CKLF and IL1B transcript levels at diagnosis are predictive of relapse in children with pre‐B‐cell acute lymphoblastic leukaemia
Date
2021
Authors
Fitter, S.
Bradey, A.L.
Kok, C.H.
Noll, J.E.
Wilczek, V.J.
Venn, N.C.
Law, T.
Paisitkriangkrai, S.
Story, C.
Saunders, L.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Journal article
Citation
British Journal of Haematology, 2021; 193(1):171-175
Statement of Responsibility
Stephen Fitter, Alanah L. Bradey, Chung Hoow Kok, Jacqueline E. Noll, Vicki J. Wilczek, Nicola C. Venn ... et al.
Conference Name
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.
School/Discipline
Dissertation Note
Provenance
Description
First published: 23 February 2021
Access Status
Rights
© 2021 British Society for Haematology and John Wiley & Sons Ltd.