AI Framework Surfaces New CAR T Target with Multi‑Cancer Potential

A new study in Cell describes an AI‑enabled strategy that could accelerate the search for next‑generation CAR T cell targets—an enduring bottleneck in expanding the therapy beyond blood cancers. The work, titled “AI‑driven discovery of GPNMB CAR T cells as a multi‑cancer therapy,” was led by researchers at the Perelman School of Medicine at the University of Pennsylvania and Penn’s Abramson Cancer Center, with collaborators at the Icahn School of Medicine at Mount Sinai and RWTH Aachen University.

The Penn team developed a human‑in‑the‑loop AI framework designed to systematically nominate antigens suitable for CAR T cell therapy. Rather than replacing expert judgment, the system integrates large language models (LLMs) with single-cell RNA sequencing datasets from human skin cancer and healthy tissue to generate and refine target lists that scientists then evaluate experimentally.

The challenge is well known: while CAR T therapies have transformed treatment for several hematologic malignancies, identifying safe, selective targets in solid tumors remains slow and labor‑intensive. “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,” said lead author Daniel Baker, PhD, who completed the work under the mentorship of Carl June, MD, and Zoltan Arany, MD, PhD. LLMs, Baker added, excel at scanning broad datasets, while human experts “go deep”—a complementary pairing the team sought to formalize.

To test the framework, the researchers focused on skin cancer, integrating four publicly available single‑cell RNA‑seq datasets with additional public resources. More than 10,000 potential antigens were filtered using criteria relevant to CAR T design, including tumor composition, tissue specificity, and clinical feasibility. Multiple LLMs then repeatedly simulated target nomination—1,000 independent runs—to reduce noise and mitigate hallucinations. The resulting consensus list was reviewed by the team, who selected Glycoprotein non-metastatic melanoma protein B (GPNMB) as the top candidate.

The researchers then engineered a GPNMB‑directed CAR T cell and validated its activity across several preclinical models. In mouse studies, the CAR T cells eliminated tumors not only in melanoma—the original focus of the dataset—but also in monoblastic leukemia and colorectal adenocarcinoma, suggesting broader therapeutic potential. These findings align with the paper’s highlight that GPNMB is expressed across a wide range of tumor types.

The full framework is included in the methods section to enable adoption by other groups. The Penn team plans to apply the approach to additional cancer types and continue advancing the GPNMB CAR T candidate toward potential clinical translation.

According to June, “this study represents one of the first uses of large language models in the field of cell and gene therapy, including CAR T cell therapy.” The framework is intentionally modular and disease‑agnostic, designed to accommodate new datasets and future LLMs as they evolve. Arany emphasized the broader implications: “This is only the tip of the iceberg, as agentic AI is on the rise.”

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