Rapid molecular response to dasatinib in Ph-like acute lymphoblastic leukemia patients with ABL1 rearrangements: case series and literature review.
Kai-Wen TanYi-Yan ZhuQiao-Cheng QiuMan WangHong-Jie ShenSi-Man HuangHan-Yu CaoChao-Ling WanYan-Yan LiHai-Ping DaiSheng-Li XuePublished in: Annals of hematology (2023)
Philadelphia chromosome-like acute lymphoblastic leukemia (Ph-like ALL) is a high-risk subtype with a poor prognosis under conventional chemotherapy. Ph-like ALL has a similar gene expression profile to Philadelphia chromosome-positive (Ph+) ALL, but is highly heterogeneous in terms of genomic alterations. Approximately 10-20% of patients with Ph-like ALL harbor ABL class (e.g. ABL1, ABL2, PDGFRB, and CSF1R) rearrangements. Additional genes that form fusion genes with ABL class genes are still being researched. These aberrations result from rearrangements including chromosome translocations or deletions and may be targets of tyrosine kinase inhibitors (TKIs). However, due to the heterogeneity and rarity of each fusion gene in clinical practice, there is limited data on the efficacy of tyrosine kinase inhibitors. Here, we report three cases of Ph-like B-ALL with ABL1 rearrangements treated with the dasatinib backbone for the CNTRL::ABL1, LSM14A::ABL1, and FOXP1::ABL1 fusion genes. All three patients achieved rapid and profound remission with no significant adverse events. Our findings suggest that dasatinib is a potent TKI for the treatment of ABL1-rearranged Ph-like ALL and can be used as a first-line treatment option for such patients.
Keyphrases
- chronic myeloid leukemia
- acute lymphoblastic leukemia
- tyrosine kinase
- copy number
- genome wide
- poor prognosis
- genome wide identification
- end stage renal disease
- ejection fraction
- newly diagnosed
- long non coding rna
- prognostic factors
- dna methylation
- rheumatoid arthritis
- radiation therapy
- squamous cell carcinoma
- peritoneal dialysis
- epidermal growth factor receptor
- transcription factor
- regulatory t cells
- autism spectrum disorder
- gene expression
- cerebrospinal fluid
- big data
- single cell
- deep learning