Identification and analysis of mutational hotspots in oncogenes and tumour suppressors

Abstract

Background: The key to interpreting the contribution of a disease-associated mutation in the development and progression of cancer is an understanding of the consequences of that mutation both on the function of the affected protein and on the pathways in which that protein is involved. Protein domains encapsulate function and position-specific domain based analysis of mutations have been shown to help elucidate their phenotypes.Results: In this paper we examine the domain biases in oncogenes and tumour suppressors, and find that their domain compositions substantially differ. Using data from over 30 different cancers from whole-exome sequencing cancer genomic projects we mapped over one million mutations to their respective Pfam domains to identify which domains are enriched in any of three different classes of mutation; missense, indels or truncations. Next, we identified the mutational hotspots within domain families by mapping small mutations to equivalent positions in multiple sequence alignments of protein domainsWe find that gain of function mutations from oncogenes and loss of function mutations from tumour suppressors are normally found in different domain families and when observed in the same domain families, hotspot mutations are located at different positions within the multiple sequence alignment of the domain.Conclusions: By considering hotspots in tumour suppressors and oncogenes independently, we find that there are different specific positions within domain families that are particularly suited to accommodate either a loss or a gain of function mutation. The position is also dependent on the class of mutation.We find rare mutations co-located with well-known functional mutation hotspots, in members of homologous domain superfamilies, and we detect novel mutation hotspots in domain families previously unconnected with cancer. The results of this analysis can be accessed through the MOKCa database (http://strubiol.icr.ac.uk/ extra/MOKCa).

Endogenous Retroelements in Cancer: Molecular Roles and Clinical Approach

“…Identification of somatic mutation hotspots associated with cancer is very important for functional analysis and diagnosis [137]. Several methods have been developed for the identification of somatic RE insertions in cancers (L1-seq, TIPseq, and ERVcaller), and many bioinformatics tools to discover somatic L1 insertions in silico using WGS or WES data have been developed [138,139].…”

Mapping genetic variations to three-dimensional protein structures to enhance variant interpretation: a proposed framework

“…Several integrated tools have been developed that combine the prediction of the effects of mutations, annotation by functional information, and visual mapping of mutation sites onto 3D protein structures and multiple sequence alignments. Examples include 3DHotspots.org [ 93 ], cBioPortal [ 11 ], COSMIC-3D [ 10 ], CRAVAT [ 31 ], Jalview [ 32 ], LS-SNP/PDB [ 94 ], MOKCA [ 95 ], MuPIT [ 33 ], RCSB PDB [ 21 ], SNP2Structure [ 96 ], and Cancer3D [ 36 ]. These tools might help elucidate the effect of mutations in the context of both 3D structure and other available annotations.…”

‘Big data’ approaches for novel anti-cancer drug discovery

“…The classification of known driver genes as either tumour suppressor or oncogene is often well documented in the literature, although when a new driver is identified via a high-throughput approach, its class can often be unclear. However, the mutational patterns observed in cohorts of tumour samples differ markedly between tumour suppressor and oncogenes and several groups have used data from whole exome sequencing of large data sets to automatically distinguish between them on that basis [34][35][36].…”