Quantitative protein network profiling to improve CAR design and efficacy

定量蛋白质网络分析以改进 CAR 设计和功效

• 批准号: 10578701
• 负责人: Stephen Edward Paucha Smith
• 金额: $ 48.03万
• 依托单位: SEATTLE CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-15 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10578701
• 关键词: Affinity; Antibodies; Antigens; Antineoplastic Agents; Autoimmune Diseases; B-Cell Lymphomas; Binding; Bioinformatics; Biological Markers; Biomedical Engineering; CD19 gene; CD22 gene; CD28 gene; Cancerous; Cell Communication; Cell Surface Receptors; Cells; Child; Clinical; Clinical Trials; Co-Immunoprecipitations; Collecting Cell; Communities; Custom; Data; Development; Disease; Disease remission; Engineering; Event; Fc Receptor; Genetic; Goals; Grant; ITAM; Immunology; In Vitro; Individual; K-562; Lead; Logic; Lymphocyte; Machine Learning; Mass Spectrum Analysis; Measurable; Measurement; Measures; Molecular; Monitor; Network-based; Outcome; Outcome Measure; Pathway Analysis; Patient-Focused Outcomes; Patients; Performance; Population; Production; Proteins; Proteomics; Publishing; Receptor Signaling; Recording of previous events; Refractory; Relapse; Research Institute; Research Personnel; Research Project Grants; Sampling; Science; Signal Transduction; Source; T-Cell Activation; T-Cell Receptor; T-Lymphocyte; Techniques; Technology; Testing; Translating; Variant; Viral Vector; autism spectrum disorder; biosignature; cancer cell; cancer therapy; cell behavior; cell killing; cell type; cellular transduction; chimeric antigen receptor; chimeric antigen receptor T cells; clinical efficacy; clinical implementation; clinical predictors; clinical translation; clinically relevant; computer infrastructure; cytokine; cytokine release syndrome; density; design; extracellular; graphical user interface; improved; in vivo; individual variation; interest; leukemia; lymphoblast; machine learning prediction algorithm; manufacture; molecular modeling; nano-string; neurochemistry; neuropsychiatric disorder; neurotoxicity; new technology; novel; personalized medicine; personalized predictions; prediction algorithm; predictive marker; predictive test; programs; protein protein interaction; receptor; research and development; research clinical testing; response; side effect; transcriptome

PROJECT SUMMARY This grant is in response to PAR-18-206, Bioengineering Research Grants (BRG). Our goal is to adapt a cutting-edge proteomic network analysis platform, Quantitative Multiplex co-Immunoprecipitation or QMI, to chimeric antigen receptor (CAR) T cell signaling. We will then use CAR-QMI to characterize signal transduction network activation downstream of the CAR, to both understand how the CAR instructs a T cell to attack and destroy cancerous targets, and to make batch-specific predictions about efficacy and side-effect profiles of CAR T cell products. CAR T cells are a breakthrough anti-cancer therapy that recently won FDA approval for relapsed B cell lymphomas. A true “personalized medicine”, CAR T cells are manufactured for each patient from that patient's own T cells by transducing T cells collected by leukopheresis with a viral vector encoding a CAR. However, since each batch is unique, some batches perform better than others in terms of producing remissions and/or deleterious and sometimes fatal side effects including cytokine storms and neurotoxicity. The goal of this project is to develop a “personalized signal transduction network analysis platform” that can screen each batch of CAR T cells and predict the efficacy and side-effect potential of that specific batch. Because signal transduction networks integrate information from multiple input sources- for example costimulatory and immunosuppressive cell surface receptors, patient genetic background, and T-cell specific history of activation- we hypothesize that this readout will be a powerful predictor of function. Our preliminary data show that small changes in CAR design parameters such as scFV binding domain affinity produce measurable changes in signal transduction network state that correlate with functional variables such as target killing ability and cytokine release. Further, we show that there exists considerable individual-to- individual variation in batches of CAR T cells produced from different donors. Therefore, the two prerequisites for an individualized predictive assay are present- variation in our measurement across the population, and the functional relevance of our measurement to outcome parameters. Our interdisciplinary team consists of experts in CAR development, signal transduction, proteomics, and bioinformatics. Our ambitious but achievable goals are to expand the QMI panel to include CAR-specific components; to understand how CAR design parameters influence both signal transduction network states and functional performance measures; and to develop a predictive machine learning algorithm that translates QMI-derived signal transduction network states into a functional biomarker of in vivo clinical efficacy. Successful completion these aims will (1) identify specific proteins or protein interactions that determine clinically-relevant outcomes such as cytokine production or cell killing ability, allowing CAR designers to rationally modify the design of CARs to target specific signaling outcomes; (2) provide clinicians with a test to predict the clinical performance of CAR T cells on a batch-to- batch basis; and (3) provide the community with a novel analytical platform to measure CAR activity.

项目总结 这项资助是对PAR-18-206生物工程研究资助(BRG)的响应。我们的目标是使一个 尖端蛋白质组网络分析平台，定量多重免疫共沉淀或QMI， 嵌合抗原受体(CAR)T细胞信号转导。然后我们将使用CAR-QMI来表征信号 CAR下游的转导网络激活，以了解CAR如何指示T细胞 攻击和摧毁癌症靶点，并对疗效和副作用进行批次特定预测 CAR T细胞产品简介。CAR T细胞是一种突破性的抗癌疗法，最近赢得了FDA 批准复发性B细胞淋巴瘤。真正的“个人化药物”，CAR T细胞是为 每个患者通过用病毒载体转导白细胞分离术收集的T细胞来从患者自己的T细胞中分离出来 对一辆车进行编码。但是，由于每个批次都是唯一的，因此某些批次在以下方面比其他批次表现更好 产生缓解和/或有害的有时是致命的副作用，包括细胞因子风暴和 神经毒性。该项目的目标是开发一种个性化的信号转导网络分析 可以对每一批CAR T细胞进行筛选并预测其疗效和副作用的平台 特定批次。因为信号转导网络集成了来自多个输入源的信息-例如 共刺激和免疫抑制细胞表面受体、患者遗传背景和T细胞的例子 特定的激活历史-我们假设这个读数将是一个强大的功能预测因子。我们的 初步数据显示，单链抗体结合域亲和力等汽车设计参数的微小变化 在与功能变量相关的信号转导网络状态中产生可测量的变化，如 作为靶向杀伤能力和细胞因子的释放。进一步地，我们证明了存在相当大的个体对个人的关系。 不同供者生产的CAR T细胞批次的个体差异。因此，这两个先决条件 对于个性化的预测分析是存在的-我们的测量在整个人群中存在差异，并且 我们的测量结果与结果参数的功能相关性。我们的跨学科团队包括 汽车开发、信号传导、蛋白质组学和生物信息学方面的专家。我们雄心勃勃的 可实现的目标是扩大QMI面板，以包括汽车特定组件；了解汽车如何 设计参数既影响信号转导网络状态，又影响功能性能指标； 并开发了一种预测机器学习算法，用于翻译QMI派生的信号转导网络 转化为体内临床疗效的功能性生物标志物。成功完成这些目标将(1)确定 决定临床相关结果的特定蛋白质或蛋白质相互作用，如细胞因子的产生 或细胞杀伤能力，使汽车设计师能够合理修改汽车的设计，以针对特定的信号 结果；(2)为临床医生提供一种测试，以预测CAR T细胞的临床性能 (3)为社区提供一个新的分析平台来衡量汽车活动。