Fig 1.
Principle of the protein dependence inference framework.
(A) Summary of tumor samples and drugs in the three datasets used in this study, and the number of proteins with non-zero coefficients in the inferred protein dependence matrix. (B) Distribution of the number of kinases bound by each drug (kd < 1000nM) and (C) distribution of the number of drugs binding each kinase from the kinobeads profiling data used in our analysis. (D) Inference of the protein dependence matrix using a multivariate multi-response regression model with L1 regularization. A known drug-protein affinity matrix (independent variables) and a known drug-effect matrix (response variables) are used as input for the model to infer the unknown protein dependence matrix.
Fig 2.
Results of protein dependence inference on the GDSC dataset.
(A) Shown are the data for nine kinases for which we found significant differences in the protein dependence coefficients between cancer types (one-way ANOVA, BH adjusted p-value < 0.05, Fold Change > 0.1). Within each panel, each point corresponds to a cell line. The coefficients were centered and scaled to obtain a per-protein z-score, and the points are grouped and colored by cancer type (ALL, red; AML, orange; DLBCL, green, BRCAHer-, blue; BRCAHer+, purple). (B) Radar plots of protein dependence coefficients across the different cancer types. Dashed line represents a protein dependence coefficient of zero. (C) Association between NRAS mutation status and MAP2K2 dependence. Association testing was performed using Student’s t-test (two-sided, with equal variance). (D) A heatmap showing the -log10(P-values) with signs determined by direction of fold changes of the associations between mutational background of the cell lines and protein dependence coefficients (Student’s t-test). Blue: associations with higher dependence coefficients in mutated cases; red: lower dependence coefficients in mutated cases; stars indicate the associated pass 10% FDR control.
Fig 3.
Results of protein dependence inference on primary CLL samples.
(A) PCA visualization of the CLL samples according to protein dependence matrix (right) and drug sensitivity matrix (left). The points are colored by IGHV mutational status (mutated: blue, unmutated: red), and the results of k-means clustering (k = 2) is indicated by the shading. The cross-tabulation and Rand indices show that the protein dependence matrix-based clustering is more consistent with IGHV mutational status, a known strong stratifying factor in CLL biology (Rand index = 0.623), than the raw drug sensitivity matrix (Rand index = 0.146). (B) A heatmap showing the -log10(P-values) with signs determined by direction of fold changes of the associations between mutational background of the cell lines and protein dependence coefficients (Student’s t-test). Blue: associations with higher dependence coefficients in trisomy 12 positive / U-CLL; red: higher dependence coefficients in Trisomy12 negative / M-CLL; stars indicate the associated pass 10% FDR control. (C) Examples of associations, visualized in beeswarm plots: associations between IGHV mutational status and SIK2 / INPPL1 / BTK dependence and association between trisomy 12 and MAP2K2 dependence. Association testing was performed using Student’s t-test (two-sided, with equal variance) and Benjamini-Hochberg correction was applied.
Fig 4.
Association of CHEK1 dependence with IGHV status in CLL.
(A) Beeswarm plot of the CHEK1 protein dependence values in all samples, visualizing the increased dependence on CHEK1 in U-CLL tumors. Association testing was performed using Student’s t-test test (two-sided, with equal variance) and Benjamini-Hochberg correction was applied. (B) A network illustration of the off-target effect of CHEK1 inhibitors that involves BCR components (BTK, SYK, YES1 and LYN). Only the high confidence pairs in the kinobeads dataset are considered. The drugs that only target CHEK1 are colored in red. (C) Beeswarm plots showing the effect of three CHEK1-specific inhibitors in U-CLL and M-CLL samples. Association testing was performed using Student’s t-test test (two-sided, with equal variance) and Benjamini-Hochberg correction was applied. (D) Differentially expressed hallmark gene sets in CLL cells after BCR pathway stimulation through anti-IgM treatment (ArrayExpress ID: E-GEOD-39411). Highlighted upregulated hallmark gene sets are associated with DNA-damage response and cell cycle checkpoint.