Bay Area Cancer Target Discovery and Development

湾区癌症靶标的发现和开发

• 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
• 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

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 我们的总体战略是利用我们开发的新工具和方法 在我们的前两个CTD^2资助期间-更具体地说， 帮助发现和表征新型癌症靶点及其调节剂的技术- 使用创新的高通量技术。我们的最终目标是找出 靶点具有消除所有癌细胞的潜力，尽管它们具有克隆异质性， 环境背景。这就要求我们更好地理解肿瘤的生物发生，即 驱动肿瘤发生的基因组合，以及使有效治疗复杂化的肿瘤异质性， 治疗性治疗 在这个建议中，我们建立在令人兴奋的系统，使我们能够定量基因型和表型细胞 在细胞培养和体内的异质性。总的目标是鉴定合成基因组合 临床耐药所必需的，并与肿瘤间和肿瘤内异质性有关。我们假设 改变细胞状态，如炎症表型和谱系可塑性， 和抵抗我们应用单细胞方法和尖端的谱系追踪工具来研究 致病细胞状态变化的发生，并使用遗传筛选，计算和 药理学方法和临床相关的体外和体内肿瘤模型，以确定 机械校准的，特定的治疗弱点。这些方法将应用于两个 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，主要是由于细胞的复杂性， 癌症的类型和行为以及驱动癌症类型和亚型的基因组合， 明白我们已经开发了下一代GEMM来询问基因组合， 促进癌症。在这个目标中，将产生含有遗传修饰的组合的小鼠模型。 前30个TCGA复发突变的干扰。这些研究将结合以下因素 干扰与特定的细胞状态，尽管他们的克隆异质性和细胞状态，并奠定了坚实的 为确定复发基因的哪种组合对哪种疗法产生反应奠定了基础， 帮助患者分层。这部分的研究计划集中在肺癌，因为它协同 提案的其他组成部分。我们应用一种进化的血统跟踪技术， 细胞RNA-seq读数，让我们以前所未有的分辨率跟踪肿瘤演变。这些研究 将帮助我们了解肿瘤可塑性如何使癌症逃避治疗挑战。和 重要的是，肿瘤抑制基因或基因组合的丢失如何改变首选的肿瘤抑制基因。 单个转化细胞达到侵袭性和转移性状态的进化路径。