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Development of ex-vivo tumor culture for systems network biology and personalized medicine

用于系统网络生物学和个性化医疗的离体肿瘤培养的开发

基本信息

批准号：
10830630
负责人：
Trey Ideker
金额：
$ 15.23万
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：
2022-09-14 至 2027-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10830630
关键词：
Advanced Malignant Neoplasm Area Atlas of Cancer Mortality in the United States Attention Binding Bioinformatics Biological Assay Biology Breast California Cancer Center Cataloging Catalogs Cell Line Cell Proliferation Clinical Clustered Regularly Interspaced Short Palindromic Repeats Collection Complex Copy Number Polymorphism Cryoelectron Microscopy Data Dependence Development Disease Expert Systems FDA approved Faculty Funding Gene Mutation Genes Genetic Diseases Genetic Screening Head and Neck Neoplasms Heterogeneity Image Immunofluorescence Microscopy Information Networks Institution Laboratories Logic Lung Malignant Neoplasms Maps Mind Mission Modeling Molecular Mutation NCI Center for Cancer Research Neoplasm Metastasis Other Genetics PIK3CA gene Pathogenesis Pathway interactions Patients Pattern Phenotype Physiological Point Mutation Principal Investigator Proliferating Proteins Research Personnel Resources Science Scientist Sister Squamous Cell Structural Models Structure System Systems Biology TP53 gene Techniques The Cancer Genome Atlas Time Training Translating Universities anticancer research cancer cell cancer genome cell motility combinatorial computerized tools deep learning driver mutation drug response prediction experimental study genome-wide interest knockout gene molecular modeling mouse model neoplastic cell next generation patient response personalized medicine precision medicine precision oncology pressure protein complex rare cancer scale up simulation spatiotemporal training opportunity transfer learning treatment response tumor tumor initiation

项目摘要

THE CANCER CELL MAP INITIATIVE v2.0 OVERALL SUMMARY The Cancer Genome Atlas and sister projects have now sequenced over 20,000 tumor genomes, providing a catalog of gene mutations, copy number variants and other genetic alterations associated with cancer. These data have made it clear that every cancer is a distinct genetic disease, with tumors that look physiologically similar often driven by patterns of gene mutations that are strikingly different. Due to this molecular heterogeneity, it is typically unclear what are the key driver mutations or dependencies in a given cancer and how these influence pathogenesis and response to therapy. One key observation for interpreting tumor genomes is that the many rare tumor mutations can be shown to converge on common molecular networks. Based on this premise we created the Cancer Cell Map Initiative (CCMI), whose mission is to create comprehensive maps of cancer molecular networks and to use these maps in intelligent systems for personalized therapy. In 2017, the CCMI was funded as an NCI U54 Research Center for Cancer Systems Biology, integrating expertise in network mapping, bioinformatic analysis and cancer research from leading academic laboratories at two University of California campuses (UCSF and UCSD). We have since generated comprehensive networks of protein interactions in breast and head-and-neck tumor cells and, from these data, identified several hundred protein complexes under selective mutational pressure in cancer (NeST v1.0). We have piloted deep learning systems (DCell, DrugCell and TCRP) that can use this protein network information to translate a patient’s tumor mutation profile to a predicted drug response, including FDA-approved and exploratory agents. We have implemented a rich portfolio of training opportunities and, leveraging UC institutional support, expanded the CCMI consortium to include more than a dozen faculty at UC and, most recently, Stanford. In the next five years, the CCMI will seek to: (1) Generate comprehensive protein interaction networks centered on key cancer driver genes in lung squamous cells (in healthy and diseased states) as well as the PIK3CA and TP53 pathways, which are central to many tumor types; (2) Systematically extend the CCMI collection of cancer protein interaction data with protein immunofluorescent imaging and cryo-electron microscopy to formulate multi-scale cancer cell maps; (3) Dissect the functional logic of these networks and maps by systematic genetic screening experiments in the same tumor types and pathways, using a panel of scalable cell proliferation, phenotype and pathway readouts; (4) Significantly advance and harden our DrugCell interpretable deep learning system for cancer precision medicine; (5) Train the current and next generation of scientists in network biology and its applications to cancer research; and (6) Continue to build a cadre of leading investigators to expand CCMI into a global coordinated partnership.

癌症细胞地图倡议v2.0