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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要这笔赠款是为了响应 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网络分析平台，定量多重免疫共沉淀或 QMI，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 细胞攻击并摧毁癌症靶点，并对疗效和副作用进行批次特异性预测 CAR T 细胞产品简介 CAR T 细胞是一种突破性的抗癌疗法，最近获得了 FDA 的认证。 CAR T 细胞是一种真正的“个性化药物”，旨在治疗复发性 B 细胞淋巴瘤。通过使用病毒载体转导白细胞分离术收集的 T 细胞，从每位患者自身的 T 细胞中提取然而，由于每个批次都是唯一的，因此某些批次的性能优于其他批次。产生缓解和/或有害且有时致命的副作用，包括细胞因子风暴和该项目的目标是开发“个性化信号转导网络分析”。 “平台”可以筛选每批CAR T细胞并预测其功效和潜在副作用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 细胞特定的激活历史——我们追求这一读数将成为功能的强大预测因子。初步数据显示，CAR 设计参数（例如 scFV 结合域亲和力）发生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的变化，例如作为目标杀伤能力和细胞因子释放，我们进一步表明存在相当大的个体间差异。不同捐赠者生产的批次 CAR T 细胞存在个体差异，因此需要满足两个先决条件。对于个体化预测分析来说，我们在人群中的测量结果存在差异，并且我们的测量与结果参数的功能相关性我们的跨学科团队由以下人员组成。 CAR 开发、信号转导、蛋白质组学和生物信息学方面的专家。可实现的目标是扩展 QMI 面板以包含 CAR 特定组件，以了解 CAR 的作用；设计参数影响信号转导网络状态和功能性能指标；并开发一种预测机器学习算法来翻译 QMI 衍生的信号转导网络成功完成这些目标将（1）确定体内临床功效的功能生物标志物。确定临床相关结果（例如细胞因子产生）的特定蛋白质或蛋白质相互作用或细胞杀伤能力，使 CAR 设计者能够合理修改 CAR 的设计，以针对特定信号传导结果；(2) 为上级提供预测 CAR T 细胞批次临床表现的测试批次基础；(3) 为社区提供一个新颖的分析平台来测量 CAR 活性。