Modeling Dynamic Immune Cell Modulation in a 3-D Tissue Engineered Platform to Enhance Patient-specific Immunotherapy for Lung Cancer

在 3D 组织工程平台中模拟动态免疫细胞调节，以增强肺癌患者特异性免疫治疗

• 批准号: 10518637
• 负责人: Jessy Satyadas Deshane
• 金额: $ 20.83万
• 依托单位: UNIVERSITY OF ALABAMA AT BIRMINGHAM
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-27 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10518637
• 关键词: 3-Dimensional; Address; Affect; Antigens; Apoptotic; Architecture; Atlases; Attenuated; Autologous; Benchmarking; Biochemical; Biological Markers; Biological Models; Bioreactors; Blocking Antibodies; CD8-Positive T-Lymphocytes; Cancer Etiology; Cancer Patient; Cell Culture Techniques; Cell Density; Cell model; Cell physiology; Cells; Ceramides; Cessation of life; Characteristics; Clinical; Combined Modality Therapy; Complex; Data; Dimensions; Effector Cell; Equilibrium; Evaluation; Growth; Heterogeneity; Homeostasis; Human; Human Engineering; Hypoxia; Immune; Immune Targeting; Immune checkpoint inhibitor; Immune response; Immunoglobulin G; Immunosuppression; Immunosuppressive Agents; Immunotherapeutic agent; Immunotherapy; In Vitro; Intervention; Lung; Lung Neoplasms; Lymphoid Cell; Malignant neoplasm of lung; Mediator of activation protein; Metabolism; Modeling; Molecular; Molecular Profiling; Mus; Mutation; Myeloid-derived suppressor cells; Non-Small-Cell Lung Carcinoma; Oncogenic; Outcome; Pathway interactions; Patients; Peripheral Blood Mononuclear Cell; Pharmacology; Preclinical Testing; Prediction of Response to Therapy; Production; Prognosis; Prognostic Marker; Resistance; Signal Pathway; Signal Transduction; Sphingolipids; Sphingosine; Stromal Neoplasm; Suppressor-Effector T-Lymphocytes; System; Systemic Therapy; T cell response; T-Lymphocyte; Technology; Testing; Therapeutic; Tissue Engineering; Tissue Model; Tissues; Treatment Efficacy; Treatment Protocols; Tumor-Derived; Tumor-infiltrating immune cells; anti-PD-1; base; cancer immunotherapeutics; cancer immunotherapy; checkpoint therapy; clinical translation; density; diagnosis design; digital; dihydroceramide desaturase; driver mutation; effector T cell; enzyme pathway; exhaustion; genetic signature; glucose metabolism; human disease; human model; human tissue; immune resistance; improved; individualized medicine; inhibitor therapy; insight; metabolic fitness; mimetics; molecular diagnostics; nano-string; neoplastic cell; novel; patient population; patient response; peripheral blood; personalized diagnostics; predictive marker; programmed cell death ligand 1; recruit; resistance mechanism; response; spatial relationship; sphingosine 1-phosphate; sphingosine kinase; targeted treatment; three-dimensional modeling; transcriptome; translational approach; treatment strategy; tumor; tumor growth; tumor immunology; tumor microenvironment; two-dimensional

PROJECT SUMMARY/ABSTRACT Immune suppression and resistance to immune checkpoint inhibitors (ICI) are major obstacles for successful immunotherapy for non small cell lung cancer (NSCLC). In NSCLC, the density and diversity of tumor-infiltrating immune cells in the tumor microenvironment (TME) are closely related to prognosis, prediction of treatment efficacy of frontline combination therapies with ICI and favorable survival. The patient heterogeneity in the immune cell composition within the TME indicates that mapping the composition of immune infiltrates and their functional state within the TME is important for diagnosing and designing treatment strategies and for predicting biomarkers. The central objective of this project is to utilize our novel three dimensional human tissue model (3D-LTB) that recapitulates tissue dimensionality and microenvironment of human lung tumors to test the hypothesis that modulation of tumor-stromal crosstalk and sphingolipid signaling pathways that influence infiltration of immune suppressive myeloid-derived suppressor cells (MDSCs) in the lung TME alters the spatial dynamics of resident and recruited effector cells to enhance response to immune targeted therapies for NSCLC. In Aim1, patient-derived tumors and cutting edge GeoMx Digital Spatial Profiling platform will be utilized to define the dynamics and spatial profiles of effector T cells within the 3D-LTBs in response to immunotherapy. Studies in Aim 2 will determine if pharmacological targeting of sphingolipid rheostat alters tumor-stromal crosstalk and enhances response to immunotherapy using the same platform described in Aim 1. To our knowledge, this is the first fully developed 3D model of NSCLC that fully recapitulate lung cancer-immune interactions. Our studies have the power to define patient heterogeneity and identify spatially informed biomarkers in response to ICI in NSCLC. This optimized model system mimics extrapolatable growth characteristics and molecular signatures of resistance mechanisms in the human disease.

项目摘要/摘要 免疫抑制和对免疫检查点抑制剂（ICI）的抗性是成功治疗的主要障碍。 非小细胞肺癌（NSCLC）的免疫治疗。在非小细胞肺癌中，肿瘤浸润的密度和多样性 肿瘤微环境（TME）中的免疫细胞与预后、治疗预测等密切相关 一线联合治疗与ICI的疗效和有利的生存期。患者的异质性 TME内的免疫细胞组成表明，绘制免疫浸润细胞的组成及其 TME内的功能状态对于诊断和设计治疗策略以及预测 生物标志物。该项目的中心目标是利用我们新颖的三维人体组织模型 （3D-LTB），其概括了人类肺肿瘤的组织维度和微环境，以测试肺肿瘤的生物学特性。 肿瘤间质串扰和鞘脂信号通路调节 肺TME中免疫抑制性髓源性抑制细胞（MDSC）的浸润改变了TME的空间分布。 动态的驻留和募集效应细胞，以增强对NSCLC免疫靶向治疗的反应。 在Aim1中，将利用患者源性肿瘤和最先进的GeoMx数字空间分析平台来定义 3D-LTB内效应T细胞响应免疫疗法的动力学和空间分布。研究 目的2将确定鞘脂变阻器的药理学靶向是否改变肿瘤间质串扰， 使用目标1中描述的相同平台增强对免疫疗法的应答。据我们所知，这是 第一个完全开发的NSCLC 3D模型，完全重现了肺癌-免疫相互作用。我们的研究 有能力定义患者异质性，并识别响应ICI的空间信息生物标志物， NSCLC。这种优化的模型系统模拟了生长因子的可外推生长特征和分子特征。 人类疾病的抵抗机制。