Cancer is rarely the result of a single mutation in a single gene. Rather, tumors arise from the complex interplay between any number of mutually exclusive abnormal changes in the genome, the combinations of which can be unique to each individual patient. To better characterize the functional context of genomic variations in cancer, researchers developed a new computer algorithm they call REVEALER.
The team tested REVEALER using The Cancer Genome Atlas (TCGA), the National Institutes of Health's database of genomic information from more than 500 human tumors representing many cancer types. REVEALER revealed gene alterations associated with the activation of several cellular processes known to play a role in tumor development and response to certain drugs. Some of these gene mutations were already known, but others were new.
For example, the researchers discovered new activating genomic abnormalities for beta-catenin, a cancer-promoting protein, and for the oxidative stress response that some cancers hijack to increase their viability.