Domain landscapes of somatic mutations in cancer

Abstract

BackgroundLarge-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.ResultsWe 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.ConclusionsUsing 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, themethod 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.

Pan-Cancer Analysis of Mutation Hotspots in Protein Domains

“…For example, some of the most frequent oncogenic mutations in human cancer affect analogous residues of the activation segment of the kinase domain and cause constitutive activation of several oncogenes, including FLT3 D835 mutations in acute myeloid leukemia, KIT D816 mutations in gastrointestinal stromal tumors, and BRAF V600 mutations in melanoma (Dibb et al, 2004; Greenman et al, 2007). Proteome-wide bioinformatics analysis of mutations in domains have been performed to identify domains enriched for alterations (Nehrt et al, 2012; Peterson et al, 2012; Yang et al, 2015) as well as to detect significantly mutated domain hotspots using multiple sequence analysis (Peterson et al, 2010; Yue et al, 2010). We here extend upon these analyses by performing a systematic pan-cancer analysis of recurrence of mutations in protein domains (hotspots and enrichment of mutations across the domain body) and identify dozes of unreported cancer-associated mutations that are not detection using standard gene-based approaches.…”

Revealing ETC-1922159 Affected Unknown 3<em>^rd</em> order WNT10B-X-X Combinations, in Silico

“…Tuupanen et al 152 observe mutations in CTTNBP2 in colorectal cancer cases. Nehrt et al 153 also somatic mutations in colon cancer. After the treatment of ETC-1922159, CT-TNBP2 was found to be down regulated and this is indicated by the pipeline with a low rank of 93.…”

Mutational patterns in oncogenes and tumour suppressors

“…Although most of the analysis of cancer mutations is based around a gene centric view, a few studies have focused on domain-based analyses [45,46][50] and they may be particularly fruitful when studying mechanisms of activation of proteins. Larger proteins comprise recognizable smaller sequence domains, which recur in other proteins in various combinations.…”

“…Proteome-wide analyses have been performed to identify domains enriched in missense mutations [45,47] [50] and to identify domain-centric positions of hotspot missense mutations [48,49] [50]. These studies focused exclusively on missense mutation and as yet, little attempt was to use these data to distinguish between activating and loss of function mutations in the majority of cases.…”