Broad analytical tool finds mutations in the driver's seat
Earlier this week, a team of scientists including Broad researcher Rameen Beroukhim, also a physician at Dana-Farber Cancer Institute, published exciting results from a study of squamous cell lung cancer, a disease linked to smoking. The scientists analyzed samples of lung tumors and discovered a mutation in the gene known as fibroblast growth factor receptor 1 (FGFR1) that was more common in samples of squamous cell lung cancer tumors than those of other types of lung cancer.
A key component of this work is the analytical tool known as GISTIC — Genomic Identification of Significant Targets in Cancer — devised in 2005 by Rameen along with fellow Broad researcher Gaddy Getz and associate member Bill Sellers. The tool addresses a problem faced by scientists studying the genetics of cancer: not all mutations are causal in cancer. Some mutations are so-called “drivers” that enable tumor cells to divide quickly, while others are merely “passenger” mutations that are along for the ride. While analytical tools existed to sort out small-scale “point” mutations, such as single-letter misspellings in DNA, that drive cancer, at that time no tools existed to do the same for mutations of DNA copy number — extra or missing DNA known as “amplifications” or “deletions.”
As Rameen explains, “Copy number mutations are more difficult to analyze.” In order to spotlight the driving mutations, scientists need to first establish the rate of background mutation in a cell, or the amount of randomly generated passenger mutations. “Some mutations lead to cancer, but cancer cells have mutations everywhere because of the way mutations are generated at random as cells divide,” he says. Any mutations that are seen more often than the background rate are possible driver mutations.
For point mutations, scientists know that some are “silent,” meaning that they do not change the protein encoded by the gene, and because they have no significant effect on the function of the cell, are considered to arise at the background rate. “But for copy number changes, there are none that we know have no effect,” says Rameen. Because all instances of extra or missing DNA may change the cell’s behavior in some way, it’s difficult to calculate the background mutation rate for copy number mutations, and therefore difficult to spotlight the important drivers. GISTIC determines the background rate of copy number mutation in cancer cells, and enables the discovery of driver mutations occuring more often than the background rate.
GISTIC also addresses the issue of what genes are targeted by a mutation. Point mutations affect a single letter of DNA, “so you have a better sense of what gene it’s targeting,” says Rameen. Copy number mutations can be chunks of DNA spanning many genes, so the one or more genes that are important can be difficult to spot. “Identifying the genes that are targets of copy number mutations is more difficult than it might seem,” he says. GISTIC includes methods to root out the targets among large amplifications.
In addition to the new finding of FGFR1 amplifications in squamous cell lung cancer, Rameen and his colleagues have used GISTIC in collaborations with researchers from Norway, Australia, and elsewhere to discover mutations associated with aggressive, recurrent cancers such as endometrial and ovarian cancer, in addition to uncovering driver mutations in cancers like lung adenocarcinoma, colorectal cancer, esophageal and gastric cancer, hepatocarcinoma, and renal cancer. The tool was also used in The Cancer Genome Atlas project to identify important regions in glioblastoma and other cancers.
GISTIC is one of the many analytical tools developed at the Broad to help researchers study the genetic variants underlying disease and shared broadly with the scientific community. Broad researchers continue to improve and expand the capabilities of GISTIC, an important tool in the search for therapeutic targets of cancer.