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Type: Journal article
Title: CKLF and IL1B transcript levels at diagnosis are predictive of relapse in children with pre‐B‐cell acute lymphoblastic leukaemia
Author: 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.
Dalla Pozza, L.
Marshall, G.M.
White, D.L.
Sutton, R.
Zannettino, A.C.W.
Revesz, T.
Citation: British Journal of Haematology, 2021; 193(1):171-175
Publisher: Wiley
Issue Date: 2021
ISSN: 0007-1048
Statement of
Stephen Fitter, Alanah L. Bradey, Chung Hoow Kok, Jacqueline E. Noll, Vicki J. Wilczek, Nicola C. Venn ... et al.
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.
Keywords: Humans
Acute Disease
Treatment Failure
Risk Assessment
Survival Analysis
Predictive Value of Tests
Child, Preschool
Precursor B-Cell Lymphoblastic Leukemia-Lymphoma
MARVEL Domain-Containing Proteins
Machine Learning
Description: First published: 23 February 2021
Rights: © 2021 British Society for Haematology and John Wiley & Sons Ltd.
DOI: 10.1111/bjh.17161
Published version:
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