AI-driven discovery of GPNMB CAR T cells as a multi-cancer therapy

Abstract

Chimeric antigen receptor (CAR) T cells have demonstrated curative potential in hematologic cancers and increasing efficacy in solid tumors and non-malignant diseases. However, target identification remains a major bottleneck. We developed an artificial intelligence (AI)-driven approach for CAR T cell target discovery by integrating single-cell RNA sequencing datasets from human skin cancer and healthy tissue. Candidates were refined using public datasets to optimize for tumor composition, tissue specificity, and clinical feasibility. Large language models were applied to prioritize and nominate targets with therapeutic promise. Glycoprotein non-metastatic melanoma protein B (GPNMB) was the most frequently nominated target. We validated its expression across hematologic and solid tumors. We engineered a human GPNMB-directed CAR T cell, which showed potent anti-tumor activity in mouse models of monoblastic leukemia, melanoma, and colorectal adenocarcinoma. These findings establish a scalable pipeline for CAR T cell target discovery and support the translation of GPNMB-directed CAR T cells as a multi-cancer therapeutic.