Department of Computer Science, Princeton University, Princeton, NJ 08540, USA; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.
Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA; Division of Hematology and Oncology, Department of Medicine, University of California, San Francisco, San Francisco, CA 94143, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94158, USA; Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA 94158, USA; Center for Systems and Synthetic Biology, University of California, San Francisco, San Francisco, CA 94158, USA; Cell Design Institute and Center for Synthetic Immunology, University of California, San Francisco, San Francisco, CA 94158, USA.
Cell Syst. 2020 Sep 23;11(3):215-228.e5. doi: 10.1016/j.cels.2020.08.002. Epub 2020 Sep 10.
Precise discrimination of tumor from normal tissues remains a major roadblock for therapeutic efficacy of chimeric antigen receptor (CAR) T cells. Here, we perform a comprehensive in silico screen to identify multi-antigen signatures that improve tumor discrimination by CAR T cells engineered to integrate multiple antigen inputs via Boolean logic, e.g., AND and NOT. We screen >2.5 million dual antigens and ∼60 million triple antigens across 33 tumor types and 34 normal tissues. We find that dual antigens significantly outperform the best single clinically investigated CAR targets and confirm key predictions experimentally. Further, we identify antigen triplets that are predicted to show close to ideal tumor-versus-normal tissue discrimination for several tumor types. This work demonstrates the potential of 2- to 3-antigen Boolean logic gates for improving tumor discrimination by CAR T cell therapies. Our predictions are available on an interactive web server resource (antigen.princeton.edu).
精确区分肿瘤组织和正常组织仍然是嵌合抗原受体 (CAR) T 细胞治疗效果的主要障碍。在这里，我们进行了全面的计算机筛选，以确定多抗原特征，这些特征通过通过布尔逻辑（例如 AND 和 NOT）整合多个抗原输入的 CAR T 细胞设计来改善肿瘤的区分。我们筛选了超过 250 万个双抗原和大约 6000 万个三抗原，涵盖 33 种肿瘤类型和 34 种正常组织。我们发现，双抗原的性能明显优于最佳的单一临床研究 CAR 靶点，并通过实验证实了关键预测。此外，我们还确定了三抗原组合，这些组合预计在几种肿瘤类型中具有接近理想的肿瘤与正常组织区分能力。这项工作证明了 2 到 3 个抗原布尔逻辑门在提高 CAR T 细胞治疗的肿瘤区分能力方面的潜力。我们的预测可在一个交互式网络服务器资源（antigen.princeton.edu）上获得。
