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: 10704647
• 负责人: Christina N Curtis
• 金额: $ 153.22万
• 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 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10704647
• 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 转移性乳腺癌和治疗后的复发取决于 (1) 内在耐药性的发展 靶向和内分泌治疗，以及（2）对癌细胞识别和破坏的抵抗力 免疫系统。斯坦福乳腺转移中心 (SBMC) 的重点是 (1) 量化发生转移的时间 乳腺癌的转移性播散 (2) 功能性地描绘了细胞和细胞的贡献 微环境串扰对转移倾向的影响，以及（3）通过以下方式表征反应机制： 转移细胞接受治疗。 为了实现这些目标，捕获动态和数据的机械计算模型 需要新出现的肿瘤细胞的内在和外在特性，以及临床注释的纵向 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问题 突出的挑战。首先，我们建立了无与伦比的临床注释乳房数据库 通过治疗和转移取样的癌症队列，包括前瞻性和回顾性 纵向队列，具有多个转移部位。我们利用乳腺癌患者的活体生物库- 来自原发性肿瘤和转移瘤的衍生类器官（PDO），概括了肿瘤的异质性 疾病、复发亚组的高风险以及肿瘤-免疫相互作用，并极大地促进了拟议的 功能研究。我们使用最先进的技术来描述这些巨大的组织资源和模型系统 分子分析技术以单细胞和亚细胞分辨率原位探测肿瘤组织。具体来说， 采用飞行时间多重离子束成像 (MIBI-TOF) 和基质辅助激光解吸电离 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，并对糖组进行联合注册分析。我们将这些数据整合到 转移性疾病的基因组景观，并在强大的机器学习中分析这些数据， 揭示与临床结果相关的疾病动态和特征的计算框架。 最后，我们在 3D 乳腺癌模型中进行基因组规模的 CRISPR 筛选，以系统地定义 致癌依赖性、治疗脆弱性和巨噬细胞-肿瘤细胞相互作用。 这种综合系统生物学和功能基因组学方法将有助于定量和 对转移性乳腺癌的机制认识以及肿瘤细胞与乳腺癌之间的动态关系 宿主，对治疗靶向有影响。