Researchers at Penn Medicine developed an artificial intelligence framework that integrates large language models with human expertise to discover new targets for CAR T cell therapy, according to a report published Wednesday in the journal Cell. The study was led by Daniel Baker, who completed his doctorate at Penn in December 2025 under the mentorship of CAR T cell therapy pioneer Carl June and Zoltan Arany, chair of Physiology at Penn.
The AI system, described as a “human-in-the-loop” model, aims to mitigate the challenge of identifying antigens for CAR T cells. “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,” Baker said. He noted that AI is well-suited for this task since large language models can analyze vast amounts of data effectively, while human experts can provide in-depth insights.
The framework combines single-cell RNA sequencing datasets with LLM-based simulations to nominate and prioritize potential CAR T targets, producing a shortlist for expert validation. The design is disease-agnostic and compatible with future AI models.
As a proof of concept, the team focused on skin cancer and identified glycoprotein non-metastatic melanoma protein B (GPNMB) as a top candidate. CAR T cells engineered to target GPNMB demonstrated significant tumor-killing activity in mouse models of melanoma, leukemia, and colorectal cancer. A related commentary in Cell highlighted that GPNMB CAR T treatment resulted in remission without relapse in xenograft models.
While CAR T cell therapy has significantly improved treatment for blood cancers, current FDA-approved therapies predominantly target antigens in these malignancies. The researchers claim their framework can reduce the target discovery process from months to weeks, facilitating its application to a variety of disease types without requiring redesign of the underlying architecture.
