Johns Hopkins engineers have devised innovative computer software that can sift through hundreds of genetic mutations and highlight the DNA changes that are most likely to promote cancer. The goal is to provide critical help to researchers who are poring over numerous newly discovered gene mutations, many of which are harmless or have no connection to cancer. According to its inventors, the new software will enable these scientists to focus more of their attention on the mutations most likely to trigger tumors.
A description of the method and details of a test using it on brain cancer DNA were published in the Aug. 15 issue of the journal Cancer Research.
The new process focuses on missense mutations, meaning protein sequences that each possess a single tiny variation from the normal pattern. A small percentage of these genetic errors can reduce the activity of proteins that usually suppress tumors or hyperactivate proteins that make it easier for tumors to grow, thereby allowing cancer to develop and spread. But finding these genetic offenders can be difficult.
"It's very expensive and time-consuming to test a huge number of gene mutations, trying to find the few that have a solid link to cancer," said Rachel Karchin, an assistant professor of biomedical engineering who supervised the development of the computational sorting approach. "Our new screening system should dramatically speed up efforts to identify genetic cancer risk factors and help find new targets for cancer-fighting medications."
The new computational method is called CHASM, short for Cancer-specific High-throughput Annotation of Somatic Mutations.
Developing this system required a partnership of researchers from diverse disciplines. Karchin and doctoral student Hannah Carter drew on their skills as members of the university's Institute for Computational Medicine, which uses powerful information management and computing technologies to address important health problems, and collaborated with leading Johns Hopkins cancer and biostatistics experts from the university's School of Medicine, its Bloomberg School of Public Health and the Johns Hopkins Kimmel Cancer Center.
The team first narrowed the field of about 600 potential brain cancer culprits using a computational method that would sort these mutations into "drivers" and "passengers." Driver mutations are those that initiate or promote the growth of tumors. Passenger mutations are those that are present when a tumor forms but appear to play no role in its formation or growth. In other words, the passenger mutations are only along for the ride.