Nature Genetics Advance Online Publication a N a Ly S I S 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 noncoding drivers. We employed density-based clustering methods in 2 tumor types to detect variably sized significantly mutated regions (SMRs). SMRs reveal recurrent alterations across a spectrum of coding and noncoding elements, including transcription factor binding sites and untranslated regions mutated in up to ~5% of specific tumor types. SMRs demonstrate spatial clustering of alterations in molecular domains and at interfaces, often with associated changes in signaling. Mutation frequencies in SMRs demonstrate that distinct protein regions are differentially mutated across tumor types, as exemplified by a linker region of PIK3CA in which biophysical simulations suggest that 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. In cancer, driver mutations alter functional elements of diverse nature and size. For example, melanoma drivers include hyperacti-vating mutations mapping to single amino acid residues (for example, BRAF Val600; ref. 1), inactivating mutations along tumor-suppressor exons (for example, in PTEN 1) and regulatory mutations (for example, in the TERT promoter 2). Cancer genomics projects such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) have substantially expanded our understanding of the landscape of somatic alterations by identifying frequently mutated protein-coding genes 3–5. However, these studies have focused little attention on systematically analyzing the positional distribution of coding mutations or characterizing noncoding alterations 6. Algorithms to identify cancer driver genes often examine non-synonymous-to-synonymous mutation rates across the gene body or recurrently mutated amino acids called mutation hotspots 5 , as observed in BRAF 7 , IDH1 (ref. 8) and DNA polymerase ε (encoded by POLE) 9. Yet, these analyses ignore recurrent alterations in the vast intermediate scale of functional coding elements, such as those affecting protein subunits or interfaces. Moreover, where mutation clustering within genes has been examined 10–12 , analyses have employed windows of fixed length or identified clusters of non-synonymous mutations, assuming that driver mutations exclusively influence protein sequence and ignoring the importance of exon-embedded regulatory elements 13–18. A significant proportion of regulatory elements in the genome are located proximal to or even in exons 15,19 , suggesting that many may be captured by whole-exome sequencing. Efforts to characterize noncoding regulatory variation …