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

项目3：控制乳腺癌进展和耐药因素的系统表征

基本信息

批准号：
10704691
负责人：
MICHAEL C BASSIK
金额：
$ 34.57万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
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/10704691
关键词：
3-Dimensional Breast Breast Cancer Cell Breast Cancer Risk Factor Breast Cancer cell line CD47 gene CRISPR interference CRISPR screen CRISPR-mediated transcriptional activation Cancer Model Cancer Patient Candidate Disease Gene Cell Communication Cell Line Cells Clinical Clinical Trials Clustered Regularly Interspaced Short Palindromic Repeats Dependence Development Drug Targeting Drug resistance Effectiveness Engineering Estrogen receptor positive Exhibits Fluorescent Probes Gene Combinations Gene Expression Gene Expression Profile Gene Targeting Genes Genetic Genetic Transcription Genomics Growth Immune Evasion Immune system Immunosuppression Immunotherapy In Vitro Individual Infiltration MCF10A cells MCF7 cell Macrophage Magnetism Malignant Neoplasms Mass Spectrum Analysis Measurement Measures Mediating Metastatic breast cancer Modeling Molecular Nature Neoplasm Metastasis Organoids Outcome Patients Phagocytes Phagocytosis Phenotype Post-Translational Protein Processing Process Property Proteins Proteomics Relapse Repression Resistance Resistance development Role Signal Transduction Subgroup System Testing Therapeutic Trastuzumab Tumor Escape Tumor-associated macrophages adaptive immune response anti-cancer anticancer activity biomarker driven breast cancer progression cancer cell cancer therapy candidate identification differential expression druggable target experimental study genome-wide high risk hormone therapy improved in vivo malignant breast neoplasm mammary epithelium neoplastic cell next generation novel overexpression receptor relapse risk resistance mechanism sialylation targeted treatment therapeutic target therapy resistant three dimensional cell culture transcriptomics treatment response tumor tumor initiation tumor microenvironment tumor progression tumorigenesis

项目摘要

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.

摘要/项目摘要转移性乳腺癌和治疗后的复发取决于 (1) 对识别和治疗的抵抗力免疫系统破坏癌细胞，以及（2）对靶向和治疗产生内在抵抗力内分泌治疗。使用体外癌症模型对这些过程的研究在规模和规模上都受到限制。通常缺乏肿瘤微环境的关键特性。我们最近开发了一个可扩展的癌症球体首次在 3D 培养中实现全基因组 CRISPR 筛选的系统；该系统中的表型很多更好地反映体内肿瘤（Nature，2020）。此外，我们开发了磁分离策略，以快速鉴定巨噬细胞吞噬作用的调节因子（Nature Genetics，2018）并已成功扩展这种策略用于研究巨噬细胞与肿瘤细胞的相互作用。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子。为了研究治疗后复发的机制，我们将重点关注四个 ER+ 乳腺癌亚组，其中柯蒂斯实验室先前确定的高复发风险（项目 1）。这形成了生物标志物的基础—— 针对这些高风险亚组中假定的候选驱动因素的驱动临床试验。由于扩增子定义这些亚组每个都包含多个基因，我们将使用功能性 CRISPR 扰动来测试哪些基因基因（或其组合）是真正的驱动力。此外，我们将在全面表征的基础上通过转录组学（项目 1）和空间蛋白质组学（项目 2）对这些肿瘤进行分析，添加功能使用高通量测量生长和治疗抵抗力中每个改变因素的需求高通量 CRISPR 筛选。这些研究将极大地增强我们对以下问题的理解：基因是改善高复发风险乳腺癌治疗的关键靶点。为了研究转移性肿瘤如何逃避免疫系统，我们将重点关注巨噬细胞肿瘤互动。令人惊讶的是，尽管巨噬细胞占某些肿瘤细胞量的 50%，但乳腺癌癌细胞似乎对巨噬细胞的杀伤有抵抗力。这主要是由于表达的抗吞噬信号癌细胞，包括CD47；然而，越来越多的证据表明存在额外的、乳腺癌中未识别的抗吞噬细胞信号。此外，肿瘤相关巨噬细胞（TAM）重新连接以支持肿瘤发展并减少吞噬作用。然而，目前尚不清楚是哪一个基因介导高危 IC 亚型对吞噬作用的抵抗，以及哪些巨噬细胞基因是其基础转移性乳腺癌的免疫抑制。在这里，我们将系统地鉴定限制抗癌的基因通过在巨噬细胞和癌细胞中进行 CRISPR 筛选，利用巨噬细胞的活性复杂的 ALI 患者衍生的类器官模型用于验证命中。这些互补的方法将在功能上定义乳腺癌驱动基因和控制治疗反应的治疗靶点免疫逃避，为下一代临床试验提供信息。