by Ryan Layer, Assistant Professor of Computer Science, University of Colorado Boulder
Using big data to find cancerous mutations – When determining what type of cancer mutation a patient has, the gold standard is to compare two samples from the patient: one from the tumor and one from healthy tissue (typically blood). Since both samples came from the same person, most of their DNA is identical; focusing only the genetic regions that differ from each other drastically narrows the location of a possible cancer-causing mutation.
The problem is that healthy tissue isn’t always collected from patients, for reasons ranging from clinical costs to narrow research protocols.
One way to get around this is to look at massive public DNA databases. Since cancer-driving mutations are detrimental to survival, natural selection tends to eliminate them over time in successive generations. Of all the mutations in a tumor, the ones that occur less frequently in a given population are more likely to be harmful than changes that are shared by many people. By counting how often a mutation occurs in these databases, researchers can distinguish between genetic changes that are common and likely benign and those that are rare and potentially cancerous.