MOSAIC: Imaging Human Tissue State Dynamics In Vivo

MOSAIC：体内人体组织状态动态成像

• 批准号: 10729423
• 负责人: Kristin R Swanson
• 金额: $ 34.29万
• 依托单位: MAYO CLINIC ARIZONA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-18 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10729423
• 关键词: Adult; Automobile Driving; Biological Markers; Biopsy; Brain; Brain Neoplasms; Cell model; Cells; Clinical; Diagnosis; Disease; Disease Progression; Epidermal Growth Factor Receptor; Facility Accesses; Genomic Segment; Glioblastoma; Glioma; Guidelines; Human; Image; Image Analysis; Immune; Immune Targeting; Immune response; Immunocompetent; Immunooncology; Immunotherapy; Inflammation; Inflammatory; Magnetic Resonance Imaging; Malignant - descriptor; Mathematics; Measures; Mediating; Modeling; Molecular; Monitor; Nature; Oncology; Outcome; Patient-Focused Outcomes; Patients; Phenotype; Physiological; Population; Primary Brain Neoplasms; Proliferating; Recurrence; Residual Neoplasm; Resources; Sampling; Signal Transduction; Study models; Systems Analysis; Therapeutic; Time; Tissue Model; Tissue Sample; Tissues; Treatment Protocols; Work; angiogenesis; clinical imaging; cohort; cost; human imaging; human tissue; imaging facilities; immunotherapy clinical trials; improved; in vivo; individual patient; individualized medicine; neoplastic cell; noninvasive diagnosis; novel; patient safety; physical property; predictive modeling; quantitative imaging; radiomics; response; standard of care; tool; treatment response; tumor; tumor behavior

SUMMARY: PROJECT 2: IMAGING THE DYNAMIC TISSUE STATE IN PATIENTS IN VIVO With a dismal median survival of 16 months, glioblastoma (GBM) is the most common malignant primary brain tumor within adult patients. Response to the standard-of-care (SOC) is widely variable across patients. Identifying optimal targeted treatments traditionally relies on tissue sampling to identify patient-relevant targets. Yet, tissue sampling has many severe limitations and costs (time, money, and facility access), and ultimately provides only limited scope both spatially and temporally thus always leaving behind residual tumor cells that have not been sampled. Multi-parametric magnetic resonance imaging (MRI) measures an array of complementary physiologic biomarkers that correspond with diverse tumor phenotypes (e.g., proliferation, inflammation, angiogenesis), and it serves as the clinical mainstay for monitoring therapeutic response and disease progression. As tumor cell signaling may be mediated through interactions (i.e.,“cross-talk”) with surrounding non-tumoral cells in the regional microenvironment, there is a critical need to define the degree to which this cross-talk influences local tissue state, phenotypic expression, and disease progression. Understanding these associations should help refine the clinical interpretations of imaging phenotypes to improve guidelines for non-invasive diagnosis and disease monitoring. There is an urgent need for image-based radiomics tools that can 1) predict which patients will respond to a given treatment and 2) can observe/track that response over time. Overall Hypothesis: Tissue states, represented as combinations of cellular constituents and phenotypes, can be resolved on clinical imaging to a level sufficient to identify transitions in these states with and without treatments in individual patients in vivo. Our two aims in this project investigate this hypothesis in two separate settings, Aim 1) Standard of Care, Aim 2) Immunotherapy. In these aims, we will characterize the landscape of phenotypic states, build image-based models to predict tissue state from images, investigate how predicted tumor states correspond with outcomes, quantify dynamics of states from pre- to post-therapy, and finally build mechanistic models to understand the critical driving differences in the flow of cells in local phenotype state space leading to the overall tumor state.

摘要：项目2：成像体内患者的动态组织状态 胶质母细胞瘤（GBM）是最常见的恶性原发性大脑的中位生存期的中位生存期。 成人患者的肿瘤。对护理标准（SOC）的反应在患者之间存在很大变化。 传统上，识别最佳靶向疗法依赖于组织采样来识别与患者相关的靶标。 但是，组织采样有许多严重的限制和成本（时间，金钱和设施访问），最终 仅在空间和临时提供有限的范围，因此总是留下残留的肿瘤细胞 没有被采样。多参数磁共振成像（MRI）测量 完全物理生物标志物与潜水肿瘤表型相对应（例如增殖， 炎症，血管生成），它是监测治疗反应和 疾病进展。由于肿瘤细胞信号传导可以通过与 围绕区域微环境中的非肿瘤细胞，至关重要的是定义程度 这种串扰会影响局部组织状态，表型表达和疾病 进展。了解这些关联应有助于完善想象力的临床解释 表型改善非侵入性诊断和疾病监测指南。迫切需要 对于基于图像的放射素学工具1）预测哪些患者会对给定的治疗做出反应，2）可以 随着时间的推移观察/跟踪该响应。 总体假设：表示为细胞构成和表型组合的组织态可以是 在临床成像上解决至一个足以识别有和没有治疗的状态过渡的水平 在体内的个别患者中。 我们在该项目中的两个目标在两个单独的环境中调查了这一假设，目的是1）护理标准，目标 2）免疫疗法。在这些目标中，我们将表征表型状态的景观，建立基于图像的状态 从图像预测组织状态的模型，研究预测的肿瘤状态如何与结果相对应 量化从治疗前到治疗后状态的动态，最后建立机械模型以了解 局部表型状态空间中细胞流动的关键驱动差异，导致整体肿瘤状态。