Researchers have developed a way to predict how lung cancer cells will respond to different therapies, allowing people with the most common form of lung cancer to receive more effective individualized treatment.

The research, published Oct. 10 in Nature Genetics, was led by Thazin Aung, Ph.D., in the laboratory of Yale School of Medicine’s David Rimm, MD, Ph.D., in collaboration with scientists at the Frazer Institute at the University of Queensland. Researchers studied the tumors of 234 patients with non-small cell lung cancer (NSCLC) across three cohorts in Australia, the United States, and Europe.

“Using AI and spatial biology, we mapped NSCLC, cell-by-cell, to understand and predict its response to drug treatment,” Aung says. “This ‘Google Maps’ approach can pinpoint areas within tumors that are both responsive and resistant to therapies, which will be a gamechanger for lung cancer treatment. Rather than having to use a trial-and-error approach, oncologists will now know which treatments are most likely to work with new precision medicine tools.”

Researchers at McMaster University and the Massachusetts Institute of Technology (MIT) have made two scientific breakthroughs at once: they not only discovered a brand-new antibiotic that targets inflammatory bowel diseases (IBD), but also successfully used a new type of AI to predict exactly how the drug works. To their knowledge, this is a global first for the AI.

Detailed in the journal Nature Microbiology, the discovery unveils a promising new treatment option for millions of people affected by Crohn’s disease and other related conditions, while also showcasing important new applications for AI in drug discovery research.

“This work shows that we’re still just scratching the surface as far as AI-guided drug discovery goes,” says Jon Stokes, an assistant professor in McMaster’s Department of Biochemistry and Biomedical Sciences and principal investigator on the new study.