The effects of mutations on the kinase and TF activities were assessed by associating the activity of a given protein with the mutational status of the same protein or other proteins it might interact throughout the cellular regulatory networks. First, we built a binary mutation matrix M where the index Mij corresponds to 1 if the sample i has a mutation in gene j and 0 otherwise. To do that, we selected the mutations classified as frameshift and in‐frame Indels, missense, nonsense and stop codon loss. Given the proteins X and Y, the association between the activity of Y (Yact) and the mutational status of X (Xmut) was assessed across samples by fitting a linear model that took into account possible confounding effects: Yact=β0+β1Study+β2Xmut+ɛ where Yact represents the activity of protein Y, β0 the intercept, β1 the regression coefficient for the covariate experimental study, β2 the regression coefficient for the mutational status of X and ɛ the noise term. This model was applied to assess the effect of Xmut on the activity of the same protein (Xact ∼ Xmut) and on the activity of other proteins (Yact ∼ Xmut). The P‐values from the coefficients of Xmut (β2) were calculated using the t‐statistic over a Student's t‐distribution and adjusted for false discovery rate (FDR) using the Benjamini–Hochberg method. The linear models and respective statistics were calculated using the lm and p.adjust R functions.
The associations were performed with the genes mutated in more than 20 samples and with the protein activities estimated in at least 10 samples. An association between a pair Yact ∼ Xmut or Xact ∼ Xmut was performed if Xmut was mutated in at least five of all the samples in the pair. Regarding the Yact ∼ Xmut associations, we tested 520,938 pairs between 208 kinases and 3,590 genes and 1,048,216 pairs between 292 TFs and 3,590 genes. In relation to the Xact ∼ Xmut associations, we tested 40 pairs and 64 pairs with the kinases and TFs, respectively.
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