Bay Area Cancer Target Discovery and Development

湾区癌症靶标的发现和开发

基本信息

批准号：
10704172
负责人：
Sourav Bandyopadhyay
金额：
$ 97.66万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-13 至 2027-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10704172
关键词：
Area Automobile Driving Benign Biogenesis Biological Breast Adenocarcinoma CRISPR/Cas technology Calibration Cancer cell line Cell Culture Techniques Cells Clinical Clustered Regularly Interspaced Short Palindromic Repeats Data Development Distal Drug Targeting Drug Tolerance Drug resistance Epigenetic Process Event Evolution Exhibits Fertilization Foundations Funding Gene Combinations Genes Genetic Genetic Screening Genotype Goals Growth Heterogeneity Human In Vitro Inflammatory Joints Lung Adenocarcinoma Malignant - descriptor Malignant Neoplasms Malignant neoplasm of lung Methodology Modeling Molecular Molecular Target Mutation Oncogenes Pathogenicity Pathway interactions Phenotype Phylogenetic Analysis Process Reagent Recurrence Research Research Project Grants Resistance Resolution Role Solid Synthetic Genes System Systems Biology Technology The Cancer Genome Atlas Therapeutic Time Tissues Tumor Suppressor Genes Work cancer cell cancer subtypes cancer type cell behavior cell transformation cell type clinical translation clinically relevant high throughput technology improved in vivo innovation insight mouse model neoplastic cell next generation novel novel therapeutics patient derived xenograft model patient stratification pharmacologic premalignant programs single-cell RNA sequencing synergism synthetic biology therapeutic target therapeutically effective therapy resistant tool treatment response tumor tumor growth tumor heterogeneity tumorigenesis

项目摘要

PROJECT SUMMARY Our general strategy is to take advantage of novel tools and methodologies that we have developed during our first two CTD^2 funding periods– more specifically pioneering and applying CRISPR based technologies to aid the discovery and characterization of novel cancer targets and their modulators– using innovative high throughput technologies. Our end goal is to uncover optimal combinations of targets with the potential to eliminate all cancer cells, despite their clonal heterogeneity and environmental context. This requires us to better understand tumor biogenesis, namely the combinations of genes that drive oncogenesis, and tumor heterogeneity which complicates effective therapeutic treatment. In this proposal we build upon exciting systems allowing us to quantitate genotypic and phenotypic cell heterogeneity in cell culture and in vivo. The overall goal is to identify synthetic gene combinations necessary for clinical resistance and related to inter- and intra-tumor heterogeneity. We hypothesize that altered cell states such as inflammatory phenotypes and lineage plasticity fuels therapy tolerance and resistance. We apply single-cell approaches and cutting-edge lineage tracing tools to investigate the genesis of pathogenic cellular state changes and use genetic screening, computational and pharmacologic approaches, and clinically relevant in vitro and in vivo tumor models to identify mechanistically calibrated, specific therapeutic vulnerabilities. These approaches will be applied to two cancer, lung and breast adenocarcinoma. Tumor biogenesis and evolution is a challenging area of research, largely due to the complexity of cell types and behaviors and the combinations of genes that drive cancer types and subtypes is poorly understood. We have developed next generation GEMMs to interrogate gene combinations that promote cancer. In this aim, mouse models will be generated that contain combinations of genetic perturbations of the top 30 TCGA recurrent mutations. These studies will associate the combination of perturbagens with specific cell states, despite their clonal heterogeneity and cell state and lay a solid foundation for identifying which combinations of recurrent genes respond to which therapy, thus helping to stratify patients. This part of the research program focuses on lung cancer as it synergizes with other components of the proposal. We apply an evolved lineage tracing technology with single cell RNA-seq readout that lets us follow tumor evolution with unprecedented resolution. These studies will help us understand how tumor plasticity enables cancers to evade therapeutic challenges. And importantly, how the loss of tumor suppressor genes or gene combinations, alters the preferred evolutionary paths a single transformed cell takes to reach aggressive and metastatic states.

项目摘要我们的一般策略是利用我们开发的新颖工具和方法在我们的前两个CTD^2资金期间 - 更具体地开拓并应用了基于CRISPR 帮助发现和表征新型癌症靶标及其调节剂的技术 - 使用创新的高通量技术。我们的最终目标是发现最佳组合具有消除所有癌细胞的潜力，dostite的靶标的目标环境环境。这要求我们更好地了解肿瘤生物发生，即驱动肿瘤发生的基因和肿瘤异质性的组合，使有效有效治疗。在此提案中，我们以令人兴奋的系统为基础，使我们能够定量基因型和表型细胞细胞培养和体内的异质性。总体目标是识别合成基因组合对于临床抵抗，与肿瘤内和肿瘤内异质性有关。我们假设改变了细胞状态，例如炎症表型和谱系可塑性燃料疗法的耐受性和阻力。我们应用单细胞方法和尖端的谱系跟踪工具来调查致病性细胞状态的起源改变并使用遗传筛查，计算和药理方法以及临床上相关的体外和体内肿瘤模型，以鉴定机械校准的特定治疗漏洞。这些方法将应用于两个癌症，肺和乳腺癌。肿瘤生物发生和进化是研究的挑战领域，主要是由于细胞的复杂性类型和行为以及驱动癌症类型和亚型的基因的组合效果很差理解。我们已经开发了下一代宝石来询问基因组合促进癌症。在此目标中，将生成鼠标模型，其中包含通用的组合前30个TCGA复发突变的扰动。这些研究将使具有特定细胞状态的急性脉冲，其克隆异质性和细胞状态并铺设固体识别哪些复发基因组合对哪种治疗的反应，因此帮助分层患者。研究计划的这一部分侧重于肺癌，因为它合成了与该提案的其他组成部分。我们将进化的谱系跟踪技术应用于单一细胞RNA-seq读数使我们能够以前所未有的分辨率遵循肿瘤演化。这些研究将有助于我们了解肿瘤可塑性如何使癌症避免治疗挑战。和重要的是，肿瘤抑制基因或基因组合的丧失如何改变首选进化路径单个转化的细胞需要达到侵略性和转移状态。