CKLF and IL1B transcript levels at diagnosis are predictive of relapse in children with pre-B-cell acute lymphoblastic leukaemia.
Stephen FitterAlanah L BradeyChung Hoow KokJacqueline E NollVicki J WilczekNicola C VennTamara LawSakrapee PaisitkriangkraiColin StoryLynda SaundersLuciano Dalla PozzaGlenn M MarshallDeborah L WhiteRosemary SuttonAndrew C W ZannettinoTamas ReveszPublished in: British journal of haematology (2021)
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.
Keyphrases
- gene expression
- liver failure
- machine learning
- end stage renal disease
- dna methylation
- free survival
- respiratory failure
- newly diagnosed
- chronic kidney disease
- ejection fraction
- healthcare
- intensive care unit
- primary care
- drug induced
- emergency department
- aortic dissection
- young adults
- peritoneal dialysis
- prognostic factors
- hepatitis b virus
- rna seq
- combination therapy
- bioinformatics analysis