MOSAIC: Imaging Human Tissue State Dynamics In Vivo

摘要：项目2：对患者体内的动态组织状态进行成像 胶质母细胞瘤(GBM)是最常见的恶性原发脑瘤，中位生存期为16个月 成年患者体内的肿瘤。对护理标准(SOC)的反应在不同患者之间差异很大。 确定最佳靶向治疗传统上依赖于组织采样来确定与患者相关的靶点。 然而，组织采样有许多严重的限制和成本(时间、金钱和设施访问)，并最终 在空间和时间上只提供有限的范围，因此总是留下残留的肿瘤细胞， 还没有被抽样。多参数磁共振成像(MRI)测量了一系列 与不同的肿瘤表型(例如，增殖， 炎症、血管生成)，并作为临床监测治疗反应和 疾病的发展。因为肿瘤细胞信号可能通过相互作用(即，串扰)与 在非肿瘤细胞周围的区域微环境中，迫切需要定义 这种串扰影响局部组织状态、表型表达和疾病 进步。理解这些联系应该有助于改进对影像的临床解释 表型以改进非侵入性诊断和疾病监测指南。有一个迫切的需要 对于基于图像的放射组学工具，可以1)预测哪些患者对给定的治疗有反应，以及2)可以 随着时间的推移，观察/跟踪这种反应。 总体假设：组织状态，表现为细胞成分和表型的组合，可以是 在临床影像上的分辨率达到足以识别这些状态在治疗和不治疗情况下的转变的水平 在活体内的个别患者中。 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一假设，目标1)护理标准，目标 2)免疫治疗。在这些目标中，我们将表征表型状态的景观，构建基于图像的 从图像中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，最终建立机制模型来理解 导致整体肿瘤状态的关键驱动因素是局部表型状态空间中细胞流动的差异。

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.