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.

项目概要我们的总体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 资助期间 - 更具体地说是基于 CRISPR 的开拓和应用帮助发现和表征新的癌症靶点及其调节剂的技术– 我们的最终目标是使用创新的高通量技术来发现最佳组合。尽管具有克隆异质性，但具有消除所有癌细胞的潜力的目标这需要我们更好地了解肿瘤的生物发生，即肿瘤的发生。驱动肿瘤发生的基因组合以及使有效治疗变得复杂的肿瘤异质性治疗性治疗。在这项提案中，我们建立了令人兴奋的系统，使我们能够定量基因型和表型细胞细胞培养和体内异质性的总体目标是识别合成基因组合。临床耐药性所必需的，并且与肿瘤间和肿瘤内异质性相关。改变细胞状态，如炎症表型和谱系可塑性，增强治疗耐受性我们应用单细胞方法和尖端的谱系追踪工具来研究。致病性细胞状态变化的起源并使用遗传筛选、计算和药理学方法以及临床相关的体外和体内肿瘤模型来识别这些方法将应用于两个经过机械校准的特定治疗漏洞。癌症、肺癌和乳腺癌。肿瘤的生物发生和进化是一个具有挑战性的研究领域，很大程度上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组合很差我们已经开发了下一代 GEMM 来询问基因组合。为了实现这一目标，将产生包含遗传组合的小鼠模型。这些研究将前 30 个 TCGA 复发突变的扰动与以下组合相关联。具有特定细胞状态的扰动物，尽管它们的克隆异质性和细胞状态并奠定了坚实的基础确定哪些重复基因组合对哪种治疗有反应的基础，从而帮助对患者进行分层，这部分研究项目重点关注肺癌，因为它具有协同作用。与该提案的其他组成部分一起，我们应用了一种进化的谱系追踪技术。细胞 RNA 测序读数让我们能够以前所未有的分辨率追踪肿瘤的进化。将帮助我们了解肿瘤可塑性如何使癌症逃避治疗挑战。重要的是，肿瘤抑制基因或基因组合的丢失如何改变首选单个转化细胞达到侵袭和转移状态所需的进化路径。