Mapping the single cell state basis of metastasis in space and time

绘制空间和时间转移的单细胞状态基础

• 批准号: 10738579
• 负责人: Andrew Josef Ewald
• 金额: $ 67.96万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-12 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10738579
• 关键词: Age; Algorithms; Architecture; Automobile Driving; Behavior; Biological Assay; Biological Models; Birth Rate; Cancerous; Cause of Death; Cells; Cellular Morphology; Cessation of life; Clustered Regularly Interspaced Short Palindromic Repeats; Data; Development; Developmental Gene; Disease; Distant; Ecosystem; Environment; Enzymes; Epithelium; Exhibits; Genes; Genetic Transcription; Genetically Engineered Mouse; Gland; Goals; Growth; Hybrids; Invaded; Keratin; Learning; Link; Literature; Longevity; Machine Learning; Malignant Neoplasms; Mammary Neoplasms; Mammary gland; Maps; Measurement; Modeling; Molecular; Morphogenesis; Neoplasm Metastasis; Noninfiltrating Intraductal Carcinoma; Organ; Patient-Focused Outcomes; Patients; Periodicity; Pharmaceutical Preparations; Primary Neoplasm; Process; Proteins; Publishing; RNA; Regulator Genes; Resolution; Source; Structure; Systems Biology; Tail; Testing; Time; Tissues; United States; Veins; Vimentin; Weight; cancer cell; cancer site; candidate validation; cell type; computerized tools; differential expression; high dimensionality; high resolution imaging; in vivo; learning algorithm; malignant breast neoplasm; mammary epithelium; migration; neoplastic; new therapeutic target; patient derived xenograft model; prevent; programs; single cell sequencing; single-cell RNA sequencing; small hairpin RNA; small molecule; spatiotemporal; statistics; three dimensional cell culture; tool; transcriptome; transcriptomics; transfer learning; tumor; tumor progression

We propose to leverage recent advances in machine learning and systems biology to enable high dimensional molecular assessment of the dynamic cell state transitions driving metastasis. We hypothesize that the interaction between a cancer cell's intrinsic reactivation of developmental programs with its spatiotemporal context determines its metastatic potential. We will exploit developmental changes in the mammary epithelium to define their cell state basis and map the aberrant reuse of these transcriptional programs in metastatic disease. Both normal mammary epithelium and breast tumors undergo dramatic changes in differentiation and tissue architecture, and loss of differentiation correlates with poor patient outcomes. We developed 3D culture assays that recapitulate epithelial morphogenesis and cancer growth, invasion, and metastatic colony formation. The key concepts arising are that: (1) a conserved process of dedifferentiation and loss of polarity accompanies both normal and neoplastic morphogenesis and (2) the cancer cells in luminal and basal breast cancer recapitulate basal epithelial and hybrid EMT programs. Recent advances in single cell sequencing, spatial transcriptomics, and machine learning enable transcriptome-wide resolution of these states in tissue, quantitative comparison of normal and cancerous cell states, and the identification of targetable cell state regulators. Aim 1: Map cell states in space and time during development, tumor formation, and metastasis. We will generate scRNA-seq data from normal glands, ductal carcinoma in situ, and invasive tumors collected at different ages and also longitudinally in 3D culture. We will use our CoGAPS algorithm to infer cell states and their temporal progression. We will then use our patternMarker2 statistic to identify cell state makers for MERSCOPE analysis in tissue. We will map these states in normal glands, primary tumors, and metastases isolated from genetically engineered mouse models (GEMM) and patient derived xenografts (PDX). Aim 2: Model the dynamics of differentiation state during development and cancer progression. To define the effect of cell state on metastatic progression, we will construct an ecosystem-style multinomial diversity model. We will initialize the model with literature-based parameter values to predict the interactions between cell type and cell state. We will then extend the model to use the weights assigned by CoGAPS to each cell, thereby linking gene regulatory programs to the cell state changes driving metastasis. Aim 3: Validate candidate regulators of metastatic cell state transitions in 3D culture and in vivo. To isolate the genes regulating metastasis, we will use our transfer learning algorithm, projectR, to score each cancer cell for its relative utilization of scRNA-seq-defined molecular programs. We will then use our projectionDriver statistic to identify differentially expressed (DE) genes at sites of cancer invasion, relative to the tumor interior. DE genes will be tested genetically in 3D culture assays modeling invasion and colony formation and then in orthotopic and tail vein metastasis assays in vivo.

我们建议利用机器学习和系统生物学的最新进展， 驱动转移的动态细胞状态转变的三维分子评估。我们假设 癌细胞内在的发育程序再激活与其时空的相互作用 背景决定了它的转移潜力。我们将利用乳腺上皮细胞的发育变化 以确定它们的细胞状态基础，并绘制这些转录程序在转移性疾病中的异常重复使用。 正常乳腺上皮和乳腺肿瘤都经历了分化和分化的显著变化， 组织结构和分化的丧失与不良的患者结果相关。我们发展了3D文化 重现上皮形态发生和癌症生长、侵袭和转移性集落形成的测定。 由此产生的关键概念是：（1）保守的去分化和极性丧失过程伴随着 正常和肿瘤形态发生和（2）腔和基底乳腺癌中的癌细胞 概括基底上皮和混合EMT计划。单细胞测序的最新进展，空间 转录组学和机器学习使得能够在组织中、定量地 正常和癌细胞状态的比较，以及可靶向的细胞状态调节剂的鉴定。 目的1：绘制细胞在发育、肿瘤形成和转移过程中的空间和时间状态。我们将 从正常腺体、导管原位癌和在不同时间收集的浸润性肿瘤中生成scRNA-seq数据， 年龄和纵向在3D文化。我们将使用CoGAPS算法来推断单元格状态及其 时间进程然后，我们将使用我们的patternMarker 2统计来识别MERSCOPE的单元状态标记 组织分析我们将在正常腺体、原发性肿瘤和转移瘤中绘制这些状态， 基因工程小鼠模型（GEMM）和患者来源的异种移植物（PDX）。 目的2：模拟发育和癌症进展过程中分化状态的动态。以限定 细胞状态对转移进展的影响，我们将构建一个生态系统风格的多项式多样性 模型我们将使用基于文献的参数值初始化模型，以预测细胞之间的相互作用 类型和细胞状态。然后，我们将扩展模型，以使用CoGAPS为每个单元分配的权重，从而 将基因调控程序与驱动转移的细胞状态变化联系起来。 目的3：在三维培养和体内筛选转移性细胞状态转换的候选调节剂。 为了分离调控转移的基因，我们将使用我们的迁移学习算法projectR对每个基因进行评分。 癌症细胞对scRNA-seq定义的分子程序的相对利用。我们将使用我们的 projectionDriver统计，以确定在癌症侵袭部位的差异表达（DE）基因，相对于 肿瘤内部DE基因将在3D培养试验中进行遗传测试，建模入侵和菌落形成 然后在体内原位和尾静脉转移测定中。