Project 1

项目1

• 批准号: 10700935
• 负责人: David J. Odde
• 金额: $ 55.27万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 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/10700935
• 关键词: Adult Glioblastoma; Anisotropy; Apoptosis; Brain; Brain Neoplasms; CD 200; Cancer Prognosis; Cells; Cessation of life; Chemotaxis; Clinical; Clinical Trials; Cytotoxic agent; Data; Development; Dissociation; Engineering; Environment; Exclusion Criteria; Failure; Flu virus; Fluorescence Microscopy; Frequencies; Genetic Engineering; Genetically Engineered Mouse; Genome engineering; Glioblastoma; Glioma; Human; Immune; Immune response; Immunocompetent; Immunologic Stimulation; Immunologics; Immunosuppression; Immunotherapy; In Vitro; Infiltration; 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; 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; human disease; immunoengineering; immunotherapy trials; in vivo; innovation; insight; migration; mouse model; multi-scale modeling; neoplasm immunotherapy; neoplastic cell; novel therapeutic intervention; outcome prediction; patient stratification; receptor; simulation; standard of care; success; tool; transcriptomics; tumor; tumor progression; two-photon; unpublished works

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 In our unpublished work, we discovered key mechanical signatures of these two subtypes.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.

摘要 在胶质母细胞瘤（GBM）中，癌细胞脱离肿瘤块并浸润到邻近的脑组织中。 像其他预后不良的癌症一样，GBM已经通过全基因组转录组学进行了广泛的分析。 分析。这导致了3-4个亚型的识别，这些亚型跨越了“前神经”（PN）的状态谱。 间充质干细胞（MES）。虽然亚型的鉴定是有趣的，但它还没有产生临床- 可操作的机械洞察力在我们未发表的工作中，我们发现了这两个关键的机械特征， 亚型使用我们的睡美人（SB）免疫活性基因诱导的小鼠胶质瘤模型，我们 发现致癌驱动因子NRasG 12 V促进MES样表型，而致癌驱动因子PDGFβ 促进PN样表型。此外，我们发现NRAS驱动的肿瘤迁移速度快，并产生大量的肿瘤细胞。 牵引力，而PDGFβ驱动的肿瘤迁移缓慢，产生较弱的牵引力，我们还 用脑组织中的人类细胞进行观察。因此，这两种亚型可各自具有其自身不同的机械性质。 可以有效针对的弱点。由于对可能的目标进行强力试错是不可行的， 我们将使用工程中广泛使用的建模方法来管理复杂性。正如在《 总的来说，癌细胞和抗肿瘤T细胞的流动性都是至关重要的 肿瘤进展/消退的决定因素，所以我们将应用我们最近发表的“细胞迁移”， 模拟器”（CMS1.0）对癌症和免疫细胞迁移的研究，并使用实验显微镜进行测量 以确定两种GBM亚型的模型参数。这将使我们能够识别 将使用数字多路复用T细胞基因组工程（如所描述的）来测试关键的机械弱点 项目3中），并将提供一个应用于胰腺癌和免疫细胞的计算平台（在 项目2）。为了模拟多细胞迁移、增殖和免疫介导的杀伤动力学，我们将 应用我们的“布朗动力学肿瘤模拟器”（BDTS1.0）来预测肿瘤的整体动力学。 NRas（MES）和PDGFβ（PN）肿瘤。有趣的是，像人类疾病一样，NRAS（MES）肿瘤是 PDGFβ（PN）肿瘤在免疫学上是“热”的，而PDGFβ（PN）肿瘤在免疫学上是“冷”的。因此，BDTS 1.0一旦 为这两种亚型的脑肿瘤开发的，将使我们能够预测紧急免疫治疗的效果， 由我们的团队开发的概念，包括CD 200肽疗法和肽报警疗法。通过 通过活细胞荧光显微镜获得的数据约束的模拟器，我们将开发一个多尺度 提供机械去风险和优化以最大化物理接近度的计算模型 以及抗肿瘤T细胞和癌细胞之间的相遇频率。结合建模和实验 这将使我们能够测试我们的中心假设，即T细胞与癌细胞的接近性是癌症的主要决定因素。 实体瘤的成功免疫治疗。