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

• 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
• 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

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.

癌细胞图计划v2.0 总结 癌症基因组图集和姊妹项目现在已经测序了20,000多个肿瘤基因组，提供了一种 基因突变，拷贝数变异和其他与癌症相关的遗传改变的目录。这些 数据清楚地表明，每种癌症都是一种独特的遗传疾病，肿瘤在物理上看起来 相似之处通常是由截然不同的基因突变模式驱动的。由于这种分子异质性， 通常不清楚给定癌症中的关键驱动突变或依赖性是什么？ 影响发病机理和对治疗的反应。解释肿瘤基因组的一个关键观察是 许多罕见的肿瘤突变可以证明在公共分子网络上汇聚。基于这个前提 我们创建了癌细胞图计划（CCMI），其任务是创建癌症的全面地图 分子网络并在智能系统中使用这些图进行个性化治疗。在2017年，CCMI 被资助为NCI U54癌症系统生物学研究中心，将专业知识纳入网络 两所大学的领先学术实验室的映射，生物信息分析和癌症研究 加利福尼亚校园（UCSF和UCSD）。从那以后，我们产生了蛋白质的综合网络 乳房和头颈肿瘤细胞中的相互作用，从这些数据中鉴定出数百种蛋白 癌症选择性突变压力下的复合物（Nest V1.0）。我们已经试行了深度学习系统 （DCELL，DRUDCEL和TCRP）可以使用此蛋白质网络信息来翻译患者的肿瘤突变 预测药物反应的概况，包括FDA批准和探索剂。我们已经实施了 丰富的培训机会组合以及利用UC机构支持，扩大了CCMI财团 在UC和最近的斯坦福大学中包括十多个教职员工。在接下来的五年中，CCMI将 寻求：（1）生成以肺部关键癌症基因为中心的综合蛋白质相互作用网络 鳞状细胞（在健康和否认状态下）以及PIK3CA和TP53途径，它们是中心的 许多肿瘤类型； （2）系统地扩展了与蛋白质的CCMI癌蛋白相互作用数据的收集 免疫荧光成像和冷冻电子显微镜以制定多尺度癌细胞图； （3）解剖 这些网络的功能逻辑和地图通过同一肿瘤中的系统遗传筛查实验 使用一系列可扩展细胞增殖，表型和途径读数的类型和途径； （4） 大幅度进步并加强了我们的药物可解释的深度学习系统，用于癌症精确医学； （5）培训网络生物学的当前和下一代科学家及其在癌症研究中的应用； （6）继续建立一群领先的调查人员的干部，以将CCMI扩展到全球协调的合作伙伴关系中。