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
基本信息
- 批准号:10704611
- 负责人:
- 金额:$ 47.68万
- 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:AffectAffinity ChromatographyArchitectureBiologicalBiological AssayBiological MarkersBreastCancer ModelCell modelCellsCellular StructuresClinicalClinical DataComplexCryoelectron MicroscopyDNA Sequence AlterationDataData AnalysesData SetDevelopmentERBB3 geneEvaluationExpert SystemsFRAP1 geneFundingGenerationsGenesGeneticHead and Neck CancerHead and Neck NeoplasmsHead and neck structureHeterogeneityHumanImageImmunofluorescence ImmunologicKnowledgeLearningLungLung NeoplasmsMalignant NeoplasmsMalignant neoplasm of lungMapsMass Spectrum AnalysisMedicineMethodologyMethodsModelingMolecularMutateMutationOrganellesPIK3CA genePathway interactionsPatientsPhenotypePopulationProtein DynamicsProteinsResearch PersonnelResolutionRiskSamplingSomatic MutationStructural ModelsStructureSystemTechniquesTrainingTranslationsTreatment outcomeWorkXenograft procedurecancer cellcancer genomecancer therapycancer typeclinical translationclinically relevantcombinatorialcomputer frameworkconfocal imagingcrosslinkdata modelingdeep learningdesigndrug response predictionimprovedmachine learning modelmalignant breast neoplasmmultimodalityneoplastic cellpatient derived xenograft modelprecision medicinepredictive modelingprotein complexprotein distributionresponsestructural biologythree-dimensional modelingtransfer learningtumor
项目摘要
CCMI v2.0
Project 3: From Networks and Structures to Hierarchical Whole-Cell Models of Cancer
Project Leads: Trey Ideker and Andrej Sali; Co-Investigators: Emma Lundberg, Jennifer Grandis, J. Silvio
Gutkind, and Laura van ’t Veer
SUMMARY
One of the striking discoveries of the cancer genome projects is that each tumor presents a unique set of genetic
mutations and molecular alterations. To understand how these alterations give rise to cancer and treatment
outcomes, the Cancer Cell Map Initiative (CCMI) has launched systematic efforts to map the physical and
functional architecture of tumor cells, capturing the molecular components and pathways on which cancer
mutations converge. While parts of this effort are experimental, this Project 3 presents the central computational
framework.
A first computational theme concerns methods to assemble the structure of the multiscale tumor cell map. Aim
1 focuses on creating 3D models of cancer-associated protein complexes. It will apply established methods of
integrative structural biology to data from other projects, including cryo-electron microscopy (cryo-EM), affinity
purification mass spectrometry (AP-MS), cross-linking mass spectrometry (XL-MS), and genetic interaction
datasets. Initial efforts will focus on PIK3CA-HER3 and mTOR complexes, identified in previous work by the
CCMI, then move to new protein complexes identified by our ongoing mapping activities. Aim 2 focuses on
mapping tumor cellular components at scales at and above the protein complex, extending to larger cellular
components, compartments, and organelles. It will expand on a compelling proof-of-concept for creating an
unbiased hierarchical map of human cell components by integration of AP-MS data with protein distribution data
from immunofluorescence confocal images. These whole-cell maps will be analyzed to reveal specific cellular
components under mutational selection in breast, head-and-neck, and lung cancers.
A second computational theme concerns methods to integrate tumor cell maps with functional analysis and
predictive medicine. Aim 3 uses the maps to build interpretable deep learning systems for prediction of drug
responses. This aim draws from our previous work to establish “visible” learning models (DCell and DrugCell),
which are not black boxes but have internal organization determined by prior knowledge of biological structure.
We will construct such models from CCMI tumor cell maps, incorporating key improvements over our first-
generation pilots. Finally, Aim 4 will use visible deep learning systems alongside other machine learning models
to design and evaluate combinatorial biomarkers for breast, head-and-neck, and lung tumors in the patient-
derived xenograft (PDX) and clinical settings. Clinical samples and data will be drawn from molecular
tumor boards and the I-SPY breast cancer trial. PDX and clinical data will be used for further optimization
of our predictive models using nascent techniques from transfer learning.
Through these aims, we will advance our basic knowledge of the structure and function of tumors while
embedding this knowledge within intelligent systems for precision medicine.
CCMI v2.0
项目3:从网络和结构到癌症的分层全细胞模型
项目负责人:Trey Ideker和Andrej Sali;共同研究者:Emma Lundberg、Jennifer Grandis、J. Silvio
古特金德和劳拉货车没有转向
总结
癌症基因组计划的一个惊人发现是,每个肿瘤都呈现出一组独特的基因表达。
突变和分子改变。为了了解这些改变如何引起癌症和治疗,
结果,癌症细胞图谱倡议(CCMI)已经启动了系统的努力,以绘制物理和
肿瘤细胞的功能结构,捕获癌症的分子成分和途径
突变会趋同虽然这项工作的一部分是实验性的,这个项目3提出了中央计算
框架.
第一个计算主题涉及组装多尺度肿瘤细胞图的结构的方法。目的
1专注于创建癌症相关蛋白质复合物的3D模型。它将采用既定的方法,
将结构生物学与其他项目的数据相结合,包括冷冻电子显微镜(cryo-EM),亲和性
纯化质谱(AP-MS)、交联质谱(XL-MS)和遗传相互作用
数据集。最初的努力将集中在PIK 3CA-HER 3和mTOR复合物上,这些复合物是在以前的工作中由
CCMI,然后转移到我们正在进行的绘图活动确定的新蛋白质复合物。目标2侧重于
在蛋白质复合物处和蛋白质复合物上方的尺度上映射肿瘤细胞组分,延伸到更大的细胞,
组件、隔室和细胞器。它将扩展一个令人信服的概念验证,
通过整合AP-MS数据和蛋白质分布数据的人类细胞组分的无偏分层图
免疫荧光共聚焦图像。这些全细胞图谱将被分析,以揭示特定的细胞
在乳腺癌、头颈癌和肺癌的突变选择下,
第二个计算主题涉及将肿瘤细胞图谱与功能分析整合的方法,
预测医学Aim 3使用这些地图构建可解释的深度学习系统,用于预测药物
应答这一目标借鉴了我们以前建立“可见”学习模型(DCell和DrugCell)的工作,
它们不是黑箱,而是由生物结构的先验知识决定的内部组织。
我们将从CCMI肿瘤细胞图谱构建这样的模型,结合我们第一个模型的关键改进-
一代飞行员。最后,Aim 4将与其他机器学习模型一起使用可视深度学习系统
设计和评估患者乳腺、头颈和肺肿瘤的组合生物标志物-
衍生异种移植物(PDX)和临床环境。临床样本和数据将从分子生物学
肿瘤委员会和I-SPY乳腺癌试验。PDX和临床数据将用于进一步优化
我们的预测模型使用了迁移学习的新生技术。
通过这些目标,我们将推进我们对肿瘤结构和功能的基础知识,
将这些知识嵌入到精准医疗的智能系统中。
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:
$ 47.68万 - 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:
$ 47.68万 - 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:
$ 47.68万 - 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:
$ 47.68万 - 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
- 资助金额:
$ 47.68万 - 项目类别:
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:
$ 47.68万 - 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:
$ 47.68万 - 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:
$ 47.68万 - 项目类别:
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