Fig. 1

Divergent transcriptomic programs are a recurring feature of frequently mutated genes in breast cancer. 772 subgroupings within the point mutations of 38 genes having known links to cancer processes in METABRIC-(LumA) were enumerated by grouping together variants with shared properties. A logistic ridge regression classifier was trained to predict the presence of any point mutation in each of these genes as well as the presence of each enumerated subgrouping. Comparing the classification performance (AUC) for each gene-wide task (x-axis) to the best performance across all tested subgroupings of the gene (y-axis) reveals subgroupings within genes such as GATA3 and MAP3K1 with downstream effects that are consistently separable from the remaining mutations of the gene. The pie charts’ areas are proportional to the number of samples in the cohort that carry any point mutation of the corresponding gene; the darker slice inside each pie is scaled according to the proportion of these samples carrying a mutation in the best subgrouping. A gene label is included wherever the AUC of the best task exceeded 0.7; a description of the best subgrouping is also included wherever its task performance was cv-significantly higher than that of its gene-wide counterpart. Six genes in which no subgroupings were found have been omitted from this plot. The corresponding plots for the other cohorts used for training in this study can be found at Additional file 12: Figure S11