Project 1

项目1

• 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
• 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receptor; simulation; standard of care; success; tool; transcriptomics; tumor; tumor progression; two-photon; unpublished works

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 In our unpublished work, we discovered key mechanical signatures of these two subtypes.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，癌细胞从肿瘤块中分离出来，并渗入邻近的脑组织。 像其他预后不良的癌症一样，gbm已经通过全基因组转录进行了广泛的分析。 分析。这导致了3-4个亚型的识别，这些亚型跨越了“原神经性”(PN)的一系列状态。 至“间充质干细胞”(MES)。虽然亚型的鉴定很耐人寻味，但它尚未在临床上产生- 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 子类型。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 发现致癌驱动基因NRasG12V促进MES样表型，致癌驱动基因pDGFβ 促进PN样表型。此外，我们发现由NRAS驱动的肿瘤迁移速度很快，并产生了大量的 牵引力，虽然pdgfβ驱动的肿瘤迁移缓慢，产生的牵引力较弱，但我们还 观察脑组织中的人类细胞。因此，这两个子类型可能各自具有其不同的机械 可以有效地针对的弱点。由于对可能的目标进行暴力试错是不可行的， 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/退化的决定因素，因此我们将应用我们最近发表的《细胞迁移》 癌症和免疫细胞迁移的模拟器“(CMS1.0)和使用实验显微镜测量 在脑组织中制作，以确定两种GBM亚型的模型参数。这将使我们能够确定 将使用数字多重T细胞基因组工程(如上所述)测试的关键机械漏洞 在项目3中)，并将提供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，与人类疾病一样，NRAS(MES)肿瘤 免疫“热”，而血小板衍生生长因子β(PN)肿瘤是免疫“冷”。因此，BDTS1.0曾经 为这两种脑瘤亚型开发的，将使我们能够预测紧急免疫治疗的效果 我们团队开发的概念，包括CD200多肽疗法和多肽警报疗法。通过 用活细胞荧光显微镜获得的数据约束模拟器，我们将开发一个多尺度 提供机械降低风险和优化以最大限度地提高物理接近程度的计算模型 并在抗肿瘤T细胞和癌细胞之间频繁相遇。将建模和实验结合起来 将使我们能够检验我们的中心假设，即T细胞与癌细胞的接近是 实体瘤的免疫治疗成功。