Fusion gene map of acute leukemia revealed by transcriptome sequencing of a consecutive cohort of 1000 cases in a single center.
Xue ChenFang WangYang ZhangXiaoli MaPanxiang CaoLili YuanLan WangJiaqi ChenXiaosu ZhouQisheng WuMing LiuDavid JinHongxing LiuPublished in: Blood cancer journal (2021)
Fusion genes (FGs) are important genetic abnormalities in acute leukemias, but their variety and occurrence in acute leukemias remain to be systematically described. Whole transcriptome sequencing (WTS) provides a powerful tool for analyzing FGs. Here we report the FG map revealed by WTS in a consecutive cohort of 1000 acute leukemia cases in a single center, including 539 acute myeloid leukemia (AML), 437 acute lymphoblastic leukemia (ALL), and 24 mixed-phenotype acute leukemia (MPAL) patients. Bioinformatic analysis identified 792 high-confidence in-frame fusion events (296 distinct fusions) which were classified into four tiers. Tier A (pathogenic), B (likely pathogenic), and C (uncertain significance) FGs were identified in 61.8% cases of the total cohort (59.7% in AML, 64.5% in ALL, and 63.6% in MPAL). FGs involving protein kinase, transcription factor, and epigenetic genes were detected in 10.7%, 48.5%, and 15.1% cases, respectively. A considerable amount of novel FGs (82 in AML, 88 in B-ALL, 13 in T-ALL, and 9 in MPAL) was identified. This comprehensively described real map of FGs in acute leukemia revealed multiple FGs with clinical relevance that have not been previously recognized. WTS is a valuable tool and should be widely used in the routine diagnostic workup of acute leukemia.
Keyphrases
- acute myeloid leukemia
- genome wide
- single cell
- allogeneic hematopoietic stem cell transplantation
- acute lymphoblastic leukemia
- dna methylation
- transcription factor
- liver failure
- genome wide identification
- rna seq
- gene expression
- end stage renal disease
- copy number
- ejection fraction
- respiratory failure
- risk assessment
- protein kinase
- newly diagnosed
- high density
- drug induced
- prognostic factors
- intensive care unit
- bioinformatics analysis
- peritoneal dialysis
- dna binding