Spatial functional genomics to identify regulators of the tumor microenvironment and cancer immunity

空间功能基因组学识别肿瘤微环境和癌症免疫的调节因子

• 批准号: 10720979
• 负责人: Brian D Brown
• 金额: $ 103.23万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-19 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10720979
• 关键词: Affect; Antibodies; Architecture; CD8-Positive T-Lymphocytes; CRISPR screen; Cancer Biology; Candidate Disease Gene; Categories; Cell Communication; Cell Cycle; Cell Death; Cell Fraction; Cell membrane; Cells; Clustered Regularly Interspaced Short Palindromic Repeats; Collagen; Collection; Complementary DNA; Computer Analysis; Data; Development; Drug Targeting; Environment; Fibroblasts; Gene Expression; Genes; Genomics; Goals; Growth; Head Cancer; Immune; Immunity; Immunocompetent; Immunologic Surveillance; Immunotherapy; In Situ; Infiltration; Invaded; Knock-out; Knowledge; Ligands; Machine Learning; Macrophage; Malignant Neoplasms; Malignant neoplasm of lung; Malignant neoplasm of ovary; Malignant neoplasm of pancreas; Maps; Measures; Mediating; Modeling; Morphology; Mutate; Mutation; Neck Cancer; Neoplasm Metastasis; Non-Small-Cell Lung Carcinoma; Oncogenes; Outcome Study; Ovarian Serous Adenocarcinoma; Pancreatic Adenocarcinoma; Pancreatic Ductal Adenocarcinoma; Patients; Pharmaceutical Preparations; Phenotype; Positioning Attribute; Process; Publishing; Resistance; Resolution; Role; Selection for Treatments; Serous; Solid Neoplasm; T cell therapy; Testing; The Cancer Genome Atlas; Tumor Biology; Tumor Immunity; Tumor-Associated Process; antiangiogenesis therapy; cancer cell; cancer imaging; cancer therapy; candidate identification; cell stroma; chemokine; cohort; cytokine; extracellular; fitness; functional genomics; gene function; genomic platform; immune checkpoint blockade; immunosuppressed; improved; in vivo; innovation; insight; malignant breast neoplasm; mouse model; mouth squamous cell carcinoma; multiplexed imaging; novel therapeutic intervention; overexpression; personalized medicine; predicting response; preservation; recruit; refractory cancer; response; therapy resistant; transcriptomics; treatment response; tumor; tumor behavior; tumor growth; tumor microenvironment

PROJECT SUMMARY Tumor growth and response to therapy, particularly immunotherapy, are all highly dependent on the tumor microenvironment (TME): the collection of cells and extracellular factors (cytokines, chemokines, collagens, etc.) that form around cancer cells. This is evident from the major impact drugs targeting TME components can have on cancers, including immune checkpoint blockade (ICB). Though there is a relatively good understanding of key genes regulating cancer cell intrinsic processes, such as cell cycle, there is less known about genes controlling the extrinsic environment that protects cancer cells from immunity and aids growth. The objective of this project is to determine the genes controlling tumor composition and facilitating tumor growth and resistance to immunity & immunotherapy, with the goal of identifying vulnerability factors that can be targeted to enhance tumor immunity and improve cancer treatment. The overarching hypothesis, which forms the rationale for this U01, is that malignant cells turn on or off genes, including intrinsically operating genes, through mutations and selective gene expression, that act extrinsically to recruit, position, & polarize immune & stroma cells into a state that subverts immunity & facilitates tumor growth1, 2. To reach our objective, we will employ a first-of-its-kind spatial functional genomics platform, called Perturb-map, which permits extensive phenotypic analysis of dozens of single or multiple gene perturbations in a tumor at single cell resolution and with spatial architecture preserved. With Perturb-map, CRISPR knockout (KO) or cDNA overexpression (OE) screens are resolved by multiplex imaging & spatial transcriptomics (ST), and this allows study of entire classes of genes (e.g. secreted factors) and phenotypes (e.g. TME composition) not feasible with existing screens. We will use Perturb-map to determine the role of 100s of genes in controlling many critical tumor processes, including tumor: (i) growth, (ii) morphology, (iii) metastasis, (iv) cell-cell interactions, (v) subclonal interactions, (vi) immune/stroma recruitment & polarization, (vii) resistance to immunotherapy & other treatments. The breadth & depth of analysis of each gene will be achieved at a scale and efficiency not previously feasible. We will focus on 3 broad categories of genes, identified through analysis of TCGA, ICB-treated cohorts, single cell-omics, and other patient data, including: commonly mutated cancer genes in solid tumors (Aim 1), genes correlating with resistance or response to ICB immunotherapy (Aim 2), and cancer cell-derived ligands and secreted molecules (Aim 3). Studies will be carried out in immunocompetent, orthotopic models of non-small cell lung carcinoma, high grade serous ovarian carcinoma, pancreatic adenocarcinoma, and oral squamous cell carcinoma. The study outcome will determine the roles of 100s of genes in many processes critical to unimpeded cancer growth, including identifying genes shielding cancers from immunity. In doing so, they will generate insights into mechanisms of aggressive tumor behavior and treatment resistance that will help to improve and personalize treatment selection and drive the immediate next steps towards the development of novel therapeutic strategies.

