Xin He & Matthew Stephens Featured: Software tools help researchers find genetic mutations that can cause cancer

Sep 12, 2019
By Nancy Averett
Software identified the METTL3 gene (left) as a potential "driver gene" for bladder cancer. The closeups below show areas with genetic mutations.

Software identified the METTL3 gene (left) as a potential "driver gene" for bladder cancer. The closeups below show areas with genetic mutations.

Identifying which gene mutations are most likely to propel cancer forward can help doctors treat cancer patients more effectively and help researchers better understand the biology of cancer. But finding these “driver” genes isn’t easy. Any cell can and will acquire gene mutations, but only a fraction of those aberrations have the potential to survive, proliferate and create tumors.

Driver genes tend to have higher numbers of mutations than non-driver genes. Researchers have been able to find them by sequencing the DNA of cancer tissue from many different patients and then counting the number of mutations on each gene. This method works well with some common cancers, but isn’t as effective with other malignancies because there often isn’t a large enough sample size to find a pattern in the data. It also misses a large number of potential genes that drive the formation of tumors in only a small fraction of cancer patients.

Xin He, PhD, assistant professor of human genetics at the University of Chicago, Matthew Stephens, professor of human genetics and statistics, and their colleagues have developed a computational software program that can tease out driver genes from non-driver genes much more effectively than previous methods. Their program, called driverMAPS (Model-based Analysis of Positive Selection), does more than just count the number of mutations on genes. It also considers the functional importance of the mutation, or how much it affects the gene’s ability to do its job.