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receptor; simulation; standard of care; success; tool; transcriptomics; tumor; tumor progression; two-photon; unpublished works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 In our unpublished work, we discovered key mechanical signatures of these two subtypes.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.

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。这导致了3-4个亚型的鉴定，这些亚型跨越了“胸膜”（PN）的一系列状态 要“间充质”（MES）。尽管亚型的鉴定很有趣，但尚未在临床上产生 可行的机械洞察力。在未发表的工作中，我们发现了这两个的关键机械签名 亚型。使用我们的睡美人（SB）免疫能力遗传诱导的小鼠神经胶质瘤模型，我们 发现致癌驱动器NRASG12V促进了MES样表型和致癌驱动器PDGFβ 促进PN样表型。此外，我们发现NRAS驱动的肿瘤迁移迅速并产生较大 牵引力，而PDGFβ驱动的肿瘤迁移缓慢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。那两个亚型可以每个都有自己独特的机械 可以有效针对的弱点。由于可能目标的蛮力反复试验是不可行的，因此 我们将使用广泛用于工程的建模方法来管理复杂性。正如在 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 确定肿瘤进展/回归，因此我们将应用最近发表的“细胞迁移 模拟器”（CMS1.0）进行癌症和免疫球迁移并使用实验显微镜测量 在脑组织中制成以识别两个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细胞基因组工程测试将测试的关键机械漏洞（如所述 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）。为了模拟多细胞迁移，增殖和免疫介导的杀戮动力学，我们将 应用我们的“布朗动力学肿瘤模拟器”（BDTS1.0）来预测总体肿瘤动力学 NRA（MES）和PDGFβ（PN）肿瘤。有趣的是，像人类疾病一样，NRA（MES）肿瘤是 免疫学上“热”，而PDGFβ（PN）肿瘤在免疫学上是“冷”。那是BDTS1.0，一次 为这两个亚型的脑肿瘤开发，将使我们能够预测新兴免疫疗法的影响 我们的团队开发的概念，包括CD200胡椒疗法和肽警报疗法。经过 通过通过活细胞荧光显微镜获得的数据来限制模拟器，我们将开发一个多尺度 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。一起建模和实验 将使我们能够检验中心假设，即T细胞与癌细胞的接近是 实体瘤的成功免疫疗法。