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Molecular Cancer Therapeutics
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Three-kinase inhibitor combination recreates multipathway effects of a geldanamycin analogue on hepatocellular carcinoma cell death

Justin R. Pritchard, Benjamin D. Cosgrove, Michael T. Hemann, Linda G. Griffith, Jack R. Wands and Douglas A. Lauffenburger
Justin R. Pritchard
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Benjamin D. Cosgrove
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Michael T. Hemann
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Linda G. Griffith
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Jack R. Wands
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Douglas A. Lauffenburger
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DOI: 10.1158/1535-7163.MCT-08-1203 Published August 2009
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    Figure 1.

    A plot for the cleaved caspase-3/cleaved PARP double-positive population at 48 h measures the susceptibility of Hep3B and the resistance of Huh7 cells to 17AAG. Typical measurements of 24-h signaling degradation in response to 17AAG fail to correlate with this distinction. A, a representative flow cytometry scatter plot depicts Hep3B-fixed and Hep3B-permeabilized cells that are stained with antibodies for active cleaved caspase-3 and cleaved PARP at 48 h after treatment with 1 μmol/L 17AAG or 0.1% methanol vehicle control. The double-positive population is denoted by the gating in the upper right-hand corner. Different gatings were used for Huh7 and Hep3B but the scatter plots looked very similar. B, the average size of the population of three replicates (±SE) of Hep3B and Huh7 cells as gated in A represents the percentage of double-positive apoptotic cells at 48 h ± SE. C, late time signals do not correlate with 17AAG susceptibility. Measured at 24 h, the fold change of the mean of duplicates ± SE, treated with 1 μmol/L 17AAG, is normalized to a 0.1% methanol control. Phospho-AKT (Ser473), total AKT, phospho-ERK1/2 (Thr202/Tyr204), and phospho–IκB-α (Ser32/Ser36) were measured by a bead-based Bio-Rad phosphoprotein (Bioplex) assay.

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    Figure 2.

    A dynamic signaling time course reveals differences between Hep3B and Huh7. A, a signaling time course in Hep3B and Huh7 cells depicts mean fold changes in phosphoprotein signaling in response to 1 μmol/L 17AAG relative to the vehicle only (0.1% methanol) control. B, the integral (discussed in Materials and Methods) from 0 to 4 h of 17AAG-induced signaling shows large cumulative differences in early signaling between Huh7 and Hep3B cells.

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    Figure 3.

    A PCA reduces data complexity and provides testable hypotheses. A, percentage of the total variance captured by a model consisting of one, two, or three principal components. There is a marginal increase in the benefit of including principal component 3, indicating an obvious cutoff criterion. B, percentage of total variance explained by two-component models that are built on 1,000 perturbed data matrices (see Materials and Methods). A histogram plots the data from the empirical 1,000 matrix sampling. A normal distribution was fit to the histogram data, and our model fit was calculated to have a highly significant P value of 0.0002. C, a PCA yielded a two-component model that accurately explains 91% of the cumulative variance. PC1 strongly captures variation at early time points and in the 0- to 4-h integral metric. PC2 captures variation at 24 h. These results from the loadings plot are summarized in the upper right-hand corner of the plot. The ellipse represents Hotelling's outlier criteria at a 95% confidence level. The original signaling measurements are plotted in the principal components space. PC1 visually seems to capture cell line variation. The colored ellipses are simply a visualization tool used to bring the readers attention to the distinct clusters in the scores plot. D, contributions vectors describe how signals vary in principal components space and are derived computationally by measuring the latent variable distance in terms of the Euclidean distance of the measured variables between Huh7 and Hep3B for a given signal and then comparing that distance to an average distance of all the distances between the cell lines (in units of SD). This plot asks the question of how distinct are two signals between Huh7 and Hep3B cells relative to the average distance between lines. Quantitatively, D affirms qualitative observations made in C.

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    Figure 4.

    Adding signaling data from another HCC cell line (FOCUS) to the PCA correctly clusters FOCUS cells with sensitive Hep3B cells. A, mean of triplicate measurements of the percentage of double-positive FOCUS cells in response to 1 μmol/L 17AAG at 48 h. The gating strategy was the same as in Fig. 1A. B, signaling time course of FOCUS cells in response to 1 μmol/L 17AAG. Time point measurements are represented as mean signaling fold change relative to vehicle only controls. C, a principal components scores plot, as in Fig. 3, correctly classifies FOCUS cell phosphoprotein signaling measurements as homologous to 17AAG-sensitive Hep3B cells in the two-component model.

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    Figure 5.

    Where inhibition of singular nodes fails, pretreatment with a combination therapy sensitizes Huh7 cells to 17AAG and works as well or better than 17AAG in all cell lines tested. A, double-positive populations of Huh7 cells (mean of triplicate ± SE as gated in Fig. 1A) in response to different treatments at 48 h. The IKK inhibitor BMS-345541 was used at 15 μmol/L, the PI3K inhibitor PI103 was used at 5 μmol/L, and the JAK inhibitor JAK inhibitor 1, pyridone 6, was used at 3 μmol/L. The P values were obtained by a t test. B, double-positive populations for the listed treatments of Hep3B and FOCUS cells at 48 h. Columns, mean of triplicates; bars, SE. The P values were obtained by a t test. The drug combination is the same as in A.

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    Figure 6.

    FOCUS, Huh7, and Hep3B cells exhibit different amounts of multinode effects in a JAK inhibitor background. A, a matrix of the mean of duplicate measurements of the percentage of double-positive cells in response to varying concentrations of IKK inhibitor (IKKi) and PI3K inhibitor (PI3Ki) in a JAK (3 μmol/L) inhibitor background. The compounds are the same as in Fig. 4. Concentrations are as follows: IKK inhibitor: high (H), 20 μmol/L; medium (M), 6.66 μmol/L; low (L), 2.22 μmol/L; PI3K inhibitor: high, 5 μmol/L; medium, 1.66 μmol/L; low, 0.55 μmol/L. B, the synergy plots display the fold deviation from predictions based on the assumption of additivity. Briefly, additivity predictions for any double-drugged entry (i,j) were calculated by adding the ith row of the first column (i.e., the singular IKK effect at that concentration) to the first row, jth column (i.e., the singular PI3K effect at that concentration). Then, the measured value at the double-drugged entry (i,j) was divided by the additivity prediction. This created a metric that describes synergy versus additivity.

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Molecular Cancer Therapeutics: 8 (8)
August 2009
Volume 8, Issue 8
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Three-kinase inhibitor combination recreates multipathway effects of a geldanamycin analogue on hepatocellular carcinoma cell death
Justin R. Pritchard, Benjamin D. Cosgrove, Michael T. Hemann, Linda G. Griffith, Jack R. Wands and Douglas A. Lauffenburger
Mol Cancer Ther August 1 2009 (8) (8) 2183-2192; DOI: 10.1158/1535-7163.MCT-08-1203

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Three-kinase inhibitor combination recreates multipathway effects of a geldanamycin analogue on hepatocellular carcinoma cell death
Justin R. Pritchard, Benjamin D. Cosgrove, Michael T. Hemann, Linda G. Griffith, Jack R. Wands and Douglas A. Lauffenburger
Mol Cancer Ther August 1 2009 (8) (8) 2183-2192; DOI: 10.1158/1535-7163.MCT-08-1203
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