Protein structural and functional consequences of missense mutations in the human cancer genome

The rapid improvement of sequencing technologies has resulted in the creation of multiple mutational databases, allowing researchers to scrutinise the human mutational landscape with an incredibly high coverage. As these resources grow, new variants get incorporated into the pool and older variants get updated annotations, further refining the quality of the information stored. Sometimes, however, the sequenced variants are of unknown significance, and they cannot be interpreted contextually. This scenario is a potential source of bias and therefore a challenge that needs to be addressed, and it is particularly prevalent in cancer. The replication and repair mechanisms of tumour cells are defective, causing an extremely chaotic and messy mutational landscape, where the contribution of most variants to tumour progression cannot be quantified easily. There are some key genes that are known to initiate many different types of cancer, and they have distinct mutational features that are well-known, such as mutational hotspots or highly conserved and delicate residues. The protein-level is ultimately where evolution exert its force, so it is arguably the most interesting area of study for cancer variants. However, less than the human proteome has experimentally available structures, meaning that pan-cancer studies are lacking approaches and resources to identify the molecular mechanisms of variants. This bottleneck now can be avoided thanks to the advances in structure determination and computational modelling. In this project, we use a vast repertoire of computational tools — Variant Effect Predictors (VEPs) — to evaluate the phenotypical impact of cancer missense mutations in the entire human proteome. We detect interesting patterns and are able to distinguish to some extent the molecular mechanisms that each of the affected genes exert in cancer. Our observations allow us to identify candidate driver genes and speculate about their molecular roles, which we expect will have general utility in the analysis of cancer sequencing data.

Grantor dc:publisher

The University of Edinburgh

Year dc:date.issued

2025

Author dc:creator

• Chillón Pino, Diego

Advisors dc:contributor.advisor

• Marsh, Joseph
• Semple, Colin

• cancer-related genetic changes
• protein function
• mutational databases
• human proteome
• Variant Effect Predictors
• analysis of cancer sequencing data

Language dc:language.iso

en

Identifier URI

http://dx.doi.org/10.7488/era/6617

OAI identifier oai:identifier

oai:era.ed.ac.uk:1842/44091