Using big data to better understand cancerous mutations

Artificial intelligence and machine learning are among the latest tools being used by cancer researchers to aid in detection and treatment of the disease.

One of the scientists working in this new frontier of cancer research is University of Colorado Cancer Center member Ryan Layer, PhD, who recently published a study detailing his research that uses big data to find cancerous mutations in cells.

“Identifying the genetic changes that cause healthy cells to become malignant can help doctors select therapies that specifically target the tumor,” says Layer, an assistant professor of computer science at CU Boulder. “For example, about 25% of breast cancers are HER2-positive, meaning the cells in this type of tumor have mutations that cause them to produce more of a protein called HER2 that helps them grow. Treatments that specifically target HER2 have dramatically increased survival rates for this type of breast cancer.”

Scientists can evaluate cell DNA to identify mutations, Layer says, but the challenge is that the human genome is massive, and mutations are a normal part of evolution.

“The human genome is long enough to fill a 1.2 million-page book, and any two people can have about 3 million genetic differences,” he says. “Finding one cancer-driving mutation in a tumor is like finding a needle in a stack of needles.”

Scanning the data

The ideal method of determining what type of cancer mutation a patient has is to compare two samples from the same patient, one from the tumor and one from healthy tissue. Such tests can be complicated and costly, however, so Layer hit upon another idea — using massive public DNA databases to look for common cell mutations that tend to be benign, so that researchers can identify rarer mutations that have the potential to be cancerous.

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