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:
- 金额:$ 232.14万
- 依托单位:
- 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:
- 关键词:Advanced Malignant NeoplasmAreaAtlas of Cancer Mortality in the United StatesAttentionBindingBioinformaticsBiological AssayBiologyBreastCaliforniaCancer CenterCatalogingCatalogsCell LineCell ProliferationClinicalClustered Regularly Interspaced Short Palindromic RepeatsCollectionComplexCopy Number PolymorphismCryoelectron MicroscopyDataDependenceDiseaseExpert SystemsFDA approvedFacultyFundingGene MutationGenesGenetic DiseasesGenetic ScreeningHead and Neck NeoplasmsHeterogeneityImageImmunofluorescence MicroscopyInformation NetworksInstitutionLaboratoriesLogicLungMalignant NeoplasmsMapsMindMissionModelingMolecularMutationNCI Center for Cancer ResearchNeoplasm MetastasisOther GeneticsPIK3CA genePathogenesisPathway interactionsPatientsPatternPhenotypePhysiologicalPoint MutationPrincipal InvestigatorProliferatingProteinsResearch PersonnelResourcesScienceScientistSisterSquamous CellStructural ModelsStructureSystemSystems BiologyTP53 geneTechniquesThe Cancer Genome AtlasTimeTrainingTranslatingUniversitiesanticancer researchcancer cellcancer genomecell motilitycombinatorialcomputerized toolsdeep learningdriver mutationdrug response predictionexperimental studygenome-wideinterestknockout genemolecular modelingmouse modelneoplastic cellnext generationpatient responsepersonalized medicineprecision medicineprecision oncologypressureprotein complexrare cancerscale upsimulationspatiotemporaltraining opportunitytransfer learningtreatment responsetumortumor 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,
通常尚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产生的
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。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Trey Ideker其他文献
Trey Ideker的其他文献
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{{ truncateString('Trey Ideker', 18)}}的其他基金
Next generation massively multiplexed combinatorial genetic screens
下一代大规模多重组合遗传筛选
- 批准号:
10587354 - 财政年份:2023
- 资助金额:
$ 232.14万 - 项目类别:
Core 2: Software Infrastructure for Network Models and Cell Maps
核心 2:网络模型和小区地图的软件基础设施
- 批准号:
10704622 - 财政年份:2022
- 资助金额:
$ 232.14万 - 项目类别:
Project 3: From Networks and Structures to Hierarchical Whole Cell Models of Cancer
项目 3:从网络和结构到癌症的分层全细胞模型
- 批准号:
10704611 - 财政年份:2022
- 资助金额:
$ 232.14万 - 项目类别:
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 - 财政年份:2022
- 资助金额:
$ 232.14万 - 项目类别:
Project 3: From Networks and Structures to Hierarchical Whole Cell Models of Cancer
项目 3:从网络和结构到癌症的分层全细胞模型
- 批准号:
10525590 - 财政年份:2022
- 资助金额:
$ 232.14万 - 项目类别:
Core 2: Software Infrastructure for Network Models and Cell Maps
核心 2:网络模型和小区地图的软件基础设施
- 批准号:
10525593 - 财政年份:2022
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CYTOSCAPE: AN ECOSYSTEM FOR NETWORK GENOMICS
CYTOSCAPE:网络基因组学的生态系统
- 批准号:
10411738 - 财政年份:2022
- 资助金额:
$ 232.14万 - 项目类别:
Cytoscape: A Modeling Platform for Biomolecular Networks
Cytoscape:生物分子网络建模平台
- 批准号:
10415596 - 财政年份:2021
- 资助金额:
$ 232.14万 - 项目类别:
Cytoscape: A Modeling Platform for Biomolecular Networks
Cytoscape:生物分子网络建模平台
- 批准号:
10166303 - 财政年份:2020
- 资助金额:
$ 232.14万 - 项目类别:
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