Project 1

项目1

基本信息

批准号：
10270393
负责人：
David J. Odde
金额：
$ 57.6万
依托单位：
UNIVERSITY OF MINNESOTA
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-16 至 2026-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10270393
关键词：
Adult Glioblastoma Anisotropy Apoptosis Brain Brain Neoplasms CD 200 Cancer Prognosis Cells Cessation of life Chemotaxis Clinical Clinical Trials Computer Models Cytotoxic agent Data Development Diffuse Engineering Environment Exclusion Criteria Failure Flu virus Fluorescence Microscopy Frequencies Genetic Engineering Genetically Engineered Mouse Genome engineering Glioblastoma Glioma Human Immune Immune response Immunization Immunocompetent Immunological Models Immunologics Immunosuppression Immunotherapy In Vitro Malignant - descriptor Malignant Neoplasms Malignant neoplasm of pancreas Measurement Mechanics Mediating Mesenchymal Microscopy Modeling Mus Oncogenic Operative Surgical Procedures Optics Outcome Patients Peptides Pharmaceutical Preparations Phenotype Positioning Attribute Prognosis Proliferating Publishing Radiation Risk Sleeping Beauty Slice Solid Neoplasm T-Lymphocyte Testing Therapeutic Traction Translating Two-Parameter Model Vaccination Vaccines anti-tumor immune response brain tissue cancer cell cell killing cell motility chemotherapy clinical risk clinically actionable combinatorial computational platform computer framework confocal imaging cytokine digital engineered T cells exhaustion experience experimental study genome-wide analysis human disease immunoengineering immunotherapy trials in vivo innovation insight migration mouse model neoplasm immunotherapy neoplastic cell outcome prediction patient stratification receptor simulation standard of care success tool transcriptomics tumor tumor progression two-photon unpublished works

项目摘要

Abstract In glioblastoma (GBM), cancer cells break away from the tumor mass and infiltrate into adjacent brain tissue. Like other poor-prognosis cancers, GBM has been extensively analyzed by genome-wide transcriptomic analyses. This has led to the identification of 3-4 subtypes that span a spectrum of states from “Proneural” (PN) to “Mesenchymal” (MES). While the identification of subtypes is intriguing, it has yet to produce clinically- actionable mechanistic insight. In our unpublished work, we discovered key mechanical signatures of these two subtypes. Using our Sleeping Beauty (SB) immunocompetent genetically-induced mouse glioma model, we found that the oncogenic driver NRasG12V promotes a MES-like phenotype and the oncogenic driver PDGFβ promotes a PN-like phenotype. In addition, we found that NRas-driven tumors migrate fast and generate large traction forces, while PDGFβ-driven tumors migrate slowly and generate weaker traction forces, features we also observe with human cells in brain tissue. Thus, the two subtypes may each have their own distinct mechanical weaknesses that can be effectively targeted. Since brute force trial-and-error of possible targets is not feasible, we will manage complexity using the modeling approach that is widely used in engineering. As pointed out in the Overall section of this proposal, the mobility of the cancer cells and the antitumoral T cells are both critical determinants of tumor progression/regression, so we will apply our recently published “Cell Migration Simulator” (CMS1.0) to cancer and immune cell migration and use experimental microscopy measurements made in brain tissue to identify the model parameters for the two GBM subtypes. This will then allow us to identify key mechanical vulnerabilities that will be tested using digital multiplex T cell genome engineering (as described in Project 3) and will provide a computational platform for application to pancreatic cancer and immune cells (in Project 2). To simulate the multicellular migration, proliferation, and immune-mediated killing dynamics, we will apply our “Brownian Dynamics Tumor Simulator” (BDTS1.0) to predict the overall tumor dynamics of the NRas (MES) and PDGFβ (PN) tumors. Interestingly, like the human disease, the NRas (MES) tumors are immunologically ‘hot’, while the PDGFβ (PN) tumors are immunologically ‘cold’. Thus, the BDTS1.0, once developed for these two subtypes of brain tumors, will allow us to predict the effects of emergent immunotherapy concepts developed by our team, including CD200 peptide therapy and Peptide Alarm Therapy. By constraining the simulators with data obtained by live cell fluorescence microscopy, we will develop a multiscale computational model that provides mechanistic de-risking and optimization to maximize the physical proximity and encounter frequency between antitumoral T cells and cancer cells. Together the modeling and experiments will allow us to test our central hypothesis that T cell proximity to cancer cells is a major determinant of successful immunotherapy of solid tumors.

抽象的在胶质母细胞瘤（GBM）中，癌细胞脱离肿瘤质量并浸润到相邻的脑组织。像其他不良预知癌一样，GBM已通过基因组转录组进行了广泛的分析分析。对于“间充质”（MES），亚型的鉴定是有趣的，但它尚未产生临床在我们未发表的工作中，可行的机械洞察力。亚型。发现致癌驱动器NRASG12V促进了MES样表型和致癌驱动器PDGFββ 促进PN样表型。牵引力，而PDGFβ驱动的肿瘤迁移并产生较弱的牵引力，但我们还具有在脑组织中观察到人类细胞。可以有效地针对目标的弱点我们将使用工程广泛的建模方法来管理压缩方法。该提案的总体部分，癌细胞和抗肿瘤T细胞的迁移率都是关键的肿瘤程序/回归的决定因素/因此我们将应用我们最近发表的“细胞迁移模拟器“（CMS1.0）进行癌症和免疫细胞迁移并使用实验测量在脑组织中识别两个GBTYPE的模型参数。关键的机械漏洞，使用数字多链体T细胞基因组工程测试在项目3中），并将为应用于胰腺癌和免疫细胞的计算平台（在项目2）。应用我们的“布朗动力学肿瘤模拟器”（BDTS1.0）来预测您的整体肿瘤动力学 NRA（MES）和PDGFβ（PN）肿瘤。在免疫学上“热”，而PDGFβ（PN）肿瘤一次是bdts1.0 为脑肿瘤的两个亚型开发由我们的团队开发的概念，包括CD200肽疗法和肽警报疗法通过通过活细胞荧光显微镜获得的数据来限制模拟器，我们将开发多尺度提供机械抗风险和优化的计算模型，以最大化物理接近度并在抗肿瘤T细胞和癌细胞之间遇到频率。将使我们能够检验我们的中心假设，即对癌细胞的心电图是AA的主要决定实体瘤的成功免疫疗法。