Evolutionary dynamics and microenvironmental determinants of metastatic breast cancer

转移性乳腺癌的进化动力学和微环境决定因素

• 批准号: 10819066
• 负责人: Christina N Curtis
• 金额: $ 6.62万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-14 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10819066
• 关键词: 3-Dimensional; Address; Biological Models; Breast; Breast Cancer Model; Breast Cancer Patient; Breast cancer metastasis; CRISPR screen; CRISPR/Cas technology; Cancer Relapse; Cell Communication; Cells; Classification; Clinical; Collection; Computer Models; Data; Dependence; Development; Diagnosis; Disease; ERBB2 gene; Estrogen receptor positive; Experimental Models; Genomic approach; Genomics; Goals; Image; Immune; Immune system; In Situ; Lead; Longitudinal cohort; Machine Learning; Macrophage; Malignant Neoplasms; Metastatic breast cancer; Modeling; Molecular; Molecular Profiling; Multiplexed Ion Beam Imaging; Nature; Neoplasm Metastasis; Non-linear Models; Oncogenic; Organoids; Outcome; Pathologist; Pathology; Patients; Phagocytosis; Population; Primary Neoplasm; Process; Property; Relapse; Research Project Grants; Research Support; Resistance; Resolution; Resource Sharing; Sampling; Site; Spatial Distribution; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; Stromal Cells; Subgroup; System; Systems Biology; Technology; Therapeutic; Time; Tissue Model; Tissue Sample; Tissues; Tumor Tissue; Vision; Visualization; bean; biobank; breast cancer progression; cancer cell; cohort; computer framework; computerized tools; data integration; disease heterogeneity; functional genomics; genome-wide; high risk; hormone therapy; innovation; invention; malignant breast neoplasm; multidisciplinary; neoplastic cell; prospective; relapse risk; response; single cell technology; targeted treatment; therapeutic target; therapy resistant; tissue resource; triple-negative invasive breast carcinoma; tumor

Abstract/Project Summary Metastatic breast cancer and relapse following therapy are dependent on (1) development of intrinsic resistance to targeted and endocrine therapies, and (2) resistance to recognition and destruction of cancer cells by the immune system. The Stanford Breast Metastasis Center (SBMC) is focused on (1) quantifying the timing of metastatic dissemination in breast cancer (2) functionally delineating the contribution of cellular and microenvironmental crosstalk on metastatic proclivity, and (3) characterizing the mechanisms of responses by metastatic cells to therapies. In order to achieve these goals, mechanistic computational models that capture dynamic and emergent tumor cell intrinsic and extrinsic properties are needed as are clinically annotated longitudinal tissue cohorts and experimental models that capture disease heterogeneity. The SBMC addresses each of these outstanding challenges. First, we have established an unparalleled collection of clinically annotated breast cancer cohorts sampled through treatment and metastasis, including both prospective and retrospective longitudinal cohorts, with multiple metastatic sites. We leverage a living biobank of breast cancer patient- derived organoids (PDOs) from primary tumors and metastases that recapitulate the heterogeneity of disease, high-risk of relapse subgroups and tumor-immune interactions and greatly facilitating the proposed functional studies. We characterize these vast tissue resources and model systems using state-of-the-art molecular profiling technologies to probe tumor tissue in situ at single cell and subcellular resolution. Specifically, with Multiplexed Ion Beam Imaging by Time of Flight (MIBI-TOF) and matrix-assisted laser desorption ionization imaging (MALDI) we simultaneously visualize the composition, lineage, function and spatial distribution of tumor and stromal cell populations and perform co-registered analysis of the glycome. We integrate these data within the genomic landscape of metastatic disease and analyze these data within robust machine learning and computational frameworks to uncover disease dynamics and features associated with clinical outcomes. Lastly, we conduct genome-scale CRISPR screens in 3D breast cancer models to systematically define oncogenic dependencies, therapeutic vulnerabilities and macrophage-tumor cell interactions. This integrated systems biology and functional genomics approach will contribute to a quantitative and mechanistic understanding of metastatic breast cancer and the dynamic relationship between tumor cells and the host, with implications for therapeutic targeting.

摘要/项目摘要 乳腺癌转移性乳腺癌和治疗后的复发取决于（1）固有抗性的发展 对靶向和内分泌疗法，以及（2）抗识别和破坏癌细胞的抗性 免疫系统。斯坦福乳房转移中心（SBMC）的重点是（1）量化的时间 乳腺癌中的转移性传播（2）在功能上描述了细胞和细胞的贡献 微环境串扰转移性倾向，（3）表征反应的机理 转移细胞对疗法。 为了实现这些目标，捕获动态和的机械计算模型 需要临床注释的纵向，需要出现的肿瘤细胞固有和外在特性 捕获疾病异质性的组织队列和实验模型。 SBMC解决了其中的每一个 出色的挑战。首先，我们建立了无与伦比的临床注释乳房集合 通过治疗和转移取样的癌症队列，包括前瞻性和追溯性 纵向人群，具有多个转移性位点。我们利用乳腺癌患者的活生物库 - 来自原发性肿瘤和转移的衍生类器官（PDOS），这些肿瘤和转移均概括了异质性 疾病，复发亚组的高风险和肿瘤免疫相互作用，并极大地促进了所提出的 功能研究。我们使用最先进的 分子分析技术以探测单细胞和亚细胞分辨率的原位肿瘤组织。具体来说， 通过飞行时间（MIBI-TOF）和基质辅助激光解吸电离，与多路复用离子光束成像 成像（MALDI）我们同时可视化肿瘤的组成，谱系，功能和空间分布 和基质细胞群体并对糖的共同注册分析。我们将这些数据集成到其中 转移性疾病的基因组景观，并在强大的机器学习和 计算框架以发现与临床结果相关的疾病动态和特征。 最后，我们在3D乳腺癌模型中进行基因组尺度CRISPR筛选以系统定义 致癌依赖性，治疗脆弱性和巨噬细胞肿瘤的相互作用。 这种集成的系统生物学和功能基因组学方法将有助于定量和 机械理解转移性乳腺癌以及肿瘤细胞与肿瘤细胞之间的动态关系 宿主，对治疗靶向有影响。