Gene expression prognostic of early relapse risk in low-risk B-cell acute lymphoblastic leukaemia in children.
Xiaowen GongTianyuan HuQiujin ShenLuyang ZhangWei ZhangXueou LiuSuyu ZongXiaoyun LiTiantian WangWen YanYu HuXiaoli ChenJiarui ZhengAoli ZhangJunxia WangYahui FengChengwen LiJiao MaXin GaoZhen SongYingchi ZhangRobert Peter GaleXiao-Fan ZhuJunren ChenPublished in: EJHaem (2024)
ETV6 :: RUNX1 is the most common fusion gene in childhood acute lymphoblastic leukaemia (ALL) and is associated with favorable outcomes, especially in low-risk children. However, as many as 10% of children relapse within 3 years, and such early relapses have poor survival. Identifying children at risk for early relapse is an important challenge. We interrogated data from 87 children with low-risk ETV6 :: RUNX1 -positive B-cell ALL and with available preserved bone marrow samples (discovery cohort). We profiled somatic point mutations in a panel of 559 genes and genome-wide transcriptome and single-nucleotide variants. We found high TIMD4 expression (> 85th-percentile value) at diagnosis was the most important independent prognostic factor of early relapse (hazard ratio [HR] = 5.07 [1.76, 14.62]; p = 0.03). In an independent validation cohort of low-risk ETV6 :: RUNX1 -positive B-cell ALL ( N = 68) high TIMD4 expression at diagnosis had an HR = 4.78 [1.07, 21.36] ( p = 0.04) for early relapse. In another validation cohort including 78 children with low-risk ETV6 :: RUNX1 -negative B-cell ALL, high TIMD4 expression at diagnosis had an HR = 3.93 [1.31, 11.79] ( p = 0.01). Our results suggest high TIMD4 expression at diagnosis in low-risk B-cell ALL in children might be associated with high risk for early relapse.
Keyphrases
- young adults
- genome wide
- gene expression
- poor prognosis
- acute lymphoblastic leukemia
- bone marrow
- dna methylation
- transcription factor
- copy number
- intensive care unit
- type diabetes
- mesenchymal stem cells
- binding protein
- skeletal muscle
- electronic health record
- insulin resistance
- deep learning
- big data
- aortic dissection
- bioinformatics analysis