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.

癌细胞分布图倡议v2.0 总体汇总 癌症基因组图谱和姐妹项目现在已经测序了20，000多个肿瘤基因组， 与癌症相关的基因突变、拷贝数变异和其他遗传改变的目录。这些 数据已经清楚地表明，每一种癌症都是一种独特的遗传疾病，肿瘤在生理上看起来 相似性通常是由基因突变的模式所驱动的，而这些模式是惊人的不同。由于这种分子异质性， 通常还不清楚特定癌症中的关键驱动突变或依赖性是什么，以及这些突变或依赖性是如何发生的。 影响发病机制和对治疗反应。解释肿瘤基因组的一个关键观察结果是， 许多罕见的肿瘤突变可以显示出在共同的分子网络上聚集。基于这样的前提 我们创建了癌症细胞图谱计划（CCMI），其使命是创建全面的癌症图谱 分子网络，并在智能系统中使用这些地图进行个性化治疗。2017年，CCMI 被资助为NCI U 54癌症系统生物学研究中心，整合网络专业知识 来自两所大学的领先学术实验室的绘图，生物信息学分析和癌症研究。 加州校区（UCSF和UCSD）。从那时起，我们已经建立了全面的蛋白质网络， 在乳腺和头颈部肿瘤细胞中的相互作用，并从这些数据中，确定了数百种蛋白质 癌症中选择性突变压力下的复合物（NeST v1.0）。我们已经试验了深度学习系统 （DCell，DrugCell和TCRP），可以使用这种蛋白质网络信息来翻译患者的肿瘤突变 包括FDA批准的和探索性药物。我们实施了 丰富的培训机会组合，并利用UC机构支持，扩大了CCMI联盟 包括加州大学和最近的斯坦福大学的十几名教师。未来五年，CCMI将 寻求：（1）生成以肺中关键癌症驱动基因为中心的全面蛋白质相互作用网络 鳞状细胞（健康和患病状态）以及PIK 3CA和TP 53途径，它们是核心 （2）系统地扩展了CCMI收集的癌症蛋白相互作用数据， 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 这些网络和地图的功能逻辑通过系统的遗传筛选实验在同一肿瘤 类型和途径，使用一组可扩展的细胞增殖，表型和途径读数;（4） 显著推进和强化我们用于癌症精准医疗的DrugCell可解释深度学习系统; (5)培训当前和下一代科学家网络生物学及其在癌症研究中的应用; 以及（6）继续建立一支主要的调查人员队伍，以将CCMI扩展为全球协调的伙伴关系。