Advanced DNA sequencing methods coupled with improved computational techniques have changed the scope of what researchers can learn about cancer genetics, and allow them to apply that knowledge to improving patient care.

A researcher now at The University of Texas at Austin is developing advanced computational methods that can comb through vast amounts of data from cancer genomics and find correlations that would be impossible to find otherwise.

Computational geneticist Can Cenik was recruited in 2018 from Stanford University School of Medicine with a First-Time Tenure-Track Award from CPRIT. He joined the Department of Molecular Biosciences.

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Advanced DNA sequencing methods coupled with improved computational techniques have changed the scope of what researchers can learn about cancer genetics, and allow them to apply that knowledge to improving patient care.

A researcher now at The University of Texas at Austin is developing advanced computational methods that can comb through vast amounts of data from cancer genomics and find correlations that would be impossible to find otherwise.

Computational geneticist Can Cenik was recruited in 2018 from Stanford University School of Medicine with a First-Time Tenure-Track Award from CPRIT. He joined the Department of Molecular Biosciences.

Cenik’s hope is that by better understanding how specific genetic mutations are related to cancer initiation, progression, and prognosis; that patient care and outcomes will improve. His lab is unusual in that it combines the development of sophisticated computational algorithms with biological experimentation in cell lines.

“Over the past decade or so, we’ve seen a huge increase in our ability to map the mutations that contribute to cancer,” Cenik says, “so today we can actually look at this information from tens of thousands of patients and analyze the mutations.”

Given the vast scale of the data, it’s impossible for a person or even a desktop computer to comb through it. Cenik utilizes the resources of UT Austin’s Texas Advanced Computing Center, which is one of the leading academic supercomputer centers in the U.S., and also one of the reasons Cenik chose to come to UT Austin.

“Any particular patient will likely have hundreds of mutations,” Cenik says, “and it’s important to understand which mutations are more important and which are just passengers coming along for the ride.”

Cenik recently found that a fifth of all bladder cancers share the same mutation in a regulatory region of a specific gene — a discovery that wasn’t picked up by other methods. Interestingly, patients who had this mutation had a better chance of surviving the disease than patients who lacked it. Cenik is turning to the biological side of his lab to try to find out what role this gene plays in bladder cancers.

“We are looking at bladder cancer cell line models, and we can either make the mutation in these cell lines, or correct it if it’s already there,” Cenik says. “We’re looking to see what impact this has. Does it change how fast the cells grow, or how well they survive?”

Cenik’s computational methods are generally applicable to all cancers; he’s looking at 20 different types of cancers. “We’re particularly interested in mutations that don’t necessarily change a protein’s function, but rather, affect how much of it is around,” he says.

He says CPRIT funding is crucial for attracting top scientists to Texas, and having a critical mass of top-notch researchers is what will drive life-saving innovations.

Cenik studied applied mathematics as an undergraduate at Harvard College and completed his Ph.D. in genetics at Harvard Medical School. He joined Stanford as a postdoctoral fellow in genetics in 2012.

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