Discoveries at the Institute
Mathematical tool searches for cancer genes
The advent of technology for scanning thousands of genes at a time to see which are mutated in cancer cells has presented researchers with a downpour of data – and a quandary. How to sort through the dozens or even hundreds of genes that often turn up abnormal on such scans to determine which are truly involved in cancer, and which are merely decoys?
Investigators at Dana-Farber and Memorial Sloan-Kettering Cancer Center have developed a set of mathematical formulas, or an algorithm, to do just that. The algorithm, now freely available to scientists online, ranks genes by their likelihood of being involved in cancer. In a study published this spring, the researchers showed that a gene identified by the algorithm as a likely tumor restrainer indeed plays that role in a common type of brain cancer, and is not a mere "bystander" to another restrainer gene.
The algorithm promises to be especially valuable to The Cancer Genome Atlas (TCGA) pilot project, a federally led effort to explore genomic changes linked to human cancer, according to Dana-Farber's Lynda Chin, MD, senior author of the study and a leading participant in TCGA.
"As the project begins to map the genetic alterations in different kinds of cancer, we need to be able to discriminate between alterations that truly are relevant to the disease and those that are not," she says. "The new algorithm, which was developed in collaboration with Dr, Cameron Brennan of Memorial Sloan-Kettering, will help us do that."
