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Leading CAR T cell therapy researchers have developed a human-in-the-loop artificial intelligence (AI) framework that firmly centers scientists’ expertise to find viable target antigens for CAR T cell therapy. The work, published in Cell, is led by experts from Penn’s Perelman School of Medicine and the Abramson Cancer Center.
As proof-of-concept, the team developed a CAR T targeting glycoprotein non-metastatic melanoma protein B (GPNMB), the top candidate nominated by this AI-driven approach, which showed robust tumor-killing activity in mouse models of multiple cancer types.
“Discovering a good CAR target is like trying to find a needle in a haystack, except the haystack keeps growing as more sequencing data becomes available,” says lead author Daniel Baker, who earned his doctorate from Penn in December 2025 and completed this work under the mentorship of CAR T cell therapy pioneer Carl June, as well as Zoltan Arany, professor of physiology at Penn. “We thought this would be a strong use-case for AI because one of the strengths of large language models (LLMs) is the amount of data they can consider. Human experts excel at going deep, while LLMs are good at looking across a broad range of data. So, we created a framework that combines these strengths to build a systematic way to nominate and prioritize potential targets.”
To build and test their AI framework, the research team chose to focus on skin cancer. They integrated four publicly available single-cell RNA sequencing skin cancer datasets—along with data from public databases—with specific guidelines to prioritize the 10,000+ potential targets for critical CAR T cell target features. Next, they used several frontier LLMs to nominate ideal targets from that prioritized list. These simulations were then independently repeated 1,000 times to weed out some of the inherent risks and known issues with AI, such as hallucinations. The results were combined to create a final short list of priority targets for expert review and biological validation.
“By building this AI framework to work with public data sets, we hope to democratize target discovery so that it’s broadly available beyond teams who have access to clinical samples or major institutions that are able to do their own sequencing,” Baker says.
Once the framework was built, the entire process took less than a few weeks, far quicker and less expensive than the current manual methods for target discovery, which can take several months to several years.
Read more at Penn Medicine News.
Meagan Raeke
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