An innovative algorithm identifies 460 genes that are important for the development of cancer, uncovering tumor-gene associations that had not previously been identified. The study, published today in Nature Genetics, provides a comprehensive resources of cancer driver genes to date.

Cancer driver genes are genes that give cells a growth advantage when they are mutated, helping tumours proliferate. Identifying these genes is a crucial step towards personalising treatment for cancer, but the complexity and diversity of cancerous cells make finding these genes difficult.

Researchers develop sophisticated algorithms to look for genetic mutations that specifically drive tumour development, discarding mutations which confer no advantage. These cancer driver mutations are catalogued and can be used as reference points to diagnose and treat the disease.

In this study, researchers at the Centre for Genomic Regulation in Barcelona, Harvard Medical School and the Dana-Farber Cancer Institute in Boston describe a new method that outperforms seven current methods for cancer driver gene detection in 26 out of 28 cancer types.

The innovative algorithm considers “unusual” mutation contexts in the DNA of cancers, which untangles some of the nuances in interpreting whether a gene will drive tumour proliferation.

“Our paper describes a new computational tool to detect cancer-causing genes at unprecedented sensitivity, which I hope will be a useful resource to the community of cancer researchers” says Donate Weghorn, one of the lead authors of the paper and researcher at the Centre for Genomic Regulation in Barcelona. “We also provide a list of predicted novel tumour type-gene associations, based on a large cohort of over 11,000 tumours. This can directly inform cancer therapy” she added.