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 涉及不同癌细胞之间的相互作用以及与肿瘤微环境之间的内在3D过程 (TME)。我们开发了创新的3D分析方法来模拟体外转移的每一步。我们用这些化验方法 提出关于癌细胞如何完成转移以及哪些分子信号应该是 有针对性的治疗。这些假说的体内验证在技术上和概念上都是限速的。我们 可以通过实时成像和分子读数在体外比较多种扰动的影响。在……里面 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， 2D切片上有少量分子标记。迫切需要实现对 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。这个 TECH1和Tech2中的3D成像和空间多组学方法非常适合让我们能够 了解血管侵犯，这是从局部疾病到转移性疾病的关键过渡。以前的研究一般 评估单个细胞类型或几个标记，主要是2D。CODA(TECH1)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(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。我们将把这些结合起来 实现空间多组学和解决乳腺癌转移过程中癌细胞状态变化的技术。 目的1：使CODA适用于小鼠模型和人类乳腺肿瘤，重点是静脉侵袭。我们会 First提供人类乳腺肿瘤档案，使科技公司能够将其3D深度学习算法应用于乳房 癌症。我们将从现有的250个带有数字化连续切片的人类乳腺肿瘤系列开始。到时候我们会的 收集、固定和切片新鲜的人乳腺肿瘤样本，免疫和癌细胞标记物染色。我们 将使用CODA重建血管入侵的3D结构和相关的间质反应。我们 还将使CODA技术用于小鼠临床前模型。然后，我们将利用这些见解来 采用我们开发的新型微流控平台，在体外重建血管侵袭生态位。 目的2：使DBiT-seq适用于小鼠和人乳腺肿瘤，重点是癌细胞状态的转变。 我们将使dbit-seq应用于3D人类乳腺肿瘤样本，以了解癌症之间的空间关系。 血管侵入过程中的细胞状态。此分析将从单元状态和我们推断的状态转换 在我们的3D转移分析中使用单细胞RNA-SEQ在体外定义。然后我们将收集一系列阶段性的 肿瘤和远离GEMM的器官来定义跨转移过程的细胞状态转换 很难在人类身上采集样本。然后，我们将使用在体内确定的转录和信号动力学 利用DBiT-seq确定血管侵袭功能验证的候选分子调控因子 体外微流控装置。经过验证的候选者将在乳腺癌GEMM中进行体内测试。