Genomic alterations in chronic myeloid leukaemia patients who failed second generation tyrosine kinase inhibitor

Abstract

The practise of prescribing chronic myeloid leukaemia (CML) patients with indefinite tyrosine kinase inhibitors (TKIs) has gone uncontested, and the capacity of TKIs to eliminate the CML clone is also still unknown. Although the vast majority of CML patients do respond to TKIs, nonetheless, resistance may develop either de novo or during treatment. TKI resistance pathways are commonly classified as BCR-ABL1-dependent or BCR-ABL1-independent. The molecular evolution causal for this subset of individuals to lose molecular remission is still unknown. In this study, our goal was to explore the molecular mechanisms involved in resistance to TKI in patients who failed second-generation TKIs in order to identify potential genetic signatures and pathways that lead to TKI resistance. A total of 34 samples (2 responder and 32 non-responder) were subjected to whole-transcriptomic analysis. mRNA gene expression, gene fusion and single nucleotide variation (SNV) were determined using Illumina DRAGEN Bio-IT Platform pipeline. A preliminary results for a total of 386 differentially regulated genes (DEGs) were identified, with 148 genes up-regulated and 238 genes down-regulated in the non-responder group. The DEGs were enriched in histone acetylation/deacetlylation (HDACs/HATs), DNA methylation, and RNA polymerase regulation pathway. Further analyses demonstrated that CCCDC32-CBX3 fusion is significantly associated with non-responder. Techniques with enhanced sensitivity such as next generation sequencing and the use of artificial intelligence techniques coupled with the development of mathematical modelling and computational prediction methods could reveal the underlying mechanism of drug resistance and facilitate the design of more effective treatment strategies for improving drug efficacy in CML patients. This preliminary analysis found that individuals who failed second-generation TKIs express a potentially unique genetic signature. Analysis using larger sample size is necessary to validate these findings.