Project 2 Human Tumor Analysis

项目2 人类肿瘤分析

基本信息

批准号：
10729467
负责人：
SYLVIA KATINA PLEVRITIS
金额：
$ 52.27万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-19 至 2028-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10729467
关键词：
Architecture Awareness Biological Markers Biology Cancer Etiology Cancer Patient Cell Communication Cell model Cells Cessation of life Clinical Clustered Regularly Interspaced Short Palindromic Repeats Complex Data Disseminated Malignant Neoplasm Drug Targeting Ecosystem Endothelial Cells Environment Epithelium Extracellular Matrix Formalin Freezing Genes Genetic Transcription Growth Head and Neck Squamous Cell Carcinoma Heterogeneity Host Defense Mechanism Human Immune Immune Tolerance Immune system Link Lung Adenocarcinoma Lymph Node Involvement Lymph Node Tissue Malignant - descriptor Malignant Neoplasms Maps Metastatic Neoplasm to Lymph Nodes Modeling Molecular Analysis Neoplasm Metastasis Organ Organoids Paraffin Embedding Patients Pattern Positive Lymph Node Primary Neoplasm Process Property Proteomics Research Role Sampling Sentinel Lymph Node Series Site Solid Specimen Stromal Cells Stromal Neoplasm Systems Biology Technology Testing Time Tissues Tumor Biology biocomputing cancer cell cell type cohort human model human tissue imaging platform in situ imaging insight lymph nodes markov model mouse model neoplastic cell novel pre-clinical single cell sequencing single-cell RNA sequencing spatial integration spatiotemporal temporal measurement treatment strategy tumor tumor microenvironment

项目摘要

SUMMARY/ABSTRACT: PROJECT 2 Metastasis is the primary cause of cancer-related death. Our recent evidence establishes a new paradigm, “lymph node tolerization,” in which the lymph node (LN) tissue environment creates a conditioned, systemic metastatic state across tissues and organs. This proposal aims to identify and characterize unidentified changes that create tolerize LNs, leading to the discovery of new biomarkers, drug targets, and treatment strategies. Using a spatial systems biology approach that combines multiplexed in situ imaging with single-cell RNA sequencing technologies, we will characterize tumor microenvironments across the host metastatic ecosystem, in HNSCC and LUAD. We hypothesize that spatially resolved stromal-immune interactions in LNs together with stromal-malignant properties in a primary tumor set the stage for metastasis. We will explore mechanistic properties of these processes using organoid models of human-derived cells and mouse models. In Aim 1, we will use using single-cell spatial proteomics to identify a pro-metastatic microenvironment in uninvolved LNs of HNSCC and LUAD cancer patients by reconstructing and comparing spatially resolved tumor-stroma-immune colocalization patterns of patient-derived uninvolved LNs, involved LNs, and primary tumors. These analyses will compare cell types and cell-cell co-localization patterns in the tissue environments of N0 and N+ patients. In addition, we will probe cell composition and colocalization patterns together with extracellular matrix architecture to ascertain the role of ECM in establishing and maintaining the pro-metastatic microenvironment in uninvolved LNs. In Aim 2, we will discover cell-cell interactions in uninvolved LNs that predispose them to colonization by malignant cells by reconstructing and comparing cell-cell interactions uninvolved LNs, involved LNs, and primary tumors inferred through integrative analysis of spatial information with single-cell RNA-sequencing. We will develop novel biocomputational approaches to integrate spatial features from CODEX with single cell RNA sequencing data to identify proximal cell-cell interactions among tumor-stromal and stromal-immune cell types associated with LN metastases. We will then evaluate selected cell-cell interactions in organoid models of human-derived cells, including perturbation with CRISPR-facilitated gene editing to reveal mechanistic insights. In Aim 3, we will predict spatiotemporal progression in tumor- stromal and stromal-immune colocalization patterns through spatially-aware Markov modeling and relate these patterns to changes in human-derived LNs and primary tumors associated with metastatic progression. We will build a spatially aware Markov model using spatially resolved time series data generated using tumor-stromal and immune-stromal organoids generated from human-derived cells, to identify spatial motifs of tumor-stromal and immune-stromal spatial patterning. This approach will illuminate spatiotemporal features of human LN metastasis, toward defining mechanistic features of the metastatic cascade.

摘要/摘要：项目 2 我们最近的证据建立了一个新的范例，转移是癌症相关死亡的主要原因。 “淋巴结耐受”，其中淋巴结 (LN) 组织环境创造了一种条件性、全身性的环境。该提案旨在识别和表征未识别的转移状态。产生耐受 LN 的变化，从而发现新的生物标志物、药物靶点和治疗方法使用将多重原位成像与单细胞相结合的空间系统生物学方法。 RNA测序技术，我们将表征跨宿主转移的肿瘤微环境我们在 HNSCC 和 LUAD 中研究了 LN 中的空间分辨基质免疫相互作用。与原发肿瘤的间质恶性特性一起为转移奠定了基础。使用人源细胞的类器官模型和小鼠模型来研究这些过程的机械特性。在目标 1 中，我们将使用单细胞空间蛋白质组学来识别促转移微环境通过重建和比较空间分辨的 HNSCC 和 LUAD 空间癌症患者的未受累淋巴结患者来源的未受累淋巴结、受累淋巴结和原发淋巴结的肿瘤-基质-免疫共定位模式这些分析将比较组织环境中的细胞类型和细胞间共定位模式。此外，我们将与 N0 和 N+ 患者一起探讨细胞组成和共定位模式。细胞外基质结构以确定 ECM 在建立和维持促转移细胞中的作用在目标 2 中，我们将发现未受累 LN 中的细胞间相互作用。通过重建和比较细胞间相互作用，使它们易于被恶性细胞定植通过空间信息综合分析推断未受累淋巴结、受累淋巴结和原发肿瘤我们将开发新的生物计算方法来整合空间。 CODEX 的功能与单细胞 RNA 测序数据可识别近端细胞之间的相互作用然后我们将评估与 LN 转移相关的肿瘤基质和基质免疫细胞类型。人源细胞类器官模型中的细胞间相互作用，包括 CRISPR 促进的扰动在目标 3 中，我们将预测肿瘤的时空进展。通过空间感知马尔可夫建模来实现基质和基质免疫共定位模式，并将这些模式关联起来我们将研究与转移进展相关的人源性淋巴结和原发性肿瘤的变化模式。使用肿瘤基质生成的空间解析时间序列数据构建空间感知马尔可夫模型和从人源细胞产生的免疫基质类器官，以识别肿瘤基质的空间基序这种方法将阐明人类 LN 的时空特征。转移，以确定转移级联的机制特征。