Health
UM Researchers Use AI to Understand Vulnerability of Lung Cancer Cells
Missoula – A scientific team including University of Montana biologist Mark Grimes recently used artificial intelligence to better understand how groups of proteins in lung cancer cells regulate cell division and metabolism.
This study may lead to a better understanding of lung cancer vulnerability and future anticancer therapies. Here are the findings: Published in PLOS Computational Biology.
“We looked at how the cells responded to anticancer drugs used to treat lung cancer,” Grimes said. “The human brain is not very good at understanding patterns in large spreadsheets, so we used machine learning algorithms to detect patterns in data that are hard to see.”
He said lung cancer remains the leading cause of death. New drugs to treat lung cancer are effective for a while, but cancer cells can evolve to form new tumors and cause recurrence. Combining drugs to attack cancer cells may help solve this problem, but only if researchers have a better understanding of cancer cells’ weaknesses.
“The beauty of this study is that we turned the patterns we found into networks representing mutated genes, called oncogenes, and cell-signaling pathways that are affected by drugs that target oncogenes,” Grimes said. said Mr. “This study takes this approach to the next level by examining interactions between pathways, groups of proteins that work together in cells.”
This, he says, has given his research team both a higher-level and molecular-level view of the interplay between pathways that cause cancer cells to divide and regulate their metabolism.
Grimes said cancerous tumors are often hypermetabolic and have a limited supply of oxygen.
“Thus, identifying links between these pathways offers an opportunity to add vulnerability to nutrient uptake and utilization in combination with other anticancer therapies.”
Other partners in research include Georgetown University. Moffitt Hospital, Tampa, Florida. and the University of Manitoba.
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contactBy: Mark Grimes, UM Professor of Biology, 406-243-4977, [email protected].
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