Project 3: Systematic characterization of factors controlling breast cancer progression and resistance

摘要/项目摘要 转移性乳腺癌和治疗后复发取决于(1)对识别和治疗的抵抗力 免疫系统对癌细胞的破坏，以及(2)对靶向和 内分泌疗法。使用体外癌症模型对这些过程的研究在规模和 往往缺乏肿瘤微环境的关键特性。我们最近开发了一种可伸缩的癌症球体 在3D培养中实现了第一个全基因组CRISPR筛选的系统；这个系统中的表型 更好地反映体内肿瘤(《自然》，2020)。此外，我们开发了一种磁选策略，以快速 确定巨噬细胞吞噬的调节因素(自然遗传学，2018)，并成功地扩展了 这种研究巨噬细胞-肿瘤细胞相互作用的策略。在这里我们将使用这些系统来识别 转移性乳腺癌治疗复发和免疫逃避的调节因素。 为了研究治疗后复发的机制，我们将关注四个ER+乳腺癌亚组 柯蒂斯实验室以前确定的高复发风险(项目1)。这构成了生物标记物的基础- 针对这些高风险亚组中的推定候选司机的驱动型临床试验。因为扩增器 定义这些子组每个包含多个基因，我们将使用功能性CRISPR扰动来测试哪些 基因(或其组合)才是真正的驱动力。此外，我们将在全面描述的基础上 来自转录组学(项目1)和空间蛋白质组学(项目2)的这些肿瘤，增加了功能 使用高密度脂蛋白测定生长和抗药性中每个改变因子的需要。 吞吐量CRISPR屏幕。总之，这些研究将极大地增强我们对 基因是高复发风险乳腺癌改进治疗的关键靶点。 为了研究转移性肿瘤如何逃避免疫系统，我们将重点研究巨噬细胞肿瘤。 互动。令人惊讶的是，尽管巨噬细胞占某些肿瘤细胞质量的50%，但乳房 癌细胞似乎对巨噬细胞的杀伤有抵抗力。这在很大程度上是由于由 癌细胞，包括CD47；然而，积累的证据表明存在额外的， 乳腺癌中未知的反吞噬信号。此外，肿瘤相关巨噬细胞()是 重新布线以支持肿瘤的发展并减少吞噬作用。然而，目前还不清楚是哪一种 在高危IC亚型中，基因介导吞噬抵抗，而巨噬细胞基因是其基础 转移性乳腺癌引起的免疫抑制。在这里，我们将系统地识别限制抗癌的基因 巨噬细胞的活性通过在巨噬细胞和癌细胞中进行CRISPR筛选，利用 复杂的ALI患者衍生的器官模型，以验证命中率。这些互补的方法将 功能性定义乳腺癌驱动基因和治疗靶点，控制治疗反应和 免疫逃避，通知下一代临床试验。

Abstract/Project Summary Metastatic breast cancer and relapse following therapy are dependent on (1) resistance to recognition and destruction of cancer cells by the immune system, and (2) development of intrinsic resistance to targeted and endocrine therapies. The study of these processes using in vitro cancer models have been limited in scale and often lack key properties of the tumor microenvironment. We recently developed a scalable cancer spheroid system that enabled the first genome-wide CRISPR screens in 3D culture; phenotypes in this system much better reflect in vivo tumors (Nature, 2020). In addition, we developed a magnetic separation strategy to rapidly identify regulators of phagocytosis by macrophages (Nature Genetics, 2018) and have successfully extended this strategy to study macrophage-tumor cell interactions. Here we will use these systems to identify regulators of therapeutic relapse and immune evasion in metastatic breast cancer. To investigate mechanisms of relapse after therapy, we will focus on four ER+ breast cancer subgroups with high relapse risk previously identified by the Curtis Lab (Project 1). This has formed the basis of a biomarker- driven clinical trial targeting the presumed candidate drivers in these high-risk subgroups. Since the amplicons defining these subgroups each contain multiple genes, we will use functional CRISPR perturbations to test which genes (or combinations thereof) are the true drivers. Further, we will build on the comprehensive characterization of these tumors from transcriptomics (Project 1) and spatial proteomics (Project 2), adding functional measurements of the requirement for each altered factor in growth and resistance to therapy using high- throughput CRISPR screens. Together these studies will dramatically enhance our understanding of which genes are critical targets for improved therapies in high-relapse risk breast cancers. To investigate how metastatic tumors evade the immune system, we will focus on macrophage-tumor interactions. Surprisingly, although macrophages comprise 50% of the cell mass of some tumors, breast cancer cells appear resistant to macrophage killing. This is largely due to anti-phagocytic signals expressed by cancer cells, including CD47; however, accumulating evidence points to the existence of additional, unidentified anti-phagocytic signals in breast cancer. In addition, tumor-associated macrophages (TAM) are re-wired to support tumor development and have reduced phagocytosis. It remains unclear, however, which genes mediate resistance to phagocytosis in high-risk IC subtypes, and which macrophage genes underlie immunosuppression by metastatic breast cancers. Here, we will systematically identify genes limiting anti-cancer activity by macrophages by conducting CRISPR screens in both macrophages and cancer cells, making use of sophisticated ALI patient-derived organoid models to validate hits. These complementary approaches will functionally define breast cancer driver genes and therapeutic targets that control therapeutic response and immune evasion, informing the next generation of clinical trials.