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 转移是与癌症相关死亡的主要原因。我们最近的证据建立了一个新的范式， “淋巴结耐受化”，其中淋巴结（LN）组织环境会产生条件，全身性跨组织和器官的转移状态。该建议旨在识别和表征未知的会产生耐受性LN的变化，从而发现新的生物标志物，药物靶标和治疗策略。使用空间系统生物学方法，该方法结合了原位成像与单细胞的多路复用 RNA测序技术，我们将表征整个宿主转移的肿瘤微环境生态系统，HNSCC和LUAD。我们假设LNS中的空间分辨基质 - 免疫相互作用在原发性肿瘤中，与基质 - 恶性特性一起构成了转移的阶段。我们将探索这些过程的机械性能使用人类衍生细胞和小鼠模型的类器官模型。在AIM 1中，我们将使用单细胞的空间蛋白质组学来识别促成中转移的微环境通过重建和比较空间解决患者衍生的未涉及LN的肿瘤 - 基质 - 免疫共定位模式，涉及LN和原发性肿瘤。这些分析将比较组织环境中细胞类型和细胞细胞共定位模式 N0和N+患者的。此外，我们将探测细胞组成和共定位模式以及细胞外基质体系结构，以确定ECM在建立和维持促生物中的作用未参考的LNS中的微环境。在AIM 2中，我们将发现未参与LN的细胞细胞相互作用通过重建和比较细胞 - 细胞相互作用，使它们易于恶性细胞定植未参考的LN，涉及LN和通过空间信息的综合分析推断出的原发性肿瘤带有单细胞RNA序列。我们将开发新型的生物计算方法来综合空间与单细胞RNA测序数据的Codex的功能，以识别在与LN转移相关的肿瘤 - 基质和基质 - 免疫细胞类型。然后我们将评估选定的人类衍生细胞的器官模型中的细胞 - 细胞相互作用，包括扰动CRISPR-FAIRET的扰动基因编辑以揭示机械见解。在AIM 3中，我们将预测肿瘤中的空间时间进展通过空间意识的马尔可夫建模，基质和基质免疫共定位模式并将其相关与转移性进展相关的人类衍生的LN和原发性肿瘤变化的模式。我们将使用使用肿瘤 - 基质生成的空间分辨时间序列数据构建空间意识的马尔可夫模型以及由人来源细胞产生的免疫巨细胞，以鉴定肿瘤 - 基质的空间基序和免疫质的空间图案。这种方法将阐明人类LN的空间时间特征转移，以定义转移级联的机械特征。