Probabilistic Multiscale Modeling of the Tumor Microenvironment

肿瘤微环境的概率多尺度建模

• 批准号: 10586545
• 负责人: Wesley Tansey
• 金额: $ 68.73万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2027-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10586545
• 关键词: Address; Algorithms; Architecture; Bayesian Analysis; Bayesian Method; Benchmarking; Biological; Biological Markers; Biological Testing; Cancer Biology; Cancer Patient; Cells; Clinical; Code; Collection; Communities; Complement; Computer software; Computing Methodologies; Cytometry; Data; Data Analyses; Data Set; Development; Dimensions; Generations; Genetic Transcription; Goals; Image; Immunofluorescence Immunologic; In Situ; Individual; Intestines; Investigation; Location; Lung; Machine Learning; Malignant Neoplasms; Malignant neoplasm of ovary; Measurement; Measures; Methodology; Methods; Modality; Modeling; Molecular Profiling; Morbidity - disease rate; Neoplasm Metastasis; Ovarian; Pathologist; Patient-Focused Outcomes; Patients; Pattern; Phenotype; Property; Proteins; Proteomics; Recurrence; Research Project Grants; Resolution; Sampling; Signal Transduction; Software Tools; Spatial Design; Specimen; Statistical Algorithm; Statistical Methods; Statistical Models; Techniques; Technology; Testing; Tissues; Tumor Biology; Validation; Visualization software; Woman; base; cancer type; cell community; cohort; computer framework; data integration; experimental study; improved; innovation; insight; large scale data; malignant phenotype; melanoma; mortality; multi-scale modeling; multidisciplinary; next generation; novel; open source; patient biomarkers; patient response; patient stratification; programs; rational design; simulation; technology development; theories; tool; transcriptome; transcriptomics; treatment response; tumor; tumor microenvironment; web interface

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.

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要， 分析肿瘤微环境（TME）的空间结构。TME数据表明， 空间结构中的临床和生物学重要性，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， 使用高分辨率测量肿瘤的空间特性，包括空间转录组学， 蛋白质组学然而，充分解释这些测量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，以在 从单个细胞水平到顶级患者分层的多个尺度。我们将开发 BayesTME是一套用于贝叶斯多尺度空间建模的创新统计方法， 一类新的空间统计模型，定量评估的TME的属性。我们聚集 一个多样化和规模化的空间剖面数据集集合，用于测试、基准测试和评估 贝叶斯TME。在开发BayesTME时，我们将在Aim 2中创建模块化工具，这些工具可以使用相同的统计 跨不同技术的概念，例如多重免疫荧光、成像质谱细胞术和 空间转录组学这将允许对可能已经生成的数据集进行统计整合， 多种技术。此外，我们将包括对基本BayesTME的扩展，以识别经常性的 一组样本的空间特性，能够发现和定量描述空间 与特定癌症表型相关的社区。最后，在目标3中，我们提出了验证实验 这将产生平行的空间分析数据--以空间转录组学和成像质量细胞术的形式 来自同一个卵巢癌样本该数据集将有助于解决卵巢癌中的一个关键问题 这就是TME动力学如何使肠转移-女性发病率和死亡率的主要决定因素 卵巢癌总而言之，本提案的目标是开发一种强大的新统计类别， 分析TME空间架构的方法，生成强大的开源软件， 我们的方法在多种空间分析技术中的应用，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的空间结构。实现这些目标将导致新的量化编码， TME的统计学基础属性，这将反过来导致一类新的空间生物标志物， 定义癌症的恶性表型。