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.

癌细胞图谱计划v2.0 总体总结 癌症基因组图谱和姊妹项目现在已经对20,000多个肿瘤基因组进行了测序，提供了一个 与癌症相关的基因突变、拷贝数变异和其他基因改变的目录。这些 数据表明，每一种癌症都是一种不同的遗传性疾病，肿瘤看起来像是生理上的。 相似的通常是由截然不同的基因突变模式驱动的。由于这种分子的异质性， 通常还不清楚特定癌症中的关键驱动因素突变或依赖关系是什么，以及这些因素是如何 影响发病机制和治疗反应。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被证明聚集在共同的分子网络上。基于这一前提 我们创建了癌症细胞地图倡议(CCMI)，其使命是创建全面的癌症地图 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 U54癌症系统生物学研究中心，整合网络中的专业知识 地图绘制、生物信息学分析和癌症研究-来自两所大学领先的学术实验室 加州大学(加州大学旧金山分校和加州大学洛杉矶分校)。自那以后，我们已经产生了全面的蛋白质网络 乳腺和头颈部肿瘤细胞之间的相互作用，并从这些数据中识别出数百种蛋白质 癌症中选择性突变压力下的复合体(Nest v1.0)。我们已经试行了深度学习系统 (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 对预测的药物反应的简介，包括FDA批准的和试探性药物。我们已经实施了一个 丰富的培训机会组合，并利用UC机构支持，扩大了CCMI财团 包括加州大学十几名教职员工，最近还有斯坦福大学。在未来五年，CCMI将 寻求：(1)建立以肺癌关键致癌基因为中心的全面蛋白质相互作用网络 鳞状细胞(在健康和疾病状态下)以及PIK3CA和TP53通路，这是中心 到多种肿瘤类型；(2)系统地扩展了癌症蛋白质与蛋白质相互作用数据的CCMI收集 免疫荧光成像和冷冻电子显微镜绘制多尺度癌细胞图谱；(3)解剖 在同一肿瘤中进行系统基因筛查实验的这些网络和图谱的功能逻辑 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) 显著提高和强化我们的DrugCell可解释的癌症精确医学深度学习系统； (5)培养网络生物学及其在癌症研究中的应用的当代和下一代科学家； (6)继续建设一支领导调查队伍，将CCMI扩展为全球协调伙伴关系。