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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改变，并导致大多数癌症死亡。转移也是