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: 10374037
• 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/10374037
• 关键词: Affinity; Antibodies; Antigen Targeting; 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; 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; base; 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 algorithm; 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.

项目摘要 此补助金是响应PAR-18-206，生物工程研究赠款（BRG）。我们的目标是适应 尖端的蛋白质组学网络分析平台，定量多重免疫共沉淀或QMI， 嵌合抗原受体（CAR）T细胞信号传导。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如何指导T细胞 攻击和摧毁癌性靶点，并对疗效和副作用进行批次特异性预测 CAR T细胞产物的概况。CAR T细胞是一种突破性的抗癌疗法，最近赢得了FDA 批准用于复发性B细胞淋巴瘤。作为一种真正的“个性化药物”，CAR T细胞被制造用于 通过用病毒载体转导通过白细胞去除术收集的T细胞， 编码CAR。但是，由于每个批次都是唯一的，因此在以下方面，某些批次的性能优于其他批次： 产生缓解和/或有害的和有时致命的副作用，包括细胞因子风暴， 神经毒性本项目的目标是开发一个“个性化的信号转导网络分析 该平台可以筛选每批CAR T细胞并预测其功效和副作用潜力， 具体批次。由于信号转导网络整合了来自多个输入源的信息， 共刺激和免疫抑制细胞表面受体的实例、患者遗传背景和T细胞 特定的激活历史-我们假设这个读数将是功能的有力预测器。我们 初步数据显示CAR设计参数如scFV结合结构域亲和力的微小变化 产生与功能变量相关的信号转导网络状态的可测量变化， 如靶杀伤能力和细胞因子释放。此外，我们发现，存在相当大的个人- 从不同供体产生的CAR T细胞批次中的个体差异。因此，这两个先决条件 存在个体化预测分析的差异-我们在人群中的测量结果存在差异， 我们的测量与结果参数的功能相关性。我们的跨学科团队包括 CAR开发、信号转导、蛋白质组学和生物信息学专家。我们雄心勃勃，但 可实现的目标是扩展QMI面板，以包括CAR特定组件;了解CAR如何 设计参数影响信号转导网络状态和功能性能测量; 并开发一种预测机器学习算法， 将其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 确定临床相关结果的特定蛋白质或蛋白质相互作用， 或细胞杀伤能力，允许CAR设计者合理地修改汽车的设计以靶向特异性信号传导 结果;（2）为临床医生提供一种测试，以预测CAR T细胞在批量生产中的临床表现。 批量基础;和（3）为社区提供一个新的分析平台来测量CAR活性。