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.

转移需要细胞行为的根本变化，并导致大多数癌症死亡。转移也是固有的3D过程，涉及各种癌细胞之间的相互作用以及与肿瘤微环境的相互作用（TME）。我们开发了创新的3D测定方法，以模拟转移外体内的每个步骤。我们使用这些测定关于癌细胞如何完成转移的假设以及哪些分子信号应为针对治疗的目标。这些假设的体内验证在技术上和概念上都是限制速率的。我们可以将许多在体外扰动，实时成像和分子读数中的影响进行比较。在对比度，体内验证通常仅限于肿瘤直径，CTC和转移数的测量以及2D部分中的一些分子标记。迫切需要实现3D的理解转移，包括细胞类型之间的复杂相互作用和细胞状态之间的过渡。这技术1和Tech2中的3D成像和空间多摩学方法非常适合使我们能够了解血管侵袭，这是从局部疾病到转移性疾病的关键过渡。通常的研究评估了单细胞类型或几个标记，主要在2D中。 CODA（Tech1）将使我们能够对单元类型进行分类以及他们在3D中的空间关系。 DBIT-SEQ（Tech2）使我们能够重建转录组并选择高分辨率区域（〜10微米）的蛋白质组在整个人类肿瘤的整个部分中。我们将结合这些在乳腺癌转移过程中，实现空间多词并解决癌细胞状态变化的技术。目标1：将尾声适应鼠模型和人类乳腺肿瘤，重点是静脉侵袭。我们将首先提供档案档案的人类乳腺肿瘤使技术能够使其3D深度学习算法适应乳房癌症。我们将从现有的250种人体乳腺肿瘤系列开始，并具有数字化的连续部分。然后我们会收集，固定和截面新鲜的人类乳腺肿瘤样品，并用免疫和癌细胞标记染色。我们将使用CODA重建血管侵袭和相关基质反应的3D结构。我们还将适应用于鼠临床前模型的尾声技术。然后，我们将利用这些见解通过适应我们开发的新型微流体平台来重建体外血管入侵生态位。 AIM 2：将DBIT-SEQ适应鼠和人类乳腺肿瘤，重点是癌细胞态过渡。我们将将DBIT-SEQ调整为3D人类乳腺肿瘤样品，以了解癌症之间的空间关系血管入侵期间的细胞状态。该分析将从细胞状态进行，并推断出状态过渡我们在我们的3D转移测定中，使用单细胞RNA-Seq在体外定义。然后，我们将收集一系列的一系列肿瘤和遥远的器官从宝石中定义细胞状态在跨转移过程中定义的细胞状态过渡，在人类中很难采样。然后，我们将使用体内识别的转录和信号动力学使用DBIT-seq鉴定候选分子调节剂进行血管侵袭的功能验证体外微流体设备。然后，经过验证的候选者将在乳腺癌宝石中的体内进行测试。