Integrated expression profiling of BCR-ABL and computational identification of ABL1 inhibitors in pediatric acute lymphoblastic leukemia.

Abstract

Pediatric B-cell acute lymphoblastic leukemia achieves overall survival approaching 90% with modern therapy, though high-risk subtypes, relapse, and drug resistance remain critical clinical challenges. Among its genetic abnormalities, the BCR-ABL fusion oncogene, resulting from the Philadelphia chromosome translocation t (9; 22), is associated with aggressive disease progression, poor prognosis, and high relapse rates, particularly in pediatric patients. This study aimed to investigate the expression profile of BCR-ABL in pediatric ALL and to identify potential inhibitors targeting the ABL1 tyrosine kinase using computational approaches. A cohort of 50 pediatric ALL patients was analysed and fusion oncogene expression was quantified using quantitative real-time PCR (qRT-PCR). BCR-ABL exhibited heterogeneous expression with several samples showing marked upregulation. To explore novel therapeutic options, a virtual screening workflow was employed using drug-like compounds from publicly available databases. Molecular docking identified four lead compounds (LIG1-LIG4) with higher binding affinities toward ABL1 compared to the reference inhibitor imatinib. These interactions were further characterised by favourable hydrogen bonding and hydrophobic interactions within the active site. Molecular dynamics simulations over 100 ns demonstrated the structural stability of the protein-ligand complexes, supported by acceptable RMSD and RMSF profiles. Additionally, in silico ADMET and pharmacokinetic analyses indicated that the selected compounds possess favourable drug-like properties, including high gastrointestinal absorption and acceptable safety profiles. In conclusion, this study identified BCR-ABL as the most highly expressed fusion oncogene in this cohort, consistent with its known oncogenic role in Philadelphia chromosome-positive paediatric ALL, and identifies promising ABL1 inhibitor candidates through computational screening. These findings provide a foundation for further experimental validation and the development of targeted therapies for BCR-ABL-positive ALL.