Fig. 5: Cancer cells’ phospho-catalytic signatures reveal a wide spectrum of kinase activities indicative of differential dependencies and vulnerabilities to kinase-targeting agents.

a, Unsupervised hierarchical clustering of the peptide phosphorylation activity signatures of 20 cancer cell lines. For each experimental run, the average value of ATP consumption in sample-containing wells measured across 228 peptides and 14 peptide-free controls was used for internal normalization (Supplementary Table 26). The activity per peptide was then calculated as the difference in ATP consumption between individual peptide-derived readouts and the internal mean. Phospho-catalytic activities are colour-coded based on the relative level of activity measured in the presence of each peptide for each cell line (blue, low activity; white, intermediate or mean activity; red, high activity; in nM of ATP consumption). The bottom red / gold / grey streak indicates the category of biological peptides / generic CON⁺ peptides / reference peptides out of the 228 peptides. The heatmap on the right represents cells’ activity measured in the 14 control, peptide-free wells. b,c, Peptide phosphorylation activity patterns across cancer cells based on the range of activity per peptide, average level of phosphorylation intensity per peptide, and peptide class. In b, peptides are first sorted by peptide category and then range of peptide-phosphorylation activity per peptide. In the plots of mean-centred ranges of phospho-activity, the data points represent the mean, while the error bars represent maximum and minimum values. In c, peptides are first sorted by average peptide-phosphorylation activity per peptide. d, Unsupervised hierarchical clustering of the kinase activity signatures of 20 cancer cell lines. For each cell line, kinase activities were calculated as the average of the phosphorylation activities measured in presence of kinases’ respective biological peptide subsets (deconvoluted from the peptide phosphorylation profiles in a). Profiles were then mean-centred across cell lines. Profiles highlight the heterogeneity of kinases’ activities across cancer cells. e, Comparison of cancer cells’ drug sensitivity (x axis; GI50 concentration per drug per cell line) versus the kinase activity (y axis) derived from the biological peptide subsets in d. Each data point represents a cell line. Pearson correlation coefficients (r) are included within each plot. CHEK, checkpoint kinase; SYK, spleen tyrosine kinase. f, Pearson correlation heat maps highlighting the functional relationship between cancer cells based on their kinase activity signatures established from kinases’ subsets of biological peptides (left), versus kinases’ subsets of generic CON⁺ peptides (middle), or individual generic CON⁺ peptides of kinases (right). Cells are arranged by tumour origin (BC, breast cancer; LC, lung cancer; MEL, melanoma; PC, prostate cancer). g, Comparison of cancer cells’ drug sensitivity versus the kinase activity derived from the kinases’ subsets of generic CON⁺ peptides. Each data point represents a cell line. Pearson correlation coefficients (r) are included within each plot. h, Pearson correlation between kinase activity and drug sensitivity in A375 (melanoma) versus WiDr (CRC) BRAF^V600E cells (n = 2 per data point). i, Pearson correlation between kinase activity and drug sensitivity across all results (373 data points derived from 35 drug treatments targeting 23 kinases are plotted).