lymphoblastic leukaemia
نویسندگان
چکیده
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 relapse. Herein, we used a machine-learning approach identify B-ALL blast-secreted factors that are associated with poor survival outcomes. Using this approach, identified two-gene expression signature (CKLF and IL1B) allowed identification high-risk diagnosis. This enhances predictive value current diagnosis or end-of-induction stratification suggesting model can be applied continuously help guide implementation risk-adapted therapies.
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ژورنال
عنوان ژورنال: British Journal of Haematology
سال: 2021
ISSN: ['0007-1048', '1365-2141']
DOI: https://doi.org/10.1111/bjh.17161