Predictability of the response to tyrosine kinase inhibitors via in vitro analysis of Bcr-Abl phosphorylation

Hematology and Oncology, Osaka University, Graduate School of Medicine, Suita, Osaka, Japan.
Leukemia research (Impact Factor: 2.35). 03/2011; 35(9):1205-11. DOI: 10.1016/j.leukres.2011.01.012
Source: PubMed


It would be of great value to predict the efficacy of tyrosine kinase inhibitors (TKIs) in the treatment of individual CML patients. We propose an immunoblot system for detecting the phosphorylation of Crkl, a major target of Bcr-Abl, in blood samples after in vitro incubation with TKIs. When the remaining phosphorylated Crkl after treatment with imatinib was evaluated as the "residual index (RI)", high values were found in accordance with imatinib resistance. Moreover, RI reflected the outcome of imatinib- as well as second generation TKIs with a high sensitivity and specificity. Therefore, this system should be useful in the selection of TKIs.

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    ABSTRACT: ABCB1/P-glycoprotein (Pgp) and ABCG2/BCRP overexpression have been described as related to imatinib resistance in chronic myeloid leukemia (CML). We showed in CML cells from 55 patients that Pgp activity was more frequently detected than BCRP activity (p=0.0074). Imatinib-induced Crkl phosphorylated protein (pCrkl) reduction was more pronounced in K562 (Pgp-negative) than in K562-Lucena (Pgp-positive) CML cell line. Expressive pCrkl reduction levels after in vitro imatinib treatment was observed in samples from patients exhibiting lower Pgp activity levels compared with patients exhibiting higher Pgp activity levels (p=0.0045). Pgp activity in association with pCrkl reduction levels might help to distinguish between imatinib-resistant and imatinib-sensitive CML cells.
    Leukemia research 09/2013; 37(12). DOI:10.1016/j.leukres.2013.09.021 · 2.35 Impact Factor
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    ABSTRACT: Imatinib, a targeted tyrosine kinase inhibitor, is the gold standard for managing chronic myeloid leukemia (CML). Despite its wide application, imatinib resistance occurs in 20-30% of individuals with CML. Multiple potential biomarkers have been identified to predict imatinib response; however, the majority of them remain externally uncorroborated. In this study, we set out to systematically identify gene/microRNA (miRNA) whose expression changes are related to imatinib response. Through a Gene Expression Omnibus search, we identified two genome-wide expression datasets that contain expression changes in response to imatinib treatment in a CML cell line (K562): one for mRNA and the other for miRNA. Significantly differentially expressed transcripts/miRNAs post imatinib treatment were identified from both datasets. Three additional filtering criteria were applied 1) miRbase/miRanda predictive algorithm; 2) opposite direction of imatinib effect for genes and miRNAs; and 3) literature support. These criteria narrowed our candidate gene-miRNA to a single pair: IL8 and miR-493-5p. Using PCR we confirmed the significant up-regulation and down-regulation of miR-493-5p and IL8 by imatinib treatment, respectively in K562 cells. In addition, IL8 expression was significantly down-regulated in K562 cells 24 hours after miR-493-5p mimic transfection (p = 0.002). Furthermore, we demonstrated significant cellular growth inhibition after IL8 inhibition through either gene silencing or by over-expression of miR-493-5p (p = 0.0005 and p = 0.001 respectively). The IL8 inhibition also further sensitized K562 cells to imatinib cytotoxicity (p<0.0001). Our study combined expression changes in transcriptome and miRNA after imatinib exposure to identify a potential gene-miRNA pair that is a critical target in imatinib response. Experimental validation supports the relationships between IL8 and miR-493-5p and between this gene-miRNA pair and imatinib sensitivity in a CML cell line. Our data suggests integrative analysis of multiple omic level data may provide new insight into biomarker discovery as well as mechanisms of imatinib resistance.
    PLoS ONE 12/2014; 9(12):e115003. DOI:10.1371/journal.pone.0115003 · 3.23 Impact Factor

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