New study sheds light on potential cancer driver genes

A new study into the protein structure of human cancer-associated mutations has shed light on potential driver genes which lead to the progression of the disease.

Using advances in computational modelling, scientists at the Institute of Genetics and Cancer were able to investigate the protein structural context of cancer-associated missense mutations on an “unprecedented scale”. 

A missense mutation occurs when a change in the DNA sequence leads to a single amino acid substitution in the protein, potentially altering its structure and function.

A major challenge faced by the researchers was the fact that datasets of cancer mutations are dominated by ‘passenger’ mutations that do not significantly impact tumour growth. 

While they were able to find that properties of known cancer-driving mutations resemble those of other pathogenic missense mutations, analysing all cancer-associated mutations together revealed weaker trends. 

However, by searching for genes enriched in these mutational properties, the team was able to identify new genes where mutations are potentially important for driving cancer and obtain insight into the molecular mechanisms by which mutations in these genes might act. 

A significant challenge the researchers faced was the need to combine data from multiple cancer types to find meaningful patterns. This approach, known as a ‘pan-cancer’ analysis, gave them the statistical power to identify potential driver genes. However, because they combined mutations from different tissues, they were unable to fully explore the unique genetic drivers of specific cancers.This study was published in the journal Cell Reports in an article entitled 'Protein structural context of cancer mutations reveals molecular mechanisms and candidate driver genes'.

Our study shows that consideration of 3D protein structure can provide new insights into the molecular mechanisms underlying cancer-associated mutations. While combining data across cancers allowed us to find these novel genes, the power of our approach will continue to grow in the future as more sequencing data becomes available. This will ultimately enable us to pinpoint mutations that drive individual cancer types.

Professor Joe Marsh Personal Chair of Computational Protein Biology

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