Evolutionary dynamics and microenvironmental determinants of metastatic breast cancer

摘要/项目摘要 转移性乳腺癌和治疗后复发取决于(1)内在抵抗力的发展 靶向和内分泌治疗，以及(2)对识别和摧毁癌细胞的抵抗力 免疫系统。斯坦福大学乳房转移中心(SBMC)专注于(1)量化 乳腺癌中的转移扩散(2)从功能上描述细胞和 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 转移性细胞的治疗。 为了实现这些目标，捕捉动态和动态的机械性计算模型 新出现的肿瘤细胞的内在和外在属性是需要的，就像临床上注明的纵向 捕捉疾病异质性的组织队列和实验模型。SBMC解决了所有这些问题 突出的挑战。首先，我们建立了一个无与伦比的临床注释乳房集合。 通过治疗和转移抽样的癌症队列，包括前瞻性和回顾性研究 纵向队列，有多个转移部位。我们利用乳腺癌患者的活生物库- 来自原发肿瘤和转移瘤的衍生有机物(PDO)概括了 疾病、复发高危亚群和肿瘤-免疫相互作用，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庞大的组织资源和模型系统 单细胞和亚细胞分辨率的原位探测肿瘤组织的分子图谱技术。具体来说， 多路离子束飞行时间成像(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，并对糖糖进行共同注册分析。我们将这些数据集成到 转移性疾病的基因组图谱，并在稳健的机器学习和 揭示疾病动力学和与临床结果相关的特征的计算框架。 最后，我们在3D乳腺癌模型中进行基因组规模的CRISPR筛查，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。 这种集成的系统生物学和功能基因组学方法将有助于 转移性乳腺癌的机制认识及肿瘤细胞与肿瘤细胞的动态关系 宿主，与治疗靶向的含义。

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.