Protein structural context of cancer mutations reveals molecular mechanisms and identifies novel candidate driver genes

Abstract

Advances in structure determination and computational modelling are enabling us to study the protein structural context of human genetic variants at an unprecedented scale. Here, we investigate millions of human cancer-associated missense mutations in terms of their structural locations and predicted perturbative effects. We find that, while cancer-driving mutations have properties similar to other known disease-causing mutations, this is obscured by the abundance of passenger mutations in cancer sequencing datasets. Nevertheless, by considering the collective properties of mutations at the level of individual proteins, we identify distinct mutational signatures associated with tumour suppressors and oncogenes. Tumour suppressors are enriched in structurally damaging mutations, consistent with loss-of-function mechanisms. In contrast, oncogene mutations tend to be structurally mild, reflecting selection for gain-of-function driver mutations and against loss-of-function mutations. Although oncogenes are difficult to distinguish from genes with no role in cancer using only structural damage, we find that an alternate metric based on the clustering of mutations in three-dimensional space is highly predictive of oncogenes, particularly when mutation recurrence is considered. These observations allow us to identify novel candidate driver genes and speculate about their molecular roles, which we expect to have general utility in the analysis of cancer sequencing data.