项目概要 肿瘤生长和对治疗（尤其是免疫治疗）的反应都高度依赖于肿瘤 微环境（TME）：细胞和细胞外因子（细胞因子、趋化因子、胶原蛋白等）的集合 形成于癌细胞周围。从针对 TME 成分的药物可能产生的重大影响中可以明显看出这一点 癌症，包括免疫检查点阻断（ICB）。虽然有了比较好的了解 调节癌细胞内在过程的关键基因，例如细胞周期，目前对基因知之甚少 控制保护癌细胞免受免疫并帮助生长的外部环境。 该项目的目标是确定控制肿瘤组成和促进肿瘤发生的基因 生长以及对免疫和免疫治疗的抵抗力，目的是确定可影响的脆弱因素 有针对性地增强肿瘤免疫力并改善癌症治疗。总体假设 形成这个 U01 的基本原理是，恶性细胞打开或关闭基因，包括内在运行的基因， 通过突变和选择性基因表达，从外部起到招募、定位和极化免疫的作用 基质细胞进入破坏免疫力并促进肿瘤生长的状态1, 2。为了实现我们的目标，我们将 采用首个空间功能基因组学平台，称为 Perturb-map，该平台允许广泛 以单细胞分辨率对肿瘤中数十个单基因或多基因扰动进行表型分析 保留了空间建筑。使用 Perturb-map、CRISPR 敲除 (KO) 或 cDNA 过表达 (OE) 屏幕通过多重成像和空间转录组学 (ST) 进行解析，这使得可以对整个类别进行研究 现有筛选无法实现基因（例如分泌因子）和表型（例如 TME 成分）的分析。 我们将使用扰动图来确定数百个基因在控制许多关键肿瘤过程中的作用， 包括肿瘤：（i）生长，（ii）形态，（iii）转移，（iv）细胞间相互作用，（v）亚克隆相互作用， (vi) 免疫/基质募集和极化，(vii) 对免疫疗法和其他治疗的抵抗。广度 对每个基因的深度分析将以前所未有的规模和效率实现。我们将重点 通过 TCGA、ICB 治疗组、单细胞组学分析确定的 3 大类基因 其他患者数据，包括：实体瘤中常见突变的癌症基因（目标 1）、与 对 ICB 免疫疗法（目标 2）以及癌细胞衍生的配体和分泌分子的耐药性或反应 （目标 3）。研究将在免疫功能正常的非小细胞肺癌原位模型中进行， 高级别浆液性卵巢癌、胰腺腺癌和口腔鳞状细胞癌。 该研究结果将确定数百个基因在许多对癌症畅通无阻至关重要的过程中的作用 生长，包括识别保护癌症免受免疫的基因。在此过程中，他们将深入了解 侵袭性肿瘤行为和治疗抵抗的机制将有助于改善和个性化 治疗选择并推动下一步开发新的治疗策略。