Fig. 3

Damaging alteration pattern of drivers in TCGA. a Identification of damaged drivers in 7953 TCGA samples. Mutations, gene deletions, and amplifications were annotated according to their predicted damaging effect. This allowed to distinguish drivers acquiring loss-of-function (LoF) or gain-of-function (GoF) alterations. b Number of TCGA samples with damaging alterations (all, LoF, GoF) in canonical drivers that were detected (421) or undetected (170) by cancer driver detection methods. c Proportion of TCGA samples with GoF and LoF alterations in tumor suppressors, oncogenes, and canonical drivers with a dual or unclassified role. Proportion of TCGA samples with GoF and LoF alterations in (d) canonical drivers and (e) candidate drivers. Genes mentioned in the text are highlighted. The two-dimensional Gaussian kernel density estimations were calculated for each driver group using the R density function. f Number of TCGA samples with damaging alterations (all, LoF, GoF) in drivers previously reported in coding and non-coding sequences. g Proportion of samples with variable numbers of all damaged drivers or only canonical drivers. h Proportion of TCGA samples with GoF and LoF alterations in healthy drivers. Canonical and candidate healthy drivers correspond to genes with a known or predicted cancer driver role. i Number of TCGA samples with damaged canonical, candidate, and remaining healthy drivers and the rest of human genes. All distributions were compared using a two-sided Wilcoxon rank-sum test