The Cancer Cell Map Initiative v2.0

癌细胞图谱计划 v2.0

基本信息

批准号：
10704587
负责人：
Trey Ideker
金额：
$ 232.14万
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/10704587
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 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.

癌细胞图谱倡议 v2.0 总体总结癌症基因组图谱和姐妹项目现已对 20,000 多个肿瘤基因组进行了测序，提供了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。数据清楚地表明，每种癌症都是一种独特的遗传性疾病，肿瘤在生理学上看起来很相似由于这种分子异质性，相似往往是由显着不同的基因突变模式驱动的。通常不清楚特定癌症的关键驱动突变或依赖性是什么，以及这些突变如何发生影响发病机制和治疗反应的一个关键观察结果是解释肿瘤基因组。基于这个前提，许多罕见的肿瘤突变可以聚集在共同的分子网络上。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 2017 年，CCMI 开发了分子网络，并在智能系统中使用这些图谱进行个性化治疗。被资助作为 NCI U54 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领先学术实验室的绘图、生物信息分析和癌症研究加州大学校园（加州大学旧金山分校和加州大学圣地亚哥分校）从此全面生成了蛋白质网络。乳腺和头颈肿瘤细胞中的相互作用，并从这些数据中识别出数百种蛋白质癌症选择性突变压力下的复合物（NeST v1.0）我们已经试点了深度学习系统。（DCell、DrugCell 和 TCRP）可以使用该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 批准的和探索性的药物。丰富的培训机会组合，并利用加州大学机构的支持，扩大了 CCMI 联盟未来五年，CCMI 将包括加州大学和斯坦福大学的十多名教员。寻求：（1）生成以肺部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（健康和患病状态下）以及 PIK3CA 和 TP53 通路，这是核心 (2) 系统地扩展 CCMI 收集的癌症蛋白与蛋白质的相互作用数据 (3) 解剖通过在同一肿瘤中进行系统的基因筛选实验来绘制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）；显着推进和强化我们用于癌症精准医学的 DrugCell 可解释深度学习系统； (5) 培训当前和下一代网络生物学及其在癌症研究中的应用的科学家； (6) 继续建立一支领先的研究人员队伍，将 CCMI 扩展为全球协调伙伴关系。