权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

RTB 2

实时出价2

基本信息

批准号：
10375195
负责人：
Andrew Josef Ewald
金额：
$ 34.5万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-12-01 至 2026-11-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10375195
关键词：
3-Dimensional Animal Model Architecture Archives Basic Science Biological Biological Assay Blood Vessels Breast cancer metastasis Caliber Cells Cellular Structures Cellular biology Cessation of life Clinical Complex Cytometry Data Data Set Disease Distant Ensure Evaluation Extracellular Matrix Extravasation Fibroblasts Gene Expression Genetic Transcription Human Image Immune Immunofluorescence Immunologic Immunohistochemistry In Vitro Individual Malignant Neoplasms Malignant neoplasm of pancreas Mammary Neoplasms Masks Measurement Mechanics Metastatic to Microfluidic Microchips Microfluidics Modeling Molecular Mus Neoplasm Metastasis Organ Organoids Pancreatic Adenocarcinoma Patients Phenotype Pre-Clinical Model Process Proteome Proteomics Publishing Research Resolution Sampling Series Signal Transduction Site Stains Structure Techniques Technology Testing Three-Dimensional Imaging Tissues Training Tumor Tissue Validation Venous Wood material Work cancer cell cell behavior cell type classification algorithm deep learning algorithm imaging approach in vivo in vivo evaluation innovation insight malignant breast neoplasm metastatic process molecular marker mouse model multiple omics novel real-time images reconstitution reconstruction response segmentation algorithm single-cell RNA sequencing spatial relationship synergism targeted treatment three dimensional cell culture transcriptome transcriptome sequencing tumor tumor microenvironment

项目摘要

Metastasis requires fundamental changes in cell behavior and causes most cancer deaths. Metastasis is also an inherently 3D process involving interactions among diverse cancer cells and with the tumor microenvironment (TME). We developed innovative 3D assays to model each step in metastasis ex vivo. We use these assays to generate hypotheses about how cancer cells accomplish metastasis and which molecular signals should be targeted therapeutically. In vivo validation of these hypotheses is rate limiting, technically and conceptually. We can compare the effects of many perturbations in vitro, with real-time imaging and molecular readouts. In contrast, in vivo validation is generally limited to measurements of tumor diameter, CTC and metastasis numbers, and a few molecular markers in 2D sections. There is an urgent need to achieve a 3D understanding of metastasis, including the complex interactions among cell types and transitions between cell states. The 3D imaging and spatial multi-omics approaches in TECH1 and TECH2 are ideally suited to allow us to understand vascular invasion, the key transition from local to metastatic disease. Prior studies generally evaluated single cell types or a few markers, largely in 2D. CODA (TECH1) will enable us to classify cell types and their spatial relationships in 3D. DBiT-seq (TECH2) enables us to reconstruct the transcriptome and select proteome of high-resolution regions (~10 micron) across whole sections of human tumors. We will combine these techniques to achieve spatial multi-omics and resolve cancer cell state changes during breast cancer metastasis. Aim 1: Adapt CODA to murine models and human breast tumors, focusing on venous invasion. We will first supply archival human breast tumors to enable TECH to adapt their 3D deep learning algorithms to breast cancer. We will start with a existing series of 250 human breast tumors with digitized serial sections. We will then collect, fix, and section fresh human breast tumor samples, stained with immune and cancer cell markers. We will use CODA to reconstruct the 3D architecture of vascular invasion and associated stromal responses. We will also adapt CODA techniques for use with murine preclinical models. We will then leverage these insights to reconstitute the vascular invasion niche in vitro by adapting a novel microfluidic platform we developed. Aim 2: Adapt DBiT-seq for murine and human breast tumors, focusing on cancer cell state transitions. We will adapt DBiT-seq to 3D human breast tumor samples to understand spatial relationships among cancer cell states during vascular invasion. This analysis will be led from cell states and inferred state transitions we defined in vitro using single cell RNA-seq in our 3D metastasis assays. We will then collect a staged series of tumors and distant organs from GEMMs to define cell state transitions spatially across metastatic processes that are difficult to sample in humans. We will then use the transcriptional and signaling dynamics identified in vivo using DBiT-seq to identify candidate molecular regulators for functional validation in vascular invasion microfluidic devices in vitro. Validated candidates will then be tested in vivo in breast cancer GEMMs.

转移需要细胞行为发生根本性改变，并导致大多数癌症死亡。转移也是一个固有的 3D 过程，涉及不同癌细胞之间以及与肿瘤微环境之间的相互作用（TME）。我们开发了创新的 3D 检测来模拟离体转移的每个步骤。我们使用这些分析来产生关于癌细胞如何实现转移以及应该使用哪些分子信号的假设有针对性的治疗。这些假设的体内验证在技术和概念上都受到限制。我们可以通过实时成像和分子读数来比较许多体外扰动的影响。在相比之下，体内验证通常仅限于测量肿瘤直径、CTC 和转移数量，以及二维切片中的一些分子标记。迫切需要实现 3D 理解转移，包括细胞类型之间复杂的相互作用和细胞状态之间的转变。这 TECH1 和 TECH2 中的 3D 成像和空间多组学方法非常适合让我们了解血管侵犯，这是从局部疾病向转移性疾病转变的关键。一般之前的研究评估单细胞类型或一些标记，主要是二维的。 CODA (TECH1) 将使我们能够对细胞类型进行分类以及它们的 3D 空间关系。 DBiT-seq (TECH2) 使我们能够重建转录组并选择人类肿瘤整个切片的高分辨率区域（~10 微米）的蛋白质组。我们将结合这些实现空间多组学并解决乳腺癌转移过程中癌细胞状态变化的技术。目标 1：将 CODA 应用于小鼠模型和人类乳腺肿瘤，重点关注静脉侵犯。我们将首先提供存档的人类乳腺肿瘤，使 TECH 能够调整其 3D 深度学习算法以适应乳腺癌症。我们将从现有的 250 个人类乳腺肿瘤系列开始，并进行数字化连续切片。我们随后将收集、固定和切片新鲜的人类乳腺肿瘤样本，并用免疫和癌细胞标记物染色。我们将使用 CODA 重建血管侵袭和相关基质反应的 3D 结构。我们还将采用 CODA 技术用于小鼠临床前模型。然后我们将利用这些见解通过采用我们开发的新型微流体平台在体外重建血管侵袭生态位。目标 2：将 DBiT-seq 应用于小鼠和人类乳腺肿瘤，重点关注癌细胞状态转变。我们将采用 DBiT-seq 来适应 3D 人类乳腺肿瘤样本，以了解癌症之间的空间关系血管侵袭过程中的细胞状态。该分析将从细胞状态和我们推断的状态转换出发在我们的 3D 转移测定中使用单细胞 RNA-seq 进行体外定义。然后我们将收集一系列分阶段的 GEMM 中的肿瘤和远处器官来定义跨越转移过程的空间细胞状态转变很难在人类身上取样。然后我们将使用体内鉴定的转录和信号动力学使用 DBiT-seq 识别候选分子调节因子，用于血管侵袭功能验证体外微流体装置。然后，经过验证的候选药物将在乳腺癌 GEMM 中进行体内测试。