Identification of significantly mutated regions across cancer types highlights a rich landscape of functional molecular alterations

Cancer sequencing studies have primarily identified cancer-driver genes by the accumulation of protein-altering mutations. An improved method would be annotation-independent, sensitive to unknown distributions of functions within proteins, and inclusive of non-coding drivers. We employed density-based clustering methods in 21 tumor types to detect variably-sized significantly mutated regions (SMRs). SMRs reveal recurrent alterations across a spectrum of coding and non-coding elements, including transcription factor binding sites and untranslated regions mutated in up to ∼15% of specific tumor types. SMRs reveal spatial clustering of mutations at molecular domains and interfaces, often with associated changes in signaling. Mutation frequencies in SMRs demonstrate that distinct protein regions are differentially mutated among tumor types, as exemplified by a linker region of PIK3CA in which biophysical simulations suggest mutations affect regulatory interactions. The functional diversity of SMRs underscores both the varied mechanisms of oncogenic misregulation and the advantage of functionally-agnostic driver identification.