Chronic Myeloid Leukemia Patients Sensitive and Resistant to Imatinib Treatment Show Different Metabolic Responses

Abstract

The BCR-ABL tyrosine kinase inhibitor imatinib is highly effective for chronic myeloid leukemia (CML). However, some patients gradually develop resistance to imatinib, resulting in therapeutic failure. Metabonomic and genomic profiling of patients' responses to drug interventions can provide novel information about the in vivo metabolism of low-molecular-weight compounds and extend our insight into the mechanism of drug resistance. Based on a multi-platform of high-throughput metabonomics, SNP array analysis, karyotype and mutation, the metabolic phenotypes and genomic polymorphisms of CML patients and their diverse responses to imatinib were characterized. The untreated CML patients (UCML) showed different metabolic patterns from those of healthy controls, and the discriminatory metabolites suggested the perturbed metabolism of the urea cycle, tricarboxylic acid cycle, lipid metabolism, and amino acid turnover in UCML. After imatinib treatment, patients sensitive to imatinib (SCML) and patients resistant to imatinib (RCML) had similar metabolic phenotypes to those of healthy controls and UCML, respectively. SCML showed a significant metabolic response to imatinib, with marked restoration of the perturbed metabolism. Most of the metabolites characterizing CML were adjusted to normal levels, including the intermediates of the urea cycle and tricarboxylic acid cycle (TCA). In contrast, neither cytogenetic nor metabonomic analysis indicated any positive response to imatinib in RCML. We report for the first time the associated genetic and metabonomic responses of CML patients to imatinib and show that the perturbed in vivo metabolism of UCML is independent of imatinib treatment in resistant patients. Thus, metabonomics can potentially characterize patients' sensitivity or resistance to drug intervention.

Cross talk between Wnt/β-catenin and Irf8 in leukemia progression and drug resistance

“…Enrichment of human CML-related genes indicated convergence of BCR-ABL with Wnt/β-catenin and Irf8 signaling in disease progression. Within the group of deregulated genes, we found association with enhanced glycolysis in accordance with gene expression analysis in human CML BP (A et al, 2010). Interestingly, leukemic GMPs also show up-regulation of Hif1α , as a potential cause of metabolic reprogramming (Zhao et al, 2010).…”

Triomics Analysis of Imatinib-Treated Myeloma Cells Connects Kinase Inhibition to RNA Processing and Decreased Lipid Biosynthesis

“…Previous studies have shown that imatinib can increase nucleotide levels. 25,88,89 In contrast, nucleosides such as inosine and guanosine, as well as other important key metabolites such as acetyl-CoA and reduced nicotinamide adenine dinucleotide phosphate (NADPH), were downregulated with imatinib treatment. Acetyl-CoA is a key player in the citric acid cycle (tricarboxylic acid or TCA cycle) as well as fatty acid metabolism, and NADPH is used as a cofactor in lipid and nucleic acid synthesis.…”

“…90 The glycolysis pathway and its intermediates such as fructose 1,6-bisphosphate (F1,6BP), glyceraldehyde 3-phosphate (G3P), and dihydroxyacetone phosphate (DHAP) were also downregulated with imatinib (Figure 3B–D) and previous reports have shown that glycolysis and the pentose phosphate pathway (PPP) can be inhibited with imatinib. 25,88,89,91 The table of Q3 peak areas for detected metabolites with and without imatinib treatment is available in Data set S3. To further assess the enhanced glycolysis and PPP dependence of BCR–ABL H929 cells and the inhibiting effect of imatinib, we examined changes in a metabolic flux tracing experiment.…”

Metabolomic approach to evaluating adriamycin pharmacodynamics and resistance in breast cancer cells

“…A few studies documented the relationship between chemotherapeutic resistance and metabolic profiles in other cancer cell lines (Klawitter et al 2009; Serkova and Boros 2005) and in cancer patients (A et al 2010). The biochemical effects of adriamycin in animal model was studied (Park et al 2009), potential metabolomic biomarkers in urine for evaluation of adriamycin efficacy were identified (Kim et al 2012), and targeted profiling of modified nucleosides was also used to characterize the metabolic signature of the breast cancer cell line MCF-7/S and compared it to the human mammary epithelial cells MCF-10A (Bullinger et al 2007).…”