Article Figures & Data
Figures
- Figure 1.
Schematic of the approach used to define biomarkers of drug response from pathway models. A, Specific predictive models for 14 colorectal cancer (CRC) cell lines were built from perturbation data and prior information about network structure (PKN). B, Drug sensitivity data for the same 14 colorectal cancer cell lines were retrieved from the GDSC panel in response to 27 different drugs targeting the PKN or first neighbor. C, Elastic Net was used to select strong associations between model parameters and drug sensitivity.
- Figure 2.
Large-scale experimental perturbation dataset. Data for 14 colorectal cancer cell lines in response to 43 different perturbed conditions with 14 measured phosphoproteins. Genomic alterations affecting investigated pathways are also shown along with molecular subtypes. Cell lines are clustered based on phosphorylation profile across all perturbations.
- Figure 3.
In silico example to illustrate modeling approach. A, In silico general network (PKN) with edges used for data simulation highlighted in black. B, One (out of 10) example of simulated perturbation data used to illustrate L1 regularization. C, Goodness of fit (QLS, i.e., sum of squared residuals) versus model sparsity (P, i.e., sum of estimated parameters) for different values of tunable regularization term λ, (λ = 0, 0.001, 0.01, 0.1, 10, 100), resulting in an L-shaped curve (each dot is the mean value obtained from the 10 in silico datasets). D, Mean accuracy (error bars, SD) of the estimated parameters (sum of the square of the difference between estimated and true parameters) as function of λ. Optimal λ is depicted in red.
- Figure 4.
Results of cell type–specific model optimization. A, Compressed PKN with edge width and values representing the level of heterogeneity of the corresponding model parameter among cell lines (percentage of pairwise tests between cell lines for which null hypothesis of equal distribution was rejected) and edge color representing median value of the corresponding parameter across all cell lines. B, Mean squared error (MSE) for each cell line; error bars, SD derived from bootstrap. C, Estimated parameters kj,i and τi, representing edges strength and nodes responsiveness, respectively, which are different from zero in at least one cell line. Clustering on the right is based on all estimated model parameters and does not correlate well with genomic alterations.
- Figure 5.
Associations between model parameters and drug sensitivity. Among the top 25 parameter–drug associations resulting from Elastic Net selection, those involving drugs with no genetic biomarkers are schematically mapped to the signaling pathways. Drug targets for the 7 drugs listed as ellipses in the bottom panel are shown in the compressed PKN (top panel) using the corresponding colored drug symbols. Corresponding model parameter associations for each drug are shown in the PKN using the same color code to mark edges corresponding to parameters kj,i (strength of edge from j to i) and nodes border for corresponding parameters τi (responsiveness of node i).
- Figure 6.
Association between MEK inhibitors and GSK3 model parameters. A–C, Scatterplots of the three GSK3 related model parameter (τGSK3, kRSKp90,GSK3, kAKT,GSK3 in A, B, and C respectively) versus the mean sensitivity of the respective associated MEK inhibitors (at least two for each parameter), with error bars representing the SE. D, Center, scatterplot of the strongest association between MEK inhibitor (AZD6244) and GSK3 node parameter (τGSK3). In the external plots, dose–response curves when combining MEK inhibitor (trametinib; at fixed concentration 0.005 μmol/L) with two GSK3 inhibitors (SB216763 and CHIR-99021) at increasing concentrations (0, 0.039, 0.156, 0.625, 2.5, 10 μmol/L). Plots for the 14 colorectal cancer cell lines are ordered clockwise according to their τGSK3 parameter value.
Additional Files
Supplementary Data
- Supplementary Table S2 - Optimised cell-line-specific models - Estimated parameters for the optimised cell-line-specific models
- Supplementary Table S1 - Signaling perturbation data - Measurement of 14 phospho-proteins in 14 CRC cell lines under perturbation with 5 stimuli and 7 targeted drugs. Data are in MIDAS format.
- Supplementary Figures - Supplementary Fig. S1. Transfer function used in logic ordinary differential equations. Supplementary Fig. S2. L-shaped curves for Ï„ parameters. Supplementary Fig. S3. L-shaped curves for k parameters. Supplementary Fig. S4. Extended version of the prior-knowledge network (PKN). Supplementary Fig. S5. Associations between model parameters and drug sensitivity. Supplementary Fig. S6. Detailed top 50 associations between model parameters and drug sensitivity. Supplementary Fig. S7. Genomic biomarkers of drug sensitivity.
- Supplementary Methods - Detailed model definition and optimisation strategy
Volume 77, Issue 12
