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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.

项目总结