Scientists discover new target in potential pancreatic cancer treatment

GAINESVILLE, Fla. — University of Florida Health scientists helped find a promising new way to attack and kill pancreatic cancer cells by identifying an overlooked weak spot on a protein that supercharges the lethal cancer’s growth.

After finding the ideal location using artificial intelligence and UF’s HiPerGator supercomputer, researchers screened nearly 140,000 compounds to find the best match to exploit this newfound vulnerability in the cancerous cells.

The top contender is a compound called striatal B, a byproduct of a family of organisms known as “bird’s nest fungi,” named for the shape of their fruiting bodies, which resemble nests.

Paired with a chemotherapy drug, striatal B turns off the protein’s messaging to cancer cells that drive them to multiply, according to the study published in the October issue of Biomedicine & Pharmacotherapy.

“These are exciting findings,” said UF Health immunologist and structural biologist David A. Ostrov, Ph.D., a co-author of the study. “This is one of the strengths of drug discovery at the University of Florida — our ability to target unique and unexplored structural pockets on proteins.”

While more research, including clinical trials, is needed to confirm safety and effectiveness in patients, Ostrov said, “these early results mark a significant step in the search for new treatments.”

The compound and chemotherapy drug combination proved effective in tests using laboratory-grown human pancreatic cancer cells and mouse cancer cell lines in the laboratory.

The road to the discovery began in Texas. Ostrov’s former UF Health colleague, Robert A. Hromas, M.D. — now dean of the University of Texas medical school in San Antonio — asked Ostrov to help his team. He wanted to tap Ostrov’s expertise in mapping proteins to find druggable spots.

“We’re all still Gators,” said Ostrov, who is a member of the UF Health Cancer Center.

The Texas researchers had identified a promising agent they believed might bind well to a protein, STAT3, that is integral to the cancer’s growth. Ostrov hunted the best location.

Ostrov is, in a sense, a cartographer, mapping the complex folds, valleys and ridges that compose a protein’s three-dimensional surface. Proteins carry out nearly every job in the body, from controlling growth to fighting infection.

STAT3, working properly, fights infection and inflammation by turning on genes in an immune response. It also regulates how cells grow and divide, and then deactivates when the job is done.

In cancerous cells, however, STAT3 acts like a car with its accelerator stuck.

“Mutations in some cancer cells turn STAT3 on, and keep it on,” said Ostrov, an associate professor in the UF College of Medicine’s Department of Pathology, Immunology and Laboratory Medicine. “So, it keeps sending signals to the cancer cells telling them to grow and keep dividing.”

Ostrov used AI software to predict STAT3’s labyrinthine shape. The AI tool he used has been trained on hundreds of thousands of other proteins whose structure has already been confirmed in the lab with older methods, such as X-ray crystallography.

The AI learned the rules of how all proteins twist, curl and fold into complex patterns and can predict an unmapped protein’s geography. Scientists can then confirm the findings in the lab.

Ostrov identified an area on STAT3 called the linker domain that had previously been thought inconsequential as a landing zone for drug candidates.

The chemical agent that Texas researchers asked Ostrov to screen attached to the linker domain better than any other part of STAT3, he said. This surprised the team. Ostrov, however, thought expanding the search might uncover an even better drug candidate.

Ostrov’s team analyzed digital copies of the 140,000 compounds from a National Cancer Institute database and found one more effective than the compound originally suggested by the Texas researchers. This was striatal B.

“No one has ever solved the full crystal structure of the STAT3 protein,” Ostrov said. “Now, with the power of artificial intelligence, we can predict its complete structure and reveal drug targets that were previously invisible. This opens the door to faster drug discovery and more personalized treatments.”