EAGER: Development of a Hybrid Knowledge- and Data-Driven Approach to Guide the Design of Immunotherapeutic Cells

在过去的十年里，免疫疗法已经迅速成为癌症治疗的“新支柱”，利用和加强患者的免疫系统来攻击肿瘤。嵌合抗原受体（car）在T细胞（一种白细胞）上进行工程改造，已经彻底改变了血癌的治疗，并显示出治疗实体瘤、自身免疫性疾病和慢性病毒感染的希望。CAR - T细胞工程化的主要目标是产生能够有效和持久清除肿瘤的T细胞表型，具有更高的抗肿瘤细胞毒性、T细胞持久性和更低的衰竭。CAR细胞内结构域在将抗原识别转化为这些抗肿瘤效应功能方面起着关键作用。从众多候选受体结构域中进行选择，并在受体上对其进行排序，以优化其对细胞功能的影响，这既带来了巨大的机遇，也带来了相当大的设计挑战。大规模的系统计算探索和推荐CAR信号域有可能通过产生新的CAR- T细胞行为来改变基于CAR的免疫治疗领域，从而导致更安全，更有效的治疗。同时，这些研究提供了优秀的跨学科培训，将合成生物学探索和基础生物学知识与创新的计算方法联系起来。为了实现这些目标，这项探索性研究的早期资助（EAGER）将探索一种完全不同的CAR - T细胞设计方法，这是一种混合人工智能方法，通过数据驱动的学习和推理方法，通过知识驱动的机械网络组装和分析，将实验数据与知识来源相结合。该项目将系统地研究受体设计管道中的步骤及其完全自动化：从文献和途径数据库中检索相关信息，细胞内T细胞网络组装，基于知识的约束生成以及在数据驱动的深度学习方法中的使用。通过这些探索，本项目将确定最有效的方法来解决以前方法的不确定性和训练运行时间，同时提供可靠的建议和解释CAR设计。最终，该项目将在合成生物学、系统生物学、生物传感和免疫治疗方面推进知识和贡献新的研究策略。该项目的成果，对新算法和方法的评估，网络模拟和分析数据，以及推荐car的机制解释，将是开源的，并公开提供给广泛的科学界来检查、利用和复制。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Over the past decade, immunotherapy has rapidly become the "new pillar" of cancer treatment, utilizing and strengthening the patient's immune system to attack tumors. Chimeric antigen receptors (CARs) engineered on T cells, a type of white blood cells, have revolutionized the treatment of blood cancers and have shown promise for treating solid tumors, as well as auto-immune diseases and chronic viral infections. The main goal when engineering CAR T cells is to generate T cell phenotypes capable of effective and durable tumor clearance with increased anti-tumor cytotoxicity, T cell persistence, and lower exhaustion. CAR intracellular domains play a key role in converting antigen recognition into these anti-tumor effector functions. Selecting among a plethora of candidate receptor domains and ordering them on a receptor, to optimize their effect on cellular function, presents both tremendous opportunities and considerable design challenges. A large-scale systematic computational exploration and recommendation of CAR signaling domains has the potential to transform the field of CAR-based immunotherapy by producing novel CAR T cell behaviors leading to safer, more effective therapies. At the same time, such studies offer excellent interdisciplinary training, bridging synthetic biology explorations and fundamental biology knowledge with innovative computational approaches.To accomplish these goals, this EArly Grant for Exploratory Research (EAGER) will explore a radically different CAR T cell design methodology, a hybrid artificial intelligence approach that integrates experimental data, through data-driven learning and inference methods, with knowledge sources, through knowledge-driven mechanistic network assembly and analysis. This project will systematically study the steps in the receptor design pipeline, and their full automation: retrieval of relevant information from literature and pathway databases, intracellular T cell network assembly, knowledge-based constraint generation and use in data-driven deep learning methods. With these explorations, this project will determine the most effective methods to address the uncertainty and training runtime of previous approaches, while providing reliable recommendations and explanations of CAR designs. Ultimately, this project would advance the knowledge and contribute novel research strategies in synthetic biology, systems biology, biosensing, and immunotherapy. The outcomes of this project, evaluation of novel algorithms and methods, network simulation and analysis data, and mechanistic explanations of recommended CARs, will be open source and publicly available for the wide scientific community to examine, utilize, and reproduce.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.