Domain landscapes of somatic mutations in cancer

Abstract

Background: Large-scale tumor sequencing projects are now underway to identify genetic mutations that drive tumor initiation and development. Most studies take a gene-based approach to identifying driver mutations, highlighting genes mutated in a large percentage of tumor samples as those likely to contain driver mutations. However, this gene-based approach usually does not consider the position of the mutation within the gene or the functional context the position of the mutation provides. Here we introduce a novel method for mapping mutations to distinct protein domains, not just individual genes, in which they occur, thus providing the functional context for how the mutation contributes to disease. Furthermore, aggregating mutations from all genes containing a specific protein domain enables the identification of mutations that are rare at the gene level, but that occur frequently within the specified domain. These highly mutated domains potentially reveal disruptions of protein function necessary for cancer development.

Results: We mapped somatic mutations from the protein coding regions of 100 colon adenocarcinoma tumor samples to the genes and protein domains in which they occurred, and constructed topographical maps to depict the "mutational landscapes" of gene and domain mutation frequencies. We found significant mutation frequency in a number of genes previously known to be somatically mutated in colon cancer patients including APC, TP53 and KRAS. In addition, we found significant mutation frequency within specific domains located in these genes, as well as within other domains contained in genes having low mutation frequencies. These domain "peaks" were enriched with functions important to cancer development including kinase activity, DNA binding and repair, and signal transduction.

Conclusions: Using our method to create the domain landscapes of mutations in colon cancer, we were able to identify somatic mutations with high potential to drive cancer development. Interestingly, the majority of the genes involved have a low mutation frequency. Therefore, the method shows good potential for identifying rare driver mutations in current, large-scale tumor sequencing projects. In addition, mapping mutations to specific domains provides the necessary functional context for understanding how the mutations contribute to the disease, and may reveal novel or more refined gene and domain target regions for drug development.

Figure 1

Gene and domain mutational landscapes for colon and breast cancer Topographical maps depicting the frequency of somatic mutations in individual genes (1A and 1C) and domains (1B and 1D) from studies of 100 colon adenocarcioma and 522 breast invasive carcinoma patients. Each gene or domain is represented by a single point, and the heights of the peaks on the maps are proportional to the length normalized frequencies of somatic mutations occurring in each gene or domain. The “*” next to the arrow for the P53 domain peak in breast cancer (1D) denotes that the height of this peak was reduced to better show the landscape for the other domains.

Figure 2

Domain peaks derived from genes with low mutation frequencies Depiction of the gene and domain landscape topographies corresponding to an instance where the individual genes contributing mutations to a shared domain do not achieve significance, yet the shared domain aggregates enough mutations to achieve significance. Top map – arrows point to genes with non-significant mutation frequencies. Bottom map – domain peak aggregates enough mutations to be significant.

Figure 3

Domain peaks retaining mutations after the removal of mutations from gene peaks Depiction of the gene and domain landscape topographies corresponding to an instance where multiple genes contribute mutations to a shared domain, yet the removal of mutations from a significantly mutated gene peak leaves a significant number of mutations in the shared domain. Left side – top map shows the significant gene peak in the lower right corner of the map, bottom map shows the gene peak removed. Right side – top map shows the original domain peak, bottom map shows the domain peak with a significant number of mutations even after the removal of mutations from a significant gene peak.

Figure 4

Shared gene and domain peaks in colon and breast cancer landscapes Venn diagram illustrating the proportion of overlap between significantly mutated genes and domains in colon and breast cancer.

Figure 5

Comparison of mutation prevalence in PIK3CA domains from colon and breast cancer Depiction of the mutation prevalence in colon and breast cancer for domains occurring on the PIK3CA gene. Each box represents a distinct domain from the PIK3CA gene. The color of the domain reflects the mutation prevalence for the domain – a mutation prevalence color scale is shown on the right. The mutation prevalence is calculated as the number of mutations occurring in the domain divided by the number of patients in either the colon (100) or breast (522) cancer sets. Each domain is labelled with the count of mutations found within the domain in the PIK3CA gene, with the mutation prevalence in parenthesis.