ABSTRACT
Imatinib Mesylate (imatinib) was once hailed as the magic bullet for chronic myeloid leukemia (CML) and remains a front-line therapy for CML to this day alongside other tyrosine kinase inhibitors (TKIs). However, TKI treatments are rarely curative and patients are often required to receive life-long treatment or otherwise risk relapse. Thus, there is a growing interest in identifying biomarkers in patients which can predict TKI response upon diagnosis. In this study, we analyze clinical data and differentially expressed miRNAs in CD34+ CML cells from 80 patients at diagnosis who were later classified as imatinib-responders or imatinib-nonresponders. A Cox Proportional Hazard (CoxPH) analysis identified 16 miRNAs that were associated with imatinib nonresponse and differentially expressed in these patients. We also trained a machine learning model with different combinations of the 16 miRNAs with and without clinical parameters and identified a panel with high predictive performance based on area-under-curve values of receiver-operating-characteristic and precision-recall curves. Interestingly, the multivariable panel consisting of both miRNAs and clinical features performed better than either miRNA or clinical panels alone. Thus, our findings may inform future studies on predictive biomarkers and serve as a tool to develop more optimized treatment plans for CML patients in the clinic.
Subject(s)
Leukemia, Myelogenous, Chronic, BCR-ABL Positive , MicroRNAs , Humans , Imatinib Mesylate/therapeutic use , Imatinib Mesylate/pharmacology , MicroRNAs/genetics , Protein Kinase Inhibitors/therapeutic use , Protein Kinase Inhibitors/pharmacology , Leukemia, Myelogenous, Chronic, BCR-ABL Positive/diagnosis , Leukemia, Myelogenous, Chronic, BCR-ABL Positive/drug therapy , Leukemia, Myelogenous, Chronic, BCR-ABL Positive/genetics , BiomarkersABSTRACT
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.
Subject(s)
Leukemia, Myelogenous, Chronic, BCR-ABL Positive , MicroRNAs , Antigens, CD34 , Canada , Chronic Disease , Humans , Leukemia, Myelogenous, Chronic, BCR-ABL Positive/drug therapy , Leukemia, Myelogenous, Chronic, BCR-ABL Positive/genetics , Leukemia, Myelogenous, Chronic, BCR-ABL Positive/metabolism , MicroRNAs/genetics , Protein Kinase Inhibitors/pharmacology , PyrimidinesABSTRACT
BACKGROUND: Imatinib mesylate (IM) induces clinical remission of chronic myeloid leukemia (CML). The Abelson helper integration site 1 (AHI-1) oncoprotein interacts with BCR-ABL and Janus kinase 2 (JAK2) to mediate IM response of primitive CML cells, but the effect of the interaction complex on the response to ABL and JAK2 inhibitors is unknown. METHODS: The AHI-1-BCR-ABL-JAK2 interaction complex was analyzed by mutational analysis and coimmunoprecipitation. Roles of the complex in regulation of response or resistance to ABL and JAK2 inhibitors were investigated in BCR-ABL (+) cells and primary CML stem/progenitor cells and in immunodeficient NSG mice. All statistical tests were two-sided. RESULTS: The WD40-repeat domain of AHI-1 interacts with BCR-ABL, whereas the N-terminal region interacts with JAK2; loss of these interactions statistically significantly increased the IM sensitivity of CML cells. Disrupting this complex with a combination of IM and an orally bioavailable selective JAK2 inhibitor (TG101209 [TG]) statistically significantly induced death of AHI-1-overexpressing and IM-resistant cells in vitro and enhanced survival of leukemic mice, compared with single agents (combination vs TG alone: 63 vs 53 days, ratio = 0.84, 95% confidence interval [CI] = 0.6 to 1.1, P = .004; vs IM: 57 days, ratio = 0.9, 95% CI = 0.61 to 1.2, P = .003). Combination treatment also statistically significantly enhanced apoptosis of CD34(+) leukemic stem/progenitor cells and eliminated their long-term leukemia-initiating activity in NSG mice. Importantly, this approach was effective against treatment-naive CML stem cells from patients who subsequently proved to be resistant to IM therapy. CONCLUSIONS: Simultaneously targeting BCR-ABL and JAK2 activities in CML stem/progenitor cells may improve outcomes in patients destined to develop IM resistance.
