Discriminatory Power of Combinatorial Antigen Recognition in Cancer T Cell Therapies.,Cell Systems

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Discriminatory Power of Combinatorial Antigen Recognition in Cancer T Cell Therapies.
Cell Systems ( IF 9.0 ) Pub Date : 2020-09-10 , DOI: 10.1016/j.cels.2020.08.002
Ruth Dannenfelser ₁ , Gregory M Allen ₂ , Benjamin VanderSluis ₃ , Ashley K Koegel ₄ , Sarah Levinson ₅ , Sierra R Stark ₆ , Vicky Yao ₇ , Alicja Tadych ₈ , Olga G Troyanskaya ₉ , Wendell A Lim ₆

Affiliation

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.
Center for Computational Biology, Flatiron Institute, New York, NY 10010, USA.
Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, 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; Division of Pediatric Oncology, Department of Pediatrics, University of California, San Francisco, San Francisco, CA 94158, USA.
Department of Cellular and Molecular Pharmacology, 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.
Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, 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.
Department of Computer Science, Princeton University, Princeton, NJ 08540, USA; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA; Department of Computer Science, Rice University, Houston, TX 77005, USA.
Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.
Department of Computer Science, Princeton University, Princeton, NJ 08540, USA; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA; Center for Computational Biology, Flatiron Institute, New York, NY 10010, USA.

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).

中文翻译：

癌症 T 细胞疗法中组合抗原识别的辨别力。

准确区分肿瘤与正常组织仍然是嵌合抗原受体 (CAR) T 细胞治疗功效的主要障碍。在这里，我们进行了全面的计算机模拟筛选以通过布尔逻辑（例如，AND 和 NOT）整合多个抗原输入的 CAR T 细胞来识别提高肿瘤辨别力的多抗原特征。我们在 33 种肿瘤类型和 34 种正常组织中筛选了超过 250 万种双抗原和约 6000 万种三重抗原。我们发现双抗原显着优于最佳的单一临床研究 CAR 目标，并通过实验确认了关键预测。此外，我们确定了预测对几种肿瘤类型显示出接近理想的肿瘤与正常组织区分的抗原三联体。这项工作证明了 2 到 3 个抗原布尔逻辑门在通过 CAR T 细胞疗法改善肿瘤识别方面的潜力。我们的预测可在交互式网络服务器资源 (antigen.princeton.edu) 上获得。

更新日期：2020-09-23

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