Probabilistic Multiscale Modeling of the Tumor Microenvironment

摘要 这里提出的研究项目解决了对更好的统计模型和方法的迫切需求 分析肿瘤微环境(TME)的空间结构。TME数据表明，有明显的 临床和生物学在空间结构中的重要性，例如作为治疗反应的决定因素 转移。鉴于TME在癌症中的重要性已被公认，技术也在飞速发展 使用高分辨率测量的肿瘤的空间特性，包括空间转录和 蛋白质组学。然而，完全解释这些测量结果所需的计算方法是滞后的。 因此，在目标1中，我们将开发一个统计框架，我们称之为BayesTME，用于在 多个级别，从单个细胞的级别到顶级患者分层。我们将发展 BayesTME作为一套用于贝叶斯多尺度空间建模的创新统计方法，将使 一类新的空间统计模型用于定量评价TME的性质。我们聚集在一起 用于测试、基准和评估的各种规模的空间剖面图数据集集合 贝斯·特姆。在开发BayesTME时，我们将在Aim 2中创建可以使用相同统计数据的模块化工具 跨不同技术的概念，如多路免疫荧光、成像质量细胞术和 空间转录学。这将允许对可能已使用生成的数据集进行统计集成 不同的技术组合。此外，我们将包括对基本BayesTME的扩展，以确定经常性的 跨样本队列的空间属性，支持发现和量化描述空间 与特定癌症表型相关的社区。最后，在目标3中，我们提出了一个验证实验 这将以空间转录学和成像质量细胞术的形式生成并行的空间轮廓数据 取自相同的卵巢癌样本。这一数据集将有助于解决卵巢癌的一个关键问题 这就是TME动力学如何使肠道转移成为女性发病率和死亡率的主要决定因素 患有卵巢癌。总而言之，这项提议的目标是发展一种强大的、新的统计类别 分析TME空间架构的方法，生成强大的开源软件，使 将我们的方法应用于多种空间剖面图技术，并验证我们的方法和软件 通过使用它们来进行大规模的数据分析，调查关于 TME的空间架构。实现这些目标将导致新的量化编码 TME的统计特性，这反过来将导致一类新的空间生物标记物 定义癌症的恶性表型。

Abstract The research project proposed here addresses the pressing need for better statistical models and methods to analyze the spatial architecture of the tumor microenvironment (TME). TME data has demonstrated there is clear clinical and biological importance in the spatial architecture, e.g. as a determinant of response to treatment and metastasis. Given the recognized importance of the TME in cancer, technology has advanced at pace to profile the spatial properties of tumors using high resolution measurements including spatial transcriptomics and proteomics. However, the requisite computational methods to fully interpret these measurements are lagging. Accordingly, in Aim 1 we will develop a statistical framework, which we call BayesTME to model the TME at multiple scales, ranging from the level of individual cells to top-level patient stratification. We will develop BayesTME as a suite of innovative statistical methods for Bayesian multiscale spatial modeling that would enable a new class of spatial statistical models to quantitatively evaluate the properties of the TME. We have gathered a diverse and scaled collection of spatial profiling datasets upon which to test, benchmark and evaluate BayesTME. In developing BayesTME, we will create modular tools in Aim 2 that can use the same statistical concepts across different technologies such as multiplexed immunofluorescence, imaging mass cytometry and spatial transcriptomics. This will permit statistical integration of datasets that may have been generated with diverse sets of technology. In addition, we will include an extension to the base BayesTME to identify recurrent spatial properties across a cohort of samples, enabling discovery and quantitative description of spatial communities that related to specific cancer phenotypes. Finally, in Aim 3 we propose a validation experiment that will generate parallel spatial profiling data–in the form of spatial transcriptomics and imaging mass cytometry from the same ovarian cancer specimens. This dataset will help to address a critical question in ovarian cancer which is how TME dynamics enable bowel metastasis - a major determinant of morbidity and mortality for women with ovarian cancer. In summary, the goals of this proposal are to develop a robust, new class of statistical methods for analyzing the spatial architecture of the TME, generate robust open-source software enabling application of our methods across multiple spatial profiling techniques, and validate our methods and software by using them to conduct large-scale data analyses investigating novel biological hypotheses regarding the spatial architecture of the TME. Accomplishing these goals will lead to new quantitative encoding of the properties of the TME that are statistically grounded that will in turn lead to a new class of spatial biomarkers to define malignant phenotypes in cancer.