Statistical Model Identifies Novel Cancer-Associated Genes
27 previously unknown genes have been linked to tumour suppressor activity by a novel statistical model and may be able to serve as targets for future anti-cancer therapies. The research, published in Nature Communications earlier this week, was led by researchers at the Francis Crick Institute in London and the University of Leuven, with contributions from researchers at the University of Oslo and the University of Chicago.
“Using this powerful toolkit, we’ve uncovered rare tumour suppressor genes that when lost in mutated cells, cause cancer,” said Jonas Demeulemeester, PhD, co-first author of the study and a Postdoctoral Research Scientist at the Francis Crick Institute. “This could pave the way for the development of personalized cancer treatments.”
The research examined data from 2,218 primary tumour samples that represented 12 different cancer types, including breast, lung, and bowel cancers. The team systematically screened the samples to identify homozygous deletions by comparing the relative proportion of genes between healthy and cancerous cells. They hoped that this copy number meta-analysis would enable them to identify rare tumour suppressor genes which had been mutated in the different cancer types.
The screening process identified 96 genomic regions which were recurrently targeted by deletion events. The screen also revealed that harmful deletions demonstrated a different DNA footprint to non-harmful ones, which were typically smaller. As the team believed that these regions were either tumour suppressing genes or regions with abnormally high genetic instability, the varying DNA footprints enabled them to develop a statistical model capable of differentiating between the two.
“Our study demonstrates that rare tumour suppressor genes can be identified through large-scale analysis of the number of copies of genes in cancer samples,” said Peter Van Loo, PhD, lead investigator and Group Leader at the Francis Crick Institute. “Cancer genomics is a growing area of research, and the computational tools we use are a powerful way to find new genes involved in cancer.”
The statistical model identified 16 tumour suppressors that had previously been established, along with a further 27 candidate suppressors which hadn’t been located before. Further work is necessary to confirm the action of the 27 genes, but it’s possible that they could become targets for future therapies against cancer.
“In this study, we aimed to identify rare tumour suppressors through a systematic pan-cancer analysis of homozygous deletions in primary tumours. Our screen detected 16 established tumour suppressors, 3 immune regions, 15 known (named) fragile sites, 24 additional intrachromosomal fragile sites, 9 regions of telomeric instability, and 32 regions showing signatures of positive selection for homozygous deletions,” the authors concluded.
“Our results provide a view on the landscape of tumour suppressors that is complementary to sequencing screens for recurrent single-nucleotide substitutions and small insertions and deletions.”