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

EAGER：开发混合知识和数据驱动的方法来指导免疫治疗细胞的设计

• 批准号: 2324742
• 负责人: Natasa Miskov-Zivanov
• 金额: $ 30万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2324742&HistoricalAwards=false
• 关键词: EAGER; Development; Hybrid; Knowledge; Data

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.

过去十年，免疫疗法迅速成为癌症治疗的“新支柱”，利用和增强患者的免疫系统来攻击肿瘤。 T 细胞（一种白细胞）上的嵌合抗原受体 (CAR) 彻底改变了血癌的治疗方法，并显示出治疗实体瘤、自身免疫性疾病和慢性病毒感染的前景。工程化 CAR T 细胞的主要目标是产生能够有效且持久清除肿瘤的 T 细胞表型，同时增强抗肿瘤细胞毒性、T 细胞持久性和较低的耗竭。 CAR 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发挥着关键作用。从大量候选受体结构域中进​​行选择并将它们排列在受体上，以优化它们对细胞功能的影响，既带来了巨大的机遇，也带来了巨大的设计挑战。 CAR 信号域的大规模系统计算探索和推荐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，从而带来更安全、更有效的治疗。同时，此类研究提供了出色的跨学科培训，将合成生物学探索和基础生物学知识与创新计算方法联系起来。为了实现这些目标，这项早期探索性研究资助（EAGER）将探索一种完全不同的 CAR T 细胞设计方法，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，将实验数据与知识源集成。该项目将系统地研究受体设计流程中的步骤及其完全自动化：从文献和通路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，该项目将确定最有效的方法来解决先前方法的不确定性和训练运行时间，同时提供 CAR 设计的可靠建议和解释。最终，该项目将推进合成生物学、系统生物学、生物传感和免疫治疗领域的知识并贡献新颖的研究策略。该项目的成果、新算法和方法的评估、网络模拟和分析数据以及推荐 CAR 的机制解释将开源并公开供广大科学界检查、利用和复制。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。