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 was led by experts from the Perelman School of Medicine at the University of Pennsylvania and Penn's Abramson Cancer Center and published today in Cell.
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.
CAR T cell therapy, a personalized form of immunotherapy that was developed at Penn Medicine and has revolutionized care for several types of blood cancer over the last decade, is increasingly being tested in other solid cancers and even non-cancerous conditions. However, identifying the best antigens for the CARs to target remains a challenge in applying CAR T cell therapy beyond blood cancers. The current FDA-approved CAR T cell therapies target surface antigens that are widely expressed in blood cancers, but not other cancer types. Finding the right targets for new CAR T applications is an incredibly time-consuming and labor-intensive process, compounded by the ever-expanding amount of data.
"Discovering a good CAR target is like trying to find a needle in in a haystack, except the haystack keeps growing as more sequencing data becomes available," explained lead author Daniel Baker, PhD, who earned his doctorate from Penn in December 2025 and completed this work under the mentorship of CAR T cell therapy pioneer Carl June, MD and Zoltan Arany MD, PhD, chair 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."
Speeding up target discovery in skin cancer
To build and test their AI framework, the research team chose to focus on skin cancer. Unlike other solid tumors, broad immunotherapy strategies, such as immune checkpoint inhibitors and, more recently, tumor-infiltrating lymphocyte (TIL) therapy, have shown efficacy in melanoma, indicating that other immune strategies, like CAR T cell therapy could make a clinical impact, if a good CAR target could be identified.
