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/项目摘要乳腺癌转移性乳腺癌和治疗后的复发取决于（1）对识别的抵抗力和免疫系统破坏癌细胞，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受到限制，并且通常缺乏肿瘤微环境的关键特性。我们最近开发了可扩展的癌症球体在3D文化中启用了第一个全基因组CRISPR屏幕的系统；该系统中的表型很多更好地反映体内肿瘤（自然，2020年）。此外，我们制定了一种磁分离策略来快速鉴定巨噬细胞的吞噬作用调节剂（自然遗传学，2018年），并成功扩展了研究巨噬细胞肿瘤相互作用的策略。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剂。为了研究治疗后复发的机制，我们将重点放在四个ER+乳腺癌亚组上柯蒂斯实验室先前确定的高复发风险（项目1）。这构成了生物标志物的基础针对这些高风险亚组中假定候选驱动因素的驱动临床试验。自扩增子定义这些子组每个包含多个基因，我们将使用功能性CRISPR扰动来测试哪个基因（或其组合）是真正的驱动因素。此外，我们将基于全面的特征在转录组学（项目1）和空间蛋白质组学（项目2）中的这些肿瘤中，增加了功能测量对每个改变因素的需求的测量吞吐量CRISPR屏幕。这些研究将大大增强我们对哪个的理解基因是改善高释放风险乳腺癌疗法的关键靶标。为了研究转移性肿瘤如何逃避免疫系统，我们将重点放在巨噬细胞肿瘤上互动。令人惊讶的是，尽管巨噬细胞占某些肿瘤的细胞质量的50％，但癌细胞似乎对巨噬细胞杀戮具有抗性。这主要是由于由癌细胞，包括CD47；但是，积累证据表明存在附加的证据，乳腺癌中未识别的抗孢细胞信号。此外，肿瘤相关的巨噬细胞（TAM）是重新结合以支持肿瘤发育并减少吞噬作用。但是，尚不清楚基因介导高风险IC亚型中吞噬作用的抗性，哪些巨噬细胞基因是基因转移性乳腺癌的免疫抑制。在这里，我们将系统地识别限制抗癌的基因巨噬细胞通过在巨噬细胞和癌细胞中进行CRISPR筛查的活动，复杂的ALI患者衍生的类器官模型以验证命中。这些补充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标免疫逃避，告知下一代临床试验。