We analyze the detected somatic events (see above) across a cohort of samples searching for genes and pathways, as well as non-coding genomic elements, that show significant signals of positive selection. To that end, we construct a statistical model of the background mutational processes and then detect genes that deviate from it. We have developed tools for detecting significantly gained or lost genes in cancer, including GISTIC (Beroukhim, Getz, et al., PNAS 2007; Mermel, et al., Genome Biology 2011), and genes with increased density or irregular patterns of mutations, including the MutSig suite of tools (Getz, Höfling H, et al. Science 2007; Chapman, et al., Nature 2011; Lawrence, Stojanov, Polak, et al., Nature 2013; Lawrence, et al., Nature 2014; Rheinbay, et al., Nature 2017), CLUMPS/ EMPRINT (Kamburov, et al., PNAS, 2015), MSMutSig (Maruvka, et al., Nature Biotechnology 2017), NetSig (Horn, Lawrence, et al., Nature Methods 2017), and “driver”/“passenger” hotspots (Hess, et al., Cancer Cell 2019). Our work demonstrated the need to accurately model the heterogeneity of mutability across patients, sequence contexts, and the genome, when searching for cancer genes.