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-猫眼课题宝

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

项目类别：

财政年份：

2022

资助国家：

美国

项目状态：

已结题

起止时间：

2022-07-27 至 2024-06-30

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

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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.

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