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.