Identification of Drug Combinations Containing Imatinib for Treatment of BCR-ABL+ Leukemias

<div><p>The BCR-ABL translocation is found in chronic myeloid leukemia (CML) and in Ph+ acute lymphoblastic leukemia (ALL) patients. Although imatinib and its analogues have been used as front-line therapy to target this mutation and control the disease for over a decade, resistance to the therapy is still observed and most patients are not cured but need to continue the therapy indefinitely. It is therefore of great importance to find new therapies, possibly as drug combinations, which can overcome drug resistance. In this study, we identified eleven candidate anti-leukemic drugs that might be combined with imatinib, using three approaches: a kinase inhibitor library screen, a gene expression correlation analysis, and literature analysis. We then used an experimental search algorithm to efficiently explore the large space of possible drug and dose combinations and identified drug combinations that selectively kill a BCR-ABL+ leukemic cell line (K562) over a normal fibroblast cell line (IMR-90). Only six iterations of the algorithm were needed to identify very selective drug combinations. The efficacy of the top forty-nine combinations was further confirmed using Ph+ and Ph- ALL patient cells, including imatinib-resistant cells. Collectively, the drug combinations and methods we describe might be a first step towards more effective interventions for leukemia patients, especially those with the BCR-ABL translocation.</p></div

The list and targets of the drugs used in the combinatorial studies.

<p>The list and targets of the drugs used in the combinatorial studies.</p

Test of optimum drug combinations in the patient cells.

<p>The best 49 drug combinations or single agents used for the combination were tested in the cells from BCR-ABL+ and BCR-ABL- ALL patients. The selectivity (vs control IMR fibroblasts) obtained in each patient specimen was color-coded from red (lowest) to green (highest). The drug combinations were also tested in Burkitt's lymphoma patient and in normal subject white blood cells as controls.</p

Results of statistical analysis using repeated measures one-way ANOVA with Dunnett's multiple comparisons between patient cells and normal mononuclear blood cells (N1) on viability of the best drug combinations originating from the cell lines search and from pairs of single agents plus imatininb.

<p>Patients B001 and B002 are BCR-ABL+ (ALL). Patients B031 to B037 are BCR-ABL- (ALL).</p

Scheme of our search strategy

<p>(a) anti-leukemic drugs were selected using three approaches: kinase inhibitor library screens, correlation analysis, and literature survey. (b) Dose responses of single agent and pair-wise analysis with a fixed dose of imatinib (pairs and triplets) were performed. (c) The drug combinations were optimized using iterative search algorithm (d) The optimal drug combinations were validated with primary patient cells.</p

Concentrations (µM) and levels of 11 small molecules used with imatinib in the combinatorial drug searches.

<p>Concentrations (µM) and levels of 11 small molecules used with imatinib in the combinatorial drug searches.</p

Iterative search for a highly selective drug combinations

<p>(a) Increased selectivity after each iteration step. The lines represent the selectivity of the drug combinations at as triplets and at each of the six iteration steps. (b) A dot plot showing corrected selectivity (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0102221#s2" target="_blank">Results</a>) through the iterative searches. The pie symbols shows the relative contributions of the 12 drugs for the best combination obtained at each iteration. (c) Cluster analysis on the optimized drug combinations. The top ranked 200 combinations clustered into two groups and the frequencies of the doses of the individual drugs are shown for each cluster.</p

Network representation of correlations between metabolite pairs.

<p>Plots represent the largest connected component of the networks obtained with the ARACNE algorithm for B0-P0 (A) and B29-P29 (B). Blue nodes indicate metabolites relevant to lipid metabolism; green nodes indicate amino acids, including derivatives and analogues; red edges indicate anti-correlation; and light green edges indicate correlation. Shorter edges denote smaller p-values (higher R2). Note the presence of a community of lipid metabolites on the right side in (A) and the predominance of amino acids in (B). </p

Untargeted multilevel principal component analysis of 1H-MRS spectra acquired on polar fractions of bone marrow and peripheral blood samples.

<p>(A, C) Scores plots obtained from mPCA performed on 1H MRS spectra of BM and PB samples collected at diagnosis (A, day 0) or after induction therapy (C, day 29). (B, D) Loadings plots for the first principal component depicts the most relevant discriminatory metabolites from BM (positive loadings) and PB (negative loadings) samples collected at diagnosis (B) and after induction therapy (D). Metabolites are defined in the Abbreviations section.</p

Untargeted multilevel principal component analysis performed on 1H-MRS spectra acquired on the whole lipid fraction of bone marrow and peripheral blood samples at the time of diagnosis.

<p>(A) mPCA scores plot shows a clear separation on the second principal component (PC2) between BM and PB specimens. (B) Loadings plot for the second principal component depicts the most relevant discriminatory functional groups from BM (negative loadings) and PB (positive loadings) collected at diagnosis. Red areas in (B) indicate significantly different regions of the MRS spectra according to a point-by-point nonparametric Wilcoxon Rank Sum Test (p < 0.05).</p