MOSAIC: Imaging Human Tissue State Dynamics In Vivo

总结：项目2：患者体内动态组织状态成像 胶质母细胞瘤（GBM）是最常见的恶性原发性脑肿瘤，平均生存期为16个月， 成年患者的肿瘤。不同患者对标准治疗（SOC）的反应差异很大。 识别最佳靶向治疗传统上依赖于组织采样来识别患者相关的靶标。 然而，组织取样具有许多严重的限制和成本（时间、金钱和设施访问），并且最终 仅在空间和时间上提供有限的范围，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）测量一系列 对应于不同肿瘤表型的互补生理生物标志物（例如，扩散， 炎症、血管生成），并且它作为监测治疗反应的临床支柱， 疾病进展。因为肿瘤细胞信号传导可以通过相互作用（即，“串扰”）， 在局部微环境中，围绕非肿瘤细胞，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 进展了解这些关联有助于完善影像学的临床解释 表型，以改善非侵入性诊断和疾病监测的指导方针。迫切需要 对于基于图像的放射组学工具，其可以1）预测哪些患者将对给定的治疗做出反应，并且2）可以 随着时间的推移观察/跟踪该响应。 总体假设：组织状态，表示为细胞成分和表型的组合，可以是 在临床成像上解决，足以识别这些状态在治疗和不治疗的情况下的转变 in individual个别patients患者in vivo体内. 我们在本项目中的两个目标是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)免疫疗法在这些目标中，我们将描述表型状态的景观，建立基于图像的 从图像中预测组织状态的模型，研究预测的肿瘤状态如何与结果相对应， 量化从治疗前到治疗后的状态动态，并最终建立机械模型来了解 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。

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.