Cancer genome sequencing studies have identified cancer-driver genes from the increased accumulation of protein-altering mutations. However, the positional distributions of coding mutations, and the 79% of somatic variants in exome data that do not alter protein sequence or RNA splicing, remain largely unstudied. We employed density-based clustering methods on ~4,700 exomes from 21 tumor types to detect variably-sized significantly mutated regions (SMRs). SMRs reveal recurrent alterations across a diverse spectrum of coding and non-coding elements, including microRNAs, transcription factor binding sites, and untranslated regions that are individually mutated in up to ~15% of samples in specific cancer types. SMRs often associated with changes in gene expression and signaling. Mapping SMRs to protein structures revealed spatial clustering of somatic mutations at known and novel cancer-driver domains and molecular interfaces. Mutation frequencies in SMRs demonstrate that distinct protein regions are differentially mutated among tumor types. The functional diversity of SMRs underscores both the varied mechanisms of oncogenic misregulation and the advantage of unbiased driver identification.