RTB 2

实时出价2

• 批准号: 10532387
• 负责人: Andrew Josef Ewald
• 金额: $ 34.19万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 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/10532387
• 关键词: 3-Dimensional; Animal Model; Architecture; Basic Science; Biological; Biological Assay; Blood Vessels; Breast cancer metastasis; Cells; Cellular Structures; Cellular biology; Cessation of life; Classification; Clinical; Complex; Cytometry; Data; Data Set; Diameter; Disease; Distant; Ensure; Evaluation; Extracellular Matrix; Extravasation; Fibroblasts; Gene Expression; Genetic Transcription; Human; Image; Immune; Immunofluorescence Immunologic; Immunohistochemistry; In Vitro; Individual; Invaded; Malignant Neoplasms; Malignant neoplasm of pancreas; Mammary Neoplasms; Masks; Measurement; Mechanics; 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; Visualization; Wood material; Work; cancer cell; candidate identification; candidate validation; 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; transcriptome; transcriptome sequencing; tumor; tumor microenvironment

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 一个固有的3D过程，涉及不同癌细胞之间的相互作用和肿瘤微环境 （TME）。我们开发了创新的3D测定法来模拟离体转移的每个步骤。我们使用这些检测来 产生关于癌细胞如何完成转移以及哪些分子信号应该被转移的假设。 有针对性的治疗。这些假设的体内验证在技术上和概念上都是有限的。我们 可以通过实时成像和分子读数来比较体外许多扰动的影响。在 相反，体内验证通常限于肿瘤直径、CTC和转移数目的测量， 和一些分子标记。迫切需要实现对以下内容的3D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转换。的 3D成像和空间多组学方法在2011年和2012年是理想的适合，使我们能够 了解血管浸润，从局部到转移性疾病的关键转变。以往的研究一般 评估单个细胞类型或一些标记物，主要是2D。CODA（CODA 1）将使我们能够对细胞类型进行分类 以及它们在3D中的空间关系。DBiT-seq（DBi 2）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，跨越人类肿瘤的整个切片。我们将联合收割机 技术，以实现空间多组学和解决乳腺癌转移过程中的癌细胞状态变化。 目的1：使CODA适应于小鼠模型和人乳腺肿瘤，重点是静脉浸润。我们将 首先提供人类乳腺肿瘤档案，使TECH能够将其3D深度学习算法应用于乳腺癌 癌我们将从现有的250例人类乳腺肿瘤的数字化连续切片开始。然后我们将 收集、固定和切片新鲜的人乳腺肿瘤样品，用免疫和癌细胞标记物染色。我们 将使用CODA重建血管浸润和相关基质反应的3D结构。我们 还将调整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。 目的2：使DBiT-seq适应小鼠和人类乳腺肿瘤，重点关注癌细胞状态转换。 我们将使DBiT-seq适应3D人类乳腺肿瘤样本，以了解癌症之间的空间关系。 细胞在血管侵袭过程中的状态。这种分析将从细胞状态和推断的状态转换， 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 肿瘤和远端器官之间的转移，以确定空间上跨越转移过程的细胞状态转换， 很难在人体中取样。然后，我们将使用体内确定的转录和信号动力学 使用DBiT-seq鉴定候选分子调节剂用于血管侵袭中的功能验证 微流控装置在体外。然后将在乳腺癌GEMM中对经验证的候选物进行体内测试。