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：患者体内动态组织状态成像 胶质母细胞瘤 (GBM) 是最常见的原发性脑恶性肿瘤，中位生存期仅为 16 个月。 成年患者对标准治疗 (SOC) 的反应在不同患者之间存在很大差异。 传统上，确定有针对性的最佳治疗方法依赖于组织采样来确定与患者相关的目标。 然而，组织采样有许多严重的限制和成本（时间、金钱和设施使用），最终 仅提供有限的空间和时间范围，因此总是留下残留的肿瘤细胞 尚未进行多参数磁共振成像 (MRI) 测量。 与不同肿瘤表型相对应的互补生理生物标志物（例如增殖、 炎症、血管生成），它是监测治疗反应和 由于肿瘤细胞信号传导可能通过与疾病的相互作用（即“串扰”）来介导。 在区域微环境中围绕非肿瘤细胞，迫切需要定义其程度 这种串扰影响局部组织状态、表型表达和疾病 了解这些关联应有助于完善影像学的临床解释 迫切需要改进非侵入性诊断和疾病监测的表型指南。 基于图像的放射组学工具可以 1) 预测哪些患者会对给定治疗做出反应，2) 可以 随着时间的推移观察/跟踪该反应。 总体假设：以细胞成分和表型的组合表示的组织状态可以是 临床成像的分辨率足以识别这些状态的转变（有或没有治疗） 在个体患者体内。 我们在这个项目中的两个目标在两个不同的环境中研究这个假设，目标 1) 护理标准，目标 2）免疫治疗在这些目标中，我们将描述表型状态的特征，建立基于图像的方法。 从图像预测组织状态的模型，研究预测的肿瘤状态与结果的对应关系， 量化从治疗前到治疗后的状态动态，最后建立机制模型来理解 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。