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.

抽象的 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）的空间结构已经证明存在明确的结果。 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 鉴于 TME 在癌症中的重要性已得到公认，技术正在迅速发展以分析转移。 使用高分辨率测量（包括空间转录组学和 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，我们将其称为 BayesTME，以对 TME 进行建模 我们将开发多种尺度，从单个细胞水平到顶级患者分层。 BayesTME 作为贝叶斯多尺度空间建模的一套创新统计方法，可以使 我们收集了一类新的空间统计模型，用于定量评估 TME 的属性。 用于测试、基准测试和评估的多样化且规模化的空间分析数据集集合 BayesTME。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，例如多重免疫荧光、成像质量流式细胞术和 这将允许对可能生成的数据集进行统计整合。 此外，我们将包括对基础 BayesTME 的扩展，以识别重复出现的情况。 一组样本的空间属性，从而能够发现和定量描述空间 最后，在目标 3 中，我们提出了一个验证实验。 这将生成并行空间分析数据——以空间转录组学和成像质谱流式细胞术的形式 该数据集将有助于解决卵巢癌的一个关键问题。 这就是 TME 动力学如何促进肠转移——女性发病率和死亡率的主要决定因素 总之，该提案的目标是开发一种强大的、新的统计方法。 分析 TME 空间架构的方法，生成强大的开源软件 将我们的方法应用于多种空间分析技术，并验证我们的方法和软件 通过使用它们进行大规模数据分析，调查有关 TME 的空间架构的实现将带来新的量化编码。 TME 的特性具有密切的基础，这反过来又会产生一类新的空间生物标记物 定义癌症的恶性表型。