Despite the effectiveness of tyrosine kinase inhibitors (TKIs) against chronic myeloid leukemia (CML), they are not usually curative as some patients develop drug-resistance or are at risk of disease relapse when treatment is discontinued. Studies have demonstrated that primitive CML cells display unique miRNA profiles in response to TKI treatment. However, the utility of miRNAs in predicting treatment response is not yet conclusive. Here, we analyzed differentially expressed miRNAs in CD34+ CML cells pre- and post-nilotinib (NL) therapy from 58 patients enrolled in the Canadian sub-analysis of the ENESTxtnd phase IIIb clinical trial which correlated with sensitivity of CD34+ cells to NL treatment in in vitro colony-forming cell (CFC) assays. We performed Cox Proportional Hazard (CoxPH) analysis and applied machine learning algorithms to generate multivariate miRNA panels which can predict NL response at treatment-naïve or post-treatment time points. We demonstrated that a combination of miR-145 and miR-708 are effective predictors of NL response in treatment-naïve patients whereas miR-150 and miR-185 were significant classifiers at 1-month and 3-month post-NL therapy. Interestingly, incorporation of NL-CFC output in these panels enhanced predictive performance. Thus, this novel predictive model may be developed into a prognostic tool for use in the clinic.
© 2022. The Author(s), under exclusive licence to Springer Nature Limited.