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）内在耐药性的发展 靶向治疗和内分泌治疗，以及（2）对癌细胞的识别和破坏的抗性， 免疫系统斯坦福大学乳腺癌转移中心（SBMC）专注于（1）量化 乳腺癌的转移性播散（2）功能性描述细胞和 微环境对转移倾向的干扰，以及（3）通过以下方式表征响应机制： 转移细胞的治疗方法。 为了实现这些目标，机械计算模型，捕捉动态和 需要新出现的肿瘤细胞的内在和外在特性， 组织队列和实验模型，捕捉疾病的异质性。SBMC解决了所有这些问题 突出的挑战。首先，我们已经建立了一个无与伦比的收集临床注释乳腺癌 通过治疗和转移采样的癌症队列，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）， 疾病，高风险复发亚组和肿瘤免疫相互作用，并大大促进了拟议的 功能研究。我们使用最先进的技术来表征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。具体地说， 通过飞行时间多路离子束成像（MIBI-TOF）和基质辅助激光解吸电离 MALDI成像（MALDI）可以同时显示肿瘤的组成，谱系，功能和空间分布 和基质细胞群，并进行糖组的共配准分析。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， 计算框架来揭示与临床结果相关的疾病动力学和特征。 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。 这种整合的系统生物学和功能基因组学方法将有助于定量和 转移性乳腺癌的机制理解以及肿瘤细胞与 宿主，并对治疗靶向产生影响。

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.