New model helps identify mutations that drive cancer: The system rapidly scans the genome of cancer cells, could help researchers find targets for new drugs.

Cancer cells can have thousands of mutations in their DNA. However, only a handful of those actually drive the progression of cancer; the rest are just along for the ride.

Distinguishing these harmful driver mutations from the neutral passengers could help researchers identify better drug targets. To boost those efforts, an MIT-led team has built a new computer model that can rapidly scan the entire genome of cancer cells and identify mutations that occur more frequently than expected, suggesting that they are driving tumor growth. This type of prediction has been challenging because some genomic regions have an extremely high frequency of passenger mutations, drowning out the signal of actual drivers

“We created a probabilistic, deep-learning method that allowed us to get a really accurate model of the number of passenger mutations that should exist anywhere in the genome,” says Maxwell Sherman, an MIT graduate student. “Then we can look all across the genome for regions where you have an unexpected accumulation of mutations, which suggests that those are driver mutations.”

In their new study, the researchers found additional mutations across the genome that appear to contribute to tumor growth in 5 to 10 percent of cancer patients. The findings could help doctors to identify drugs that would have greater chance of successfully treating those patients, the researchers say. Currently, at least 30 percent of cancer patients have no detectable driver mutation that can be used to guide treatment.

Sherman, MIT graduate student Adam Yaari, and former MIT research assistant Oliver Priebe are the lead authors of the study, which appears today in Nature Biotechnology. Bonnie Berger, the Simons Professor of Mathematics at MIT and head of the Computation and Biology group at the Computer Science and Artificial Intelligence Laboratory (CSAIL), is a senior author of the study, along with Po-Ru Loh, an assistant professor at Harvard Medical School and associate member of the Broad Institute of MIT and Harvard. Felix Dietlein, an associate professor at Harvard Medical School and Boston Children’s Hospital, is also an author of the paper.

A new tool

Since the human genome was sequenced two decades ago, researchers have been scouring the genome to try to find mutations that contribute to cancer by causing cells to grow uncontrollably or evade the immune system. This has successfully yielded targets such as epidermal growth factor receptor (EGFR), which is commonly mutated in lung tumors, and BRAF, a common driver of melanoma. Both of these mutations can now be targeted by specific drugs.

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